https://data.geods.ac.uk/catalog.ttl
type: PagedCollection
items per page: 100
total items: 96

Geographic Data Service
type: Catalog
language: en
modified: 2026-04-28T11:11:49.515400

Ethnicity Estimator
type: Dataset
description: Ethnicity Estimator is an online service which allows users to produce an estimated ethnicity distribution of a set of names supplied to it, based on the standard UK ONS ethnicity category groups. Upon supplying a CSV of names, it will return an indicative population count, split by the categories. The online service is secure and supplied names lists are automatically discarded after the categorisation is complete. The Ethnicity Estimator (EE) classifier is based on research which uses names data assembled by GeoDS. The data are taken from consumer sources and from the Office for National Statistics (ONS), which securely host data from England & Wales. The research enables estimates of the ethnic distribution from datasets which contain names, using the best methodology. Users can now apply to access the Ethnicity Estimator software online. This software provides aggregate classifications reporting on estimated population for each of the standard ONS ethnicity groups. Accepted applications will be for users who utilise the software for the public good, and applicants can be drawn from the academia, government or industry sectors. Please read our full Terms and Conditions (see document below) prior to making an application. Please note that the application review process takes a number of weeks. Once your application has been approved, you will be emailed your login credentials and a link to the tool, from where you can upload CSVs of names. The category groups are: * ABD: Asian/Asian British - Bangladeshi * ACN: Asian/Asian British - Chinese * AIN: Asian/Asian British - Indian * APK: Asian/Asian British - Pakistani * AAO: Asian/Asian British - Any Other * BAF: Black/Black British - African * BCA: Black/Black British - Caribbean * WBR: White - English/Welsh/Scottish/Northern Irish/British * WIR: White - Irish * WAO: White - Any Other (including Gypsy or Irish Traveller) * OXX: Any Other Ethnic Group (including Arab) * Unclassified: Names that could not be classified…
issued: 2025-05-06T12:41:42.637160
modified: 2026-01-15T14:09:58.982487
contact point: Organization
  fn: Oliver O'Brien
keyword: Ethnicity
landing page: Office for National Statistics

Technical Report: Ethnicity Estimator Terms & Conditions
type: Distribution
format: PDF
issued: 2025-05-06T13:05:07.032313
modified: 2025-05-06T13:05:28.588524
access URL: https://data.geods.ac.uk/dataset/05f63154-41b9-44fc-9ff0-c64190e841c3/resource/33b11ece-c6b4-4a18-8b2d-0e96d6dbe73e/download/eth3_terms.pdf
byte size: 136946
media type: application/pdf

Data Sample: Output File
type: Distribution
format: CSV
issued: 2025-05-06T13:06:29.609028
modified: 2025-05-06T13:27:34.727626
access URL: https://data.geods.ac.uk/dataset/05f63154-41b9-44fc-9ff0-c64190e841c3/resource/4e9f4ea1-ae7d-4a9c-b231-33f31774e5e0/download/testfile_results_240212_022754.csv
byte size: 431
media type: text/csv

Technical Report: Scale-based Evaluation of Ethnicity Estimator and Onomap3
type: Distribution
format: PDF
issued: 2025-05-06T13:05:28.593767
modified: 2025-05-06T13:06:29.602518
access URL: https://data.geods.ac.uk/dataset/05f63154-41b9-44fc-9ff0-c64190e841c3/resource/ac48c4f3-4490-42a6-bd73-232695ced846/download/ee_and_onomap3.pdf
byte size: 3220046
media type: application/pdf

Paper: Kandt J, Longley PA (2018) Ethnicity estimation using family naming practices. PLOS ONE 13(8): e0201774.
type: Distribution
format: HTML
issued: 2025-05-06T13:03:46.496287
modified: 2025-05-06T13:05:07.026284
access URL: https://journals.plos.org/plosone/article

Technical Report: Perturbation Examples
type: Distribution
format: PDF
issued: 2025-05-06T13:27:34.742770
modified: 2025-05-14T11:58:33.756179
access URL: https://data.geods.ac.uk/dataset/05f63154-41b9-44fc-9ff0-c64190e841c3/resource/de2ee0e0-8e63-4697-bf34-5963aab783bd/download/ee_perturbation_examples.pdf
byte size: 78947
media type: application/pdf

Great Britain Gambling Behaviours Classification (GB2C) (LAD Geography)
type: Dataset
description: The pervasive nature of online gambling has pushed it to the forefront of social concerns in Great Britain (GB). Understanding how and where gambling-related behaviours manifest is essential for informing targeted interventions and evidence-based public policy. The GB2C dataset provides the first national areal classification of gambling behaviours in GB. Uniquely, it is based on observed online transactional behaviours drawn from industry data. The classification is built using circa 1.2 million anonymised online gambling accounts recorded throughout 2022, provided by one of the ‘Big 5’ British gambling operators. This work was conducted independently by GeoDS researchers, with data access facilitated through collaboration with the gambling service provider (which had no influence over the research or reporting of it). Using this unique data resource, customers were segmented into 11 Active Subgroups, with additional estimates for non-Active account holders and the remaining adult population. Local Authority District (LAD) estimates of the incidence of each Subgroup are available through the GeoDS for bona fide research purposes. More granular Lower layer Super Output Area (LSOA) data are also available. This classification extends what is possible using conventional survey instruments alone. Linkage of georeferenced, anonymised individual customer records to neighbourhood attributes from the GeoDS UK Output Area Classification (UK-OAC) and the GeoDS Harmonised Index of Multiple Deprivation (IMD) enables GB-wide profiling of the geographic context in which actual patterns of gambling behaviour occur – rather than relying on coarser regional scale reports of stated behaviour which are subject to recall errors. This independent, ethically approved GeoDS Research Ready Data product pushes the frontiers of social science methodology. It empowers researchers at all career stages to develop deeper insights into the complexities of gambling behaviour in GB, at spatial sca…
issued: 2025-06-04T15:42:49.151618
modified: 2026-03-26T15:51:19.109833
version info: 1.0
contact point: Organization
  fn: Shunya Kimura
keyword: Gambling
keyword: Geodemographics
landing page: Online Gambling Service Provider, UK-OAC, GeoDS Harmonised IMD 2019

Data Summary
type: Distribution
format: CSV
issued: 2025-06-04T16:16:09.708772
modified: 2025-06-05T09:21:08.742847
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/08457b26-2a94-49c4-8726-c2cd312050fb/resource/09005526-a792-46f9-86d2-05df71965ebf/download/data_summary_gb2c_lad.csv
byte size: 1958
media type: text/csv

Data: Great Britain Gambling Behaviours Classification (GB2C) (LAD Geography)
type: Distribution
format: CSV
issued: 2025-06-04T16:00:19.617575
modified: 2025-06-05T02:34:21.738384
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/08457b26-2a94-49c4-8726-c2cd312050fb/resource/0e84293b-263a-4eda-8411-517a3fe4d99e/download/lad24-gb2c-v1.2.csv
byte size: 44295
media type: text/csv

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2025-06-04T16:00:50.436473
modified: 2025-06-05T09:08:13.730916
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/08457b26-2a94-49c4-8726-c2cd312050fb/resource/2003d183-8e82-4826-8717-2401b9882932/download/data_dictionary-lad.csv
byte size: 1261
media type: text/csv

Map: Mapmaker
type: Distribution
format: HTML
issued: 2025-06-05T02:42:02.774096
modified: 2026-02-02T14:19:57.770333
access URL: https://mapmaker.geods.ac.uk/#/gambling-behaviours

Technical Report: Glossary of Terms, Statistics, Pen Portraits
type: Distribution
format: PDF
issued: 2025-06-04T16:07:21.231590
modified: 2025-06-18T09:49:43.453626
access URL: https://data.geods.ac.uk/dataset/08457b26-2a94-49c4-8726-c2cd312050fb/resource/9c5b3e67-2268-4488-b220-923d06a56652/download/tech-report-v1.2.pdf
byte size: 458026
media type: application/pdf

Variable Dictionary: Classification Codes and Names
type: Distribution
format: CSV
issued: 2025-06-04T16:01:38.983085
modified: 2025-06-04T16:01:40.086992
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/08457b26-2a94-49c4-8726-c2cd312050fb/resource/acf012da-e996-4686-8a0c-6853cd9cbe65/download/classification_codes_and_names.csv
byte size: 1309
media type: text/csv

Related Record: Great Britain Gambling Behaviours Classification (GB2C) (LSOA Geography)
type: Distribution
format: HTML
issued: 2025-06-05T02:34:21.746326
modified: 2025-06-05T02:42:02.764198
access URL: https://data.geods.ac.uk/dataset/great-britain-gambling-behaviours-classification-gb2c-lsoa-geography

Flyer: Great Britain Gambling Behaviours Classification
type: Distribution
format: PDF
issued: 2026-02-02T14:19:57.776132
modified: 2026-03-26T15:51:19.113910
access URL: https://data.geods.ac.uk/dataset/08457b26-2a94-49c4-8726-c2cd312050fb/resource/faa2d81e-6ee7-4d44-b5b3-af3541c25e51/download/gb2c-2026-web.pdf
byte size: 1344012
media type: application/pdf

County Court Judgments (CCJs) (Individual Records and LSOA Geography)
type: Dataset
description: A County Court Judgment (CCJ) is a court order registered against a borrower if they fail to make debt repayments. Issued in England, Wales, and Northern Ireland, CCJs enable creditors to recover owed funds. Scotland, however, uses a different process called "enforcing a debt by diligence", where a decree or order may be issued by a sheriff court if a debtor fails to repay a debt and the pursuer seeks court intervention. These secure data include record-level County Court Judgments for England, Wales, Scotland, Northern Ireland and the Channel Islands. Two secure products are available, CCJs at the Lower Layer Super Output Area (LSOA), and individual level records. These data are supplied by the Registry Trust and are available from 2016 onwards with complete coverage for England and Wales. Data included for each type of debtor (England and Wales complete coverage - other jurisdictions may have less data): * Consumer/ Commercial * Corporate/ Non-Corporate * Value of judgment * Debtor and creditor details * Location details Record level data are only available through our secure facilities - the JDI in UCL. LSOA level aggregate secured data can be made available for use through the UCL Data Safe Haven virtual trusted research environment. The data aggregated at different Census geographies and rounded to the closest five observations is available as a Safeguarded product. ## Content The aggregate level LSOA data for Consumer and Commercial debtor types is avaialable from 2016 onwards and includes: * Total number of judgments * Total value of judgments * Average value of judgment * Number of CCJs with a value less than £250 * Number of CCJs with a value less than £500 * Number of CCJs with a value less than £1,000 * Number of CCJs with a value greater than £1,000 For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from…
issued: 2024-11-28T18:13:39.280108
modified: 2025-10-31T17:07:40.887021
contact point: Organization
  fn: Maurizio Gibin
keyword: Commercial
keyword: Consumer
keyword: Debtor
keyword: Decree
keyword: Finance
landing page: Statutory registers

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T18:15:51.821888
modified: 2025-05-05T23:23:01.601742
access URL: https://data.geods.ac.uk/dataset/0ed50b87-6d6d-4a20-8a84-b6e410eb4d02/resource/47dfd008-cf66-4b61-816c-4399342e9729/download/ccj_secure_variable_dictionary.csv
byte size: 1050
media type: text/csv

Related Record: County Court Judgments (CCJs)
type: Distribution
format: HTML
issued: 2024-11-28T18:23:21.706800
modified: 2025-10-31T17:07:40.898045
access URL: https://data.geods.ac.uk/dataset/county-court-judgments-ccjs

Data Summary
type: Distribution
format: CSV
issued: 2024-11-28T18:15:16.752021
modified: 2025-05-05T23:23:01.601451
access URL: https://data.geods.ac.uk/dataset/0ed50b87-6d6d-4a20-8a84-b6e410eb4d02/resource/9610d37d-caa8-465f-87c5-ae3e40e7911c/download/ccj_secure_data_summary.csv
byte size: 6094
media type: text/csv

Retail Type or Vacancy Classification
type: Dataset
description: Adapted from Green Street (formerly LDC) Retail Unit data which offers insights into the locations and characteristics of retail activity and vacancy across the UK, this safeguarded dataset provides retail unit classification counts aggregated annually at the Local Authority District level. Each snapshot represents the state of retail units as of September 30th of that year. ## Content These annual snapshots are derived from the Green Street Retail Unit data, which is a longitudinal record. Snapshots are provided for the years 2015–2025. For each year, retail units that were active at the end of September (i.e., created before that date and not yet closed) are identified and aggregated by Local Authority District using their geographic coordinates. The dataset includes counts for the following variables: * Total Retail Units * Vacant * Comparison * Convenience * Leisure * Service * Non-Retail * Miscellaneous For a detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. If you are interested in the record-level data, these are available from the Related Record below. Please note that this dataset cannot be accessed via our service by local authorities or organisations working with/for local authorities or associated entities such as Business Improvement Districts or Combined Authorities. ## Quality, Representation and Bias Green Street surveys retail locations across the UK, with coverage focused on high streets, shopping centres, and retail parks. Rural and smaller retail locations may be underrepresented. Survey representation varies over time, with the most recent years being the most complete; therefore, additional caution should be taken when conducting temporal analysis.
issued: 2024-11-28T14:13:51.014142
modified: 2026-04-15T13:14:14.099163
version info: 2.0
contact point: Organization
  fn: Owen Goodwin
keyword: Consumer
keyword: High Street
keyword: Retail
keyword: Retailer
keyword: Vacancy
landing page: Green Street (formerly LDC)

Related Record: Retail Type, Vacancy and Address Data
type: Distribution
format: HTML
issued: 2025-05-06T11:54:29.110599
modified: 2026-04-15T13:09:38.424589
access URL: https://data.geods.ac.uk/dataset/local-data-company-retail-type-vacancy-and-address-data

Data Summary
type: Distribution
format: CSV
issued: 2024-11-28T14:14:44.429041
modified: 2026-04-15T13:09:38.424374
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/0efe1049-f3af-462e-8d79-aa1e59d182b3/resource/871fe782-7c43-4789-9a30-0f353757a5a3/download/data_summary_ldc_safeguarded_snapshots.csv
byte size: 10088
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T14:15:23.068285
modified: 2026-01-15T16:11:43.240347
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/0efe1049-f3af-462e-8d79-aa1e59d182b3/resource/db99abe5-0d38-4072-af1b-00dfe0114b88/download/variable_dictionary_ldc_safeguarded_snapshots.csv
byte size: 1226
media type: text/csv

The Meddin Bike-Sharing World Map Data
type: Dataset
description: The Global Bike Sharing Services data forms the world's only comprehensive atlas of bikeshare services. The atlas covers virtually every active and former service across the world. It was created in 2009 and is now edited by a global team of volunteer contributors to compile bikesharing data from approximately 2007 onwards. ## Content Data are available at the city-system level and include operator name, urban area, location (approximate latitude/longitude of its centroid), type of bike, estimated size (number of bikes, pedelecs, cargo bikes and docking stations), operator and equipment. The data contains individual records for each of the approximately 3000 bikesharing systems in the world. In some cases (where automated summary feeds are available, or cell sampling or regular media updates), each record has monthly historic numbers on total size, allowing trends to be seen (some trends are inferred where data is temporally sparse.) These data are supplied via the separate Bike Share Map project run at UCL. For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. ## Quality, Representation and Bias The quality varies as most data relies on manual observations and updates made by the volunteer editorial team. The ease of obtaining and validating the data varies by country. Some key countries, such as China, will have quite substantial missing records due to the fast-changing nature of the industry in the country and the challenges in obtaining the data. As such there will be a slight bias in terms of how up-to-date and accurate the data are, towards Western countries, particularly the USA and some European countries which have a good tradition of open data access. Some cities will not have been updated for several years; however, most currently open systems have been reviewed in the last twel…
issued: 2024-12-16T13:13:32.187811
modified: 2025-05-08T15:21:39.593952
version info: HEAD
contact point: Organization
  fn: Oliver O'Brien
keyword: Bicycle Sharing System
keyword: Bicycles
keyword: Bike hire
keyword: Bikeshare
keyword: Short term rental
landing page: The Meddin Bike-Sharing World Map

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-12-16T13:14:52.547527
modified: 2025-05-05T23:16:43.718280
access URL: https://data.geods.ac.uk/dataset/0fc12952-b7c5-4e3b-ab06-b0dbdd27d508/resource/51ff1efe-efe0-4710-80a4-81baf989a78f/download/variable_dictionary_bswm.csv
byte size: 3509
media type: text/csv

Data Summary: Systems
type: Distribution
format: CSV
issued: 2024-12-16T13:15:12.122015
modified: 2025-05-05T23:16:43.718393
access URL: https://data.geods.ac.uk/dataset/0fc12952-b7c5-4e3b-ab06-b0dbdd27d508/resource/83087b5c-4f9b-45bf-bd46-8fd1367636a6/download/data_summary_bswm_systems.csv
byte size: 3092
media type: text/csv

External Website: Bike Share Map
type: Distribution
format: HTML
issued: 2024-12-16T13:17:11.630292
modified: 2025-05-05T23:16:43.718687
access URL: https://bikesharemap.com/

Data Summary: News Stories
type: Distribution
format: CSV
issued: 2024-12-16T13:15:30.633196
modified: 2025-05-05T23:16:43.718472
access URL: https://data.geods.ac.uk/dataset/0fc12952-b7c5-4e3b-ab06-b0dbdd27d508/resource/e755d7b8-89d3-40d1-b461-32ddd90ca4c0/download/data_summary_bswm_newsstories.csv
byte size: 915
media type: text/csv

External Website: The Meddin Bike-Sharing World Map
type: Distribution
format: HTML
issued: 2024-12-16T13:16:33.072836
modified: 2025-05-05T23:16:43.718619
access URL: https://bikesharingworldmap.com

Data Summary: Numbers
type: Distribution
format: CSV
issued: 2024-12-16T13:15:59.542709
modified: 2025-05-05T23:16:43.718548
access URL: https://data.geods.ac.uk/dataset/0fc12952-b7c5-4e3b-ab06-b0dbdd27d508/resource/f8a5a3c8-ee23-4275-82eb-ffd0884ff7e8/download/data_summary_bswm_numbers.csv
byte size: 717
media type: text/csv

FUSION Travel Survey and Movement Data
type: Dataset
description: These are the data from two datasets: MyWays app and survey data, produced as part of the FUSION (Fusion of User Surveys and Integrated Online Navigation) project that SYSTRA carried out for the Department for Transport (DfT) in partnership with TravelAi and Yonder Data Solutions. The datasets contain anonymised, aggregated travel information collected through the MyWays app and from 6 online surveys over an approximately six-month period. The aggregated data are based on around 970,000 routes comprising approximately 2 million journey legs. * Legs: A continuous movement using a single type of transport. * Routes: A sequence of legs (multi-modal travels) forming a complete trip between two places. * Segments: A higher-level grouping of routes, typically representing a user’s daily travel. ## Content The dataset includes two open datasets (MyWays app and survey data) aggregated by region and transport user segment, age group, and urban/rural classification, along with a web analysis tool that offers map and chart visualisations of trips captured, including origin-destination (OD) trips where available. ## Quality, Representation and Bias A threshold of 10 was applied to both the number of trips and the number of unique users. When the numbers are 10 or more, the status is "compliant"; when either was below 10, the data are suppressed or estimated ("non-compliant") across a wider area.
issued: 2025-10-20T11:57:16.586992
modified: 2026-01-15T14:11:27.046685
contact point: Organization
  fn: Meilin Shi
keyword: Active Travel
keyword: DfT
keyword: Multimodal
keyword: Survey
keyword: Transport
keyword: behaviour
keyword: demographic
keyword: mobile phone
keyword: mobility
keyword: persona
keyword: recruitment
keyword: route
keyword: rural
keyword: travel
keyword: urban
landing page: SYSTRA

Data Summary: LEG_DAILYCOUNTS_D
type: Distribution
format: CSV
issued: 2025-10-21T13:39:36.156417
modified: 2025-10-22T12:30:18.140942
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/1006edc5-d771-492a-9610-3d548d176331/resource/274a49da-82e9-41e4-a3ef-8a0a4575a7d9/download/data_summary_leg_dailycounts_d.csv
byte size: 869
media type: text/csv

External Website: FUSION project: multimodal transport user data
type: Distribution
format: HTML
issued: 2025-10-30T13:37:58.206300
modified: 2025-11-07T10:49:24.976175
access URL: https://www.gov.uk/government/publications/fusion-project-multimodal-transport-user-data

Variable Dictionary
type: Distribution
format: CSV
issued: 2025-10-20T11:58:09.937909
modified: 2025-10-20T11:58:11.571569
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/1006edc5-d771-492a-9610-3d548d176331/resource/4dadbb4d-8432-4aa4-afb1-0eb423c6ddcf/download/variable_dictionary.csv
byte size: 2816
media type: text/csv

Data Sample: LEG_DAILYCOUNTS_D
type: Distribution
format: CSV
issued: 2025-10-21T13:23:24.474322
modified: 2025-11-07T10:49:29.103048
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/1006edc5-d771-492a-9610-3d548d176331/resource/d6ac14dd-223a-4aaf-9a45-5c0f886753ac/download/sample_data_leg_dailycount_d.csv
byte size: 437
media type: text/csv

Technical Report: User Guide
type: Distribution
format: PDF
issued: 2025-10-21T13:43:42.120181
modified: 2025-10-30T13:37:58.180273
access URL: https://data.geods.ac.uk/dataset/1006edc5-d771-492a-9610-3d548d176331/resource/dfbd82c9-e5ca-453a-843a-79b59ebcbe72/download/user-guide-poc-tool-and-open-datasets-v2.pdf
byte size: 2365513
media type: application/pdf

London OAC (2011)
type: Dataset
description: The London Output Area Classification (LOAC) 2011 relates to the 2011 Census, and is specified for 2011 Output Areas. This classification has been superseded by a new edition based on 2021 Census data and geography, see the Related Record below. This classification was created using a two-tier classification based on 2011 Census data for Greater London. LOAC can be used to understand the variegated structure of neighbourhoods across London and is an open geodemographic that assigns each London neighbourhood into 8 Supergroups that are further divided into 21 Groups. The groups profile neighbourhood geography using 60 salient socio-demographic characteristics. LOAC can provide valuable context to Borough and regional level planning, enabling analysts to understand local needs and to tailor policy and services accordingly. ## Content The data are available for download below. Also available are a final report on LOAC 2011, a lookup file for codes, names and colours, as well as a classification flyer. ## Quality, Representation and Bias A journal article accompanies LOAC which provides a thorough evaluation. All data used for this classification are sourced from the 2011 Census so bounded by the usual operational quality / representation and bias of a national census. The geodemographic classification created presents a best effort of the authors to represent the characteristics of the population and geographic context of London, however, there are decisions made during the classification process that guide these representations. For a full overview of these decisions and their rationale, see the published paper.
issued: 2024-12-16T16:47:45.888541
modified: 2025-08-21T10:16:14.509727
contact point: Organization
  fn: Dr Maurizio Gibin
keyword: Census
keyword: Demographics
keyword: LOAC
keyword: London
keyword: OAC
landing page: Office for National Statistics

Variable Dictionary: Code, Name, Colour and Pen Portrait Lookup
type: Distribution
description: This provides a lookup file for the codes, names, colours (as used in GeoDS Mapmaker) and Pen Portraits (descriptions) for LOAC 2011.
format: CSV
issued: 2024-12-16T16:55:33.201676
modified: 2025-05-07T09:04:24.765857
access URL: https://data.geods.ac.uk/dataset/16625d66-4afb-4fb9-807a-79c766b552b3/resource/1e8acb56-511e-42b5-bf2a-7b60cb642768/download/variable_dictionary_loac11_lookups.csv
byte size: 11150
media type: text/csv

Flyer: London Output Area Classification 2011
type: Distribution
description: A flyer for LOAC
format: PDF
issued: 2024-12-16T16:56:19.732047
modified: 2025-05-05T23:17:05.916506
access URL: https://data.geods.ac.uk/dataset/16625d66-4afb-4fb9-807a-79c766b552b3/resource/33e489d5-f7a8-4cd8-b0c7-3eca79f49e97/download/1.8.4-loac_pal.pdf
byte size: 318193
media type: application/pdf

Data Summary
type: Distribution
format: CSV
issued: 2024-12-16T16:56:48.274658
modified: 2025-05-05T23:17:05.916584
access URL: https://data.geods.ac.uk/dataset/16625d66-4afb-4fb9-807a-79c766b552b3/resource/742be1da-9663-4563-a96f-065be64dda4d/download/data_summary_loac11.csv
byte size: 6699
media type: text/csv

https://data.geods.ac.uk/dataset/16625d66-4afb-4fb9-807a-79c766b552b3/resource/8cc5061e-d4ad-4355-9a9e-7bc91b0bed60
type: Distribution
format: HTML
issued: 2025-05-07T09:05:59.783853
modified: 2025-08-21T10:14:38.616996
title: Paper: Singleton, A. D., and Longley, P. (2015) The internal structure of Greater London: a comparison of national and regional geodemographic models. Geo: Geography and Environment, 2:1, 69–87, doi: 10.1002/geo2.7.
access URL: https://doi.org/10.1002/geo2.7

Data: London Output Area Classification 2011
type: Distribution
description: Shapefile: LOAC_London (.shp,.prj,.dbf,.shx). Tables: LOACInputData.csv: Input data, and LOAC_Lookup.csv: LOAC lookup table. Maps: AtlasSuper.pdf - Borough maps of Supergroups, AtlasGroups.pdf - Borough maps of Groups, GroupLondon.pdf - London Group Map, SuperGroupLondon.pdf - London Supergroup Map. Other files: metadata.xml: Meta Data, readme.txt: Information about the CDRC 2011 LOAC Geodata, LOACOverviewReport.pdf: Overview report about LOAC.
format: ZIP
issued: 2024-12-16T16:53:36.717462
modified: 2025-08-21T10:14:54.666071
access URL: https://data.geods.ac.uk/dataset/16625d66-4afb-4fb9-807a-79c766b552b3/resource/bcb0b86a-92d7-49fa-8b49-4550482ab279/download/london-output-area-classification.zip
byte size: 45864915
media type: application/zip

Related Record: London OAC (2021)
type: Distribution
format: HTML
issued: 2025-05-07T09:04:24.779501
modified: 2025-05-08T15:24:14.643157
access URL: https://data.geods.ac.uk/dataset/london-oac

Technical Report: London Output Area Classification (LOAC) Final Report
type: Distribution
description: This report is the same file that is contained within the Geodata Pack zip file, it is presented separately here for convenience.
format: PDF
issued: 2024-12-16T16:54:48.762541
modified: 2025-05-05T23:17:05.916354
access URL: https://data.geods.ac.uk/dataset/16625d66-4afb-4fb9-807a-79c766b552b3/resource/c72e46a7-5b9b-4ced-a829-9ae5d35bd81b/download/loacoverviewreport.pdf
byte size: 15853964
media type: application/pdf

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2024-12-16T16:57:11.357019
modified: 2025-08-21T10:16:14.515012
access URL: https://data.geods.ac.uk/dataset/16625d66-4afb-4fb9-807a-79c766b552b3/resource/cf14ab21-d911-485e-8ff1-00a4b2f396c8/download/variable_description_loac11.csv
byte size: 230
media type: text/csv

MaPS Financial Wellbeing Survey
type: Dataset
description: The Money and Pensions Service's Financial Wellbeing Survey 2021 was a nationally representative survey of over 10,000 adults living in the UK. The questionnaire covered the building blocks required for people to feel financially resilient, confident, and empowered. It was about how people spend, save and generally manage their money and bills both day to day and longer term” The 2021 survey built upon previous Financial Capability Surveys carried out in 2015 and 2018. The Financial Wellbeing Survey has been superseded by MoneyView, which is also available as a product here at GeoDS. The survey questions and answers are grouped into the following sections: * Basic demographic questions (age, sex, location (using 2011 LSOAs to approximate), employment status, internet usage) * Household composition * Life/financial satisfaction and confidence * Debt/current finances * Money attitudes * Goals * Current account, budgeting, shopping around * Managing credit use * Saving * Resilience and insurance/protection * Planning for Later Life (66+) * Advice, guidance and life events * Retirement planning (working age) * Financial Numeracy (quiz) * Income * Demographics (e.g. ethnicity, religion, health, education) ## Content GeoDS holds the anonymised individual records containing the responses from each survey participant. Access to this data is available through the GeoDS Data service. The survey answers are available as two record-level CSVs (one with codes and one with labels). There is also a code to a label lookup file, a questionnaire copy and a technical report. Please note that the supplied data does not include the extra variables mentioned in the linked technical report (Section 6.3 Table 7) apart from LSOA11CD. It should however be possible for a user of the data to map both of these from the LSOA11CD using lookup tables from the ONS or other statistical authorities. ## Quality, Representation and Bias The survey is a high quality survey organised by a professional cu…
issued: 2024-11-28T15:02:10.393489
modified: 2026-01-15T14:11:55.188512
version info: 1.0
contact point: Organization
  fn: Oliver O'Brien
keyword: Capability
keyword: Financial Wellness
keyword: Wellness
landing page: Money and Pensions Service

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T15:03:38.804652
modified: 2025-09-17T13:03:12.927910
access URL: https://data.geods.ac.uk/dataset/185de700-a39b-48c6-8838-8b14fa447b6b/resource/023882f5-8e0a-470c-bba1-4d5cdbd80050/download/maps_fws_variable_descriptions.csv
byte size: 3866
media type: text/csv

Technical Report
type: Distribution
format: PDF
issued: 2024-11-28T15:03:04.677504
modified: 2025-09-17T13:03:12.927770
access URL: https://data.geods.ac.uk/dataset/185de700-a39b-48c6-8838-8b14fa447b6b/resource/ac3f0886-01d4-4389-8f09-e2dafd868a2b/download/adult-financial-wellbeing-survey-2021-technical-report.pdf
byte size: 607367
media type: application/pdf

Related Record: MaPS MoneyView Survey
type: Distribution
format: HTML
issued: 2025-09-17T13:03:12.942010
modified: 2025-09-17T13:05:02.233401
access URL: https://data.geods.ac.uk/dataset/maps-moneyview-survey

Retail Centre Boundaries and Open Indicators
type: Dataset
description: The Retail Centre Boundaries are an openly available suite of data products representing the location, extent and function of retail agglomeration areas across the UK. The product contains multiple files to describe the spatial boundaries, catchment zones, local open indicators and a retail typology. Retail centres, or agglomerations, are identified based on the clustering and connectivity patterns of individual retail units over space. They have been delineated using consistent methods and data, where possible, for England, Wales, Scotland and Northern Ireland. The spatial granularity and detail of retail agglomerations provide a national picture of retail spaces and hierarchy. Developing indicators that can help to understand their characteristics and economic performance can provide useful supporting information for policy in identifying underperforming and/or thriving retail centres across the UK. ## Content __Retail Centre Boundaries__ Boundaries are delineated using openly available and geocoded retail-specific unit locations and land use. Self-contained mutually exclusive tracts of consistently-sized hexagon geometries are overlaid on retail clusters, with a network-based algorithmic fine-tuning based on absolute sizes and densities. The retail centres are developed consistently for all countries across the UK. In total, there are 6,423 agglomerations of retail across the countries. A hierarchical classification based on retail count, density and ranking within the respective local area serves to identify the prominence of each retail centre and captures variation between regional centres, market towns, small local centres, shopping centres and retail outlets (among 11 classification tiers). Conventional place names are drawn from the Ordnance Survey (OS) Open Names file. __Retail Centre Catchments__ The Retail Centre Catchments are spatial delineations of both drive-time and walking-time distances from the centre of each retail centre not classified as “Smal…
issued: 2024-12-17T12:37:15.011453
modified: 2026-04-15T13:02:20.186648
version info: 3.0
contact point: Organization
  fn: Oliver O'Brien
keyword: High Street
keyword: Retail
keyword: Retail Centre
keyword: Retailer
landing page: Valuation Office Agency, OpenStreetMap, Green Street, Ordnance Survey Open Data

Data: Retail Centre Boundaries
type: Distribution
description: DOI: 10.20390/retailcentres2022 Creator Name: Consumer Data Research Centre Contributor Name: Consumer Data Research Centre, Local Data Company Identifier: https://dx.doi.org/10.20390/retailcentres2022 Publisher: Consumer Data Research Centre Publication Year: 2022 Subject: Retail, Retail Centre, Shopping, High Streets, CDRC Language: English Resource Type: Area Boundaries and Attributes Version: 1 Description: Dataset containing the location, extent and function of retail agglomeration areas across the UK.
format: ZIP
issued: 2024-12-17T12:53:58.652727
modified: 2025-05-05T23:00:58.228237
access URL: https://data.geods.ac.uk/dataset/19db8f01-7727-4d35-91de-44166b85b3b6/resource/00b4e06a-8189-4771-8ff3-c2dbe9cbd606/download/retail_boundaries_uk.zip
byte size: 41176712
media type: application/zip

Data: Retail Centre Catchments (Driving and Walking time)
type: Distribution
description: Two retail catchment geo packages including walking time and driving time catchment data created by road network.
format: ZIP
issued: 2024-12-17T12:55:06.408257
modified: 2025-05-05T23:00:58.228352
access URL: https://data.geods.ac.uk/dataset/19db8f01-7727-4d35-91de-44166b85b3b6/resource/250d45d3-6097-4c8a-ad12-a6b0e71dcb54/download/cdrc_retailcentre_2021_walking-time-catchment.zip
byte size: 10570185
media type: application/zip

Variable Dictionary: Catchment Durations
type: Distribution
format: CSV
issued: 2024-12-17T13:15:42.242763
modified: 2025-05-05T23:00:58.229469
access URL: https://data.geods.ac.uk/dataset/19db8f01-7727-4d35-91de-44166b85b3b6/resource/30337c04-bde0-4e5d-bfd7-5acc1a1806bb/download/variable_dictionary_rc_catchment_durations.csv
byte size: 373
media type: text/csv

Variable Dictionary: Retail Centres Classification Hierarchy
type: Distribution
format: CSV
issued: 2024-12-17T12:58:06.080453
modified: 2025-05-05T23:00:58.228802
access URL: https://data.geods.ac.uk/dataset/19db8f01-7727-4d35-91de-44166b85b3b6/resource/33037e5e-202d-466c-8866-3a6b0a49589a/download/variable_dictionary_retailcentres_classification_hierarchy.csv
byte size: 1141
media type: text/csv

Source Code: Github repository (Retail Centre Boundaries)
type: Distribution
format: HTML
issued: 2024-12-17T12:59:42.285361
modified: 2025-05-06T17:16:43.153077
access URL: https://github.com/ESRC-CDRC/Retail-Centre-Boundaries

Data: Retail Centre Typology (2022) (GeoPackage format)
type: Distribution
description: The geographies are identical for those in the main geography files on this dataset page, but for a subset of retail centres.
format: ZIP
issued: 2024-12-17T12:57:36.229673
modified: 2025-05-05T23:00:58.228655
access URL: https://data.geods.ac.uk/dataset/19db8f01-7727-4d35-91de-44166b85b3b6/resource/42baf912-1a7d-484c-adee-ed6ca6f0943e/download/typology_2022.gpkg_.zip
byte size: 17986133
media type: application/zip

Related Record: Retail Centre Indicators
type: Distribution
format: HTML
issued: 2024-12-17T12:59:34.348389
modified: 2025-05-06T17:16:28.903935
access URL: https://data.geods.ac.uk/dataset/retail-centre-indicators

Technical Report: Retail Centre Typology (2022) User Guide and Pen Portraits
type: Distribution
format: PDF
issued: 2024-12-17T12:57:53.115408
modified: 2025-05-05T23:00:58.228729
access URL: https://data.geods.ac.uk/dataset/19db8f01-7727-4d35-91de-44166b85b3b6/resource/5e03090a-e289-4ed7-b8ec-9c390e768d60/download/typology2022userguide_v2.pdf
byte size: 864484
media type: application/pdf

Data: Retail Centre Administrative Geography Mappings
type: Distribution
description: Lookup tables between retail centres and common administrative geographies (Output Areas OA11CD and Workplace Zones WZ11CD)
format: ZIP
issued: 2024-12-17T12:56:11.679206
modified: 2025-05-05T23:00:58.228507
access URL: https://data.geods.ac.uk/dataset/19db8f01-7727-4d35-91de-44166b85b3b6/resource/66f43016-11bc-485c-a576-43e758cbf9ee/download/rc_lookups.zip
byte size: 997166
media type: application/zip

Variable Dictionary: Retail Centre Typology (2022) Lookup
type: Distribution
format: CSV
issued: 2024-12-17T12:56:25.517359
modified: 2025-05-05T23:00:58.228581
access URL: https://data.geods.ac.uk/dataset/19db8f01-7727-4d35-91de-44166b85b3b6/resource/960f803c-21d8-4e0a-acb3-4151bffe6277/download/typology_lookup_2022.csv
byte size: 129938
media type: text/csv

Source Code: Github repository (Retail Centre Indicators)
type: Distribution
format: HTML
issued: 2024-12-17T13:00:02.189767
modified: 2025-05-08T15:24:22.635705
access URL: https://github.com/patrickballantyne/CDRC-Retail-Indicators/

Map: Mapmaker
type: Distribution
format: HTML
issued: 2025-06-05T08:32:13.209394
modified: 2025-06-05T08:32:13.197701
access URL: https://mapmaker.geods.ac.uk/#/retail-centres

Variable Dictionary: Columns and Sources
type: Distribution
format: CSV
issued: 2024-12-17T12:58:26.119715
modified: 2025-05-05T23:00:58.228945
access URL: https://data.geods.ac.uk/dataset/19db8f01-7727-4d35-91de-44166b85b3b6/resource/b12421f1-0e63-409a-af58-a2c2b9d72ff2/download/variable_dictionary_rc_open.csv
byte size: 3446
media type: text/csv

Related Record: Retail Centre Boundaries (Previous Versions)
type: Distribution
format: HTML
issued: 2024-12-17T12:59:25.449011
modified: 2025-05-06T17:16:13.113574
access URL: https://data.geods.ac.uk/dataset/retail-centre-boundaries-previous-versions

Paper: Macdonald, J.L., Dolega, L. & Singleton, A. An open source delineation and hierarchical classification of UK retail agglomerations. Sci Data 9, 541 (2022).
type: Distribution
format: HTML
issued: 2024-12-17T12:59:50.825550
modified: 2025-05-06T17:17:34.185876
access URL: https://doi.org/10.1038/s41597-022-01556-3

Data Summary
type: Distribution
format: CSV
issued: 2024-12-17T12:58:16.030908
modified: 2025-05-05T23:00:58.228873
access URL: https://data.geods.ac.uk/dataset/19db8f01-7727-4d35-91de-44166b85b3b6/resource/feb3c4a6-a981-48c7-8590-e4605798e946/download/data_summary_rc_boundariesopenindicators.csv
byte size: 4092
media type: text/csv

Data: Open Indicators
type: Distribution
format: CSV
issued: 2024-12-17T12:55:56.546686
modified: 2025-05-05T23:00:58.228432
access URL: https://data.geods.ac.uk/dataset/19db8f01-7727-4d35-91de-44166b85b3b6/resource/ff7f2b65-d890-427f-8f8c-34430985c888/download/rc_indicators_openupdated_2022.csv
byte size: 203339
media type: text/csv

FCA Financial Lives Survey
type: Dataset
description: The Financial Lives Survey (FLS) is a robust large-scale quantitative survey, conducted by the Financial Conduct Authority (FCA). It establishes, from all respondents, levels of product ownership, in retail banking, retail investment, savings, credit including mortgages, general insurance and protection, and pension products. It also establishes the level of use of regulated financial advice (advice incidence) and has a section covering assets and debts. Each respondent is also allocated a single detailed question set about one product area (such as retail banking or first charge mortgages) based on their product holdings, or about advice. ## Content and Size of the Data The data available are microdata (individual responses) - there is no aggregated data, however the FCA website does included aggregated forms of this data, see the resource links below. There are 2 datasets available: * The anonymised individual responses from the Financial Lives Survey exercises that were carried out in 2024. The full data includes approximately 18,000 individual level records from across the UK. * A tracker file of anonymised responses from the 2017, 2020, 2022 and 2024 surveys for those variables that can be tracked from wave to wave. Demographic data includes racial/ethnic origin, long-term health problems, sexual orientation, and what government benefits the participant is in receipt of. Each available data collection (2024 full survey and tracker for 2017, 2020, 2022 and 2024) contains two data files, (a values and labels version) a metadata file (codebook) to match up labels and values, and a weighting reference sheeting. A data user guide is also available to instruct users on how use the data. In detail, the files are: * labels/captions data file - Individual responses (full text version) - each field (response part) is delimited. Some fields contain multiple responses, these are sub-delimited. Please note that many of the records are very long. * codes data file - A copy o…
issued: 2024-11-28T13:43:56.440116
modified: 2026-04-17T10:57:11.161603
version info: 4.2
contact point: Organization
  fn: Dr Maurizio Gibin
keyword: Finance
landing page: FCA

External Website: FCA - FLS 2024 Technical Report
type: Distribution
format: HTML
issued: 2025-11-05T13:03:07.950146
modified: 2025-11-12T21:50:47.188304
access URL: https://www.fca.org.uk/publication/financial-lives/financial-lives-survey-2024-technical-report.pdf
media type: application/pdf

External Website: FCA - FLS 2024 Questionnaire
type: Distribution
format: HTML
issued: 2025-11-12T21:48:17.526657
modified: 2025-11-12T21:50:47.188235
access URL: https://www.fca.org.uk/publication/financial-lives/financial-lives-survey-2024-questionnaire.pdf
media type: application/pdf

Data Summary
type: Distribution
description: Updated to mention 2024 wave.
format: CSV
issued: 2024-11-28T13:45:14.007926
modified: 2025-11-05T13:01:49.515577
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/1d8618f9-4839-4b9b-a138-22571e33d880/resource/4b254015-7292-4d66-98bb-266573d0fccd/download/fca_fls_data_summary.csv
byte size: 2664
media type: text/csv

External Website: FCA - Financial Lives survey
type: Distribution
format: HTML
issued: 2025-11-05T13:09:59.976259
modified: 2025-11-12T21:47:42.146466
access URL: https://www.fca.org.uk/financial-lives

External Website: FCA - Questionnaires, Aggregated Data Tables and Reports
type: Distribution
format: HTML
issued: 2024-11-28T15:06:16.454430
modified: 2025-11-12T21:50:38.277142
access URL: https://www.fca.org.uk/financial-lives/resources-library

Salad Money Open Banking Transaction Data
type: Dataset
description: The Salad Money Open Banking Transaction level data were created in collaboration with Salad Money. Salad Money offers a novel approach to lending that does not utilize traditional credit score ratings. Salad Money uses data obtained via the Open Banking standard to securely gather financial data directly from applicants' banks and employs advanced data analytics and affordability checks to evaluate loan eligibility. This approach ensures precision in assessing an individual's ability to meet loan obligations without compromising sensitive login credentials and assesses applicants based on their current financial situation, recognising that a credit score is not the sole indicator of creditworthiness. The majority of Salad Money applicants are key workers. Initially providing loans only to NHS key workers, Salad Money extended its offer to private sector key workers and set the prerequisites to apply for a loan as follows: - not self-employed - have a monthly income of at least £1,400 - maximum amount allowed for a loan is £1,000 payable in 12 or 18 months. The dataset contains transactions amounts in GBP for categories of spending created by Salad Money. Additional demographic information on the loan applicants is provided. ## Content The dataset contains total transactions and corresponding total amounts in GBP by: day; Salad Money spending category; Salad Money spending subcategory; Salad Money transaction type; Individual user. The dataset also includes the bank account’s balance minimum and maximum amount for the transaction day. When a user provided more than one bank account, both transactions and balances are summed together to produce total spending and balances by user. Additional geodemographics, age and sex are provided. The data has been anonymised to avoid identification and protect Salad Money users’ privacy. However, the relatively fine-grained spatial and temporal resolution means it might be possible, in certain circumstances, to infer the identity…
issued: 2024-12-16T12:33:22.767678
modified: 2026-02-26T18:20:13.812691
contact point: Organization
  fn: Maurizio Gibin
keyword: Cost Of Living
keyword: Economy
keyword: Ethical Lenders
keyword: Finance
keyword: Financial Precarity
keyword: Loan
keyword: Open Banking
keyword: Spending
landing page: Salad Money Data

Variable Dictionary: Category Options
type: Distribution
format: CSV
issued: 2024-12-16T12:34:06.293281
modified: 2025-05-09T22:41:06.947778
access URL: https://data.geods.ac.uk/dataset/1e0bce51-66b6-4fe1-86ae-1c5c84181e9a/resource/69c29ba9-2c32-4188-a635-b632375d9630/download/variable_dictionary_saladmoneycatoptions.csv
byte size: 2305
media type: text/csv

Data Summary: Year 2022
type: Distribution
description: Data summary for the year 2022
format: CSV
issued: 2025-05-13T15:43:38.297896
modified: 2025-05-13T15:43:55.118081
access URL: https://data.geods.ac.uk/dataset/1e0bce51-66b6-4fe1-86ae-1c5c84181e9a/resource/cdf8e929-f322-493a-a3e9-b51b05f06ad8/download/data_summary_salad_money_secure_open_banking_transaction_data.csv
byte size: 866
media type: text/csv

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2024-12-16T12:34:48.984171
modified: 2026-02-10T11:10:39.780119
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/1e0bce51-66b6-4fe1-86ae-1c5c84181e9a/resource/cf6d54c3-179a-403c-b8eb-6b54e7704b01/download/variable_dictionary_saladmoneysecure.csv
byte size: 1041
media type: text/csv

Related Record: Salad Money Daily Transaction Volumes and Values
type: Distribution
format: HTML
issued: 2024-12-16T12:35:45.130634
modified: 2025-05-09T22:41:00.121257
access URL: https://data.geods.ac.uk/dataset/salad-money-daily-transaction-volumes-and-values

Residential Property Counts (LSOA Geography)
type: Dataset
description: This dataset provides yearly small area estimates (1997-2022) of the number of ‘active’ residential properties within each neighbourhood (Lower layer Super Output Area (LSOA: England and Wales), Data Zone (DZ: Scotland) and Super Output Area (SOA: Northern Ireland)) in the UK. Acquiring historical lifecycle information about individual properties in the UK poses challenges, as most data providers primarily focus on monitoring 'active' properties for facilitating mail and package deliveries around the country. This dataset is derived from a large dataset tracing the names and addresses of more than one billion individuals dating back to 1997 (LCRs: Linked Consumer Registers) to calibrate property lifecycle information within an authoritative geolocated address and property dataset. The Residential Property Counts data allow researchers to take a temporal perspective (limited to the years pertaining to 1997-2022) on, for instance, the geography and development of the residential housing stock in the UK. Potential applications include assessing changes in geography and levels of vulnerability in the context of extreme weather events like droughts and floods at the small area level. While property counts are technically not considered as personal data under the GDPR, counts of LSOAs with fewer than 10 properties are obfuscated. This is done by replacing the estimates with a random number ranging from 1-9. Estimates of geolocated active properties for each year between 1997 and 2022 are available as a secure dataset (see link below). ## Content Available as a CSV file. For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. ## Quality, Representation and Bias As the dataset is compiled by combining data from various organisations, data products, and providers, it is unlikely to contain 100% of al…
issued: 2024-11-28T14:01:20.299619
modified: 2025-05-08T15:21:44.274243
version info: 1.0
contact point: Organization
  fn: Justin van Dijk
keyword: mobility
keyword: property
landing page: GeoDS Linked Consumer Registers

Data Summary
type: Distribution
format: CSV
issued: 2024-11-28T14:03:02.493396
modified: 2025-05-05T23:15:20.778780
access URL: https://data.geods.ac.uk/dataset/227a7be4-80df-4368-96b5-1550cb9f1253/resource/43bdabae-5631-4c9f-9846-ad14dead22dd/download/data_summary_rpc_safeguarded.csv
byte size: 1686
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T14:02:40.294080
modified: 2025-05-05T23:15:20.778671
access URL: https://data.geods.ac.uk/dataset/227a7be4-80df-4368-96b5-1550cb9f1253/resource/4fa86947-bd63-4e55-9f11-13f318263b04/download/variable_dictionary_rpc_safeguarded.csv
byte size: 350
media type: text/csv

Related Record: Residential Property Counts
type: Distribution
format: HTML
issued: 2025-05-06T15:58:06.756239
modified: 2025-05-08T15:21:44.278733
access URL: https://data.geods.ac.uk/dataset/residential-property-counts

https://data.geods.ac.uk/dataset/227a7be4-80df-4368-96b5-1550cb9f1253/resource/7965b127-55cc-4e5f-b5bd-a2c7432648ac
type: Distribution
format: HTML
issued: 2024-11-28T14:51:00.090280
modified: 2025-05-05T23:15:20.778928
title: Paper: Lansley G, Li W, Longley P A 2019. Creating a linked consumer register for granular demographic analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society) DOI:10.1111/rssa.12476
access URL: https://discovery.ucl.ac.uk/id/eprint/10078650/

https://data.geods.ac.uk/dataset/227a7be4-80df-4368-96b5-1550cb9f1253/resource/fe33824b-0b89-4b8a-a704-ad2e88548956
type: Distribution
format: HTML
issued: 2024-11-28T14:51:41.740896
modified: 2025-05-05T23:15:20.778998
title: Paper: Van Dijk J, Lansley G, Longley P A 2021. Using linked consumer registers to estimate residential moves in the United Kingdom. Journal of the Royal Statistical Society Series A (Statistics in Society). DOI:10.1111/rssa.12713
access URL: https://discovery.ucl.ac.uk/id/eprint/10128043/

https://data.geods.ac.uk/dataset/227a7be4-80df-4368-96b5-1550cb9f1253/resource/ff702702-11bd-4d1b-a9f0-f37ffa208179
type: Distribution
format: HTML
issued: 2024-11-28T14:50:07.448018
modified: 2025-05-05T23:15:20.778857
title: Paper: van Dijk, J., Todd, J. and Lan, T. (2024) ‘Leveraging digital footprints data for accurate estimation of the residential housing stock in the United Kingdom, 1997–2022’, Annals of GIS, pp. 1–16.
access URL: https://doi.org/10.1080/19475683.2024.2360206

Classification of Multidimensional Open Data of Urban Morphology (MODUM)
type: Dataset
description: The Classification of Multidimensional Open Data of Urban Morphology (MODUM) is a national classification for England and Wales at the Output Area level (2011 Census Geography). It describes the typology of neighbourhoods based on a number of built environment and urban morphology attributes, such as street & railway network, green spaces, retail access and historic buildings, calculated from open data sources. ## Content The data is available for download from the bottom of this page. The clustering methodology is Self-Organising Maps (SOM). Additional information about the cluster profiles is available for download below, as well as in the open access academic paper linked below. For detailed descriptions of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can also be downloaded from the bottom of this page. ## Quality, Representation and Bias Full details of the quality, representation and bias can be found in the publication accompanying this data product. The methodology effectively integrates built environment and socioeconomic data into a multidimensional classification system. However, representation and data issues—such as the constraints of spatial units, open data limitations, and methodological sensitivity—highlight the importance of careful variable selection, aggregation practices, and validation processes. _The Historic England GIS Data contained in this material was obtained on 2015. The most publicly available up to date Historic England GIS Data can be obtained from https://www.historicengland.org.uk/._
issued: 2024-11-28T14:31:48.173547
modified: 2025-05-08T15:29:23.268789
contact point: Organization
  fn: Alex Singleton
keyword: Land
keyword: Land Development
keyword: Land Planning
keyword: Network

Data: MODUM_EW_2016
type: Distribution
description: Cluster labels per Output Area Code in CSV format
format: CSV
issued: 2024-11-28T14:33:54.216987
modified: 2025-05-05T23:15:31.584188
access URL: https://data.geods.ac.uk/dataset/23488f1b-f0b6-4de5-bbc2-a3ba2308d752/resource/25bb2466-0406-4821-8708-de4b8a99ae35/download/modumew2016.csv
byte size: 10434033
media type: text/csv

Paper: Alexiou, A., Singleton, A., and Longley, P.A., 2016. A Classification of Multidimensional Open Data of Urban Morphology. Built Environment, Volume 42:3, pp. 382-395.
type: Distribution
format: HTML
issued: 2024-11-28T14:40:12.074910
modified: 2025-05-05T23:15:31.584703
access URL: https://doi.org/10.2148/benv.42.3.382

Data: Cluster Profiles for MODUM
type: Distribution
description: A pdf describing the cluster profiles, including pen-portraits and attribute plots for every MODUM typology.
format: PDF
issued: 2024-11-28T14:36:07.879262
modified: 2025-05-07T13:39:43.159924
access URL: https://data.geods.ac.uk/dataset/23488f1b-f0b6-4de5-bbc2-a3ba2308d752/resource/2a9a2f87-62ba-4463-8c28-04f7a706634d/download/modumclusterprofiles.pdf
byte size: 215977
media type: application/pdf

Data: MODUM_EW_2016 shapefile
type: Distribution
description: A ZIP file containing an ESRI shapefile version of the classification using 2011 OA boundaries for England and Wales.
format: ZIP
issued: 2024-11-28T14:35:22.519372
modified: 2025-05-05T23:15:31.584414
access URL: https://data.geods.ac.uk/dataset/23488f1b-f0b6-4de5-bbc2-a3ba2308d752/resource/3fe4fb35-2bfb-4f09-b295-cb366424202a/download/modumew2016.zip
byte size: 59972512
media type: application/zip

Data: Cluster Centres per Cluster Group
type: Distribution
description: Units are z-scores - standard deviation.
format: CSV
issued: 2024-11-28T14:34:39.145632
modified: 2025-05-05T23:15:31.584298
access URL: https://data.geods.ac.uk/dataset/23488f1b-f0b6-4de5-bbc2-a3ba2308d752/resource/41bb1a5a-4084-48c0-b539-486a9133dfe0/download/modum_clustercentres.csv
byte size: 2580
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T14:36:29.879618
modified: 2025-05-05T23:15:31.584563
access URL: https://data.geods.ac.uk/dataset/23488f1b-f0b6-4de5-bbc2-a3ba2308d752/resource/617bde45-82e5-496f-b1fe-eda15b6ecf2b/download/variable_dictionary_modum.csv
byte size: 241
media type: text/csv

Data Summary
type: Distribution
format: CSV
issued: 2024-11-28T14:37:17.644048
modified: 2025-05-05T23:15:31.584635
access URL: https://data.geods.ac.uk/dataset/23488f1b-f0b6-4de5-bbc2-a3ba2308d752/resource/cd88f23c-b9f3-473f-bb00-ea722c9f91c8/download/data_summary_modum.csv
byte size: 390
media type: text/csv

Regional Transport Provider - Customer and Ticket Sales Data
type: Dataset
description: Regional Transport Provider - Customer and Ticket Sales Data contains information about customer and ticket sales data (electronic smartcards and paper tickets) in a largely urban commuter travel area in central England. It contains information about concessionary card holders, smart card holders and their boarding records, which can be used to depict regional use of public transport (mainly bus use) between November 2009 and March 2024. ## Content The dataset consists of three extracts of two tables. Typically these extracts are further divided into multiple CSV files, each with millions of rows. There are minor schema differences between the three extracts, please see the variable dictionary for full details. The two tables are transaction records (with columns on timestamps, payment methods, origin and destination) and customer demographics (with columns on age, gender and residential location (postcode, postcode sector or LSOA depending on the extract), along with an indication of whether the user is a concessionary card (e.g. disabled or elderly) or "commercial" smart card holder. Sometimes this latter indication is instead presented by the data being further split into two tables. The data is also available via a database login to a postgreSQL server. It can be accessed by using pgAdmin4 or a similar tool in the secure environment. Historically only concessionary users would have smartcards, however more recently the general population also now typically uses them too. Therefore, the proportion of concessionary vs commercial users has changed significantly through the dataset's time range. For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. ## Quality, Representation and Bias The dataset contains small percentages of missing values and covering multiple years. This dataset is limit…
issued: 2024-12-06T09:57:38.255827
modified: 2025-05-08T15:21:21.509273
version info: 2.0
contact point: Organization
  fn: Dr Jens Kandt
keyword: Bus
keyword: Buses
landing page: Regional Transport Provider

Related Record: Regional Transport Provider - Travel Demand Data
type: Distribution
format: HTML
issued: 2024-12-06T10:57:30.682755
modified: 2025-05-08T15:21:21.513906
access URL: https://data.geods.ac.uk/dataset/regional-transport-provider-travel-demand-data

Data Summary: Customer (Concessionary) Details
type: Distribution
description: This is a summary of one of the 247 CSV files supplied as part of the fourth tranche of data, covering 2021-2024.
format: CSV
issued: 2024-12-06T10:42:08.681865
modified: 2025-05-05T23:15:45.947784
access URL: https://data.geods.ac.uk/dataset/273ee289-5bf0-4323-89c6-c42f0ca575b1/resource/3957dc85-3aa3-4961-bbf3-a21d3012c15a/download/data_summary_rtpcustomer_custconc.csv
byte size: 531
media type: text/csv

Data Summary: Tickets/Journeys (Concessionary)
type: Distribution
description: This is a summary of one of the 247 CSV files supplied as part of the fourth tranche of data, covering 2021-2024.
format: CSV
issued: 2024-12-06T10:41:47.856852
modified: 2025-05-05T23:15:45.947707
access URL: https://data.geods.ac.uk/dataset/273ee289-5bf0-4323-89c6-c42f0ca575b1/resource/617bd26e-55d9-47ac-a514-c5b0c26081c6/download/data_summary_rtpcustomer_ticketconc.csv
byte size: 598
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-12-06T10:41:22.690485
modified: 2025-05-05T23:15:45.947600
access URL: https://data.geods.ac.uk/dataset/273ee289-5bf0-4323-89c6-c42f0ca575b1/resource/f5ac851d-8c0a-4165-973b-57662cd8cb25/download/variable_summary_rtpcustomer.csv
byte size: 6440
media type: text/csv

Event Listings and Venue Data
type: Dataset
description: Data Thistle collects event listings and venue information from numerous sources, compiles and standardises their format. Data Thistle aims to have complete coverage of short-duration events and venues, particularly in the cultural and sports sector, such as live music events and sports matches. ## Content For this initial release, seven areas in the UK have been identified. A JSON file is supplied for each area, along with a CSV containing a simplification of the individual performances data in the JSON file. Two additional concatenated CSV files also be supplied (see below). Two metadata files that describe the fields/formats are also supplied, and are additionally accessible below. Please note that, if referring to these metadata files, we do not supply images. Impact (importance of the event) and capacity information, are included. The summary and data dictionary files below are based on a conversion of a concatenation of the JSON files for the seven areas, to a pair of CSV files - one containing the performances (events happening in a particular place and at a particular date/time) and one containing the place (venue) information including locations - across all the areas. ## Quality, Representation and Bias The selected regions available in this initial product represent a reasonably representative selection of areas across the UK, with inclusion of at least one area in each UK nation. The completeness improves towards the current date. As a curated dataset managed by the data provider, the data quality is in general excellent. The nature of the source data and diverse nature of the upstream data providers does mean there are a small number of inconsistencies present in the data. Approximately 37% of the performances are categorised with film, with the next two most populous categories being Kids and Music. Together, these three categories form two-thirds of the listed performances. Selected other categories include Sport, Talks, Exhibition, Days out, Festival…
issued: 2025-12-02T13:00:01.099705
modified: 2025-12-22T17:40:01.368909
version info: 1.0
contact point: Organization
  fn: Carol Yin
keyword: conferences
keyword: economy
keyword: event
keyword: exhibitions
keyword: festival
keyword: music
keyword: night
keyword: talks
landing page: Data Thistle

External Website: Data Thistle Default Feed Specification
type: Distribution
format: HTML
issued: 2025-12-22T17:17:42.932374
modified: 2025-12-22T17:18:07.611512
access URL: https://files.datathistle.com/feeds/docs/feedspec.pdf
media type: application/pdf

External Website: Data Thistle Impact Feed Specification
type: Distribution
format: HTML
issued: 2025-12-22T17:18:07.617238
modified: 2025-12-22T17:18:07.611624
access URL: https://files.datathistle.com/feeds/docs/impactfeedspec.pdf
media type: application/pdf

External Website: Data Thistle Publishing API
type: Distribution
format: HTML
issued: 2025-12-18T12:54:33.787628
modified: 2025-12-18T12:55:17.725376
access URL: https://api.datathistle.com/

Data Summary: Places
type: Distribution
description: Venues, with location and addressing information.
format: CSV
issued: 2025-12-17T17:24:07.547765
modified: 2025-12-18T12:52:28.634341
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/30bc3838-5404-4cd6-9bff-9ed671ec6f14/resource/adf2b419-daae-40a6-acf3-998bb4c087f4/download/data_summary_places_geods20251202.csv
byte size: 1577
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2025-12-18T12:43:59.887960
modified: 2025-12-18T12:44:01.588854
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/30bc3838-5404-4cd6-9bff-9ed671ec6f14/resource/e0609431-8729-4dfa-b00f-6c5719e27205/download/variable_dictionary_datathistle.csv
byte size: 2276
media type: text/csv

Data Summary: Performances
type: Distribution
description: Performances are events taking place as part of a schedule, in a particular place (venue).
format: CSV
issued: 2025-12-02T13:00:31.257078
modified: 2025-12-18T12:25:28.058494
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/30bc3838-5404-4cd6-9bff-9ed671ec6f14/resource/e5230c8b-adc6-4a0d-b78f-85b6dc02d212/download/data_summary_performances_geods20251202.csv
byte size: 1839
media type: text/csv

External Website: Data Thistle Event listings - what's on
type: Distribution
format: HTML
issued: 2025-12-18T12:52:28.643132
modified: 2025-12-22T17:17:42.923792
access URL: https://www.datathistle.com/events/

GambleAware Treatment and Support Survey Data
type: Dataset
description: The GambleAware Treatment and Support Survey dataset comprises two annual robust large-scale quantitative surveys, using quota samples to recruit respondents from YouGov’s online panel. In total, 18,879 respondents aged 18+ across Great Britain participated in the 2020 survey, with similar participation for other years available (2019 (two phases), 2021, 2022). The survey was coordinated by the national designated body for preventing and reducing harmful gambling (GambleAware) and so is believed unique. The dataset includes key demographic variables and information on gambling participation, harms, and reported demand and uptake for treatment and support services. It also includes information on ‘affected others’ (those who are currently or have been affected by another person’s gambling). Individual responses have been weighted to build a representative profile across the country and complemented by the approximate Lower Super Output Area (LSOA) location for 2019, 2020, and 2022 records. ## Content The response data, mapping files that describe the data columns, and the questionnaires themselves, are included. 2019 data includes two phases of surveys- general population; problem gamblers and affected others only. 2020-2022 data includes the general population. 2021 data for individuals only includes UK regions instead of LSOAs. 2022 data includes UK regions or postcode sectors for different records. ## Quality, Representation and Bias Weighting adjusts the contribution of individual respondents to aggregated figures and is used to make surveyed populations more representative of a project-relevant, and typically larger, population by forcing it to mimic the distribution of that larger population’s significant characteristics, or its size. In order to make this study representative, the sample was weighted to be representative of all GB adults (aged 18+) by age, gender, UK region, socio-economic group and ethnic group. Please see below for links to the technical rep…
issued: 2024-11-28T14:01:17.113212
modified: 2025-05-08T15:21:31.095919
contact point: Organization
  fn: Maurizio Gibin
keyword: Demographic
keyword: Health
keyword: Socioeconomic
landing page: YouGov on behalf of GambleAware

Data Summary: 2019 (Phase 2)
type: Distribution
format: CSV
issued: 2024-12-04T14:50:00.928182
modified: 2025-05-05T23:15:29.676780
access URL: https://data.geods.ac.uk/dataset/34fdeb8e-1cd5-4d3d-81ba-b8dc274102ff/resource/16a4b023-32cc-45f8-9c91-e6995c669b93/download/data_summary_ga19phase2labels.csv
byte size: 46373
media type: text/csv

Data Summary: 2020
type: Distribution
format: CSV
issued: 2024-12-04T14:50:10.856729
modified: 2025-05-05T23:15:29.676849
access URL: https://data.geods.ac.uk/dataset/34fdeb8e-1cd5-4d3d-81ba-b8dc274102ff/resource/23607f13-7730-4b12-8765-0da07eb6cb40/download/data_summary_ga20codes_manualdisclosurecontrolled.csv
byte size: 84000
media type: text/csv

Data Summary: 2021
type: Distribution
format: CSV
issued: 2024-12-04T14:50:22.441032
modified: 2025-05-05T23:15:29.676919
access URL: https://data.geods.ac.uk/dataset/34fdeb8e-1cd5-4d3d-81ba-b8dc274102ff/resource/6606b63b-3934-4a62-bb54-5fdcf82dce5a/download/data_summary_ga21labels.csv
byte size: 57716
media type: text/csv

Technical Report: 2019 Survey
type: Distribution
format: PDF
issued: 2024-11-28T14:02:30.577332
modified: 2025-05-05T23:15:29.676316
access URL: https://www.gambleaware.org/media/g3ln3414/gambling-treatment-and-support.pdf
media type: application/pdf

Data Summary: 2019 (Phase 1)
type: Distribution
format: CSV
issued: 2024-12-04T14:49:48.680333
modified: 2025-05-05T23:15:29.676710
access URL: https://data.geods.ac.uk/dataset/34fdeb8e-1cd5-4d3d-81ba-b8dc274102ff/resource/9b2b19b9-ee43-44c1-8d16-4522d63c7e51/download/data_summary_ga19phase1codes_manualdisclosurecontrolled.csv
byte size: 19771
media type: text/csv

Technical Report: 2020 Survey
type: Distribution
format: PDF
issued: 2024-11-28T17:12:59.449012
modified: 2025-05-05T23:15:29.676421
access URL: https://www.gambleaware.org/media/ahpj5smk/annual_gb_treatment_and_support_survey_2020_report_-final-_260321.pdf
media type: application/pdf

Data Summary: 2022
type: Distribution
format: CSV
issued: 2024-12-04T14:50:33.382780
modified: 2025-05-05T23:15:29.676987
access URL: https://data.geods.ac.uk/dataset/34fdeb8e-1cd5-4d3d-81ba-b8dc274102ff/resource/c752abe9-4e01-432d-94b4-9bc8c0263652/download/data_summary_ga22labels.csv
byte size: 59791
media type: text/csv

Technical Report: 2022 Survey
type: Distribution
format: PDF
issued: 2024-11-28T17:13:36.352619
modified: 2025-05-05T23:15:29.676565
access URL: https://www.gambleaware.org/media/sxgpxmad/gambleaware-2022-treatment-and-support-report.pdf
media type: application/pdf

Technical Report: 2021 Survey
type: Distribution
format: PDF
issued: 2024-11-28T17:13:19.806691
modified: 2025-05-05T23:15:29.676496
access URL: https://www.gambleaware.org/media/nklko1bh/annual-gb-treatment-and-support-survey-report-2021-final-_0.pdf
media type: application/pdf

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-12-04T14:49:36.272012
modified: 2025-05-05T23:15:29.676640
access URL: https://data.geods.ac.uk/dataset/34fdeb8e-1cd5-4d3d-81ba-b8dc274102ff/resource/f3f784bc-a9cd-4f68-9a35-03699100e687/download/variable_dictionary_ga.csv
byte size: 2176
media type: text/csv

Unified UK Census Data (2021/2)
type: Dataset
description: The Unified UK Census Dataset (2021/2022) is a harmonised, small-area dataset that brings together census data from the three UK census agencies -- ONS (England & Wales), NRS (Scotland), and NISRA (Northern Ireland) -- into a single, comparable release. The UK's census agencies each publish their data separately, with distinct variables, question wordings, formats, and disclosure controls. Through a process of manual variable matching, standardisation, and aggregation, 190 comparable variables are produced across 25 topic tables for Output Areas (OAs) in England, Wales, and Scotland, and Data Zones (DZs) in Northern Ireland, comprising 239,023 small areas across the United Kingdom. Please see the accompanying paper below for comprehensive documentation of the harmonisation methodology and data sources. The tables span demographics, ethnicity, health, housing, and employment. ## Content The dataset available for download contains the counts for all 190 variables plus 25 table totals (215 variables in total) across each of the 239,023 small-area geographies. Data Zones are relabelled as "OA" in the dataset for consistency. Both CSV and Apache Parquet formats are provided. For a detailed description of all variables contained within the data, see the Variable Metadata file; and for notes on harmonisation decisions and cross-national differences for each table, see the Table Notes file. These files can be downloaded alongside the topic tables. ## Quality, Representation and Bias The data are compiled from the official census releases of the three UK statistical agencies, each of which applies its own disclosure control and data quality procedures. The census data represent a near-complete enumeration of the usually resident population on Census Day: 21 March 2021 for England, Wales, and Northern Ireland, and 20 March 2022 for Scotland. Users should note that: - The Scottish census was conducted one year later than the rest of the UK (2022 vs 2021), which may introduce t…
issued: 2026-02-12T17:00:14.276168
modified: 2026-03-13T19:04:45.106125
contact point: Organization
  fn: Owen Goodwin
keyword: Census
keyword: Population
landing page: ONS, NRS, NISRA

https://data.geods.ac.uk/dataset/36738d76-8dc7-41d3-8eb6-a796ce1db750/resource/1821d43a-21c6-4f78-ac56-0f6f63a32801
type: Distribution
format: HTML
issued: 2026-03-02T09:26:53.142382
modified: 2026-03-02T09:29:59.168241
title: Paper: Owen Goodwin, Alex Singleton. A unified dataset of UK census variables for small areas: Harmonised data tables from the 2021 England, Wales, and Northern Ireland censuses and the 2022 Scotland census, Environment and Planning B: Urban Analytics and City Science (2026)
access URL: https://doi.org/10.1177/23998083261429563

Data Summary
type: Distribution
description: Summary statistics for each variable
format: CSV
issued: 2026-03-03T13:58:47.449254
modified: 2026-03-03T13:58:49.655223
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/36738d76-8dc7-41d3-8eb6-a796ce1db750/resource/5f206486-6b45-4c51-bd6a-973149045dd2/download/data_summary_combined_tables.csv
byte size: 19296
media type: text/csv

Data: Variable Tables (Parquet)
type: Distribution
description: Unified Cenus data tables in parquet format
format: ZIP
issued: 2026-02-19T18:21:00.867031
modified: 2026-03-03T13:47:26.808367
access URL: https://data.geods.ac.uk/dataset/36738d76-8dc7-41d3-8eb6-a796ce1db750/resource/659a00e8-9595-4ecf-b867-16c5510abc71/download/variable_tables_parquet.zip
byte size: 50454499
media type: application/zip

Data: Variable Tables (CSV)
type: Distribution
description: Unified Census data tables in csv format
format: ZIP
issued: 2026-02-19T18:19:59.740663
modified: 2026-03-03T13:38:04.714848
access URL: https://data.geods.ac.uk/dataset/36738d76-8dc7-41d3-8eb6-a796ce1db750/resource/7d501709-1301-4a50-9913-57b15907b139/download/variable_tables_csv.zip
byte size: 63088411
media type: application/zip

Variable Dictionary: Table Notes
type: Distribution
description: List of table titles and notes on any features of interest in the harmonisation process.
format: CSV
issued: 2026-02-19T17:57:33.133523
modified: 2026-03-03T13:36:48.789559
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/36738d76-8dc7-41d3-8eb6-a796ce1db750/resource/bea92a49-cb53-4435-8377-190657ffcd99/download/table_notes.csv
byte size: 8711
media type: text/csv

Variable Dictionary: Variable Metadata.
type: Distribution
description: Metadata descriptions of each variable.
format: CSV
issued: 2026-02-12T17:02:19.936855
modified: 2026-03-03T13:34:51.612192
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/36738d76-8dc7-41d3-8eb6-a796ce1db750/resource/d45a4872-a364-49e7-a4ba-29160c83851f/download/variable_metadata.csv
byte size: 21859
media type: text/csv

Related Record: Output Area Classification (2021/2)
type: Distribution
format: HTML
issued: 2026-03-03T13:47:26.818650
modified: 2026-03-03T13:58:47.433991
access URL: https://data.geods.ac.uk/dataset/output-area-classification-2021

Source Code: Github Repository
type: Distribution
format: HTML
issued: 2026-02-19T17:52:18.013323
modified: 2026-02-19T17:57:33.125428
access URL: https://github.com/GeographicDataService/unified-uk-census-2021-22

Geographic Data Science in Python
type: Dataset
description: This tutorial is a part of the course taught by Dr. Dani Arribas-Bel at the University of Liverpool. The course and materials provide students with core competencies in Geographic Data Science (GDS), covering the following topics. * Advancing their statistical and numerical literacy. * Introducing basic principles of programming and state-of-the-art computational tools for GDS. * Presenting a comprehensive overview of the main methodologies available to the Geographic Data Scientist, as well as their intuition as to how and when they can be applied. * Focusing on real world applications of these techniques in a geographical and applied context. Details on the data used in the tutorials below can be found at the Teaching Materials page, linked below. For more details about the course (slides, references, citation, license, etc.) please refer to the course page below. If you use material from this course, please used the citation listed below.
issued: 2024-11-28T12:44:23.832451
modified: 2025-07-16T10:07:14.796083
contact point: Organization
  fn: Dani Arribas-Bel
keyword: GDS
keyword: GIS
keyword: Geographic Data Science in Python
keyword: Python
keyword: Tutorial
landing page: https://darribas.org/gds19/

External Website: Course website
type: Distribution
format: HTML
issued: 2024-11-28T12:46:47.200036
modified: 2025-07-16T10:07:14.800214
access URL: https://jose.theoj.org/papers/10.21105/jose.00042

Related Record: Teaching Materials
type: Distribution
format: HTML
issued: 2025-07-16T10:05:55.598459
modified: 2025-07-16T10:06:35.207600
access URL: https://data.geods.ac.uk/dataset/teaching-materials

Residential Mobility and Deprivation (RMD) Index (LSOA Geography)
type: Dataset
description: Residential Mobility and Deprivation (RMD) data provide yearly estimates of the mean differences of residential mobility and deprivation of all known adults’ moves to, from and within each neighbourhood. Data are provided for 1997-2025 on small area estimates across the UK, comprising of Lower Layer Super Output Area for England and Wales (LSOA), Data Zones for Scotland (DZ), and Super output Areas for Northern Ireland (SOA). ## Content The data are created from multiple data sources, which are blends of English Index of Multiple Deprivation (EIMD), Welsh Index of Multiple Deprivation (WIMD), Scottish Index of Multiple Deprivation (SIMD), Northern Ireland Multiple Deprivation Measure (NIMDM), and GeoDS Secure data Linked Consumer Registers. Indices of Multiple Deprivation (IMDs) are summary measures of physical and social conditions compiled by statistical authorities for neighbourhood areas across the country for each Lower layer Super Output Area (LSOA: England and Wales), Data Zone (DZ: Scotland) and Super Output Area (SOA: Northern Ireland). Area ranks within each UK country were standardised into percentile scores, with low values denoting more deprived LSOAs. These scores from each national IMD were ranked and interleaved with the most contemporaneous ranked scores for the other UK countries. The resulting sequence was then ranked as a UK index of percentile scores. The most deprived area has the lowest rank (1), and the least deprived area has the highest rank (100). Three ‘Harmonised’ IMD datasets were assembled for circa 2004 (2004 EIMD, 2005 WIMD, 2004 SIMD and 2004 NIMDM), 2010 (2010 EIMD, 2011 WIMD, 2012 SIMD and 2010 NIMDM) and 2019 (2019 EIMD, 2019 WIMD, 2020 SIMD and 2017 NIMDM). GeoDS Linked Consumer Registers (LCRs) are based upon annual updates of the names and addresses for adult UK residents from 1997 onwards. They offer a near-complete coverage of the adult population at individual level (Lansley et al 2019; van Dijk et al 2021). ‘Harmonised’ IM…
issued: 2024-11-28T16:11:54.703654
modified: 2026-04-27T10:03:41.887487
contact point: Organization
  fn: Maurizio Gibin
keyword: Consumer Register
keyword: Deprivation
keyword: Electoral Roll
keyword: IMD
keyword: Index of Multiple Deprivation
keyword: Migration
keyword: Mobility
keyword: Population
landing page: IMD, GeoDS Linked Consumer Register

Technical Report: Research Ready Smart Data
type: Distribution
format: PDF
issued: 2024-11-28T16:12:36.242689
modified: 2025-05-07T13:45:41.741479
access URL: https://data.geods.ac.uk/dataset/3ad9fa21-5e64-4099-a5c8-860165e6d327/resource/030f7a1f-dcb9-4ee4-b06e-7852607c6e92/download/lcr_technical_report_2023.pdf
byte size: 734416
media type: application/pdf

Data Summary: RMD for Flows Into LSOAs
type: Distribution
format: CSV
issued: 2024-11-28T16:13:15.454091
modified: 2025-12-01T11:42:01.699399
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/3ad9fa21-5e64-4099-a5c8-860165e6d327/resource/1623a493-cafc-4cfe-88fd-6d5fa01837ae/download/rmd_in_lsoa_data_summary.csv
byte size: 2209
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T16:13:07.406994
modified: 2025-12-01T13:20:49.171775
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/3ad9fa21-5e64-4099-a5c8-860165e6d327/resource/33973d74-1d94-427c-a180-cbea6da39c07/download/variable_dictionary_rmd_lsoa.csv
byte size: 297
media type: text/csv

https://data.geods.ac.uk/dataset/3ad9fa21-5e64-4099-a5c8-860165e6d327/resource/a6aec451-7220-4ce8-8922-2a92dae9715a
type: Distribution
format: HTML
issued: 2024-11-28T16:17:11.369281
modified: 2025-05-06T15:09:20.435382
title: Paper: Justin T. Van Dijk, Guy Lansley, Paul A. Longley, Using Linked Consumer Registers to Estimate Residential moves in the United Kingdom, Journal of the Royal Statistical Society Series A: Statistics in Society, Volume 184, Issue 4, October 2021, Pages 1452–
access URL: https://doi.org/10.1111/rssa.12713

Data Summary: RMD for Flows Out of LSOAs
type: Distribution
format: CSV
issued: 2024-11-28T16:14:16.894609
modified: 2025-12-01T11:42:27.031461
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/3ad9fa21-5e64-4099-a5c8-860165e6d327/resource/b88c9a48-1d0e-4572-8e11-ce18e8367384/download/rmd_out_lsoa_data_summary.csv
byte size: 2195
media type: text/csv

Paper: Lansley G, Li W, Longley P A 2019. Creating a linked consumer register for granular demographic analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society)
type: Distribution
format: HTML
issued: 2024-11-28T16:16:06.986767
modified: 2025-05-05T23:09:32.767364
access URL: https://doi.org/10.1111/rssa.12476

Related Record: Residential Mobility and Deprivation (RMD) Index (LAD Geography)
type: Distribution
format: HTML
issued: 2024-11-28T16:47:18.509639
modified: 2025-12-01T11:42:00.097331
access URL: https://data.geods.ac.uk/dataset/residential-mobility-and-deprivation-rmd-index-lad-geography

Postcode Context Classification
type: Dataset
description: The Postcode Context Classification provides a national measure of urban spatial structure from high-resolution satellite imagery derived by cutting-edge convolutional neural network (CNN) techniques. By harnessing the power of the European Space Agency’s Copernicus Sentinel-2 satellites, combined with georeferenced postcodes, this data product offers unparalleled insights into the built environment across Great Britain. Theses data presents a new method of measuring local context that could be flexibly applied within different settings to meet several definitions of neighbourhood. While the method is implemented within the context of Great Britain, given the global coverage of satellite imagery, the approach can also be applied to any location in the world. The limits of the technique are centred around the resolution of the satellite data used and the interaction between the geography of the input data and the learned structure. ## Content This data are open to access, available below as ‘Data: Cluster Labels’. Detailed descriptions of the clusters are available through the pen portraits. A FAQ sheet is also provided. For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. ## Quality, Representation and Bias The source data was subject to intense cleaning to prepare for classification. These techniques may introduce biases. Please see the published paper in the 'Related Content' section for full details.
issued: 2024-11-28T14:44:59.914560
modified: 2025-06-05T08:29:07.577842
contact point: Organization
  fn: Alex Singleton
keyword: imagery and earth cover
keyword: postcode
keyword: urban form
keyword: urban function
landing page: Sentinel-2

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T14:46:41.793418
modified: 2025-05-05T23:08:39.206513
access URL: https://data.geods.ac.uk/dataset/3e2d3588-2d12-4a59-9b8c-a18f8d093947/resource/1e7333e7-0bb6-4738-80da-237b7782f06e/download/pcc_variabledesc.csv
byte size: 96
media type: text/csv

Map: Mapmaker
type: Distribution
format: HTML
issued: 2024-11-28T14:59:57.281035
modified: 2025-06-05T08:29:07.582617
access URL: https://mapmaker.geods.ac.uk/#/postcode-context-classification

https://data.geods.ac.uk/dataset/3e2d3588-2d12-4a59-9b8c-a18f8d093947/resource/43fb1d9a-631f-4c2f-af95-e9600d4787ac
type: Distribution
format: HTML
issued: 2024-11-28T14:51:49.940259
modified: 2025-05-05T23:08:39.206655
title: Paper: Alex Singleton, Dani Arribas-Bel, John Murray, Martin Fleischmann, Estimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network, Computers, Environment and Urban Systems, Volume 95, 2022, 101802, ISSN 0198-9715
access URL: https://doi.org/10.1016/j.compenvurbsys.2022.101802

Flyer: Postcode Classification.pdf
type: Distribution
format: PDF
issued: 2024-11-28T14:46:08.990271
modified: 2025-05-05T23:08:39.206363
access URL: https://data.geods.ac.uk/dataset/3e2d3588-2d12-4a59-9b8c-a18f8d093947/resource/56969884-9183-4e40-a1ac-8f146fb48a5a/download/postcode-classification.pdf
byte size: 401672
media type: application/pdf

Contextual Note: Frequently Asked Questions
type: Distribution
description: Q: What is the main objective of the research presented in the paper? A: The research develops a new national measure of urban spatial structure, focusing on the built environment through innovative analysis of high-resolution satellite-derived imagery using a convolutional neural network. Q: What data sources were used for measuring local spatial structure? A: High-resolution multispectral imagery from the European Space Agency’s Copernicus Sentinel 2 satellites and georeferenced postcodes from the Office for National Statistics. Q: How was the convolutional neural network model designed and trained? A: The model, a convolutional autoencoder (CAE), was trained to compress the dimensionality of the satellite data, preserving discriminative features of the area surrounding each GB postcode. Q: What method was used to represent the salient context of the data? A: A k-means clustering algorithm was applied to the latent vectors produced by the CAE, identifying postcodes with similar characteristics. Q: What was the outcome of the national classification of context case study? A: The study produced a classification covering the full extent of Great Britain, identifying different urban and rural areas with distinct characteristics in the Liverpool City Region. Q: What are the main conclusions and future research directions from this study? A: The study introduced a novel method to extract local contextual measures from satellite data. It suggests further exploration in the use of higher resolution data and different geographic extents to improve model performance and representation of urban context.
format: TXT
issued: 2024-11-28T14:46:27.666425
modified: 2025-05-05T23:08:39.206440

Data: Cluster Labels
type: Distribution
description: The classification for each postcode.
format: CSV
issued: 2024-11-28T14:45:46.460525
modified: 2025-05-05T23:08:39.206257
access URL: https://data.geods.ac.uk/dataset/3e2d3588-2d12-4a59-9b8c-a18f8d093947/resource/6ce00149-232c-4e51-a950-fe4c57619179/download/pcc.csv
byte size: 17875241
media type: text/csv

Data Summary
type: Distribution
description: Including pen portraits (cluster names and descriptions).
format: CSV
issued: 2024-11-28T14:47:06.948212
modified: 2025-05-05T23:08:39.206585
access URL: https://data.geods.ac.uk/dataset/3e2d3588-2d12-4a59-9b8c-a18f8d093947/resource/f2fa53f3-baee-4663-896b-e59483436e0c/download/pcc_datasummary.csv
byte size: 2470
media type: text/csv

Consilium Local Plan Housing Data
type: Dataset
description: The Consilium Local Plan Housing Dataset compiles data from all Local Planning Authorities (LPAs) in England and Wales, along with housing completions data sourced directly from central government. Developed by Edge Analytics, Consilium is a comprehensive resource for analyzing housing demand, completions, supply, and future delivery across local and regional levels. This dataset includes information on local plan status, housing needs, brownfield land registers, housing land availability, five-year housing supply, housing requirements, housing trajectories, completions (both central government and council-reported), and geocoded site data. It facilitates a detailed understanding of housing policies and trends across England and Wales. ## Content The dataset comprises five distinct data extracts, organized into macro-level (LPA-level) and micro-level (site-level) categories: 1. LP Status Data Extract: Provides information on adopted and emerging Local Plans, five-year housing supply positions, and Housing Delivery Test (HDT) results for each LPA. 2. Macro-Level Data Extract: Includes a detailed breakdown of housing delivery (e.g., completions, needs, requirements, and projections) for each LPA, covering the period from 2001/02 to 2042/43. 3. Five-Year Housing Supply Site Data: Contains site-level details such as addresses, planning statuses, coordinates, capacity, area, and annual phasing from 2016/17 to 2028/29. 4. Trajectory Site Data: Extends site-level phasing data to 2039/40, with similar attributes as the five-year supply dataset. 5. Brownfield Site Data: Features records from Brownfield Land Registers, detailing previously developed land as reported by LPAs. The dataset is accompanied by a data dictionary document that explains field definitions and relationships, providing essential context for interpreting the data. Users receive access to the five data files for detailed analysis. Please note that this dataset has a strict no-commercial-gain clause, we can…
issued: 2024-11-29T08:54:20.221823
modified: 2026-03-02T17:23:55.424918
contact point: Organization
  fn: Oliver O'Brien
keyword: Land Development
keyword: Land Planning
landing page: Local Planning Authorities, Edge Analytics

Data Summary: Trajectory
type: Distribution
format: CSV
issued: 2024-11-29T08:57:59.169246
modified: 2025-05-05T23:14:09.557880
access URL: https://data.geods.ac.uk/dataset/41a9d861-1672-4287-8e10-ff1166f5c328/resource/1de7a897-fe69-4fb6-b2d5-6c2022829931/download/data_summary_consilium_trajectory.csv
byte size: 5157
media type: text/csv

External Website: Brownfield Land Registers
type: Distribution
format: HTML
issued: 2024-11-29T08:56:11.116403
modified: 2025-05-05T23:14:09.557429
access URL: https://www.gov.uk/guidance/brownfield-land-registers

Data Summary: 5 Year Supply
type: Distribution
format: CSV
issued: 2024-11-29T08:56:49.751380
modified: 2025-05-05T23:14:09.557583
access URL: https://data.geods.ac.uk/dataset/41a9d861-1672-4287-8e10-ff1166f5c328/resource/439befcd-0549-4634-a945-77c8ced5ce10/download/data_summary_consilium_5yr_supply.csv
byte size: 3405
media type: text/csv

Data Summary: Macro-Level
type: Distribution
format: CSV
issued: 2024-11-29T08:57:42.492839
modified: 2025-05-05T23:14:09.557809
access URL: https://data.geods.ac.uk/dataset/41a9d861-1672-4287-8e10-ff1166f5c328/resource/72aae829-f66e-4ab6-a0a0-d208ef95406b/download/data_summary_consilium_macrolevel.csv
byte size: 5296
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-29T08:56:33.676637
modified: 2025-05-05T23:14:09.557509
access URL: https://data.geods.ac.uk/dataset/41a9d861-1672-4287-8e10-ff1166f5c328/resource/79863a97-1800-4574-bffa-bb9b815ceb00/download/variable_dictionary_consilium.csv
byte size: 12533
media type: text/csv

Data Summary: LP Status
type: Distribution
format: CSV
issued: 2024-11-29T08:57:23.306644
modified: 2025-05-05T23:14:09.557729
access URL: https://data.geods.ac.uk/dataset/41a9d861-1672-4287-8e10-ff1166f5c328/resource/aab0d097-f548-4d5c-869e-4fbe89c0a1ea/download/data_summary_consilium_lpstatus.csv
byte size: 2333
media type: text/csv

Contextual Note: Frequently Asked Questions about the Consilium Datasets
type: Distribution
description: This document contains a number of frequently asked questions about the Consilium datasets, along with answers. ###1. How did Edge Analytics collect the Consilium datasets, and how often is this information updated? Edge Analytics collects the source data directly from Local Planning Area websites. In some instances, Edge Analytics also approaches the local authorities to request data. Net Additional Dwellings data and Housing Delivery Test (HDT) figures are collected from the Department for Levelling Up, Housing and Communities (DLUHC) website. As each Authority publishes their planning policy data regularly but with differing frequencies, Edge Analytics are continually updating all areas, to ensure that every area is updated within each 3 to 6-month period. ###2. Will the Consumer Data Research Centre (CDRC) receive the updated Consilium data? CDRC may receive updates on behalf of users if there is evidence of demand. This could be update quarterly or annual update cycles. ###3. Why are the macro-level data not consistent between Local Authorities in England and Wales? The Consilium macro-level data is organised into six distinct categories: Council Completions; DLUHC Completions or Welsh Government (WG) Housing Stock; Need/Demand; Requirement/Targets; Supply; and Delivery. Within each category, Edge Analytics assigns a subcategory, to further distinguish between the data types. Individual Authorities may not publish data for all of these distinct categories and subcategories, and hence, there is variation between the data available across the Authorities. ###4. Is it correct to say that the Consilium dataset covers the whole planning framework, starting from the Local Authority Need (LHN), Housing Land availability, and ending with the local plan (target, 5-year supply, whole housing trajectory) and completions? This is the correct understanding of how the local authorities are meant to produce these planning policy data types. The first stage in the Local Plan (…
format: TXT
issued: 2024-11-29T08:55:35.840640
modified: 2025-05-05T23:14:09.557326

Data Summary: Brownfield
type: Distribution
format: CSV
issued: 2024-11-29T08:57:05.096623
modified: 2025-05-05T23:14:09.557658
access URL: https://data.geods.ac.uk/dataset/41a9d861-1672-4287-8e10-ff1166f5c328/resource/e032c310-08ef-4f55-9131-a7fe9c3a877e/download/data_summary_consilium_brownfield.csv
byte size: 2187
media type: text/csv

Linked Consumer Registers
type: Dataset
description: The linked consumer registers contain the names and addresses for adults in the UK as annual snapshots from 1997 onwards. They are compiled from reported outputs from linking public versions of the electoral roll and consumer registers (supplied by value-added resellers). The data represent a near-complete coverage of the adult population at an individual level. ## Content The Consumer Registers empower researchers to undertake individual level research on the adult UK population. As a set of geographic datasets available for several years, they can provide detailed estimates of local population change. They are also an invaluable tool for sample design. Research at the GeoDS has used this data base to create ethnicity estimations (using the ethnicity estimator names software) and also applied novel linkage analysis to produce estimations of origin- destination internal migration flows. Our updated data licence agreements mean that these data, which are used extensively to build other service data products, are only available for access by internal users (staff or students) at UCL. Interested potential external users can contact the us via the email address listed below - access may be arranged as part of a formal academic collaboration with this data service. It is anticipated that data will continue to be updated on an annual basis. Due to the imputation process, and with the aim towards continual improvement in the register across all years, each version of the register may be slightly different for older years as well as newer. For detailed description of the columns contained within the data, see the Variable Dictionary - and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. ## Quality, Representation and Bias As the data are inputted from several different organisations, it is possible that some names and addresses are inconsistently formatted between datasets. Despite applying…
issued: 2024-11-28T19:35:58.084882
modified: 2026-01-21T17:09:20.727857
version info: 4.0
contact point: Organization
  fn: Maurizio Gibin
keyword: Consumer
keyword: Consumer Register
keyword: Electoral Roll
keyword: Population
landing page: CACI UK Ltd, DataTalk Research Ltd, Electoral Roll Supplier

Data Summary
type: Distribution
format: CSV
issued: 2024-11-28T19:37:09.484193
modified: 2025-05-02T11:32:18.987097
access URL: https://data.geods.ac.uk/dataset/4306b26a-e336-4438-8300-e17676f8d20c/resource/0bf6df85-c92d-4b32-95e7-dfa82c9d9871/download/lcr_v3_secure_data_summary.csv
byte size: 848
media type: text/csv

https://data.geods.ac.uk/dataset/4306b26a-e336-4438-8300-e17676f8d20c/resource/8e6b94a5-fb74-424c-a1b1-5335c9396de4
type: Distribution
format: HTML
issued: 2024-11-28T19:41:19.989630
modified: 2025-05-02T11:32:18.987308
title: Paper: Lansley, G; Li, W; Longley, PA; (2019) Creating a linked consumer register for granular demographic analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society) , 182 (4) pp. 1587-1605. 10.1111/rssa.12476.
access URL: https://doi.org/10.1111/rssa.12476

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T19:36:52.801538
modified: 2025-05-02T11:32:18.987230
access URL: https://data.geods.ac.uk/dataset/4306b26a-e336-4438-8300-e17676f8d20c/resource/a9c9e326-0d5b-45b1-8bef-cba93073d83a/download/lcr_v3_secure_variable_dictionary.csv
byte size: 1102
media type: text/csv

https://data.geods.ac.uk/dataset/4306b26a-e336-4438-8300-e17676f8d20c/resource/b6cc262c-8913-462e-a35b-7f84af471349
type: Distribution
format: HTML
issued: 2024-11-28T19:42:56.535096
modified: 2025-05-02T11:32:18.987459
title: Paper: Van Dijk, J; Lansley, G; Longley, P; (2021) Using linked consumer registers to estimate residential moves in the United Kingdom. Journal of the Royal Statistical Society Series A: Statistics in Society 10.1111/rssa.12713
access URL: https://doi.org/10.1111/rssa.12713

https://data.geods.ac.uk/dataset/4306b26a-e336-4438-8300-e17676f8d20c/resource/caae71a1-a777-4d24-b1b7-a3d62cb22fcd
type: Distribution
format: HTML
issued: 2024-11-28T19:41:56.844568
modified: 2025-05-02T11:32:18.987385
title: Paper: Chen, M; Chi, B; Van Dijk, J; Longley, P; (2021) The use of Linked Consumer Registers to understand social and residential mobility. In: Proceedings of the 29th Annual GIS Research UK Conference (GISRUK). GIS Research UK: Cardiff, UK.
access URL: https://doi.org/10.5281/zenodo.4665856

Food Hygiene Rating Scheme (FHRS) Ratings
type: Dataset
description: These data provide the food hygiene rating or inspection result given to a business and reflect the standards of food hygiene found on the date of inspection or visit by the local authority. Businesses include restaurants, pubs, cafés, takeaways, hotels and other places consumers eat, as well as supermarkets and other food shops. Although the source data is freely available as open data, a process of cleaning, correcting and normalisation has taken place to produce this unique, high quality presentation of FHRS ratings and food services businesses in the UK. Weekly snapshots of active ratings or inspection results for businesses are provided back to 2012. The current snapshot is up to April 2023. The data are held on behalf of local authorities participating in either the Food Standards Agencies: National Food Hygiene Rating Scheme (FHRS) in England, Northern Ireland and Wales Food Hygiene Information Scheme (FHIS) in Scotland Data are only available for local authority areas running either of these schemes. ## Content All files are zipped CSVs or in Parquet format. For an overview of the characteristics of the data, see the Data Summary, downloadable below. Note that given the file sizes the inspection data, supply date of ratings, the business information and location are split across different files and must be combined and split for time series analysis. ## Quality, Representation and Bias All local authorities have content with the FHRS data. Discrepancies in dates may be seen due to local authority reporting procedures. The "date of opening" for example, can sometimes not match the actual opening date, and is the date that an opening was first reported by a local authority. Earliest inspection dates can be before this, suggesting the date of opening in these cases is inaccurate - and the earliest inspection date should be used (although inspections data was not published prior 2012). COVID-related lockdowns have also created some issues in the time series whic…
issued: 2024-11-28T13:54:28.019937
modified: 2026-03-18T10:34:50.189846
version info: 2.0
contact point: Organization
  fn: Dr Maurizio Gibin
keyword: Fast Food
keyword: Health
landing page: Food Standards Agency

Data Sample
type: Distribution
description: Header and first two data lines for each data file in this product.
format: CSV
issued: 2024-11-28T13:56:22.358799
modified: 2025-03-17T15:05:43.096022
access URL: https://data.geods.ac.uk/dataset/446b328d-d725-4cd3-b33f-13a91d0971fb/resource/16f0a5d7-ba75-4c10-84e4-aee0757a742a/download/data_sample_fhrs.csv
byte size: 2961
media type: text/csv

Data Summary
type: Distribution
format: CSV
issued: 2024-11-28T13:55:42.954310
modified: 2025-05-05T23:00:38.256031
access URL: https://data.geods.ac.uk/dataset/446b328d-d725-4cd3-b33f-13a91d0971fb/resource/a634d960-eec7-4c2f-a0eb-c829f44cae47/download/data_summary_fhrs.csv
byte size: 7144
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T13:56:41.233352
modified: 2025-05-08T15:24:10.985397
access URL: https://data.geods.ac.uk/dataset/446b328d-d725-4cd3-b33f-13a91d0971fb/resource/d28652a2-0894-429f-bfd9-663593adbb7e/download/variable_dictionary_fhrs.csv
byte size: 3103
media type: text/csv

MaPS Children and Young People's Financial Wellbeing Survey
type: Dataset
description: The Money and Pension Service (MaPS) Children and Young People’s Financial Wellbeing Survey is a nationally representative survey of children and young people aged 7 to 17 (and their parents/carers aged 18+) living in the UK. The findings from the survey play a major role in producing robust measures of children and young people’s financial wellbeing and capability across the UK. Previous research has shown that financial wellbeing, by the time of reaching financial independence, is in large part a consequence of what is seen, learned, and experienced during childhood and adolescence. In 2020, MaPS launched the UK Strategy for Financial Wellbeing 2020 – 2030, which is a ten-year framework to help achieve its vision. The Financial Foundations Agenda for Change, 1 of the 5 included in the UK Strategy, sets a national goal of 2 million more children and young people aged 5-17 receiving a meaningful financial education by 2030. This data product measures the progress against the national goal as the sector works towards the goal. These data are anonymised microdata (individual survey responses) for the 2022 survey, and they are available on application through our service as a Safeguarded dataset. We additionally hold the 2019 version of the survey. Applicants who are interested in both versions can apply for these together through a single application here. If you are interested in the 2019 version of the survey only, please apply for this through the UK Data Service (UKDS). ## Content The survey answers are available as, for each year, two record-level CSVs (one with codes and one with labels) or as an SPSS file. There is also a code to label lookup file, a questionnaire copy and a technical report. If using the CSV version of the data, it is important to apply the weighting factor, to make the data representative of the UK of children and young people aged 7-17. For an overview of the characteristics of a short number of the columns, see the Data Summary below. A Var…
issued: 2024-11-28T14:30:03.201360
modified: 2025-12-01T18:16:41.044387
version info: 1.0
contact point: Organization
  fn: Oliver O'Brien
keyword: Children
keyword: Kids
keyword: Young Person
landing page: Money and Pensions Service

Data Summary: Labels version (sample only)
type: Distribution
format: CSV
issued: 2024-12-12T12:44:01.481084
modified: 2025-05-02T13:20:26.936144
access URL: https://data.geods.ac.uk/dataset/4848fbfe-0664-4626-87a5-af4f0133be59/resource/2610938f-e1c9-4493-94e1-7937b8d0a8ca/download/data_summary_maps_cypfws22_labels_sampleonly.csv
byte size: 450
media type: text/csv

Technical Report: Children and Young People Financial Wellbeing Survey 2022
type: Distribution
format: HTML
issued: 2025-12-01T18:15:18.852984
modified: 2025-12-01T18:16:27.097517
access URL: https://maps.org.uk/content/dam/maps-corporate/en/publications/research/2023/maps-cyp-financial-wellneing-survey-2022-technical-report-june-2023.pdf
media type: application/pdf

External Website: UK Children and Young People’s Financial Wellbeing Survey: Financial Foundations
type: Distribution
format: HTML
issued: 2025-12-01T18:16:27.104142
modified: 2025-12-01T18:16:41.047967
access URL: https://maps.org.uk/en/publications/research/2023/uk-children-and-young-peoples-financial-wellbeing-survey-financial-foundations

Property Characteristics, Prices and Rents
type: Dataset
description: This dataset, supplied by Bricks&Logic, covers residential properties across England and Wales at the individual property level. It includes detailed attributes such as building type, construction year, number of bedrooms, and features like gardens, balconies, or roof terraces. Estimated sale and rental values are also provided for each property. Due to its commercially sensitive nature, the data are available through Secure or Safeguarded access routes. This catalogue entry pertains to the Secure data at property level. Information on LSOA-aggregated Safeguarded data can be found via the Related Record link below. ## Content The data is available in Parquet format. Detailed data descriptions provided in the accompanying Variable Dictionary and Data Summary files. ## Quality, Representation and Bias Bricks&Logic consolidates various national open data sources related to housing, including EPC records and Land Registry data. Additionally, in partnership with multiple estate agents, they acquire further details about properties and their rental features. In cases where original property attributes are missing, Bricks&Logic employs specialised modelling techniques to estimate these characteristics comprehensively, including sale and rental prices. However, it's important to note that these are modelled estimates that fill data gaps and could impact the accuracy of the data. The Bricks&Logic data are updated retrospectively over time as new information about property become known. These data represent outputs from models that were compiled in January 2024. To ensure the reliability of modelled estimates, the accuracy of the predicted fields are tested by Bricks&Logic against a subset of addresses that have been described by their Estate Agent clients. This not only allows for validation of the predictions has also provided a real-world testing environment to refine the models further. Additionally, Bricks&Logic regularly update data where new information is available, u…
issued: 2024-11-29T14:02:13.770715
modified: 2025-11-20T11:43:53.273111
contact point: Organization
  fn: Oliver O'Brien
keyword: House Prices
keyword: Houses
keyword: Rent
landing page: Bricks&Logic Ltd.

Data Summary: Property Sale and Rent (Secure)
type: Distribution
format: CSV
issued: 2024-11-29T14:03:17.183335
modified: 2025-05-05T23:15:39.993175
access URL: https://data.geods.ac.uk/dataset/484e7eca-4ed6-4f69-8fc5-ee28f052e609/resource/97859e65-70bc-4991-9dab-f2bf4e1eff60/download/data_summary_secure_prices_0.csv
byte size: 592
media type: text/csv

Data Summary: Property Characteristics (Secure)
type: Distribution
format: CSV
issued: 2024-11-29T14:05:02.522072
modified: 2025-05-05T23:15:39.993280
access URL: https://data.geods.ac.uk/dataset/484e7eca-4ed6-4f69-8fc5-ee28f052e609/resource/9eb5e001-fabe-4eb7-af2a-d2c9a3ab750b/download/data_summary_secure_properties_0.csv
byte size: 2757
media type: text/csv

External Website: Bricks&Logic
type: Distribution
format: HTML
issued: 2025-06-09T09:05:01.520530
modified: 2025-06-18T09:49:53.895534
access URL: https://www.bricksandlogic.co.uk/

Variable Dictionary: Property Sale and Rent (Secure)
type: Distribution
format: CSV
issued: 2024-11-29T14:05:15.887299
modified: 2025-05-05T23:15:39.993357
access URL: https://data.geods.ac.uk/dataset/484e7eca-4ed6-4f69-8fc5-ee28f052e609/resource/cefab314-deb4-4aee-955a-fd392e56ed5e/download/variable_dictionary_properties_secure.csv
byte size: 862
media type: text/csv

Related Record: Property Characteristics, Prices and Rents (LSOA Geography)
type: Distribution
format: HTML
issued: 2024-11-29T14:05:35.286867
modified: 2025-05-08T15:22:08.242271
access URL: https://data.geods.ac.uk/dataset/property-characteristics-prices-and-rents-lsoa-geography

Residential Mobility and Deprivation (RMD) Index (LAD Geography)
type: Dataset
description: The Residential Mobility and Deprivation (RMD) data provide yearly Local Authority District (LAD) level estimates of the mean differences of residential mobility and deprivation of all known adults’ moves to, from and within each neighbourhood in the UK for 1997-2025. ## Content The data are created from multiple data sources, which are blends of English Index of Multiple Deprivation (EIMD), Welsh Index of Multiple Deprivation (WIMD), Scottish Index of Multiple Deprivation (SIMD), Northern Ireland Multiple Deprivation Measure (NIMDM), and GeoDS Secure data Linked Consumer Registers. Indices of Multiple Deprivation (IMDs) are summary measures of physical and social conditions compiled by statistical authorities for neighbourhood areas across the country for each Lower layer Super Output Area (LSOA: England and Wales), Data Zone (DZ: Scotland) and Super Output Area (SOA: Northern Ireland). Area ranks within each UK country were standardised into percentile scores, with low values denoting more deprived LSOAs. These scores from each national IMD were ranked and interleaved with the most contemporaneous ranked scores for the other UK countries. The resulting sequence was then ranked as a UK index of percentile scores. The most deprived area has the lowest rank (1), and the least deprived area has the highest rank (100). Three ‘Harmonised’ IMD datasets were assembled for circa 2004 (2004 EIMD, 2005 WIMD, 2004 SIMD and 2004 NIMDM), 2010 (2010 EIMD, 2011 WIMD, 2012 SIMD and 2010 NIMDM) and 2019 (2019 EIMD, 2019 WIMD, 2020 SIMD and 2017 NIMDM). GeoDS Linked Consumer Registers (LCRs) are based upon annual updates of the names and addresses for adult UK residents from 1997 onwards. They offer a near-complete coverage of the adult population at individual level (Lansley et al 2019; van Dijk et al 2021). ‘Harmonised’ IMD ranks for 2004, 2010 or 2019 were assigned to LCR name and address records for proximal years (2004 values assigned to 1997-2007, 2010 values assigned to 2008-…
issued: 2024-11-28T15:35:31.741205
modified: 2025-12-01T14:15:47.339009
contact point: Organization
  fn: Maurizio Gibin
keyword: Consumer Register
keyword: Deprivation
keyword: Electoral Roll
keyword: Mobility
keyword: Population
landing page: IMD, GeoDS Linked Consumer Register

Data Summary: RMD-within Index LAD
type: Distribution
format: CSV
issued: 2024-11-28T15:40:20.850971
modified: 2025-12-01T13:39:41.083933
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/49ec5cbe-8c22-4ae0-bd1b-7fb2e1df72cd/resource/2e8c2d5e-96af-4a09-9e5c-9c7cc5284b9a/download/rmd_within_la_data_summary.csv
byte size: 1876
media type: text/csv

Data Summary: RMD-out Index LAD
type: Distribution
format: CSV
issued: 2024-11-28T15:39:38.223202
modified: 2025-12-01T13:39:21.968719
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/49ec5cbe-8c22-4ae0-bd1b-7fb2e1df72cd/resource/36954961-bccb-4eb3-95fd-5a2c2efad5b5/download/rmd_out_la_data_summary.csv
byte size: 2021
media type: text/csv

https://data.geods.ac.uk/dataset/49ec5cbe-8c22-4ae0-bd1b-7fb2e1df72cd/resource/3ecfcdfb-4082-4ea6-bc75-d7b144e49798
type: Distribution
format: HTML
issued: 2024-11-28T15:46:35.300578
modified: 2025-05-07T07:42:08.317591
title: Paper: Chen, Meixu, Chi, Bin, Van Dijk, Justin, & Longley, Paul A. (2021, April 6). The use of Linked Consumer Registers to understand social and residential mobility. 29th Annual GIS Research UK Conference (GISRUK), Cardiff, Wales, UK (Online).
access URL: https://zenodo.org/records/4665856

Data: RMD-within Index LAD 1997-2025
type: Distribution
format: ZIP
issued: 2024-11-28T15:36:57.195528
modified: 2025-12-01T14:15:22.704105
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/49ec5cbe-8c22-4ae0-bd1b-7fb2e1df72cd/resource/51727825-7523-4691-ac44-a94aae80fd77/download/rmd_within_la_releasedata_metadata_median_imd_change_pop_counts.zip
byte size: 28270
media type: application/zip

Data: RMD-out Index LAD 1997-2025
type: Distribution
format: ZIP
issued: 2024-11-28T15:36:49.861779
modified: 2025-12-01T14:15:47.348332
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/49ec5cbe-8c22-4ae0-bd1b-7fb2e1df72cd/resource/77bedd37-2d1e-41fd-b5ae-f7497172f587/download/rmd_out_la_releasedata_metadata_median_imd_change_pop_counts.zip
byte size: 32035
media type: application/zip

Map: Mapmaker
type: Distribution
format: HTML
issued: 2024-11-28T15:56:18.741768
modified: 2025-12-01T13:22:25.665149
access URL: https://mapmaker.geods.ac.uk/

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T15:43:38.229189
modified: 2025-12-01T13:25:44.817648
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/49ec5cbe-8c22-4ae0-bd1b-7fb2e1df72cd/resource/b2b466a6-01e4-4fb2-ba26-995cb1b774a3/download/variable_dictionary_rmd_lad.csv
byte size: 294
media type: text/csv

Data: RMD-in Index LAD 1997-2025
type: Distribution
format: ZIP
issued: 2024-11-28T15:36:28.836618
modified: 2025-12-01T14:15:47.347901
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/49ec5cbe-8c22-4ae0-bd1b-7fb2e1df72cd/resource/cda76629-17c0-4203-9f85-5f6e11bef17e/download/rmd_in_la_releasedata_metadata_median_imd_change_pop_counts.zip
byte size: 32121
media type: application/zip

Related Record: Residential Mobility and Deprivation (RMD) Index (LSOA Geography)
type: Distribution
format: HTML
issued: 2025-12-01T13:22:25.674648
modified: 2025-12-01T13:24:02.499404
access URL: https://data.geods.ac.uk/dataset/residential-mobility-and-deprivation-rmd-index-lsoa-geography

https://data.geods.ac.uk/dataset/49ec5cbe-8c22-4ae0-bd1b-7fb2e1df72cd/resource/d091f672-eab9-4a68-a7da-61a7be63f241
type: Distribution
format: HTML
issued: 2024-11-28T15:48:29.480664
modified: 2025-05-05T23:23:44.941923
title: Paper: Guy Lansley, Wen Li, Paul A. Longley, Creating a Linked Consumer Register for Granular Demographic Analysis, Journal of the Royal Statistical Society Series A: Statistics in Society, Volume 182, Issue 4, October 2019, Pages 1587–1605.
access URL: https://doi.org/10.1111/rssa.12476

Technical Report: Research Ready Smart Data
type: Distribution
format: PDF
issued: 2024-11-28T15:43:10.127509
modified: 2025-05-05T23:23:44.941700
access URL: https://data.geods.ac.uk/dataset/49ec5cbe-8c22-4ae0-bd1b-7fb2e1df72cd/resource/d0d431f2-9070-42f9-93e9-c8088b5456c5/download/lcr_technical_report_2023.pdf
byte size: 734416
media type: application/pdf

Data Summary: RMD-in Index LAD
type: Distribution
format: CSV
issued: 2024-11-28T15:39:09.038162
modified: 2025-12-01T13:38:56.562189
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/49ec5cbe-8c22-4ae0-bd1b-7fb2e1df72cd/resource/d258c01d-bc3f-49bf-a0e9-f4561e786e86/download/rmd_in_la_data_summary.csv
byte size: 1951
media type: text/csv

Distance of Residential Moves (DORM) Index (LSOA Geography)
type: Dataset
description: This Index provides yearly Lower layer Super Output Area (LSOA) estimates of the median straight-line distances of all known residential moves to, from, and within each LSOA (or equivalent unit) in the UK between 1997 and 2025. It is compiled from GeoDS Linked Consumer Registers secure data, which contain annual updates of the names and addresses of adults in the UK from 1997 onwards. All known adult residential moves are characterised as moving out of, moving into or moving within any UK LSOA. Some LSOA boundaries changed during the 1997-2025 period based and so boundaries of 2011 Census LSOAs were used to harmonise results. These data allow researchers to explore locality scale changes in residential mobility and migration patterns over an extended time period. The annual estimates and attribution of origins and destinations to specific LSOAs extends what is available from decennial census data. Median annual LSOA residential move data are provided for adult individuals moving out of, moving into, or moving within each UK LSOA. Estimates are triangulated with census estimates and can be used to update them during intercensal periods. ## Content For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. Files are separated in three, with different files available for moving out of, into and within any LSOA11CD. ## Quality, Representation and Bias The underpinning data are the GeoDS Linked Consumer Registers (LCRs), the provenance of which is set out in two papers in the Journal of the Royal Statistical Society Series A (Lansley et al 2019; Van Dijk et al 2021). Consumer and administrative data were acquired directly or indirectly from multiple data providers without warranties about accuracy or coverage, consistent with industry practice. Extensive internal and external validation procedures w…
issued: 2024-11-28T13:56:20.288782
modified: 2025-12-01T13:17:50.109735
contact point: Organization
  fn: Maurizio Gibin
keyword: Change
keyword: Consumer Register
keyword: Electoral Roll
keyword: Housing
keyword: Mobility
keyword: Population
landing page: Linked Consumer Register

Technical Report: Research Ready Smart Data
type: Distribution
format: PDF
issued: 2024-11-28T13:59:30.641076
modified: 2025-05-08T15:24:12.063892
access URL: https://data.geods.ac.uk/dataset/4d1b8c79-b5c2-4dc3-9ba7-4dc7f6d99b91/resource/581db47d-88f4-4fbd-bbfd-217571a07d2d/download/lcr_technical_report_2023.pdf
byte size: 734416
media type: application/pdf

Data Summary: Flows Within
type: Distribution
format: CSV
issued: 2024-11-28T14:23:58.557636
modified: 2025-12-01T11:51:39.917798
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/4d1b8c79-b5c2-4dc3-9ba7-4dc7f6d99b91/resource/5bf865a7-cd53-4fb0-9963-ea967f0b65af/download/dorm_within_lsoa_data_summary.csv
byte size: 1986
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T14:22:14.967336
modified: 2025-12-01T13:13:17.443540
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/4d1b8c79-b5c2-4dc3-9ba7-4dc7f6d99b91/resource/71c429eb-f135-469c-8bdc-b5536ae1f397/download/geods_dorm_lsoa_variabledesc.csv
byte size: 383
media type: text/csv

Data Summary: Flows In
type: Distribution
format: CSV
issued: 2024-11-28T14:22:43.290747
modified: 2025-12-01T11:51:08.467177
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/4d1b8c79-b5c2-4dc3-9ba7-4dc7f6d99b91/resource/75b69e80-c461-455a-88c4-06ed9e191d39/download/dorm_in_lsoa_data_summary.csv
byte size: 2201
media type: text/csv

https://data.geods.ac.uk/dataset/4d1b8c79-b5c2-4dc3-9ba7-4dc7f6d99b91/resource/854ab202-b3af-4d2d-a90a-a89be7dc2260
type: Distribution
format: HTML
issued: 2024-11-28T14:30:27.435751
modified: 2025-05-02T15:32:32.739544
title: Paper: Van Dijk J, Lansley G, Longley P A 2021. Using linked consumer registers to estimate residential moves in the United Kingdom. Journal of the Royal Statistical Society Series A (Statistics in Society). DOI:10.1111/rssa.12713
access URL: https://doi.org/10.1111/rssa.12713

Data Summary: Flows Out
type: Distribution
format: CSV
issued: 2024-11-28T14:23:08.033373
modified: 2025-12-01T11:51:25.408554
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/4d1b8c79-b5c2-4dc3-9ba7-4dc7f6d99b91/resource/ad27aecc-3368-4130-ad7b-1cedb8d42f8a/download/dorm_out_lsoa_data_summary.csv
byte size: 2256
media type: text/csv

Related Record: Distance of Residential Moves (DORM) Index (LAD Geography)
type: Distribution
format: HTML
issued: 2024-11-28T14:26:11.358346
modified: 2025-12-01T11:51:07.436958
access URL: https://data.geods.ac.uk/dataset/distance-of-residential-moves-dorm-index-lad-geography

https://data.geods.ac.uk/dataset/4d1b8c79-b5c2-4dc3-9ba7-4dc7f6d99b91/resource/d575d228-9877-49a5-a373-e1e5eb3137b3
type: Distribution
format: HTML
issued: 2024-11-28T14:29:05.347100
modified: 2025-05-02T15:32:32.739475
title: Paper: Lansley G, Li W, Longley P A 2019. Creating a linked consumer register for granular demographic analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society) DOI:10.1111/rssa.12476
access URL: https://academic.oup.com/jrsssa/article/182/4/1587/7068345

Speedchecker Broadband Internet Speed Tests
type: Dataset
description: Speedchecker Broadband Internet Speed Test data were provided by Speedchecker Ltd and comprise individually crowdsourced readings of internet upload / download speed from online speed tests, internet latency (ping rate/ data travel rate) and Internet Service Providers (ISP) information, between 2011 and 2020. ## Content These data are available at individual readings level and are supplied as large CSV files. For 2011-2013, each year corresponds to 1 CSV without geolocation. The dataset includes download and upload speeds (Mbps), internet latency (ms), internet provider, and internet advertised speed (where provided). For 2014-2018, each year corresponds to 2 CSV, one with geolocation (latitude, longitude) and one without location data The datasets include download and upload speeds (Mbps), internet latency (ms), internet provider, and internet advertised speed (where provided). For 2019-2020, there is a single CSV for both years, with geolocation (latitude, longitude). The datasets include download and upload speeds (Mbps) and internet providers. For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. ## Quality, Representation and Bias The content of these volunteered geographic information have reasonably complete entries. The only case where attributes are missing is with the variable “Internet Provider Advertised Speed” variable where information is provided for approximately 30% of observations only. Most of the observations are located within Europe, however the data are large enough to provide a representative sample across the globe, except for areas such as mainland Africa or Asia. There are some bad values present in the data which may need nullifying. For example, negative download and upload speeds, or speeds of 2147483.65 mbps (2^31/1000) (kbps of maxint), or latency of 0. The …
issued: 2024-12-17T13:37:30.441757
modified: 2025-05-08T15:21:35.175042
contact point: Organization
  fn: Dr Maurizio Gibin
keyword: Broadband
keyword: ISP
keyword: Internet
keyword: Latency
keyword: Speed
landing page: Speedchecker Ltd

Data Summary
type: Distribution
format: CSV
issued: 2025-01-21T11:47:11.850441
modified: 2025-05-05T23:23:14.360138
access URL: https://data.geods.ac.uk/dataset/5287ede8-a252-458c-847e-9e12a69b1352/resource/ca1fbd71-9666-4e48-b416-c5135b3679a7/download/data_summary_speedchecked_cleaned_all.csv
byte size: 10856
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-12-17T13:37:51.409210
modified: 2025-05-05T23:23:14.359953
access URL: https://data.geods.ac.uk/dataset/5287ede8-a252-458c-847e-9e12a69b1352/resource/e1fe97ee-64d4-470d-96b1-b1b9edf5f52e/download/variable_dictionary_speedchecker.csv
byte size: 1301
media type: text/csv

Retail Centre Boundaries (Previous Versions)
type: Dataset
description: These are older retail centre boundary/centroid products created by the GeoDS in 2015 and 2017. They have been superseded a newer version - please see the Related Record link below. The 2015 boundaries represent the retail centre centroids that were used on our maps website prior to October 2017. They were created in 2015 from centroid locations taken from those definitions of retail cores defined as part of the DCLG State of the Cities Report in 2005. To attribute these, please use the following text "Contains Department for Communities and Local Government Data; the data for this research have been provided by the Geographic Data Service (geods.ac.uk), a Smart Data Research UK Investment: ES/Z504464/1." The 2017 boundaries were produced from the 2015 LDC Retail units location dataset (also available on GeoDS Data), and built using the Graph-DBSCAN clustering method. The Retail Centre Typology (2018) is based on the 2017 boundaries and is a multidimensional taxonomy of retail and consumption spaces in Great Britain focussing on four domains: the composition, diversity, function and economic health of the centres. It’s a two-tier classification with 5 Supergroups and 15 nested Groups of which descriptions (Pen Portraits) are provided. Point centroids of the retail areas defined are included here. ## Content Downloadable CSVs, Shapefile and GeoPackage-format GIS files for the retail centre centroids, extent boundaries, drive/walk-time catchments and a typology/classification of each retail centre. These are all available below, along with data summaries and a variable dictionary. ## Quality, Representation and Bias The boundaries are dependent on the frequency of the source data updates - not all retail areas are surveyed every year in the source dataset, so some extent changes may not be captured. New retail areas may also not appear in the boundary products for some years after they are created, again due to source data update cycles.
issued: 2024-12-17T12:21:35.244405
modified: 2025-05-08T15:26:05.267974
version info: 1.0 and 2.0
contact point: Organization
  fn: Les Dolega
keyword: High Street
keyword: Retail Centre
keyword: Retailer
keyword: Shopping
keyword: Shops
landing page: Local Data Company; Department for Communities and Local Government

Paper: Pavlis, M., Dolega, L. and Singleton, A. (2018), A Modified DBSCAN Clustering Method to Estimate Retail Center Extent. Geogr Anal, 50: 141-161.
type: Distribution
format: HTML
issued: 2024-12-17T12:33:41.754946
modified: 2025-05-07T12:31:49.924534
access URL: https://doi.org/10.1111/gean.12138

Data: Retail Centre Comparison Catchments (2017) (GeoPackage format)
type: Distribution
description: This geodata pack provides comparison retail catchments for the year 2017 for the retail centres of Great Britain. The data represent the maximum likely catchment area for the 2017 retail centres based on their size. They were created by calculating the isochrones (based on shortest road network distance) of the comparison retail centres. The data are provided in geopackage format that is supported by open source software such as QGIS and R.
issued: 2024-12-17T12:32:35.814163
modified: 2025-05-07T12:31:16.256092
access URL: https://data.geods.ac.uk/dataset/53c51456-4e24-4525-bd5e-3db72c3c46b5/resource/2ed5812a-64fc-4eb7-8b3a-8559adfe2ea6/download/comparisoncatchments.gpkg
byte size: 6152192
media type: application/geopackage+sqlite3

Data: Typology and Centroids (2018) (Shapefile format)
type: Distribution
format: ZIP
issued: 2024-12-17T12:30:46.999246
modified: 2025-05-05T23:20:23.991794
access URL: https://data.geods.ac.uk/dataset/53c51456-4e24-4525-bd5e-3db72c3c46b5/resource/3e833fed-b6b8-46bc-8db9-4f72120ed2d5/download/tctypologygis.zip
byte size: 141909
media type: application/zip

Data: Boundaries (2017) (GeoPackage format)
type: Distribution
description: GeoPackage (.gpkg) of retail boundaries across Great Britain.
issued: 2024-12-17T12:30:33.809104
modified: 2025-05-05T23:20:23.991715
access URL: https://data.geods.ac.uk/dataset/53c51456-4e24-4525-bd5e-3db72c3c46b5/resource/447eeda5-4e2d-463e-bc73-718e45697115/download/retailcentreboundaries.gpkg
byte size: 9363456
media type: application/geopackage+sqlite3

Data: Centroids (2015)
type: Distribution
description: DOI: 10.20390/retailcentres2015 Creator: Consumer Data Research Centre Distributor: Consumer Data Research Centre Data Collector: Department for Communities and Local Government Identifier: https://dx.doi.org/10.20390/retailcentres2015 Publisher: Consumer Data Research Centre Publication Year: 2015 Subject: Retail, Retail Centre Language: English Resource Type General: Dataset Resource Type: Retail centroids maps Version: 1 Description: These data represent the retail centre centroids used on the CDRC Maps website. They were created as centroid locations taken from those definitions of retail cores defined as part of the DCLG State of the Cities Report - http://goo.gl/mtX1aB
format: CSV
issued: 2024-12-17T12:32:58.353839
modified: 2025-05-05T23:20:23.992180
access URL: https://data.geods.ac.uk/dataset/53c51456-4e24-4525-bd5e-3db72c3c46b5/resource/51e44c00-9f4a-4a2c-b477-4ad44515b5de/download/retail_centre_centroids_2015.csv
byte size: 44911
media type: text/csv

Data Summary
type: Distribution
format: CSV
issued: 2024-12-17T12:33:20.796912
modified: 2025-05-05T23:20:23.992319
access URL: https://data.geods.ac.uk/dataset/53c51456-4e24-4525-bd5e-3db72c3c46b5/resource/66d09c2a-3ff0-4961-ba80-1b0bd2f767ee/download/data_summary_retail_centres_previous.csv
byte size: 3218
media type: text/csv

Data: Centroids (2017) (Geopackage format)
type: Distribution
description: GeoPackage (.gpkg) of retail centroid points across Great Britain.
issued: 2024-12-17T12:30:18.402739
modified: 2025-05-05T23:20:23.991598
access URL: https://data.geods.ac.uk/dataset/53c51456-4e24-4525-bd5e-3db72c3c46b5/resource/7aadde03-b167-4ed8-992c-643d6fdb44e3/download/retailcentrecentroids.gpkg
byte size: 544768
media type: application/geopackage+sqlite3

Technical Report: Typology (2018) Pen Portraits
type: Distribution
format: PDF
issued: 2024-12-17T12:31:17.287623
modified: 2025-05-05T23:20:23.991965
access URL: https://data.geods.ac.uk/dataset/53c51456-4e24-4525-bd5e-3db72c3c46b5/resource/b25116ed-34eb-465b-92cb-0938bf36a190/download/clusters-descriptionupdated.pdf
byte size: 128565
media type: application/pdf

Data: Typology and Boundaries (2018) (Shapefile format)
type: Distribution
format: ZIP
issued: 2024-12-17T12:31:02.499883
modified: 2025-05-05T23:20:23.991887
access URL: https://data.geods.ac.uk/dataset/53c51456-4e24-4525-bd5e-3db72c3c46b5/resource/b7fec3b3-5379-49c1-912d-8926b398b78c/download/retail_centretyp.zip
byte size: 5244750
media type: application/zip

Related Record: Retail Centre Boundaries and Open Indicators
type: Distribution
format: HTML
issued: 2024-12-17T12:33:49.598805
modified: 2025-05-07T12:32:55.645938
access URL: https://data.geods.ac.uk/dataset/retail-centre-boundaries-and-open-indicators

Data: Retail Centre Convenience Catchments (2017) (Shapefile format)
type: Distribution
description: This geodata pack provides convenience retail catchments for the year 2017 for the retail centres in Great Britain. The data represent the maximum likely catchment area for the 2017 retail centres based on their size. They were created by calculating the shortest road network distance between a convenience retail unit and the centroids of the Output Areas in Great Britain. The data are provided in geopackage format that is supported by open source software such as QGIS and R. [N.B. For this archive, the file has been converted to a zipped shapefile and very slightly simplified using Mapshaper, due to storage constraints.]
format: ZIP
issued: 2024-12-17T12:32:08.405483
modified: 2025-05-06T17:12:29.690300
access URL: https://data.geods.ac.uk/dataset/53c51456-4e24-4525-bd5e-3db72c3c46b5/resource/dc3145b2-9ad3-4659-bc5a-c1688d5e124f/download/conveniencecatchments.zip
byte size: 51951878
media type: application/zip

Data: Retail Centre Typology (2018)
type: Distribution
format: CSV
issued: 2024-12-17T12:33:10.698078
modified: 2025-05-05T23:20:23.992250
access URL: https://data.geods.ac.uk/dataset/53c51456-4e24-4525-bd5e-3db72c3c46b5/resource/e88c636f-4fd2-4d10-b556-ec8ca9fbd5a7/download/retailcentretypology.csv
byte size: 168531
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-12-17T12:33:31.030944
modified: 2025-05-05T23:20:23.992387
access URL: https://data.geods.ac.uk/dataset/53c51456-4e24-4525-bd5e-3db72c3c46b5/resource/f08162d2-ab9c-45fb-a839-ebf7471de5c6/download/variable_dictionary_retail_centres_previous.csv
byte size: 2030
media type: text/csv

Business Census
type: Dataset
description: The Business Census dataset contains information on companies registered in the UK. The data are provided by Fusion Data Science, producing a database since May 2012 to provide a history of company formations, dissolutions and other events that are hidden in normal monthly snapshots. The GeoDS then prepares annual "Census" snapshots of the data, showing the state of all businesses at 31 March of each year. The database contains all 10+ million companies, and other corporate entities, which have existed since that time rather than the ~5 million contained in the Companies House data snapshots. These data are available at address level and are supplied as 26 zipped CSV files. ## Content These data are provided in a series of annual snapshots (13, from 2012 to 2024 inclusive) of the companies present as of 31 March of a particular year in a flattened file (except their SIC codes which are as of 1 April of the year), and as 13 entity type files (across all years). Annual snapshots of the data record, The entity type files have the Company table as the central table and the rest linked to the company table via companyID: * business_census20[12..24] - 13 files * Accounts * CompanyCategory * Company * CompanyName * CompanyStatus * ConfirmationStatements * LimitedPartnerships * Mortgages * ONSGeography * Postcodes * RegAddress * Returns * SICCodes This is the Version 11a release - mid-2024. Changes from version 11: * Snapshot date is 31 March rather than 30 March. * SIC codes are as of 1 April rather than 31 March, to cover companies incorporated the previous month. Note that prior to July 2016, SIC codes for newly incorporated companies could be supplied up to two years after incorporation. This means many new companies for these earlier censuses do not list SIC codes. They likely do appear in later censuses for such companies, typically up to month after incorporation date. Some pre-May-2012 data can also be included on request, but they may be missing some fields, values…
issued: 2024-12-06T09:10:11.654453
modified: 2026-03-18T10:36:12.092523
version info: 11a
contact point: Organization
  fn: Oliver O'Brien
keyword: Business Census
keyword: Companies
keyword: Companies House
keyword: Finance
landing page: Companies House

External Website: Companies House FAQ
type: Distribution
format: HTML
issued: 2024-12-06T09:42:19.229444
modified: 2025-05-02T15:25:10.503925
access URL: https://resources.companieshouse.gov.uk/infoAndGuide/faq/publicDataProduct.shtml
media type: text/html

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-12-06T09:11:15.582824
modified: 2025-05-02T15:25:10.503682
access URL: https://data.geods.ac.uk/dataset/54d0758f-e5db-4009-a290-7504224c0d90/resource/4859d8e0-b74e-433a-a655-134a725485e8/download/variable_dictionary_business_census.csv
byte size: 3635
media type: text/csv

External Website: Details of Companies House Data
type: Distribution
format: HTML
issued: 2024-12-06T09:41:45.250770
modified: 2025-05-07T14:38:17.383131
access URL: https://www.gov.uk/government/organisations/companies-house

Data Summary: Entities
type: Distribution
format: CSV
issued: 2024-12-06T09:10:31.219336
modified: 2025-05-02T15:25:10.503527
access URL: https://data.geods.ac.uk/dataset/54d0758f-e5db-4009-a290-7504224c0d90/resource/97de26ff-498c-44ea-b7d8-065eb5355ff6/download/data_summary_businesscensusv11a_entities.csv
byte size: 11516
media type: text/csv

Data Sample
type: Distribution
description: Headers and first two data lines of each file.
format: CSV
issued: 2024-12-06T09:11:37.013037
modified: 2025-06-18T13:45:29.525939
access URL: https://data.geods.ac.uk/dataset/54d0758f-e5db-4009-a290-7504224c0d90/resource/a1010f1a-72b7-40ec-8d9f-3275e09cfb3f/download/data_sample_business_census.csv
byte size: 3704
media type: text/csv

External Website: Business Census Data Specification at Companies House (PDF)
type: Distribution
format: HTML
issued: 2024-12-06T09:42:02.419367
modified: 2025-06-18T13:45:29.526274
access URL: https://resources.companieshouse.gov.uk/toolsToHelp/pdf/freeDataProductDataset.pdf
media type: application/pdf

Data Summary: Censuses
type: Distribution
description: For 2012, 2013 and 2024. Data summaries for intermediate years will be similar.
format: CSV
issued: 2024-12-06T09:10:55.355460
modified: 2025-05-02T15:25:10.503607
access URL: https://data.geods.ac.uk/dataset/54d0758f-e5db-4009-a290-7504224c0d90/resource/eeb15287-9a04-4cb2-9dea-acf85d243ef7/download/data_summary_businesscensusv11a_censuses.csv
byte size: 10169
media type: text/csv

Creating an Open Geodemographic Classification Using K-means Clustering in Python
type: Dataset
description: Geodemographic classifications turn complex population data into clear, actionable insights for policy, research, and commercial use. In this hands-on tutorial, we demonstrate how to build a bespoke geodemographic classification from UK census data using the Python data science stack. Participants will be guided through sourcing and preparing open data, selecting relevant variables, and clustering communities in ways that can be tailored to specific needs. We also explore how recent advances in machine learning can reduce the technical burden of creating geodemographics by using a large language model to generate cluster names and descriptions. By the end of the session, you will have the tools and workflow needed to design your own open, reproducible geodemographic classifications. The tutorial is free, but users will need to register on this website to access the materials. This tutorial contains the full workflow for producing a geodemographic classification from scratch in python using k-means clustering. The `creatinggeodem.ipynb` notebook contains the full code and explanatory text for the workshop. It can be followed from the website link or ran interactively through the linked github repository. The key steps covered in the notebook are: Data Access and Processing: * Access UK Census data and process using Pandas. * Select a specific region of interest (e.g., Liverpool City Region, Greater Manchester, Greater London). Census Data Analysis and Variable selection: * Select relevant Census variables for clustering. * Standardise variables. * Perform correlation & variance analysis to identify potentially redundant variables. * Alternative variable selection methods (e.g., PCA, Autoencoders). Clustering: * Determine optimal number of clusters using Clustergrams. * Apply K-Means clustering to classify areas based on selected variables. * Perform top-down hierarchical clustering to divide clusters into subgroups. Analytical Techniques: * Use UMAP (Uniform Manifold…
issued: 2025-09-30T15:29:20.656494
modified: 2026-03-31T14:28:14.828449
contact point: Organization
  fn: Owen Goodwin
keyword: Clustering
keyword: Geodemographic
keyword: Geographic Data Science in Python
keyword: K-Means
keyword: LLM
keyword: Python
keyword: Tutorial

Data: input_data_1.zip
type: Distribution
description: Input data for the workshop.
format: ZIP
issued: 2025-10-07T13:20:50.801261
modified: 2025-10-29T09:56:45.300135
access URL: https://data.geods.ac.uk/dataset/57532d6d-b547-4d7b-96fe-c1c48bfdf554/resource/4b87b641-96e8-4912-a6ac-5ee265543258/download/input_data_1.zip
byte size: 133292825
media type: application/zip

Data: workshop_slides.pptx
type: Distribution
description: Workshop slides presented at the Spatial Data Science Conference 2025. Includes a brief introduction to geodemographics and some suggested extentions to the tutorial.
format: PPTX
issued: 2025-10-29T09:56:45.332351
modified: 2026-01-15T14:15:17.501845
access URL: https://data.geods.ac.uk/dataset/57532d6d-b547-4d7b-96fe-c1c48bfdf554/resource/60759b00-4f38-4f08-aef1-a3b23834abdc/download/workshop_slides.pptx
byte size: 11659189
media type: application/vnd.openxmlformats-officedocument.presentationml.presentation

Related Record: Creating a Geodemographic Classification Using K-means Clustering in R
type: Distribution
format: HTML
issued: 2026-01-15T14:15:17.516630
modified: 2026-03-31T14:16:22.616611
access URL: https://data.geods.ac.uk/dataset/creating-a-geodemographic-classification-using-k-means-clustering-in-r

Source Code: Github Repository
type: Distribution
format: HTML
issued: 2025-10-02T16:44:34.623692
modified: 2025-10-29T11:56:03.727325
access URL: https://github.com/GeographicDataService/geodem-python-training

External Website: Workshop Notebook Webpage
type: Distribution
description: Webpage version of the jupyter notebook.
format: HTML
issued: 2025-10-07T15:26:48.193992
modified: 2025-11-06T13:54:02.996965
access URL: https://geographicdataservice.github.io/geodem-python-training/creatinggeodem.html
media type: text/html

Property Characteristics, Prices and Rents (LSOA Geography)
type: Dataset
description: This profile describes data held by GeoDS which has been supplied by Bricks&Logic. The data have coverage for England and Wales, and at their most detailed, presented at the scale of each residential property, with attributes such building type, construction year, number of bedrooms alongside other features such as whether a property has a garden, balcony, or roof terrace. For each property, rental and sale estimate values are also provided. Because these data are commercially sensitive, they are only available for access through either our Safeguarded or Secure routes. This catalogue entry concerns the Safeguarded data, which are aggregated from properties into a zonal geography. If you would like to apply for the more detailed Secure data, information can be found via the Related Record link below. ## Content These data are aggregated to LSOA (2021 Census version). The data is in csv format and it includes attributes for the property and annual average sale and rental values. See the Variable Dictionary and Data Summary documents below for further details. ## Quality, Representation and Bias Bricks&Logic consolidates various national open data sources related to housing, including EPC records and Land Registry data. Additionally, in partnership with multiple estate agents, they acquire further details about properties and their rental features. In cases where original property attributes are missing, Bricks&Logic employs specialised modelling techniques to estimate these characteristics comprehensively, including sale and rental prices. However, it's important to note that these are modelled estimates that fill data gaps and could impact the accuracy of the data. The Bricks&Logic data are updated retrospectively over time as new information about property become known. These data represent outputs from models that were compiled in January 2024. To ensure the reliability of modelled estimates, the accuracy of the predicted fields are tested by Bricks&Logic against …
issued: 2024-11-29T14:12:21.268576
modified: 2025-11-20T11:43:19.149435
version info: 1.0
contact point: Organization
  fn: Oliver O'Brien
keyword: House Prices
keyword: House Sales
keyword: Modelled
keyword: Rent
landing page: Bricks&Logic Ltd.

Related Record: Property Characteristics, Prices and Rents
type: Distribution
format: HTML
issued: 2024-11-29T14:13:56.015109
modified: 2025-05-07T12:13:29.485354
access URL: https://data.geods.ac.uk/dataset/property-characteristics-prices-and-rents

Data Summary: Property Sale and Rent (Safeguarded)
type: Distribution
format: CSV
issued: 2024-11-29T14:13:05.041795
modified: 2025-05-08T15:21:49.400539
access URL: https://data.geods.ac.uk/dataset/58118784-9122-451f-95b9-9c8b2bbf23ea/resource/f143466b-d861-4b07-bdde-2ee436a656b0/download/data_summary_safeguarded_prices.csv
byte size: 3811
media type: text/csv

Variable Dictionary: Property Sale and Rent (Safeguarded)
type: Distribution
format: CSV
issued: 2024-11-29T14:13:37.113714
modified: 2025-05-05T23:15:44.208820
access URL: https://data.geods.ac.uk/dataset/58118784-9122-451f-95b9-9c8b2bbf23ea/resource/f8bed828-dfce-45b5-ad97-6e8e545b5877/download/variable_dictionary_safeguarded.csv
byte size: 1362
media type: text/csv

Data Summary: Property Characteristics (Safeguarded)
type: Distribution
format: CSV
issued: 2024-11-29T14:13:24.968876
modified: 2025-05-05T23:15:44.208739
access URL: https://data.geods.ac.uk/dataset/58118784-9122-451f-95b9-9c8b2bbf23ea/resource/ffbd1bf2-45f4-44db-b698-5c00baf238b5/download/data_summary_safeguarded_properties.csv
byte size: 1796
media type: text/csv

Energy Deprivation Classification
type: Dataset
description: The Energy Deprivation Classification (EDC) is a hierarchical geodemographic classification across Great Britain (GB) that identifies small areas of the country with similar characteristics of inability and insecurity in access to adequate energy services. The classification is built from five domains: energy efficiency, energy access, energy demand and supply, housing conditions, and financial vulnerability. It includes the breakdown of 2021 Lower Super Output Areas (LSOA) for England and Wales, and 2011 Data Zones (DZs) for Scotland into 6 different Supergroups, which are further divided into 14 Groups. ## Content The EDC data for each LSOA21/DZ11, in CSV, Shapefile and GPKG format, is available on application, along with the distinct characteristics of Supergroups and Groups (Index Scores). An overview of the input variable distribution (Data Summary), input variable dictionary and classification profiles (pen portraits) can be downloaded directly from this page. ## Quality, Representation and Bias Since the Census 2021 in Scotland was delayed until 2022, and the resulting small-area aggregate Scottish statistics were not published at the point of creating this classification, this version of the EDC includes modelled data for Scotland. It is hoped that this can be updated in the future once a complete set of underlying source data are available. It is not anticipated that it will be necessary to change the classifications or definitions for any areas in England/Wales. The classification has undergone internal and external validation. Most Supergroups and Groups show good cluster performance, with lower cluster fit errors and alignment with characteristics of fuel poverty in England and Wales and the Index of Multiple Deprivation across GB. Further details on the validation process are available in a paper currently under review.
issued: 2024-11-29T13:35:19.951045
modified: 2026-01-15T14:12:13.517763
contact point: Organization
  fn: Dr Maurizio Gibin
keyword: Alternative Fuels
keyword: Classification
keyword: Deprivation
keyword: Energy
keyword: Socioeconomic

Technical Report: Data Sources, Variable List and Pen Portraits
type: Distribution
description: Where and when the data are collected, a list of all variables, and the contextual characteristics of all Supergroups and Groups.
format: PDF
issued: 2024-11-29T13:37:46.887262
modified: 2025-05-07T13:20:31.163329
access URL: https://data.geods.ac.uk/dataset/593a34d9-44e5-4db3-98a5-fcc14825bc16/resource/359cf1e6-4efb-4b5d-a917-4a3577849bd8/download/edc_data_sources_pen_portraits.pdf
byte size: 174066
media type: application/pdf

https://data.geods.ac.uk/dataset/593a34d9-44e5-4db3-98a5-fcc14825bc16/resource/60124690-0aa3-4945-af4e-9ee9f5ccc5a1
type: Distribution
format: HTML
issued: 2024-11-29T13:36:08.629067
modified: 2025-05-05T23:15:36.055921
title: Paper: Meixu Chen, Alex Singleton, and Caitlin Robinson. Exploring Energy Deprivation Across Small Areas in England and Wales (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). LIPIcs), Volume 277, pp. 20:1-20:6
access URL: https://doi.org/10.4230/LIPIcs.GIScience.2023.20

Data Summary
type: Distribution
description: The distribution summary of all variables related to the energy deprivation
format: CSV
issued: 2024-11-29T13:37:25.066497
modified: 2025-05-05T23:15:36.055747
access URL: https://data.geods.ac.uk/dataset/593a34d9-44e5-4db3-98a5-fcc14825bc16/resource/730a1190-5d02-41cb-8626-175491022dfa/download/edc_variable_summary.csv
byte size: 3218
media type: text/csv

Variable Dictionary
type: Distribution
description: The codes and classification labels/names lookup for each Supergroup and Group.
format: CSV
issued: 2024-11-29T13:36:55.425493
modified: 2025-05-05T23:15:36.055630
access URL: https://data.geods.ac.uk/dataset/593a34d9-44e5-4db3-98a5-fcc14825bc16/resource/888865d0-796d-4af7-a99b-88c0ec4776ae/download/edc_lookup_codes_names.csv
byte size: 867
media type: text/csv

Retail Centre Indicators
type: Dataset
description: Retail centres are diverse and dynamic geographical entities. Developing indicators that can help to understand their characteristics and economic performance can provide useful supporting information for policy in identifying underperforming and/or thriving retail centres across the UK. The dataset contains aggregated indicators at the retail centre level that contains some commercially sensitive information. These indicators accompany the most recent 2021 iteration of the openly available GeoDS Retail Centre Boundaries (see Related Record link below). ## Content The data supplied is a single CSV file, designed to be linked to the open data. This is a derived product for the most recent iteration of the GeoDS retail centre boundaries, utilising our Retail Type, Vacancy and Address location datasets and various openly accessible datasets. The product contains indicators at the retail centre level, which are split into four domains. * COMPOSITION – a series of variables that summarise the key compositional differences between retail centres (presence of comparison, convenience, service and leisure retail). * DIVERSITY – a series of variables summarising key differences in terms of diversity between centres, including the presence of independents and chains, and an aggregate ‘clone town’ score based on the ‘Clone Town Britain methodology’. * VACANCY – a series of variables summarising key differences in terms of vacancy, including current vacancy rate, long-term vacancy rate and change in vacancy rate. * E-RESILIENCE – series of variables summarising the relationship of retail centres to online shopping, including the index of supply vulnerability and online exposure index, which are used to create the final E-Resilience score. The indicators were derived and aggregated from Green Street (formerly LDC) data. Green Street are the primary data partner for this dataset, and applications to use this data are subject to their approval. Please note that this dataset cannot …
issued: 2024-12-17T13:24:35.226148
modified: 2026-04-15T13:08:54.372992
contact point: Organization
  fn: Alex Singleton
keyword: High Street
keyword: Retail
keyword: Retail Centre
landing page: Green Street, GeoDS

Data Summary
type: Distribution
format: CSV
issued: 2024-12-17T13:28:58.497285
modified: 2025-05-05T23:23:05.204445
access URL: https://data.geods.ac.uk/dataset/59b5789d-86d2-44da-8c66-e56a26b84bea/resource/133d934e-f621-4b34-b3d9-40942cc77c96/download/data_summary_rc_indicators_safeguarded.csv
byte size: 1565
media type: text/csv

Technical Report: Indicator Manual
type: Distribution
description: Manual describing the indicator variables and the respective process for generating them.
format: PDF
issued: 2024-12-17T13:27:09.464761
modified: 2025-05-05T23:23:05.204328
access URL: https://data.geods.ac.uk/dataset/59b5789d-86d2-44da-8c66-e56a26b84bea/resource/57e15e2d-1660-4db3-b539-13fd601a0a03/download/retail_indicators_manual_0.pdf
byte size: 69575
media type: application/pdf

Source Code: Github repository
type: Distribution
format: HTML
issued: 2024-12-17T13:29:28.116772
modified: 2025-05-09T22:39:11.962862
access URL: https://github.com/patrickballantyne/CDRC-Retail-Indicators/

Related Record: Retail Centre Boundaries and Open Indicators
type: Distribution
format: HTML
issued: 2024-12-17T13:29:20.644084
modified: 2025-05-08T15:21:13.834089
access URL: https://data.geods.ac.uk/dataset/retail-centre-boundaries-and-open-indicators

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-12-17T13:29:09.098656
modified: 2025-05-05T23:23:05.204524
access URL: https://data.geods.ac.uk/dataset/59b5789d-86d2-44da-8c66-e56a26b84bea/resource/edc5854f-d6f7-4fc9-a462-da6d4a78894b/download/variable_dictionary_rc_indicators_safeguarded.csv
byte size: 3903
media type: text/csv

Residential Mobility and Energy Performance Certificate (RMEPC) Index (LSOA Geography)
type: Dataset
description: This data profile describes the Residential Mobility and Energy Performance Certificate (RMEPC) datasets held by GeoDS, which are created through original GeoDS research. The index data are compiled through an analysis involving the linkage of GeoDS Linked Consumer Registers (LCRs), and Department for Levelling Up, Housing and Communities (DLUHC) Domestic Energy Performance Certificates (EPCs), at property level. The data provide average and median change of energy performance ratings, potential energy performance ratings and total floor area of residences involved in moves in and out of each Lower Level Super Output Area (LSOA) level in Great Britain (GB) between 2009 and 2023 These research-ready datasets allow researchers to explore changes in residential mobility at the local scale, along with changes in housing energy and space. Both the average and median changes in energy performance rating, potential energy performance rating, and total floor area of residences involved in moves are provided for adult individuals moving out of, moving into (which includes both moving into and moving within), each UK LSOA. ## Content The RMEPC (LSOA) is divided into two datasets: one for “move-outs” (mo) and another for “move-ins” (mi) for each LSOA in GB. Field level metadata are provided at the end of this profile document. These two datasets are aggregates resulting from linking GeoDS LCR and EPC data. * RMEPC move out The RMEPC move out (rmepc_lsoa11_mo) data provide average and median changes of energy performance rating, potential energy performance rating, and total floor area for the residences of individuals moving out of each home LSOA. * RMEPC move in The RMECP move into (rmepc_lsoa11_mi) data provide average and median changes of energy performance rating, potential energy performance rating, and total floor area for the residences of individuals moving into and within each LSOA in GB. ## Quality, Representation and Bias The underpinning data are from the GeoDS LC…
issued: 2024-11-28T16:53:54.851405
modified: 2025-08-12T13:31:37.482597
version info: 1.0
contact point: Organization
  fn: Maurizio Gibin
keyword: Consumer Register
keyword: Deprivation
keyword: Electoral Roll
keyword: Population
keyword: mobility
landing page: EPCs, GeoDS Linked Consumer Register

Data Summary: RMEPC (LSOA) Moves in
type: Distribution
format: CSV
issued: 2024-11-28T16:55:34.411590
modified: 2025-05-05T23:14:36.714413
access URL: https://data.geods.ac.uk/dataset/5b37cdbf-8377-4110-98d8-5012f5b7b514/resource/0776eb1d-0f83-4e67-90c0-6ffffa80fba0/download/data_summary_rmepc_lsoa_in.csv
byte size: 400
media type: text/csv

Related Record: Residential Mobility and Energy Performance Certificates (RMEPC) Index (LAD Geography)
type: Distribution
format: HTML
issued: 2024-11-28T16:58:41.266903
modified: 2025-05-08T15:21:17.467201
access URL: https://data.geods.ac.uk/dataset/residential-mobility-and-energy-performance-certificate-rmepc-index-lad-geography

Variable Dictionary: RMEPC (LSOA)
type: Distribution
format: CSV
issued: 2024-11-28T16:55:10.911745
modified: 2025-05-05T23:14:36.714333
access URL: https://data.geods.ac.uk/dataset/5b37cdbf-8377-4110-98d8-5012f5b7b514/resource/38ab8660-42fb-4c0a-aec7-909a7e737ad1/download/variable_dictionary_rmepcs_lsoa.csv
byte size: 668
media type: text/csv

Technical Report: Research Ready Smart Data
type: Distribution
format: PDF
issued: 2024-11-28T16:54:33.953324
modified: 2025-05-05T23:14:36.714177
access URL: https://data.geods.ac.uk/dataset/49ec5cbe-8c22-4ae0-bd1b-7fb2e1df72cd/resource/d0d431f2-9070-42f9-93e9-c8088b5456c5/download/lcr_technical_report_2023.pdf
media type: application/pdf

Data Summary: RMEPC (LSOA) Moves out
type: Distribution
format: CSV
issued: 2024-11-28T16:56:22.070100
modified: 2025-05-05T23:14:36.714489
access URL: https://data.geods.ac.uk/dataset/5b37cdbf-8377-4110-98d8-5012f5b7b514/resource/57406bae-c6bd-40a8-a63b-fec6392965ab/download/data_summary_rmepc_lsoa_out.csv
byte size: 400
media type: text/csv

Technical Report: RMEPC (LSOA)
type: Distribution
format: PDF
issued: 2024-11-28T16:57:35.409191
modified: 2025-05-05T23:14:36.714562
access URL: https://data.geods.ac.uk/dataset/5b37cdbf-8377-4110-98d8-5012f5b7b514/resource/ee81ab32-5f1d-4ad0-b52a-3e74eedba486/download/rmepc_lsoa_contextualnote.pdf
byte size: 1192202
media type: application/pdf

Distance of Residential Moves (DORM) Index (LAD Geography)
type: Dataset
description: This dataset provides yearly estimates of the median straight-line distances of all known residential moves to, from, and within each Local Authority District (LAD) in the UK from 1997 onwards. The index data are compiled from GeoDS Secure data Linked Consumer Registers, which contain annual updates of the names and addresses for adults in the UK from 1997 onwards. These data are used to provide yearly Local Authority District (LAD) estimates of the median straight-line distances of all known residential moves to, from and within each LAD in the UK. ## Content The data provide an estimate of median straight-line distance of residential moves between and within each UK LAD in each year between 1998 and 2025. All known adult residential moves are characterised as moving out of, moving into or moving within any UK LAD. Some LAD boundaries changed during the 1997-2025 period and so boundaries of 2023 LAD boundaries were used to harmonise results. These data allow researchers to explore locality scale changes in residential mobility and migration patterns over an extended time period. The annual estimates and attribution of origins and destinations to specific LADs extends what is available from decennial census data. Median annual LAD residential move data are provided for adult individuals moving out of, moving into, or moving within each UK LAD. Estimates are triangulated with census estimates and can be used to update them during intercensal periods. Note: The unit of DORM is in kilometres, and '*' means the figures were suppressed to avoid disclosing issues (i.e., lower count). ## Quality, Representation and Bias The underpinning data are the GeoDS Linked Consumer Registers (LCRs), the provenance of which is set out in two papers in the Journal of the Royal Statistical Society Series A (Lansley et al 2019; Van Dijk et al 2021). Consumer and administrative data were acquired directly or indirectly from multiple data providers without warranties about accuracy or cove…
issued: 2024-11-28T12:55:08.802131
modified: 2025-12-01T14:22:16.468686
contact point: Organization
  fn: Maurizio Gibin
keyword: Change
keyword: Consumer Register
keyword: Electoral Roll
keyword: Housing
keyword: Mobility
keyword: Population
landing page: GeoDS Linked Consumer Registers

Data Summary: DORM-within Index
type: Distribution
format: CSV
issued: 2024-11-28T13:10:51.181797
modified: 2025-12-01T13:08:30.723945
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/5d600931-4c90-4416-96ab-c383a8b2380d/resource/11c97456-1299-4a62-800c-a1cb5efe0e1b/download/dorm_within_la_data_summary.csv
byte size: 1777
media type: text/csv

Technical Report: Research Ready Smart Data
type: Distribution
format: PDF
issued: 2024-11-28T13:11:27.784857
modified: 2025-05-02T15:44:27.495013
access URL: https://data.geods.ac.uk/dataset/5d600931-4c90-4416-96ab-c383a8b2380d/resource/263978b8-5b41-4603-899f-5ea595806738/download/lcr_technical_report_2023.pdf
byte size: 734416
media type: application/pdf

https://data.geods.ac.uk/dataset/5d600931-4c90-4416-96ab-c383a8b2380d/resource/58d3563e-087a-4e27-a35b-49626950f1f1
type: Distribution
format: HTML
issued: 2024-11-28T13:22:28.152478
modified: 2025-05-02T15:44:27.495152
title: Paper: Van Dijk J, Lansley G, Longley P A 2021. Using linked consumer registers to estimate residential moves in the United Kingdom. Journal of the Royal Statistical Society Series A (Statistics in Society). DOI:10.1111/rssa.12713
access URL: https://doi.org/10.1111/rssa.12713

Data: DORM-within Index LAD 1997-2025
type: Distribution
format: ZIP
issued: 2024-11-28T13:09:26.739108
modified: 2025-12-01T14:22:16.472309
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/5d600931-4c90-4416-96ab-c383a8b2380d/resource/58dd03ee-9f95-4b55-bd3e-b5ff96a0122e/download/dorm_within_la_releasedata_metadata_median_dist_pop_counts.zip
byte size: 25116
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T13:11:57.287515
modified: 2025-12-01T14:21:15.567980
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/5d600931-4c90-4416-96ab-c383a8b2380d/resource/6816137f-d915-4524-ad24-633e0ae767e7/download/geods_dorm_lad_variabledesc.csv
byte size: 264
media type: text/csv

Related Record: Distance of Residential Moves (DORM) Index (LSOA Geography)
type: Distribution
format: HTML
issued: 2024-11-28T14:38:42.281621
modified: 2025-05-21T09:37:30.005104
access URL: https://data.geods.ac.uk/dataset/distance-of-residential-moves-dorm-index-lsoa-geography

https://data.geods.ac.uk/dataset/5d600931-4c90-4416-96ab-c383a8b2380d/resource/85cdd075-6df8-4952-9b8d-9111506c27de
type: Distribution
format: HTML
issued: 2024-11-28T13:17:29.954875
modified: 2025-05-02T15:44:27.495085
title: Paper: Lansley G, Li W, Longley P A 2019. Creating a linked consumer register for granular demographic analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society) DOI:10.1111/rssa.12476
access URL: https://doi.org/10.1111/rssa.12476

Data Summary: DORM-in Index
type: Distribution
format: CSV
issued: 2024-11-28T13:09:55.315009
modified: 2025-12-01T13:07:34.361404
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/5d600931-4c90-4416-96ab-c383a8b2380d/resource/9a38dfe4-3bd8-448d-bdff-e7dec87dde93/download/dorm_in_la_data_summary.csv
byte size: 1986
media type: text/csv

Data: DORM-in Index LAD 1997-2025
type: Distribution
format: ZIP
issued: 2024-11-28T13:08:26.875650
modified: 2025-12-01T14:21:47.597299
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/5d600931-4c90-4416-96ab-c383a8b2380d/resource/9d871974-3d6a-45b3-a432-e319c75c75e3/download/dorm_in_la_releasedata_metadata_median_dist_pop_counts.zip
byte size: 36933
media type: application/zip

Map: Mapmaker
type: Distribution
format: HTML
issued: 2024-11-28T16:05:58.222854
modified: 2025-12-01T12:43:51.400797
access URL: https://mapmaker.geods.ac.uk/

Data: DORM-out Index LAD 1997-2025
type: Distribution
format: ZIP
issued: 2024-11-28T13:09:06.298678
modified: 2025-12-01T14:22:16.472248
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/5d600931-4c90-4416-96ab-c383a8b2380d/resource/eb3a9285-e16e-4cfe-b36d-dee0a7b92feb/download/dorm_out_la_releasedata_metadata_median_dist_pop_counts.zip
byte size: 36589
media type: text/csv

Data Summary: DORM-out Index
type: Distribution
format: CSV
issued: 2024-11-28T13:10:18.938845
modified: 2025-12-01T13:08:12.261457
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/5d600931-4c90-4416-96ab-c383a8b2380d/resource/fd02f616-2686-4c20-98bd-e3adb864fd2f/download/dorm_out_la_data_summary.csv
byte size: 2001
media type: text/csv

Leveraging the CDRC API
type: Dataset
description: The Consumer Data Research Centre (CDRC) has developed an Application Programming Interface (API) that serves as a gateway to their open datasets. An API is a set of rules and protocols that allows different software applications to communicate and interact with each other. In the context of CDRC, the API acts as a bridge between their collection of open data, and external developers or organizations who wish to access and utilize that data in their own applications, services, or research projects. The CDRC API provides a standardized and secure way for authorized users to retrieve specific datasets, query relevant information, and integrate it seamlessly into their own software systems. Through the API, developers can access a wide range of consumer-related data, including demographics, and other socio-economic indicators. By leveraging the CDRC API, users can access and interact with the data in a programmatic manner, which opens up a plethora of possibilities for data analysis, visualization, and modelling. The API is designed to be flexible and user-friendly, providing a set of documented endpoints and methods that allow users to query the datasets based on their specific needs. To ensure security and controlled access, the CDRC API employs authentication mechanisms such as API tokens. These credentials are issued to registered users and serve as a means to identify and authorize access to the datasets. By requiring authentication, CDRC maintains control over data usage. The CDRC API can also be leveraged via the R package, “cdrcR”. A tutorial has been written to demonstrate how to access the CDRC API using "cdrcR," providing users with step-by-step instructions on effectively utilizing the API's functionalities within the R programming language.
issued: 2024-11-28T14:29:13.599107
modified: 2025-11-25T11:11:20.926260
contact point: Organization
  fn: Robert Podmore
keyword: API
keyword: Tutorial
landing page: Robert Podmore

Tool: CDRC API Homepage on CDRC Apps
type: Distribution
format: HTML
issued: 2024-11-28T14:30:15.992114
modified: 2025-05-08T15:21:36.689010
access URL: https://apps.cdrc.ac.uk/apidoc/

Data: Practical - Leveraging the CDRC API
type: Distribution
format: HTML
issued: 2024-11-28T14:29:57.484614
modified: 2025-05-07T12:44:07.928188
access URL: https://data.geods.ac.uk/dataset/61f96504-ad66-4a7d-8109-90477b926e00/resource/a3aa6a5e-7081-4981-8a57-ad2edbb58fb1/download/practical_cdrc_api.html
byte size: 1051354
media type: text/html

Local morbidity rates of Global Burden of Disease and alcohol-related conditions
type: Dataset
description: Geographical disparities in health present important research and policy challenges. Disparities can be indicative of different forms of spatially patterned advantage or disadvantage. Local age and sex-standardised hospital admissions rates can provide insights into health disparities and may act as input into health geographic research or local policy instruments, such as Joint Area Health Needs Assessments (JSNAs). Morbidity rates have been estimated in two variants: crude rates (observed cases / expected cases) and spatially smoothed using a Bayesian spatial structural model estimated separately for each year. Morbidity rates in the data have been derived from in-patient local age and sex-standardised hospital admission rates provided by HES to contribute to studies of geographical health disparities. The reference population for data is the 2001 England population as recorded in the Census 2001. ## Content 28 CSV files. These are age and sex-standardised morbidity rates for 27 categories of health conditions plus one category of alcohol-related conditions, per MSOA, per financial year (FY), for FYs 1999/00 – 2013/14. The reference population is the 2001 England population as recorded in the Census that year. The 27 health conditions are derived from the 4-digit ICD-10 (International Classification of Diseases v.10) codes recorded in Hospital Episode Statistics (HES). The codes have been aggregated to 20 categories used by the WHO’s Global Burden of Disease (GBD) study, 7 additional groups not captured by the GBD and derived from the U.S. Clinical Classification System (CCS) aggregation level 1. Alcohol-related conditions are defined by the Nuffield Trust. ## Quality, Representation and Bias The data represent in-patient data for HES. HES has near-complete coverage of NHS commissioned hospital admissions in England. Coding of diagnoses may vary in consistency but has been validated for research and auditing purposes in an earlier study. The estimates only related…
issued: 2024-11-28T14:24:32.900335
modified: 2025-05-08T15:24:18.484633
version info: 1.0
contact point: Organization
  fn: Oliver O'Brien
keyword: Alcohol
keyword: Disease
keyword: Health
keyword: Illness
keyword: NHS
landing page: NHS Digital

Variable Dictionary; Tables
type: Distribution
format: CSV
issued: 2024-11-28T14:25:41.603274
modified: 2025-05-05T23:00:13.068838
access URL: https://data.geods.ac.uk/dataset/6372b757-f331-4ce5-a078-f153de3a39eb/resource/22acaa58-7228-4a30-9dd4-73243d08818d/download/variable_dictionary_local_morbidity_tables.csv
byte size: 3623
media type: text/csv

Variable Dictionary; Columns
type: Distribution
format: CSV
issued: 2024-11-28T14:26:11.027577
modified: 2025-05-05T23:00:13.068949
access URL: https://data.geods.ac.uk/dataset/6372b757-f331-4ce5-a078-f153de3a39eb/resource/4cf715b9-f409-4f57-9911-9d2e63b56f68/download/variable_dictionary_local_morbidity.csv
byte size: 1879
media type: text/csv

External Website: Currie C, Davies A, Blunt I, Ariti C and Bardsley M (2015) Alcohol-specific activity in hospitals in England. Research report. Nuffield Trust.
type: Distribution
format: HTML
issued: 2024-12-10T14:37:30.010420
modified: 2025-05-05T23:00:13.069168
access URL: https://www.nuffieldtrust.org.uk/research/alcohol-specific-activity-in-hospitals-in-england

Data Summary
type: Distribution
format: CSV
issued: 2024-11-28T14:26:33.653294
modified: 2025-05-05T23:00:13.069027
access URL: https://data.geods.ac.uk/dataset/6372b757-f331-4ce5-a078-f153de3a39eb/resource/d7db7429-8915-4eb5-9095-3c71d43b6d50/download/data_summary_local_morbidity_gbd_1a.csv
byte size: 1116
media type: text/csv

Related Record: NHS Hospital Admission Rates by Ethnic Group and other Characteristics
type: Distribution
format: HTML
issued: 2024-12-10T14:36:50.437020
modified: 2025-05-07T09:12:10.493382
access URL: https://data.geods.ac.uk/dataset/nhs-hospital-admission-rates-by-ethnic-group-and-other-characteristics

Financial Precarity Classification (FPC)
type: Dataset
description: The Financial Precarity Classification (FPC) is a geodemographic classification for Great Britain (GB) that captures the spatial distribution of financial insecurity at a small-area level. The classification is developed to reflect financial precarity as a condition shaped by multiple interconnected factors, including poor-quality and unpredictable employment, unmanaged debt, insecure asset wealth, and insufficient financial resources. It draws on neighbourhood-level indicators covering employment patterns, income levels, asset holdings, debt obligations, and lifestyle characteristics. Using small-area measurements, the classification maps financial precarity at a fine spatial scale, enabling comparisons between local areas and revealing how economic vulnerability varies across different geographical contexts, including rural and urban areas, city centres and peripheries, and coastal and inland communities across Great Britain. ## Content The FPC data for each LSOA21, in CSV and GPKG format, is available on application. An overview of the input variable distribution (Data Summary), input variable dictionary and classification profiles (pen portraits) can be downloaded directly from this page. ## Quality, Representation and Bias All processing steps and methodological details are documented in a peer-reviewed paper published in *Computers, Environment and Urban Systems*.
issued: 2026-01-06T15:27:55.053911
modified: 2026-03-26T15:45:25.484343
contact point: Organization
  fn: Zi Ye
keyword: Classification
keyword: Economy
keyword: Finance
keyword: Poverty
keyword: socioeconomic
landing page: 22 for Great Britain, and house price datasets accessed via GeoDS.

https://data.geods.ac.uk/dataset/66900db1-4620-4b7f-a27d-3d35d3bc1d87/resource/414e9ee3-c7db-45c5-b99f-0724e94f0185
type: Distribution
format: HTML
issued: 2026-01-28T14:22:50.391678
modified: 2026-03-26T15:45:25.491605
title: Paper: Zi Ye, Alex Singleton. Thriving or surviving: Understanding the geography of financial precarity in Great Britain, Computers, Environment and Urban Systems, Volume 125, 2026, 102399, ISSN 0198-9715
access URL: https://doi.org/10.1016/j.compenvurbsys.2026.102399

Variable Dictionary
type: Distribution
description: The codes and classification labels/names lookup.
format: CSV
issued: 2026-01-06T15:31:20.376639
modified: 2026-01-06T15:31:41.607535
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/66900db1-4620-4b7f-a27d-3d35d3bc1d87/resource/4f3e5e5a-c9c4-4832-8f26-2c19c53dcbc2/download/fpc_label_colors.csv
byte size: 652
media type: text/csv

Data Summary
type: Distribution
description: The distribution summary of all variables related to the FPC.
format: CSV
issued: 2026-01-07T14:30:11.852505
modified: 2026-01-07T14:30:13.096000
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/66900db1-4620-4b7f-a27d-3d35d3bc1d87/resource/965c8a4b-f1bb-4764-8f78-cc0e1f99c82c/download/data_summary_fpc_final.csv
byte size: 294
media type: text/csv

Technical Report: Pen Portraits
type: Distribution
description: A set of pen portraits describing the contextual characteristics of each classification group.
format: PDF
issued: 2026-01-07T14:27:29.356188
modified: 2026-01-28T13:59:14.045058
access URL: https://data.geods.ac.uk/dataset/66900db1-4620-4b7f-a27d-3d35d3bc1d87/resource/aa290674-5c82-4eb0-adeb-01712e193914/download/financial-precarity-classification-pen-portrait.pdf
byte size: 61361
media type: application/pdf

External Website: The geography of economic insecurity in Great Britain (data story)
type: Distribution
format: HTML
issued: 2026-03-26T15:45:25.502345
modified: 2026-03-26T15:45:25.491743
access URL: https://geods.ac.uk/2026/02/03/financial-precarity-classification/

Map: Mapmaker
type: Distribution
format: HTML
issued: 2026-01-28T13:59:14.056139
modified: 2026-01-28T14:22:50.381860
access URL: https://mapmaker.geods.ac.uk/#/financial-precarity-classification

Point of Interest data for the United Kingdom
type: Dataset
description: This dataset contains Point of Interest (POI) data for the United Kingdom, obtained from the Overture Maps Foundation. While the Overture Maps Foundation provides a series of global datasets, this GeoDS data product provides users with easy access, without having to query the AWS hosted data that Overture Maps Foundation provide. In addition to the POI and location data, this GeoDS data product has also appended numerous UK census geographies, and an H3 spatial index. This new data product offers an openly available source of location data, capturing a broad variety of POI which can be used to support research over a variety of different domains and thematic areas including health, urban mobility, retail, transport and many more. ## Content We have provided these data as a geopackage. These data are provided at POI level. The POIs were provided to Overture Maps Foundation by Meta and Microsoft, providing a 'name', 'category', and location. For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. ## Quality, Representation and Bias This data product has been subject to academic peer review, with a published paper available here. In this, elements of the data were externally validated against the Geolytix supermarket retail points dataset. Validation revealed that our data product were well aligned with the Geolytix data, having a similar number of POIs across a subset of major supermarket retailers in the Geolytix dataset. Validation also identified that our data product exhibit good locational accuracy, through consideration of the average distance between points in both the Geolytix dataset and our data product. However, there are some additional considerations to flag for users wanting to use this dataset. Users should be cautious of utilising POIs sourced entirely from Microsoft, as these …
issued: 2024-11-08T09:42:03.517986
modified: 2026-01-15T14:12:52.939246
version info: 1.1
contact point: Organization
  fn: Dr Cillian Berragan
keyword: Meta
keyword: Microsoft
keyword: Overture
keyword: Overture Maps Foundation
keyword: POIs
landing page: Overture Maps Foundation, Meta, Microsoft

Data: Point of Interest data for the United Kingdom
type: Distribution
description: Overture Release: 2024-09-18.0
format: ZIP
issued: 2024-11-08T09:46:49.628029
modified: 2025-03-25T12:21:35.491028
access URL: https://data.geods.ac.uk/dataset/69064487-df80-4239-980a-d44f6f787a47/resource/021cbc67-94ec-4ffb-a80c-fd5ad4a4f2d9/download/poi_uk.zip
byte size: 631503167
media type: application/zip

https://data.geods.ac.uk/dataset/69064487-df80-4239-980a-d44f6f787a47/resource/2b0aa700-9a5b-48d9-a7b9-31f2f9cb6ef8
type: Distribution
format: HTML
issued: 2024-11-28T23:50:08.194821
modified: 2025-03-25T12:21:35.491098
title: Paper: Ballantyne, P., & Berragan, C. (2024). Overture Point of Interest data for the United Kingdom: A comprehensive, queryable open data product, validated against Geolytix supermarket data. Environment and Planning B: Urban Analytics and City Science, 51(8), 1974-1980.
access URL: https://doi.org/10.1177/23998083241263124

Variable Dictionary
type: Distribution
description: Overture Release: 2024-09-18.0
format: CSV
issued: 2024-11-08T09:43:36.461973
modified: 2025-03-25T12:21:35.490951
access URL: https://data.geods.ac.uk/dataset/69064487-df80-4239-980a-d44f6f787a47/resource/2dcdd725-868a-422c-b89f-94331bb8130e/download/dictionary_poi_uk_0.csv
byte size: 560
media type: text/csv

External Website: H3 Spatial Index
type: Distribution
format: HTML
issued: 2024-11-29T00:05:46.621361
modified: 2025-03-25T12:21:35.491291
access URL: https://h3geo.org/

External Website: Overture Maps Foundation
type: Distribution
format: HTML
issued: 2024-11-28T23:51:51.666453
modified: 2025-03-25T12:21:35.491227
access URL: https://overturemaps.org/

Data Summary
type: Distribution
description: Overture Release: 2024-09-18.0
format: CSV
issued: 2024-11-08T09:43:00.900035
modified: 2025-05-05T23:00:29.861436
access URL: https://data.geods.ac.uk/dataset/69064487-df80-4239-980a-d44f6f787a47/resource/8ccf0c6c-c4d3-45cb-80e1-8d15dbb9d180/download/summary_poi_uk_0.csv
byte size: 1268
media type: text/csv

External Website: Geolytix supermarket retail points dataset
type: Distribution
format: HTML
issued: 2024-11-28T23:51:03.157403
modified: 2025-03-25T12:21:35.491163
access URL: https://geolytix.com/blog/supermarket-retail-points/

Dwelling Ages and Prices
type: Dataset
description: The Dwelling Ages and Prices dataset provides insights into residential property dwelling age periods, housing transactions, and median house prices at the Lower Super Output Area (LSOA) level. The data has been sourced from the Valuation Office Agency (VOA) and the Office for National Statistics (ONS). Dwelling Ages and Prices data provide Lower Super Output Area (LSOA) level datasets related to residential property dwelling age periods, housing transactions, and median house prices. Data has been provided by the Valuation Office Agency (VOA) and the Office for National Statistics (ONS). The data offer valuable insights for analysing trends in property age and housing market activity across different periods, making it useful for research into property market dynamics, housing affordability, and regional property characteristics. ## Content There are three main datasets - Median house prices, dwelling age band counts and housing transaction counts. Data for median house prices has been aggregated to quarters from 1995-2018, and only the first annual quarter of 2020 and 2021. Dwelling Age Band Counts have been grouped into 10-year age bands from 1995 to 2015, and as a separate dataset has been represented as the modal, median, and RGB composite of age bands for houses built post-1945, post-2016 and up to 2021. Housing transaction counts are presented as quarterly aggregates from 1995-2018. The data is available for download below. For detailed descriptions of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can also be downloaded from the bottom of this page. ## Quality, Representation and Bias The quality and representation is of high quality as is provided by official statistical agencies. However it should be noted that the accuracy of the age band decreases with older buildings, due variations in quality of historic record collecting. Price information is…
issued: 2024-11-29T13:14:19.713526
modified: 2026-01-27T15:16:56.601603
contact point: Organization
  fn: Oliver O'Brien
keyword: House Prices
keyword: Housing
keyword: Property
keyword: Residential
keyword: Value
landing page: Land Registry

Data: Dwelling Age Band Counts (to 2015)
type: Distribution
description: Residential dwelling ages, grouped into approximately 10-year age bands from pre-1900 to 2015. Counts of the numbers of properties in each LSOA11CD (around 1000 properties), in each group. The original source also splits out by council tax bands. The data is generally rounded (at source) to the nearest 10. However, where there are more than 0 but less than 5 properties, then the value "1" is assigned. Because of the rounding, then the total population may not be equal to the sum of the constituent populations, for some areas. The "Mode 1" columns relate to the modal age grouping, i.e. the grouping with the most number of properties in it. "Mode 2" is the runner-up age grouping, i.e. the grouping with the second-most number of properties in it. It may be that there are two or more groupings with the same maximum population, in which case Mode 1 is assigned to the most recent one, Mode 2 to the second-most recent etc.
format: CSV
issued: 2024-11-29T13:14:56.274199
modified: 2025-05-08T07:56:09.187366
access URL: https://data.geods.ac.uk/dataset/6a70ead9-91d9-49fb-b8a9-2fb1c44b8287/resource/00ffee80-3f59-40e2-ae0a-bad9666999f0/download/voapropertyage.csv
byte size: 4034442
media type: text/csv

External Website: Price Paid Data
type: Distribution
format: HTML
issued: 2024-11-29T13:25:48.297252
modified: 2025-06-05T08:33:37.388192
access URL: https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads

Data: Median House Prices (Q1 2020 and Q2 2021)
type: Distribution
description: By LSOA11CD. Averaged across 12 months including quarter, for Q1 2020 and Q2 2021. As currently used in Mapmaker.
format: CSV
issued: 2024-11-29T13:22:04.661630
modified: 2025-05-08T07:56:34.961044
access URL: https://data.geods.ac.uk/dataset/6a70ead9-91d9-49fb-b8a9-2fb1c44b8287/resource/6c346536-cc9a-456d-b908-65a0c0a6e98f/download/hpssa202103.csv
byte size: 819512
media type: text/csv

Data: House Ages and Prices
type: Distribution
description: DOI: 10.20390/houseprice2015 Creator: GeoDS Distributor: GeoDS Data Collector: Office for National Statistics and Valuation Office Agency Identifier: https://dx.doi.org/10.20390/houseprice2015 Publisher: GeoDS Publication Year: 2015 Subject: dwellings, house prices, prices, property, residential, value Language: English Resource General Type: Dataset Resource Type: Property dwelling characteristics Version: 1 Description: Small area datasets related to residential properties that are mapped on GeoDS Maps, including dwelling age periods (at LSOA resolution) and house prices (at MSOA resolution). From the VOA and the ONS. Dwelling Age Group Counts (LSOA): Residential dwelling ages, grouped into approximately 10-year age bands from pre-1900 to 2015. Counts of the numbers of properties in each LSOA (around 1000 properties), in each group. The original source also splits out by council tax bands. Median House Prices (MSOA) by Quarter, Rolling Year (ONS): Each quarter is for the median house price for the previous 12 months. The data is made available at MSOA level (approximately 5000 houses per area).
format: ZIP
issued: 2024-11-29T13:23:00.092340
modified: 2025-05-08T07:57:52.730234
access URL: https://data.geods.ac.uk/dataset/6a70ead9-91d9-49fb-b8a9-2fb1c44b8287/resource/877fba5c-75eb-4ae9-94d1-088985f3dbad/download/houseagesandprices_doiversion.zip
byte size: 1092704
media type: application/zip

Data Summary: Prices to 2018
type: Distribution
format: CSV
issued: 2024-11-29T13:24:35.620759
modified: 2025-05-08T07:58:15.302651
access URL: https://data.geods.ac.uk/dataset/6a70ead9-91d9-49fb-b8a9-2fb1c44b8287/resource/911e4dd2-724f-4f41-8369-ba3b7977d248/download/data_summary_dwelling_prices2018.csv
byte size: 11863
media type: text/csv

Data: Dwelling Age Band Counts (to 2021)
type: Distribution
description: Counts for each LSOA11CD. Proportion of houses built post-1945, post 2016, and the modal, modal (20%+ in mode only, else X) and median ageband. A = bp_pre_1900, B = bp_1900_1918, C = bp_1919_1929, D = bp_1930_1939, E = bp_1945_1954, F = bp_1955_1964, G = bp_1965_1972, H = bp_1973_1982, I = bp_1983_1992, J = bp_1993_1999, K = bp_2000_2008, L = bp_2009_2021, U = Unknown, X = Various. Additionally, an RGB composite of the distribution of age bands: A = 0,0,255; B = 0,64,255; C = 0,128,255; D = 0,192,255; E = 0,255,192; F = 0,255,0; G = 192,255,0; H = 255,255,0; I = 255,192,0; J = 255,128,0; K = 255,64,0; L = 255,0,0; Y = 255,255,255
format: CSV
issued: 2024-11-29T13:20:05.850275
modified: 2025-05-08T07:56:22.832446
access URL: https://data.geods.ac.uk/dataset/6a70ead9-91d9-49fb-b8a9-2fb1c44b8287/resource/96754d2b-88a9-4d67-8727-bf574ff799c2/download/dwellingages.csv
byte size: 1692750
media type: text/csv

Map: Mapmaker
type: Distribution
format: HTML
issued: 2025-06-05T08:33:37.404206
modified: 2026-01-27T15:16:56.608823
access URL: https://mapmaker.geods.ac.uk/

Data: Transaction Counts (1995-2018)
type: Distribution
description: Quarterly count of housing transactions per LSOA11CD.
format: CSV
issued: 2024-11-29T13:22:37.325654
modified: 2025-05-08T07:56:40.353300
access URL: https://data.geods.ac.uk/dataset/6a70ead9-91d9-49fb-b8a9-2fb1c44b8287/resource/ae9a686c-ce8f-4ac1-a85a-1c512942c354/download/transaction_count_quarterly.csv
byte size: 8262394
media type: text/csv

Data Summary: Ages to 2021
type: Distribution
format: CSV
issued: 2024-11-29T13:24:13.066883
modified: 2025-05-08T07:58:09.515857
access URL: https://data.geods.ac.uk/dataset/6a70ead9-91d9-49fb-b8a9-2fb1c44b8287/resource/aea5f855-2877-446e-84d8-3c43231ae26b/download/data_summary_dwelling_ages2021.csv
byte size: 519
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-29T13:23:23.972093
modified: 2025-05-08T07:57:58.405473
access URL: https://data.geods.ac.uk/dataset/6a70ead9-91d9-49fb-b8a9-2fb1c44b8287/resource/b34d2b00-1d7f-400f-8932-8a75f2135376/download/variable_dictionary_dwelling.csv
byte size: 2156
media type: text/csv

Data Summary: Ages to 2015
type: Distribution
format: CSV
issued: 2024-11-29T13:23:44.514564
modified: 2025-05-08T07:58:04.168693
access URL: https://data.geods.ac.uk/dataset/6a70ead9-91d9-49fb-b8a9-2fb1c44b8287/resource/ec04ece6-ab11-4853-ae2f-6dff4484d41c/download/data_summary_dwelling_ages2015.csv
byte size: 2001
media type: text/csv

Data: Median House Prices (1995-2018)
type: Distribution
description: By LSOA11CD, quarterly data.
format: CSV
issued: 2024-11-29T13:21:34.986252
modified: 2025-05-08T07:56:29.410817
access URL: https://data.geods.ac.uk/dataset/6a70ead9-91d9-49fb-b8a9-2fb1c44b8287/resource/ed9edf00-c143-4a89-82fc-99cae4a752ac/download/median_prices_quarterly.csv
byte size: 22690414
media type: text/csv

Data Summary: Prices to 2020-21
type: Distribution
format: CSV
issued: 2024-11-29T13:24:57.897246
modified: 2025-05-08T07:58:20.915975
access URL: https://data.geods.ac.uk/dataset/6a70ead9-91d9-49fb-b8a9-2fb1c44b8287/resource/f0ead89a-4d50-4015-9dbe-e34d3df96c25/download/data_summary_dwelling_prices2021.csv
byte size: 407
media type: text/csv

Data Summary: Transactions to 2018
type: Distribution
format: CSV
issued: 2024-11-29T13:25:21.156016
modified: 2025-05-08T07:58:25.959305
access URL: https://data.geods.ac.uk/dataset/6a70ead9-91d9-49fb-b8a9-2fb1c44b8287/resource/f57a27bc-5684-495a-8c94-cbe7030e1efc/download/data_summary_dwelling_transactions2018.csv
byte size: 10360
media type: text/csv

Using AlphaEarth Foundations Embeddings for Building Analysis
type: Dataset
description: In this hands-on tutorial, we show how to use Google’s AlphaEarth Foundations embeddings to analyse buildings at scale, from exploratory clustering through to predictive modelling. Using the Liverpool City Region as a case study, participants will learn how to extract building-level embeddings, uncover meaningful structure in high-dimensional data, link to administrative datasets, and evaluate the strengths and limitations of embedding-based approaches for real-world urban analysis. By the end of the tutorial, you will have a complete, reproducible workflow for working with AlphaEarth embeddings in Python, from raw access to advanced analytics. All data required for the exercises can be downloaded from this page and be placed in a `data` folder in your working directory. The key steps covered in the notebook are: Data Access and Extraction: * Describe satellite embeddings as numerical “fingerprints” of place. * Understand the structure of Google’s AlphaEarth Foundations 64-dimensional embeddings at 10m resolution. * Set up, authenticate, and initialise the Google Earth Engine API in Python. * Access AlphaEarth embedding collections (2017–2024) for large regions and specific locations. * Extract and consolidate sampled pixel-level embeddings into efficient Parquet formats using DuckDB. Setup and Pixel-Level Analysis: * Define a study region (e.g. Liverpool City Region) and quantify its embedding footprint. * Explore the scale and structure of 64-dimensional embeddings using sampled pixels. * Understand when to work server-side in Earth Engine versus downloading local subsets. Building Cluster Analysis: * Append embeddings to building geometries (e.g. TOID-based locations) using batch extraction. * Fit K-means clustering to identify building typologies from building-level embeddings. * Export clustered outputs (GeoPackage, Parquet) for mapping and further spatial analysis. Explore the Structure of the Embeddings and Clusters using UMAP: * Use UMAP to project 64-dimens…
issued: 2025-11-09T22:19:11.378049
modified: 2026-01-28T14:26:53.740140
contact point: Organization
  fn: Alex Singleton
keyword: Clustering
keyword: Embeddings
keyword: Python
keyword: Satellite
keyword: Tutorial
landing page: AlphaEarth Foundations, Ordnance Survey, GeoDS

Data: Tutorial_data.zip
type: Distribution
format: ZIP
issued: 2025-11-19T14:59:53.348825
modified: 2025-11-27T13:37:44.514333
access URL: https://data.geods.ac.uk/dataset/6ae6290e-78bb-4481-a6d9-911a9cae7d4e/resource/a94257c5-3d78-4ff4-a236-553e0f3c6c48/download/tutorial_data.zip
byte size: 10766
media type: application/zip

External Website: Tutorial
type: Distribution
description: The main tutorial document.
format: HTML
issued: 2025-11-10T13:51:29.978105
modified: 2025-11-27T13:37:44.514221
access URL: https://geographicdataservice.github.io/Building_Level_AlphaEarth/

Output Area Classification (2001)
type: Dataset
description: This classification relates to the 2001 Census and is specified for 2001 Output Areas. This classification has been superseded by later editions based on more recent Census data (2011, 2021) and geography, but remains available for reference and historical comparison. The 2001 Classification for Output Areas (2001 OAC) is a hierarchical geodemographic classification across the UK which identifies areas of the country with similar characteristics. The 2001 OAC was developed collaboratively by the Office for National Statistics and the University of Leeds. The classification contains the following breakdown of Output Areas into different supergroups, groups and sub-groups. ## Content The data is available for download from the bottom of this page. Also available are pen portraits and a lookup table which provides additional description about this project. ## Quality, Representation and Bias A journal article accompanies 2001 OAC which provides a thorough evaluation. All data used for this classification are sourced from the 2001 Census so bounded by the usual operational quality / representation and bias of a national census. The geodemographic classification created presents a best effort of the authors to represent the characteristics of the population and geographic context, however, there are decisions made during the classification process that guide these representations. For a full overview of these decisions and their rationale, see the published paper.
issued: 2025-11-21T15:30:38.114888
modified: 2025-12-09T17:56:17.333768
version info: 1.0
contact point: Organization
  fn: Meilin Shi
keyword: Census
keyword: Classification
keyword: Geodemographics
keyword: Output Area Classification
landing page: Office for National Statistics

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2025-11-25T10:47:57.034566
modified: 2025-11-25T10:47:58.864920
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/6fe64af4-89e9-4c19-afce-a813018d18af/resource/095029d9-b4b8-4c04-ba8c-220d60dc18e2/download/variable_dictionary_oac01_columns.csv
byte size: 291
media type: text/csv

Data: Classification (GPKG format)
type: Distribution
format: ZIP
issued: 2025-11-24T12:17:44.602527
modified: 2025-11-27T14:45:27.065313
access URL: https://data.geods.ac.uk/dataset/6fe64af4-89e9-4c19-afce-a813018d18af/resource/4ca61756-9ea8-40b2-a0e4-bf65565db053/download/oac_2001.gpkg.zip
byte size: 432825
media type: application/zip

Data Summary
type: Distribution
format: CSV
issued: 2025-11-24T12:48:12.508006
modified: 2025-12-09T17:55:32.538761
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/6fe64af4-89e9-4c19-afce-a813018d18af/resource/98acd654-b016-4d8d-8956-d90b6b7eb874/download/data_summary_oa_classification_membership.csv
byte size: 686
media type: text/csv

Technical Report: Pen Portraits for the 2001 Output Area Classification
type: Distribution
format: PDF
issued: 2025-11-21T16:54:48.598697
modified: 2025-11-24T12:48:12.492772
access URL: https://data.geods.ac.uk/dataset/6fe64af4-89e9-4c19-afce-a813018d18af/resource/996ad2f3-7d6d-41dd-a2d6-196663c62b3e/download/description_supergroup_group_subgroup.pdf
byte size: 1590646
media type: application/pdf

Data: Classification
type: Distribution
format: CSV
issued: 2025-11-21T15:31:38.134338
modified: 2025-11-21T15:31:40.217077
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/6fe64af4-89e9-4c19-afce-a813018d18af/resource/9b7dbfb4-c40f-41d2-a7c7-4a516e9edb1b/download/oa_classification_membership.csv
byte size: 10331757
media type: text/csv

Paper: Vickers, D., & Rees, P. (2007). Creating the UK National Statistics 2001 output area classification. Journal of the Royal Statistical Society Series A: Statistics in Society, 170(2), 379-403.
type: Distribution
format: HTML
issued: 2025-11-25T10:18:25.440996
modified: 2025-11-25T10:47:57.019478
access URL: https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1467-985X.2007.00466.x

Related Record: Output Area Classification (2011)
type: Distribution
format: HTML
issued: 2025-11-21T16:33:26.725507
modified: 2025-11-21T16:37:20.298874
access URL: https://data.geods.ac.uk/dataset/output-area-classification-2011

Related Record: Output Area Classification (2021/2)
type: Distribution
format: HTML
issued: 2025-11-21T16:32:29.553248
modified: 2025-12-09T17:56:17.337688
access URL: https://data.geods.ac.uk/dataset/output-area-classification-2021

Variable Dictionary: Classification Codes and Names
type: Distribution
format: CSV
issued: 2025-11-21T16:40:56.619945
modified: 2025-12-09T17:56:17.337563
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/6fe64af4-89e9-4c19-afce-a813018d18af/resource/fc64bc1b-5022-463a-8e3d-7b0bf6d06d07/download/oa_classification_cluster_names.csv
byte size: 2356
media type: text/csv

Output Area Classification (2021/2)
type: Dataset
description: The UK 2021/2 Classification for Output Areas (2021/2 OAC) is a hierarchical geodemographic classification across the UK which identifies areas of the country with similar characteristics. It was developed by the Geographic Data Service (GeoDS) in partnership with the Office for National Statistics (ONS). The classification contains the following breakdown of Output Areas into 8 different supergroups, into which are nested 21 groups and 52 sub-groups. ## Content The data is available for download at the bottom of this page. Also available for download are a methodological paper, which outlines the approach taken to develop the classification, Local Authority atlases, and pen portraits, which provide additional information about the variables and terms used. Classification geodata can also be downloaded below. ## Quality, Representation and Bias A journal article (see link below) accompanies an earlier version of the classification which provides a thorough evaluation. This version of the classification was created using 2021 Census Output Area data for England and Wales; aggregate published 2021 Northern Ireland data, apportioned to 2011 small area census geography; and 2011 Scottish Census results carried forward in adjusted or unadjusted form. This version of the data was known as UK-OAC Modelled, and is available on specific request, via the article's corresponding author, for research reproducibility purposes. In general terms, a geodemographic classification presents a best effort of the authors to represent the characteristics of the population and geographic context, however, there are decisions made during the classification process that guide these representations. Again for a full overview of these decisions and their rationale, see the published paper. ## Version History * 1.0 - 2001 OAC * 2.0 - 2011 OAC * 3.0 - 2021 OAC (November 2023) Released for England/Wales. * 3.1 - UK-OAC Modelled (November 2023) [the paper] * 3.2 - 2021/2 OAC (January 2025) [this …
issued: 2024-12-16T19:18:08.931065
modified: 2026-03-26T15:46:42.144209
version info: 3.2
contact point: Organization
  fn: Oliver O'Brien
keyword: Census
keyword: Output Area Classification
keyword: Population
keyword: classification
landing page: Office for National Statistics (ONS), NISRA, Scottish Government

Data: Group Populations and Index Scores for Local Authorities
type: Distribution
description: Group populations and index scores for each Supergroup, Group and Subgroup, for each local authority (LAD23).
format: CSV
issued: 2024-12-16T19:35:23.954016
modified: 2025-12-08T13:49:21.376609
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/0237a7c7-0523-410e-89a2-15c9687c4156/download/uk_oa_lad23_indexscores.csv
byte size: 609589
media type: text/csv

Data: Classification geodata (Geopackage format)
type: Distribution
format: ZIP
issued: 2024-12-16T19:32:55.889166
modified: 2025-12-08T17:49:07.392140
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/0493e619-b57a-49fa-b8fb-2e1026dcf209/download/oac21_gpkg.zip
byte size: 96382271
media type: application/zip

Tool: Postcode Search
type: Distribution
format: HTML
issued: 2024-12-16T19:27:36.746673
modified: 2025-05-05T23:20:35.226748
access URL: https://apps.cdrc.ac.uk/oac/OACSearch

Related Record: UK LSOA / DZ / SDZ Classification (2021/2 LSOAC)
type: Distribution
format: HTML
issued: 2024-12-17T12:07:47.710552
modified: 2026-02-02T14:26:29.737197
access URL: https://data.geods.ac.uk/dataset/lsoac

Technical Report: Pen Portraits and Input Variable Listing
type: Distribution
description: Classification typology, glossary of variables, glossary of terms, distribution statistics and pen portraits.
format: PDF
issued: 2024-12-16T19:34:28.760487
modified: 2025-12-08T13:57:33.979169
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/33fd0618-b594-4451-9705-9f1e9dae2c66/download/pen_portraits_ukoac.pdf
byte size: 11258922
media type: application/pdf

Data: Classification geodata (Shapefile format)
type: Distribution
format: ZIP
issued: 2024-12-16T19:31:08.253047
modified: 2025-12-08T17:47:59.103175
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/37ff4277-2028-40ff-b4b8-90cd9f4c8f2c/download/oac21.zip
byte size: 90409178
media type: application/zip

Source Code: UK Output Area Classification (UK OAC) - Final Version (GitHub Repository)
type: Distribution
format: HTML
issued: 2024-12-17T12:08:28.332320
modified: 2026-03-26T15:46:42.148785
access URL: https://github.com/GeographicDataService/Output_Area_Classification

Data: Classification
type: Distribution
format: CSV
issued: 2024-12-17T12:04:57.188280
modified: 2025-12-08T17:49:08.974887
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/4924ae88-d384-45f0-8b82-0a4a66e49e01/download/uk_oac_final.csv
byte size: 10422395
media type: text/csv

Flyer: Output Area Classification
type: Distribution
format: PDF
issued: 2026-02-02T14:26:29.746050
modified: 2026-03-26T15:46:42.148546
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/4b87991b-82af-4642-9db1-074ea4a96f18/download/oac-2026-web.pdf
byte size: 2163373
media type: application/pdf

Technical Report: Local Authority Atlas (England A-D)
type: Distribution
format: PDF
issued: 2024-12-16T19:26:01.910924
modified: 2025-05-05T23:20:35.226416
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/4e383b7a-f99e-452c-a014-b93df50a32db/download/lad_england_a_to_d_profiles_output.pdf
byte size: 14120525
media type: application/pdf

Technical Report: Local Authority Atlas (Wales)
type: Distribution
format: PDF
issued: 2024-12-16T19:29:24.951966
modified: 2025-05-05T23:20:35.226963
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/582f60ba-9c58-49d1-8b92-b58a53228e83/download/lad_wales_profiles_output.pdf
byte size: 4530560
media type: application/pdf

Related Record: Output Area Classification (2011)
type: Distribution
format: HTML
issued: 2024-12-17T12:03:28.734309
modified: 2025-05-06T15:42:45.503499
access URL: https://data.geods.ac.uk/dataset/output-area-classification-2011

Variable Dictionary: Classification Codes and Names
type: Distribution
format: CSV
issued: 2024-12-16T19:35:49.549870
modified: 2025-12-08T13:59:51.998604
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/5ee35968-582e-4709-8433-90d9fd69d2a0/download/classification_codes_and_names_ukoac_lsoac_msoac.csv
byte size: 7378
media type: text/csv

Technical Report: Local Authority Atlas (England S-Z)
type: Distribution
format: PDF
issued: 2024-12-16T19:26:55.338211
modified: 2025-05-05T23:20:35.226679
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/63a2c4e6-909a-4e24-a574-39d8b9339f4c/download/lad_england_s_to_z_profiles_output.pdf
byte size: 18975757
media type: application/pdf

Technical Report: Upper Tier Local Authority Atlas (England M-Z)
type: Distribution
format: PDF
issued: 2024-12-16T19:28:55.921592
modified: 2025-05-05T23:20:35.226889
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/8d893262-10b9-4412-b811-b3540e2b3647/download/utla_england_m_to_z_profiles_output.pdf
byte size: 14077478
media type: application/pdf

Data Summary: Classification
type: Distribution
format: CSV
issued: 2024-12-16T19:36:24.349945
modified: 2025-12-08T13:54:53.235256
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/9cfd6c06-3760-4322-810b-caefe18294f4/download/data_summary_uk_oac_final.csv
byte size: 366
media type: text/csv

Technical Report: Local Authority Atlas (England M-R)
type: Distribution
format: PDF
issued: 2024-12-16T19:26:38.915363
modified: 2025-05-05T23:20:35.226605
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/a5915697-e664-4b6f-808e-3cee95e52ebd/download/lad_england_m_to_r_profiles_output.pdf
byte size: 12053695
media type: application/pdf

Technical Report: Local Authority Atlas (England E-L)
type: Distribution
format: PDF
issued: 2024-12-16T19:26:23.902400
modified: 2025-05-05T23:20:35.226528
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/bad6b79c-b998-48b8-8c73-36ee8aa39d80/download/lad_england_e_to_l_profiles_output.pdf
byte size: 13549920
media type: application/pdf

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2024-12-16T19:36:05.089656
modified: 2025-12-08T13:57:35.661910
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/d454e3b8-d40f-4edf-a636-e0b8b4ea0607/download/variable_dictionary_ukoac.csv
byte size: 206
media type: text/csv

https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/d87a7101-58aa-4d9f-99dc-e36d1ca8f646
type: Distribution
format: HTML
issued: 2024-12-17T12:01:21.987324
modified: 2025-05-05T23:20:35.227915
title: Paper: Wyszomierski, J., Longley, P.A., Singleton, A.D., Gale, C. & O’Brien, O. (2024) A neighbourhood Output Area Classification from the 2021 and 2022 UK censuses. The Geographical Journal, 190, e12550.
access URL: https://doi.org/10.1111/geoj.12550

Map: Mapmaker
type: Distribution
format: HTML
issued: 2024-12-17T11:58:25.549158
modified: 2025-12-08T13:48:07.253997
access URL: https://mapmaker.geods.ac.uk/#/output-area-classification-2021

Technical Report: Upper Tier Local Authority Atlas (England A-L)
type: Distribution
format: PDF
issued: 2024-12-16T19:28:38.091307
modified: 2025-05-05T23:20:35.226818
access URL: https://data.geods.ac.uk/dataset/73a16168-f2b0-4732-b36a-3ec0253e34fe/resource/f4f250ce-33fa-4189-993f-5cd81673eee8/download/utla_england_a_to_l_profiles_output.pdf
byte size: 13344252
media type: application/pdf

US Retail Centre Boundaries and Classification
type: Dataset
description: These data comprise the spatial boundaries delineating 10,956 major retail agglomerations across the United States, with an accompanying classification that describes their characteristics. The dataset was generated through the use of retailer location data supplied by SafeGraph. The data provides a replicable data product built on a heuristic categorisation of retail unit density. The product is built using consistent methods and data for the national extent of the U.S., representing the first delineation of retail centres for this country. The agglomerations are identified based on the clustering and connectivity patterns of individual retail units over space. A hexagonal high-resolution grid is superimposed over spatial clusters of retail points and a network-based algorithm is used to prune and fine-tune clusters into self-contained, mutually exclusive zones. The retail boundaries are accompanied by information about their geographical location, including state, county, place and street names, as well as a non-hierarchical classification which describes the characteristics of the different retail centres. A two-tier classification is presented, comprising four top-level groups and fourteen nested types. SafeGraph enabled the dissemination of these data, which were aggregated and derived from their own points of interest data along with OpenStreetMap, to be distributed under an open licence. SafeGraph, OpenStreetMap and GeoDS should be attributed when using these data. These data are available with a CC-BY 2.0 Licence, which permits users to copy, distribute, display, perform and make derivative works only if they give the author or licensor the attribution. For more details on the creation of the retail boundaries (and typology), please see the paper link below. ## Content These data are open and can be downloaded as a zipped Geopackage (GPKG) from the bottom of this page. For detailed description of the columns contained within the data, see the Variable Dictio…
issued: 2024-12-16T13:24:04.118604
modified: 2026-02-10T22:56:32.243774
version info: 1.0
contact point: Organization
  fn: Alex Singleton
keyword: Downtown
keyword: Retail
keyword: Retail Centre
keyword: USA
keyword: United States of America
landing page: SafeGraph, OpenStreetMap

Source Code: USRetailCentres (Github Repository)
type: Distribution
format: HTML
issued: 2024-12-16T13:39:53.539678
modified: 2026-02-10T22:56:32.250924
access URL: https://github.com/GeographicDataService/USRetailCentres

Data Summary
type: Distribution
format: CSV
issued: 2024-12-16T13:25:27.902789
modified: 2025-05-05T23:17:04.034039
access URL: https://data.geods.ac.uk/dataset/73fd543c-3d4e-47b7-9c0d-066eb3a55c1d/resource/0f16df4c-47bd-4447-9698-59bbb0fb8bb0/download/data_summary_usretail.csv
byte size: 716
media type: text/csv

Data: US_RetailCentres
type: Distribution
description: Zipped GPKG geospatial data file. 2 files in this archive - US_RetailCentres.gpkg - __MACOSX/._US_RetailCentres.gpkg
format: ZIP
issued: 2024-12-16T13:24:54.357858
modified: 2026-02-10T22:56:32.250718
access URL: https://data.geods.ac.uk/dataset/73fd543c-3d4e-47b7-9c0d-066eb3a55c1d/resource/54e01453-2b4c-49cd-8c69-044d9d62e712/download/us_retailcentres.gpkg_.zip
byte size: 34879308
media type: application/zip

https://data.geods.ac.uk/dataset/73fd543c-3d4e-47b7-9c0d-066eb3a55c1d/resource/647568d9-bc72-435e-9f7c-c30dc9892716
type: Distribution
format: HTML
issued: 2024-12-16T13:40:47.519028
modified: 2025-05-05T23:17:04.034180
title: Paper: Patrick Ballantyne, Alex Singleton, Les Dolega & Jacob Macdonald (2023) Integrating the Who, What, and Where of U.S. Retail Center Geographies, Annals of the American Association of Geographers, 113:2, 488-510,
access URL: https://doi.org/10.1080/24694452.2022.2098087

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-12-16T13:25:18.522926
modified: 2025-05-05T23:17:04.033961
access URL: https://data.geods.ac.uk/dataset/73fd543c-3d4e-47b7-9c0d-066eb3a55c1d/resource/67da6ef7-7ac1-4f04-bb37-4af40e9aec01/download/variable_dictionary_usretail.csv
byte size: 378
media type: text/csv

Creating a Geodemographic Classification Using K-means Clustering in R
type: Dataset
description: Geodemographic classifications group neighbourhoods (or sometimes even indiviudal households) into types of similar characteristics based on a range of variables. They are a useful means on segmenting the population into distinctive groups in order to effectively channel resources. Such classifications have been effective deductive tools for marketing, retail and service planning industries due to the assumed association between geodemographics and behaviour. For instance, typically a classification at the broadest level may distinguish cosmopolitan neighbourhoods with high proportions of young and newly qualified workers from suburban neighbourhoods with high proportions of settled families. Such classifications work because people of like- minded characteristics tend to cluster within cities. Whilst most geodemographic products are built within the commercial sector and sold by vendors, open source alternatives are available. The following tutorial will provide you with the basic skills to build your own geodemographic classification using R. All data and resources for this exercise are freely available. Those that are unfamiliar with R may find it useful to go through the Introduction to Spatial Data Analysis and Visualisation in R tutorial series first. The tutorial is free, but users will need to register on this website to access the materials.
issued: 2024-11-28T12:37:48.342717
modified: 2026-01-15T14:16:05.780296
contact point: Organization
  fn: James Cheshire
keyword: Cluster
keyword: Clustering
keyword: Geodemographic Classification
keyword: Geodemographics
keyword: K-Means
keyword: R Software
keyword: Tutorial
landing page: ONS, GeoDS

Data: Creating a Geodemographic Classification Using K-means Clustering in R
type: Distribution
format: PDF
issued: 2024-11-28T12:40:12.878011
modified: 2025-05-07T12:03:46.505857
access URL: https://data.geods.ac.uk/dataset/7e0fc3dc-33dd-4db5-8486-2e0481c381ac/resource/20bda233-8db1-4c73-9a89-77d7afc34ade/download/kmeans_pdfgeodemographicslondongl.pdf
byte size: 4962931
media type: application/pdf

Data: Data Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T12:40:54.589099
modified: 2025-05-08T15:21:11.766908
access URL: https://data.geods.ac.uk/dataset/7e0fc3dc-33dd-4db5-8486-2e0481c381ac/resource/a1b30fc6-326b-4d09-b7ac-fa4e397a808a/download/kmeans_datadictionary.csv
byte size: 3505
media type: text/csv

Related Record: Creating an Open Geodemographic Classification Using K-means Clustering in Python
type: Distribution
format: HTML
issued: 2026-01-15T14:15:40.216707
modified: 2026-01-15T14:16:05.783841
access URL: https://data.geods.ac.uk/dataset/creating-an-open-geodemographic-classification-using-k-means-clustering-in-python

Data: 2011 Output Area London (Shapefile)
type: Distribution
format: ZIP
issued: 2024-11-28T12:40:42.330044
modified: 2025-05-07T12:04:30.739221
access URL: https://data.geods.ac.uk/dataset/7e0fc3dc-33dd-4db5-8486-2e0481c381ac/resource/ebe9d676-1d42-4b0e-947e-c339727fdcbd/download/kmeans_londonoa11shapfile.zip
byte size: 4465428
media type: application/zip

Data: London Census Data
type: Distribution
format: CSV
issued: 2024-11-28T12:40:33.010967
modified: 2025-05-07T12:04:08.637643
access URL: https://data.geods.ac.uk/dataset/7e0fc3dc-33dd-4db5-8486-2e0481c381ac/resource/ef649468-df34-4251-bbc3-7a46371e5013/download/kmeans_london-census-data.csv
byte size: 17074223
media type: text/csv

Temporal OAC
type: Dataset
description: The 2001-2011 Temporal Output Area Classification (Temporal OAC or TOAC) is a geodemographic classification for the 2011 Output Areas in England. A geodemographic classification provides a set of categorical summaries of the built and socio-economic characteristics of small geographic areas. Many such classifications are created entirely from data extracted from a single census of population. Such classifications can be considered to become less useful over time (e.g. later in the intervening period between decennial censuses) because of the changing composition of small geographic areas. TOAC is a result of a project that utilises an innovative methodology that classifies both 2001 and 2011 census data inputs utilising a unified geography and set of attributes to create a classification that spans both census periods. Using this classification, it is possible to examine the temporal stability of the clusters and whether other secondary data sources and internal measures might usefully indicate local uncertainties in such a classification during an intercensal period. ## Content The data is available for download below, in Shapefile format. A data summary and variable dictionaries are also available. ## Quality, Representation and Bias A journal article accompanies TOAC which provides a thorough evaluation and methodological detail. All data used for this classification were sourced from the 2001 - 2011 Census so are bound by the usual operational quality / representation and bias of a national census. The geodemographic classification created presents a best effort of the authors to represent the characteristics of the population and geographic context over time, however, there are decisions made during the classification process that guide these representations. For a full overview of these decisions and their rationale, see the published paper.
issued: 2024-12-16T12:43:43.405977
modified: 2025-05-08T15:24:20.723950
contact point: Organization
  fn: Alex Singleton
keyword: Census
keyword: Demographics
keyword: England
keyword: National
keyword: OAC
keyword: Output Area Classification
keyword: Temporal
landing page: Office for National Statistics

Data: TOAC England
type: Distribution
description: 9 files in this archive - TOAC England/ - __MACOSX/._TOAC England TOAC England/.DS_Store - __MACOSX/TOAC England/._.DS_Store - TOAC England/TOAC_ENG.dbf - TOAC England/TOAC_ENG.shp - __MACOSX/TOAC England/._TOAC_ENG.shp - TOAC England/TOAC_ENG.shx - TOAC England/TOAC_ENG.prj
format: ZIP
issued: 2024-12-16T12:45:36.956722
modified: 2025-05-07T12:42:10.161148
access URL: https://data.geods.ac.uk/dataset/7e6ace0d-4a5e-4cbc-abf6-e6f52b991957/resource/30e5b16f-cf7e-43f5-b20b-dcab8f3ced16/download/toac-england_0.zip
byte size: 28565119
media type: application/zip

Data Summary
type: Distribution
format: CSV
issued: 2024-12-16T12:45:59.483665
modified: 2025-05-07T12:42:23.532182
access URL: https://data.geods.ac.uk/dataset/7e6ace0d-4a5e-4cbc-abf6-e6f52b991957/resource/9b9599c8-c083-4346-a8cf-ff45c588d9cc/download/data_summary_toac_eng.csv
byte size: 1146
media type: text/csv

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2024-12-16T12:46:37.592163
modified: 2025-05-08T15:24:20.728402
access URL: https://data.geods.ac.uk/dataset/7e6ace0d-4a5e-4cbc-abf6-e6f52b991957/resource/a5242efe-6f28-4905-9105-3d030496487c/download/variable_dictionary_toac11_columns.csv
byte size: 160
media type: text/csv

Paper: Singleton, A., Longley, P., and Pavlis, M. (2016). The stability of geodemographic cluster assignments over an inter-censal period. Journal of Geographical Systems, 8(2), 97-123.
type: Distribution
format: HTML
issued: 2024-12-16T12:47:22.897347
modified: 2025-05-05T23:15:49.210814
access URL: https://doi.org/10.1007/s10109-016-0226-x

Variable Dictionary: Lookups
type: Distribution
format: CSV
issued: 2024-12-16T12:46:18.386244
modified: 2025-05-07T12:42:41.128124
access URL: https://data.geods.ac.uk/dataset/7e6ace0d-4a5e-4cbc-abf6-e6f52b991957/resource/c38f8dcc-622d-45ee-9138-380d3f158af7/download/variable_dictionary_toac11_lookups.csv
byte size: 3268
media type: text/csv

Advanced GIS Methods Training: AHAH and Multi-Dimensional Indices
type: Dataset
description: This course presents the Access to Healthy Assets & Hazards (AHAH) dataset and the methods used to create them, multi-dimensional indices. Multi-dimensional indices are used to create many different data sets, including the Index of Multiple Deprivation. This course will explain the AHAH dataset, how and why it was created, and what it can be used for. You will also learn how to use the multi-dimensional indices method to create your own index, using AHAH as an example. It is split into two parts, each with a video clip and a series of commands to work through: * Part 1: Access to Healthy Assets & Hazards (AHAH) * Part 2: Multi Dimensional Indices (MDI) _You need some prior knowledge of R to get the most from this course. If you are new to R, we recommend you complete the Short Course on Using R as a GIS first. Use the search box above to find this course._ After completing the material, you will: * Know what AHAH is and what it can be used for * Be aware of how AHAH was created * Understand some of its key strengths and weaknesses * Know how to use Access to Healthy Assets & Hazards (AHAH) in RStudio * Be able to recreate the AHAH MDI * Understand why we need to transform some of the data * Feel confident to add/remove domains from this index and understand the results * Be able to create your own multi dimensional index To access the course, click on Download next to the 'Part 1: AHAH - Workbook' or 'Part 2: MDI - Workbook' files below. It is recommended that you have the course material open in one window, and RStudio open in another window next to it, using either a big monitor, or two monitors. If you have any comments or feedback, please email us. This course is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International licence.
issued: 2024-11-28T13:15:55.778298
modified: 2025-05-08T15:21:08.528268
contact point: Organization
  fn: Mark Green
keyword: AHAH
keyword: Advanced GIS Methods Training
keyword: Multi-Dimensional Indices
keyword: Tutorial

Data: Part 2: MDI - Workbook
type: Distribution
format: HTML
issued: 2024-11-28T13:16:29.694750
modified: 2025-05-08T15:21:08.535810
access URL: https://data.geods.ac.uk/dataset/7f72f058-8396-41aa-8722-b1e7c620ba56/resource/3427630b-0965-43e0-96ef-a67ab47c6239/download/ahahtutorial_part2-mdi-workbook.html
byte size: 673932
media type: text/html

Data: Part 1: AHAH - Workbook
type: Distribution
format: HTML
issued: 2024-11-28T13:16:16.242726
modified: 2025-05-07T12:02:35.784804
access URL: https://data.geods.ac.uk/dataset/7f72f058-8396-41aa-8722-b1e7c620ba56/resource/f0f9d683-8ad9-4bb3-a063-711a11ba3e95/download/ahahtutorial_part1-ahah-workbook.html
byte size: 2804383
media type: text/html

Great Britain Gambling Behaviours Classification (GB2C) (LSOA Geography)
type: Dataset
description: The pervasive nature of online gambling has pushed it to the forefront of social concerns in Great Britain (GB). Understanding how and where gambling-related behaviours manifest is essential for informing targeted interventions and evidence-based public policy. The GB2C dataset provides the first national areal classification of gambling behaviours in GB. Uniquely, it is based on observed online transactional behaviours drawn from industry data. The classification is built using circa 1.2 million anonymised online gambling accounts recorded throughout 2022, provided by one of the ‘Big 5’ British gambling operators. This work was conducted independently by GeoDS researchers, with data access facilitated through collaboration with the gambling service provider (which had no influence over the research or reporting of it). Using this unique data resource, customers were segmented into 11 Active Subgroups, with additional estimates for non-Active account holders and the remaining adult population. Lower-layer Super Output Area (LSOA) estimates of the incidence of each Subgroup are available through the GeoDS for bona fide research purposes. This classification extends what is possible using conventional survey instruments alone. Linkage of georeferenced, anonymised individual customer records to neighbourhood attributes from the GeoDS UK Output Area Classification (UK-OAC) and the GeoDS Harmonised Index of Multiple Deprivation (IMD) enables GB-wide profiling of the geographic context in which actual patterns of gambling behaviour occur – rather than relying on coarser regional scale reports of stated behaviour which are subject to recall errors. This independent, ethically approved GeoDS Research Ready Data product pushes the frontiers of social science methodology. It empowers researchers at all career stages to develop deeper insights into the complexities of gambling behaviour in GB, at spatial scales previously unavailable – all while maintaining the highest standar…
issued: 2025-06-05T02:21:58.168693
modified: 2026-02-02T14:24:07.859219
version info: 1.0
contact point: Organization
  fn: Shunya Kimura
keyword: Gambling
keyword: Geodemographics
landing page: Online Gambling Service Provider, UK-OAC, GeoDS Harmonised IMD 2019

Variable Dictionary: Classification Codes and Names
type: Distribution
format: CSV
issued: 2025-06-05T02:23:44.406353
modified: 2025-06-05T02:27:31.682972
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/7fbdda32-2207-4c71-8a73-a90acf480247/resource/5357e956-95ae-4228-b707-e7ae8a97f7b1/download/classification_codes_and_names.csv
byte size: 1309
media type: text/csv

Flyer: Great Britain Gambling Behaviours Classification
type: Distribution
format: PDF
issued: 2026-02-02T14:23:52.966111
modified: 2026-02-02T14:24:07.864163
access URL: https://data.geods.ac.uk/dataset/7fbdda32-2207-4c71-8a73-a90acf480247/resource/69c14081-590a-4379-9185-36d257e2f2a7/download/gb2c-2025-art-digital.pdf
byte size: 3451010
media type: application/pdf

Related Record: Great Britain Gambling Behaviours Classification (GB2C) (LAD Geography)
type: Distribution
format: HTML
issued: 2025-06-05T02:33:48.173376
modified: 2025-06-05T09:21:31.691013
access URL: https://data.geods.ac.uk/dataset/great-britain-gambling-behaviours-classification-gb2c-lad-geography

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2025-06-05T02:23:26.599855
modified: 2025-06-05T02:23:29.539669
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/7fbdda32-2207-4c71-8a73-a90acf480247/resource/79e66dbc-9d79-43b6-97ad-8d2f118ac634/download/data_dictionary-lsoa.csv
byte size: 1484
media type: text/csv

Data Summary
type: Distribution
format: CSV
issued: 2025-06-05T02:23:02.237711
modified: 2025-06-05T09:21:33.111870
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/7fbdda32-2207-4c71-8a73-a90acf480247/resource/d75c1085-210c-471a-ac29-62c1a21bad18/download/data_summary_gb2c_lsoa.csv
byte size: 1876
media type: text/csv

Technical Report: Glossary of Terms, Statistics, Pen Portraits
type: Distribution
format: PDF
issued: 2025-06-05T02:27:48.751784
modified: 2025-06-17T12:12:09.042958
access URL: https://data.geods.ac.uk/dataset/7fbdda32-2207-4c71-8a73-a90acf480247/resource/fc8e4b31-e91d-4dbb-8455-c8a34b2925ac/download/tech-report-v1.2.pdf
byte size: 458026
media type: application/pdf

Domestic Energy Provider (Postcode Sector Geography)
type: Dataset
description: ## Content This dataset, derived by the GeoDS from a UK Domestic Energy Provider, provides smart meter readings for gas and electricity consumption in Great Britain during 2015. With readings taken at 30-minute intervals, the data offers a high level of detail about consumer energy usage, supporting research into consumer behaviour and energy policy. The dataset includes energy consumption data from approximately 600,000 users, providing averages at the postcode sector level for gas and electricity consumption. It covers 1.1 million smart meters and aggregates the readings into 30-minute intervals. The dataset can be used to explore energy consumption patterns in great temporal detail, enabling studies into differences in consumption habits across households with similar usage. Additionally, this dataset allows for the potential derivation of socio-economic indicators based on energy consumption, offering a novel approach to small-area population analysis when linked with other data sources such as Census or Energy Performance Certificate data. ## Quality, Representation and Bias The dataset includes some suppressed data, with 16.47% of usage values hidden due to small sample sizes (fewer than 10 meters in a postcode sector). Missing data is minimal, affecting less than 1% of users. A very small proportion of users (less than 0.1%) are labelled as non-residential due to anomalous energy usage patterns. By the end of 2015, there were 590,000 electricity and 480,000 gas smart meters installed in Great Britain. These meters represent a small fraction of the total domestic energy consumption, 1.1% for electricity and 1.3% for gas. The geographical distribution of meters is slightly skewed, with overrepresentation in the North West and West Midlands regions, where 30% of the meters are located. In contrast, regions such as Wales and the North East are underrepresented, making up only 8% of the total installations. Given that the data reflects the early stages of the smar…
issued: 2024-11-29T09:40:51.429027
modified: 2025-05-08T15:24:05.507331
contact point: Organization
  fn: Dr Maurizio Gibin
keyword: Electricity
keyword: Energy
keyword: Gas
keyword: Smart Meter
landing page: Domestic Energy Provider

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-29T09:41:15.140144
modified: 2025-05-05T22:58:58.911213
access URL: https://data.geods.ac.uk/dataset/8006ca1b-80f2-41e1-88b8-6ea31b289d72/resource/753fd56c-73d4-4508-8115-dc677188141e/download/dep_safeguarded_variable_descriptions.csv
byte size: 606
media type: text/csv

Data Summary
type: Distribution
format: CSV
issued: 2024-11-29T09:41:24.246301
modified: 2025-05-05T22:58:58.911323
access URL: https://data.geods.ac.uk/dataset/8006ca1b-80f2-41e1-88b8-6ea31b289d72/resource/fd895bc5-93bd-48f7-9e75-3d154fc57561/download/dep_safeguarded_data_summary.csv
byte size: 5391
media type: text/csv

Advanced GIS Methods Training: Retail Centres and Catchment Areas
type: Dataset
description: This course explains what retail centres are, and different methods of constructing catchments for them, including fixed-ring buffers, drive time catchments and Huff model catchments. It is split into two parts, each with a video clip and a workbook to work through: * Part 1: Retail Centres * Part 2: Retail Catchment Areas _You need some prior knowledge of R to get the most from this course. If you are new to R, we recommend you complete the Short Course on Using R as a GIS first._ After completing the material, you will: * Understand what the Retail Centres dataset is and what it looks like * Know how to construct a basic Hierarchy for the Retail Centres * Know how to delineate fixed-ring buffers for the Retail Centres * Know how to derive drive-time catchments for Retail Centres using the HERE API * Understand what a Huff model is, and what its basic components are * Know how to build a Huff model for Retail Centres in the Liverpool City Region * Know how to delineate catchments for each Retail Centre using the Huff model To access the course, click on Download next to the 'Part 1: Retail Centres' or 'Part 2: Retail Catchment Areas' files below. It is recommended that you have the course material open in one window, and RStudio open in another window next to it, using either a big monitor, or two monitors. Also download the data zip file listed below. If you have any comments or feedback, please email us. This course is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International licence.
issued: 2024-11-28T14:20:17.806876
modified: 2025-05-08T15:21:04.523360
contact point: Organization
  fn: Alex Singleton
keyword: Advanced GIS Methods
keyword: Catchment Area
keyword: Retail Centre
keyword: Tutorial
landing page: VOA, OSM, HERE, GeoDS

Data: Data for Retail-Catchment-Training
type: Distribution
format: ZIP
issued: 2024-11-28T14:23:13.462283
modified: 2025-05-08T15:21:04.538362
access URL: https://data.geods.ac.uk/dataset/8040302e-e10e-4e0c-8258-6aa7c18aa095/resource/4eb80f22-7292-4f15-9f3a-e7bdf43b5d2f/download/retailtutorial_cdrc-retail-catchment-training.zip
byte size: 5561257
media type: application/zip

Data: Part 2: Retail Catchment Areas
type: Distribution
format: HTML
issued: 2024-11-28T14:23:01.466181
modified: 2025-05-07T11:52:07.295693
access URL: https://data.geods.ac.uk/dataset/8040302e-e10e-4e0c-8258-6aa7c18aa095/resource/59fe41a7-dfbf-41a9-bf83-4d713c25c47b/download/retailtutorial_workbook-part2.html
byte size: 43446695
media type: text/html

Data: Part 1: Retail Centres
type: Distribution
format: HTML
issued: 2024-11-28T14:22:26.296548
modified: 2025-05-07T11:51:18.876009
access URL: https://data.geods.ac.uk/dataset/8040302e-e10e-4e0c-8258-6aa7c18aa095/resource/92404d9b-fe97-43c6-9de7-808b0ca9a753/download/retailtutorial_workbook-part1.html
byte size: 3948814
media type: text/html

Residential Mobility Index
type: Dataset
description: The Residential Mobility Index provides an estimate of the "churn" of the residential population in the UK - the proportion of households that have changed between the beginning of 2025 and the end of each of each year going back to 1997. The estimates were built by linking administrative and consumer data, including electoral registers, consumer registers and land registry house sale data. These data enable research to explore annual variations in neighbourhood change at a small area geography. Crucially it also enables research to focus on yearly data rather than relying on decadal census data to estimate change. It is even possible to observe trends that have occurred since the collection of the 2011 or 2021 Census of Population. ## Content The data present a ratio of the households that are different in each Local Authority District (LAD) or Lower Layer Super Output Area (LSOA) between the beginning of 2025 and the end of each year going back to 1997. Residential mobility ("Population Churn") is estimated at the household level. Households’ start and end dates are extracted from individual level data by combining individuals that at any point in time have shared time together in the same property or have a shared surname in the same property. First household member determines the ‘start’ date (household identified as moving in), last household member determines the ‘end’ date (household identified as moving out). These data are available at LAD (2023 boundaries) and LSOA (2011) boundaries. They can be downloaded from the bottom of the page. For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. ## Quality, Representation and Bias As the data is inputted from several different organisations, it is possible that some names and addresses are inconsistently formatted between datasets. This …
issued: 2024-11-07T16:59:45.667549
modified: 2025-12-01T15:48:41.436524
contact point: Organization
  fn: Maurizio Gibin
keyword: Change
keyword: Churn
keyword: Consumer Register
keyword: Demographics
keyword: Electoral Roll
keyword: Housing
landing page: GeoDS Linked Consumer Register

Data Summary: Residential Mobility Index by LAD23
type: Distribution
description: Baselined to 2025.
format: CSV
issued: 2024-11-28T17:39:01.617188
modified: 2025-12-01T15:45:09.815459
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/836bf811-7798-470d-bae1-f0d818cdaed8/resource/10e5153f-4511-44e5-b24a-3c16782e5da2/download/hh_churn_oslaua_2025_data_summary.csv
byte size: 1890
media type: text/csv

Data: Residential Mobility Index LAD 1997-2025
type: Distribution
description: Baselined to beginning of 2025.
format: CSV
issued: 2024-11-07T17:02:20.327753
modified: 2025-12-01T15:45:59.030256
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/836bf811-7798-470d-bae1-f0d818cdaed8/resource/37c67f9a-2057-45fe-8ce6-f2b5d40f1e25/download/hh_churn_oslaua_2025.csv
byte size: 65217
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T17:39:59.367662
modified: 2025-12-01T15:40:29.134808
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/836bf811-7798-470d-bae1-f0d818cdaed8/resource/3bfbaac4-babe-4e7c-bbd2-ab5fe2e1f025/download/variable_dictionary_churn.csv
byte size: 477
media type: text/csv

Technical Report: Research Ready Smart Data
type: Distribution
format: PDF
issued: 2024-11-07T17:03:02.346857
modified: 2025-05-05T23:23:47.982254
access URL: https://data.geods.ac.uk/dataset/49ec5cbe-8c22-4ae0-bd1b-7fb2e1df72cd/resource/d0d431f2-9070-42f9-93e9-c8088b5456c5/download/lcr_technical_report_2023.pdf
byte size: 734416
media type: application/pdf

Data Summary: Residential Mobility Index by LSOA11
type: Distribution
description: Baselined to 2025.
format: CSV
issued: 2024-11-28T17:39:37.476459
modified: 2025-12-01T15:44:30.310602
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/836bf811-7798-470d-bae1-f0d818cdaed8/resource/bb1990cd-50d1-4c0a-a1c7-209b9a4dc82d/download/hh_churn_lsoa11_2025_data_summary.csv
byte size: 1848
media type: text/csv

Map: Mapmaker
type: Distribution
format: HTML
issued: 2024-11-28T17:40:40.143534
modified: 2025-12-01T15:40:27.817879
access URL: https://mapmaker.geods.ac.uk/#/population-churn

Data: Residential Mobility Index LSOA 1997-2025
type: Distribution
description: Baselined to beginning of 2025.
format: CSV
issued: 2024-11-07T17:00:17.407824
modified: 2025-12-01T15:41:19.318514
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/836bf811-7798-470d-bae1-f0d818cdaed8/resource/faa3c4fd-2fe6-4841-a05d-3256b86c0caf/download/hh_churn_lsoa11_2025.csv
byte size: 7670767
media type: text/csv

HUQ aggregated in-app location dataset
type: Dataset
description: These data have been collected and supplied by Huq Ltd. and comprise of records for the period July 2016 to October 2020. The data contain aggregated geolocated activity counts derived from mobile phone app use across Great Britain. Mobile phone applications seek user’s consent for recording and storing the mobile device’s location when the app is in use. Activity counts are derived from these locations as the sum of distinct devices per grid cell per day. These data can be used as proxy for estimating activity levels and footfall across the UK. These aggregate data were created from record level data which comprised individual phone IDs, and multiple entries for each mobile device if it is used multiple times for one app or the user accesses multiple apps. Thus, the following data cleaning and aggregation process has been used to create the activity counts: 1. Cleaning: Daily records comprise unique device ID, time-stamp and location of each entry collected by any app. The time-stamp is reformatted as a single daily date attribute. 2. Spatial linkage to OSGB grid: After turning the daily data-frames into spatial objects, the files are joined to the 1km x 1km [OSGB grid](https://github.com/OrdnanceSurvey/OS-British-National-Grids), and each impression is attributed a grid cell ID corresponding to its latitude and longitude. 3. Creation of activity counts: Activity counts are created following the previous steps by counting the number of unique device IDs per grid cell per date. This removes multiple appearances of the same device (one device may collect multiple impressions through different apps or due to frequent usage). The final activity count corresponds to the number of unique devices within a 1km square for that day. 4. Output: The output comprises cleaned aggregation counts for each grid cell and day N.B. More detail on how the data was collected and coverage is available if requesting for this detail in your initial application purpose, or if contacting us …
issued: 2024-11-28T18:42:08.813408
modified: 2025-05-08T15:21:22.965566
contact point: Organization
  fn: Dr Maurizio Gibin
keyword: Activities
keyword: Devices
keyword: Footfall
keyword: Mobile phone data
keyword: Mobility
landing page: HUQ

External Website: OS Grid Description from Digimap
type: Distribution
format: HTML
issued: 2025-05-07T08:54:02.318036
modified: 2025-05-07T09:56:18.476627
access URL: https://digimap.edina.ac.uk/help/our-maps-and-data/bng/

Data Summary
type: Distribution
format: CSV
issued: 2024-11-28T18:43:17.992510
modified: 2025-05-05T23:15:25.818550
access URL: https://data.geods.ac.uk/dataset/856a1db8-e0ae-4188-8a24-5ca904fd1475/resource/2590bc95-f041-4d5f-8c63-1912420bf485/download/huq_1kaggregates_uk_data_summary.csv
byte size: 315
media type: text/csv

Related Record: Linked Consumer Registers
type: Distribution
format: HTML
issued: 2025-05-07T08:54:58.452641
modified: 2025-05-07T09:56:25.438525
access URL: https://data.geods.ac.uk/dataset/linked-consumer-registers

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T18:42:55.980989
modified: 2025-05-05T23:15:25.818437
access URL: https://data.geods.ac.uk/dataset/856a1db8-e0ae-4188-8a24-5ca904fd1475/resource/ae76ae67-864d-4452-aaf8-623a1724b7b5/download/huq_1kaggregates_uk_variable_dictionary.csv
byte size: 104
media type: text/csv

Related Record: Local Data Company - SmartStreetSensor Footfall Data
type: Distribution
format: HTML
issued: 2025-05-07T08:55:52.716863
modified: 2025-05-08T15:21:22.970024
access URL: https://data.geods.ac.uk/dataset/local-data-company-smartstreetsensor-footfall-data

Access to Healthy Assets & Hazards (AHAH)
type: Dataset
description: AHAH (the index of 'Access to Healthy Assets and Hazards') is a multi-dimensional index developed by the Geographic Data Service for Great Britain measuring how 'healthy' neighbourhoods are based on accessibility to health-promoting and health-damaging features of the built environment. The AHAH index combines indicators under four different domains: * Retail environment (access to fast food outlets, pubs/bars, tobacconists, gambling outlets), * Health services (access to GP surgeries, hospitals, pharmacies, dentists, leisure centres), * Physical environment (Blue Space, Active Greenspace, Passive Greenspace via NDVI), and * Air quality (NO₂, PM₁₀, SO₂). The dataset available for download contains each input component used to generate the AHAH Index. For accessibility indicators (e.g. `GP`, `dentist`, `fast_food`), values represent the mean drive-time in minutes from postcode centroids within each LSOA/Data Zone to the nearest point of interest. For `greenspace` (passive), the value represents the median NDVI (Normalised Difference Vegetation Index) derived from Sentinel-2 satellite imagery. Air quality components indicate the area-weighted mean concentration of pollutants (µg/m³). For each component, the rank (`_rnk`) and percentiles (`_pct`) are normalised so that lower values indicate healthier environments in terms of expected impact on health, while higher values indicate less healthy environments. The composite AHAH score follows the same convention: higher scores = less healthy neighbourhoods. ## Content Measurement data, ranks and percentiles for the overall index, 4 domains and 15 input indicators are produced for Lower Layer Super Output Areas (2021 LSOAs) for England and Wales, and Data Zones (2022 DZs) for Scotland, comprising 43,064 small areas across Great Britain. Accessibility measures are calculated as the mean travel time (in minutes) by car along the road network, from each postcode centroid within the statistical area to the nearest point of inte…
issued: 2024-11-08T09:28:41.340522
modified: 2026-03-26T15:31:07.275598
version info: 5.0
contact point: Organization
  fn: Alex Singleton
keyword: Hazard
keyword: Health
keyword: Healthy

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-08T09:30:22.268893
modified: 2026-02-05T10:39:03.331358
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/8fa47a8b-345c-438d-a8c3-0ac758ee12be/resource/1cd4fe50-22ec-4fbb-a4da-c1f0b366b2a3/download/dictionary_ahah_v5.csv
byte size: 1143
media type: text/csv

Paper: Daras, K., Green, M.A., Davies, A. et al. Open data on health-related neighbourhood features in Great Britain. Sci Data 6, 107 (2019). (Note this is for v1 which used a different methodology.)
type: Distribution
format: HTML
issued: 2024-11-28T15:18:43.968834
modified: 2025-03-06T11:00:46.850335
access URL: https://doi.org/10.1038/s41597-019-0114-6

Data: Geofile for AHAH Overall Index, Domains and Components (v5)
type: Distribution
format: parquet
issued: 2026-02-05T10:45:36.560401
modified: 2026-03-26T15:31:07.280228
access URL: https://data.geods.ac.uk/dataset/8fa47a8b-345c-438d-a8c3-0ac758ee12be/resource/37d54f4c-52d1-4cc6-9fd0-c8cfa5ab1745/download/ahah_v5_geo.parquet
byte size: 85896963

Flyer: Access to Healthy Assets & Hazards
type: Distribution
format: PDF
issued: 2026-02-02T14:18:44.898667
modified: 2026-03-26T15:31:07.280584
access URL: https://data.geods.ac.uk/dataset/8fa47a8b-345c-438d-a8c3-0ac758ee12be/resource/691c0e47-9070-45b4-9895-33190458b5e1/download/ahah-2026-web.pdf
byte size: 1721281
media type: application/pdf

Data Summary
type: Distribution
format: CSV
issued: 2024-11-08T09:29:51.477675
modified: 2026-02-04T16:44:18.755389
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/8fa47a8b-345c-438d-a8c3-0ac758ee12be/resource/a5a1ce6a-74d1-420b-bda5-110e1ad096ca/download/data_summary_ahah_v5.csv
byte size: 6519
media type: text/csv

Source Code: AHAH (GitHub Repository)
type: Distribution
description: Note that this repository contains code starting from Version 3 (See 'releases').
format: HTML
issued: 2024-11-28T15:15:04.991005
modified: 2026-02-05T10:35:42.204923
access URL: https://github.com/ESRC-CDRC/ahah/tree/v5

Related Record: Previous versions of AHAH
type: Distribution
format: HTML
issued: 2024-11-28T15:15:48.961306
modified: 2025-05-06T13:24:39.609736
access URL: https://data.geods.ac.uk/dataset/access-to-healthy-assets-hazards-ahah-previous-versions

Data: AHAH Overall Index, Domains and Components (v5)
type: Distribution
format: CSV
issued: 2024-11-08T09:30:53.255519
modified: 2026-02-04T16:36:25.372674
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/8fa47a8b-345c-438d-a8c3-0ac758ee12be/resource/db102d9c-1515-43c4-910e-9700d13fed24/download/ahah_v5.csv
byte size: 23776766
media type: text/csv

Technical Report: Methodology
type: Distribution
description: Includes Data Sources and Indicator Components List.
format: PDF
issued: 2026-02-04T17:25:56.552541
modified: 2026-02-05T10:38:44.509645
access URL: https://data.geods.ac.uk/dataset/8fa47a8b-345c-438d-a8c3-0ac758ee12be/resource/ef9c8190-c8a1-472e-8f11-daaeeedff674/download/ahah_v5_technical_methodology.pdf
byte size: 105278
media type: application/pdf

Map: Mapmaker
type: Distribution
format: HTML
issued: 2024-11-28T15:14:36.789979
modified: 2026-02-02T14:18:44.887744
access URL: https://mapmaker.geods.ac.uk/#/access-healthy-assets-hazards

Ultra Wide Band Footfall Data
type: Dataset
description: An aggregated hourly footfall estimate for select retail locations in England obtained using Ultra Wide Band (UWB) radar devices. These data are used as a proxy for estimating footfall or the count of shoppers at retail locations. The sensors capture the wave signals sent by the UWB radar and detect people within the field of vision. The dataset includes details about the locations of UWB devices and the approximate street, town and county of shop locations. Footfall estimates are presented as hourly sums and estimated mean values. ## Content The dataset focuses exclusively on England, offering precise, localized data for towns with UWB radar sensors. The technology's limited range (~10 metres) ensures accurate identification of individuals entering specific retail locations. Data collection began after testing in January 2019, with operational data starting in August 2019 and ending in October 2022. The dataset includes data from 54 sensor locations across 9 towns in England, identified by streets. Key details regarding the dataset’s: ## Quality, Representation and Bias Unlike Wi-Fi probing, UWB radar is immune to errors related to MAC address randomization and other probing inconsistencies, improving overall data reliability. The dataset’s coverage includes 9 towns, with significant concentration in London (29 locations). This skew toward London affects the representation of national trends. The quality and representation of the Ultra Wide Band (UWB) Radar Footfall dataset are influenced by: 1. Sensor Range: The limited range (~10 meters) of UWB radar allows for accurate detection of individuals compared to fixed devices. However, sensor placement is critical for ensuring comprehensive coverage. 2. Human Error: Power disconnections at retail points may result in intermittent data gaps, affecting temporal completeness.
issued: 2024-12-17T11:45:30.090403
modified: 2026-04-15T13:14:31.561333
contact point: Organization
  fn: Dr Maurizio Gibin
keyword: Footfall
keyword: Retail
landing page: Green Street (formerly LDC)

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-12-17T11:46:07.640252
modified: 2025-05-05T23:23:03.646104
access URL: https://data.geods.ac.uk/dataset/987b4638-ce9c-4d10-882a-598ec5c11c55/resource/0daeae62-8500-42ef-bea9-5c35706ee3fe/download/uwb_footfall_safeguarded_variable_dictionary.csv
byte size: 324
media type: text/csv

Data Summary: Locations
type: Distribution
format: CSV
issued: 2024-12-17T11:45:55.313160
modified: 2025-05-05T23:23:03.645988
access URL: https://data.geods.ac.uk/dataset/987b4638-ce9c-4d10-882a-598ec5c11c55/resource/4b3e200b-0518-4096-8b95-33981118a3b3/download/uwb_footfall_safeguarded_locations_data_summary.csv
byte size: 318
media type: text/csv

Data Summary: Footfall counts
type: Distribution
format: CSV
issued: 2024-12-17T11:45:46.028907
modified: 2025-05-05T23:23:03.645879
access URL: https://data.geods.ac.uk/dataset/987b4638-ce9c-4d10-882a-598ec5c11c55/resource/682e9d41-daf4-40b4-9ca3-3f6004a646c5/download/uwb_footfall_safeguarded_data_summary.csv
byte size: 468
media type: text/csv

Related Record: Geolytix aggregated in-app location dataset
type: Distribution
format: HTML
issued: 2024-12-17T11:46:29.782841
modified: 2025-05-07T10:01:52.567845
access URL: https://data.geods.ac.uk/dataset/geolytix-aggregated-in-app-location-dataset

Related Record: HUQ aggregated in-app location dataset
type: Distribution
format: HTML
issued: 2024-12-17T11:46:37.471245
modified: 2025-05-08T15:21:51.042600
access URL: https://data.geods.ac.uk/dataset/huq-aggregated-in-app-location-dataset

Related Record: SmartStreetSensor Footfall Data
type: Distribution
format: HTML
issued: 2024-12-17T11:46:18.933506
modified: 2026-04-15T13:06:28.358005
access URL: https://data.geods.ac.uk/dataset/local-data-company-smartstreetsensor-footfall-data

County Court Judgments (CCJs) (MSOA Geography)
type: Dataset
description: These data consist of County Court Judgment Records aggregated to the Middle Layer Super Output Area (MSOA) level. They are available from 2015 onwards for England, Wales and Scotland. The data are supplied by the Registry Trust and contain information on County Court Judgment Records (CCJs). A CCJ is a type of court order that is registered against a borrower if they fail to make repayments on a debt. They are used by creditors to reclaim the money owed, and are issued in England, Wales, and Northern Ireland. Scotland uses a different method called 'enforcing a debt by diligence'. ## Content The safeguarded MSOA level CCJ dataset includes details of the numbers and values of judgments. For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. Secure level CCJ datasets are also available at the individual record level, and Lower Layer Super Output Area level . However, neither the safeguarded nor the secure datasets include valid creditor information for England nor Wales. This means that no information is included about those who have issued the CCJs. Older data from 2001 to 2015 are available upon request. From 2001 to 2008 data is provided for England and Wales only, from 2008 to 2015 data is available for both England, Wales and Scotland. Please note that this dataset has a strict no-commercial-gain clause, we cannot supply it to any commercial organisation as such where there is possibility of commercial gain from its use. ## Quality, Representation and Bias Quarterly data has been aggregated to an annual dataset. There is a total of 9 years of data, but for some years the data are not complete, with fewer than 7201 observations.
issued: 2024-11-28T17:57:40.803181
modified: 2026-03-02T17:24:39.609334
contact point: Organization
  fn: Maurizio Gibin
keyword: CCJ
keyword: Commercial
keyword: Consumer
keyword: Debtor
keyword: Decree
keyword: Finance
landing page: Statutory registers

Data Summary
type: Distribution
format: CSV
issued: 2024-11-28T18:06:19.463767
modified: 2025-05-05T23:22:57.985543
access URL: https://data.geods.ac.uk/dataset/9da4cc1e-a12d-44dd-9f46-273fb0872745/resource/6bdcb9c5-bb5f-4214-a52b-459db199e8c9/download/ccj_safeguarded_data_summary.csv
byte size: 6504
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T18:07:11.923572
modified: 2025-05-05T23:22:57.985665
access URL: https://data.geods.ac.uk/dataset/9da4cc1e-a12d-44dd-9f46-273fb0872745/resource/825a802e-8d80-4e2e-b54c-2712630a8bcb/download/ccj_safeguarded_variable_dictionary.csv
byte size: 930
media type: text/csv

Related Record: County Court Judgments (CCJs) (Individual Records and LSOA Geography)
type: Distribution
format: HTML
issued: 2024-11-28T18:22:31.850565
modified: 2025-10-31T17:08:04.460214
access URL: https://data.geods.ac.uk/dataset/county-court-judgments-ccjs-secured

Air Pollution Sensors in Liverpool
type: Dataset
description: The data consist of air pollution (particulate matter) sensor readings, generally at half-hour intervals, for various locations, generally road-side, in the City of Liverpool. They sense temperature and humidity, as well as PM1, PM2.5 and PM10 particulate matter concentration measurements. The sensors are supplied and maintained by Aeternum Innovations on behalf of GeoDS and the University of Liverpool. As well as data for a number of sensors that have been installed since late 2021/early 2022 across the city, the data from a new set of sensors along a linear bus route, the 10A, which were installed at the end of 2023, are also available. The 10A bus route is one of the city's busiest and is in the process of being converted to hydrogen bus operation. ## Content The data is in the form of one CSV file for each sensor and time period (historic to end of 2023 and quarterly onwards), and one metadata file with locations, names and IDs for each sensor. Variables include date and time, temperature, humidity, PM1.0, PM2.5 and PM10. ## Quality, Representation and Bias Each sensor has been professionally installed and calibrated by Aeternum Innovations. The pre-existing sensors have good spatial coverage across Liverpool, but with a focus on the city centre. The raw data is obtained by Professor Jonny Higham from the School of Environmental Sciences at the University of Liverpool, who has cleaned and interpolated the raw data, to fit it in to the regular time intervals, also removing or correcting obviously faulty data, and making it available via a web portal. The temporal granularity is typically every 30 minutes, normally every 15-60 minutes, with some prolonged data gaps. ## Version History * 1.0 - initial release * 1.2 - Add Q1 2024 data, updated data summary. * 1.2 - Add Q2 2024 data, updated data summary. * 1.3 - Add Q3 2024 data, updated data summary. * 1.4 - Add Q4 2024 and Q1 2025 data, updated data summary.
issued: 2024-11-08T10:59:23.399532
modified: 2025-09-09T10:53:29.154795
version info: 1.4
contact point: Organization
  fn: Oliver O'Brien
keyword: Air
keyword: Air Quality
keyword: Pollutant
keyword: Pollution
keyword: Sensor
keyword: Sensor Data
landing page: Aeternum Innovations

Variable Dictionary
type: Distribution
description: All the data files are supplied with these columns.
format: CSV
issued: 2024-11-28T17:42:59.110488
modified: 2025-04-11T13:04:27.336451
access URL: https://data.geods.ac.uk/dataset/a36045c8-fd6a-43da-b282-b42afa2b8c08/resource/1198130b-0ed5-41c3-ba61-2dfc20f926c9/download/variable_dictionary_aeternum.csv
byte size: 809
media type: text/csv

Technical Report: Air Quality Sensor Deployment for Bus Route 10
type: Distribution
description: Locations, names and location photographs for the newly deployed sensors. Report as of November 2023.
format: PDF
issued: 2024-11-08T11:02:32.294572
modified: 2025-04-11T13:04:27.336217
access URL: https://data.geods.ac.uk/dataset/a36045c8-fd6a-43da-b282-b42afa2b8c08/resource/15a19238-9462-47ad-bdee-2954ba58bf5e/download/bus_route_10_deployment.pdf
byte size: 5886110
media type: application/pdf

Data Summary: Sensor locations, names and data date ranges
type: Distribution
format: CSV
issued: 2024-11-08T11:01:32.696363
modified: 2025-05-06T11:36:07.171683
access URL: https://data.geods.ac.uk/dataset/a36045c8-fd6a-43da-b282-b42afa2b8c08/resource/2109b245-e981-4bc0-a865-ce7bc73b56f3/download/metadata_aeternum_2025-04-11.csv
byte size: 7439
media type: text/csv

Data: Historic data (2021 to 2023)
type: Distribution
description: A zip of CSVs, one for each sensor. Up to three years of historic data, covering 2021 to 2023, for 56 sensors. There is at least two years worth of data available for at least half of the sensors.
format: ZIP
issued: 2024-11-08T11:00:53.642863
modified: 2025-05-06T11:36:29.125073
access URL: https://data.geods.ac.uk/dataset/a36045c8-fd6a-43da-b282-b42afa2b8c08/resource/301b91ad-c02e-43e5-908d-25a3c20aea2a/download/data_aeternum_2021_2022_2023.zip
byte size: 44331910
media type: application/zip

Data: Aeternum Pollution Sensors Q1 2024
type: Distribution
format: ZIP
issued: 2025-05-06T11:36:29.141511
modified: 2025-05-06T11:36:52.253116
access URL: https://data.geods.ac.uk/dataset/a36045c8-fd6a-43da-b282-b42afa2b8c08/resource/41721d74-db0c-4b58-9e07-8fba4c57e886/download/data_aeternum_2024q1.zip
byte size: 8788710
media type: application/zip

Data: Aeternum Pollution Sensors Q1 2025
type: Distribution
format: ZIP
issued: 2025-05-06T11:33:46.188088
modified: 2025-05-06T11:34:10.366915
access URL: https://data.geods.ac.uk/dataset/a36045c8-fd6a-43da-b282-b42afa2b8c08/resource/74438c19-ecad-45f7-8fba-ac417848bb81/download/data_aeternum_2025q1.zip
byte size: 7281017
media type: application/zip

Data: Aeternum Pollution Sensors Q3 2024
type: Distribution
format: ZIP
issued: 2024-11-28T17:33:33.276634
modified: 2025-05-06T14:22:40.010781
access URL: https://data.geods.ac.uk/dataset/a36045c8-fd6a-43da-b282-b42afa2b8c08/resource/7de78ef1-cdb1-4571-96ab-5798406999b2/download/data_aeternum_2024q3.zip
byte size: 8375803
media type: application/zip

Data: Aeternum Pollution Sensors Q2 2024
type: Distribution
format: ZIP
issued: 2024-11-08T11:03:55.768509
modified: 2025-05-06T11:37:05.922939
access URL: https://data.geods.ac.uk/dataset/a36045c8-fd6a-43da-b282-b42afa2b8c08/resource/872258f9-2d95-4711-b66f-bfe27949e04c/download/data_aeternum_2024q2.zip
byte size: 4732333
media type: application/zip

Related Record: Access to Healthy Assets and Hazards
type: Distribution
format: HTML
issued: 2024-12-05T13:10:46.027572
modified: 2025-05-06T14:25:32.863356
access URL: https://data.geods.ac.uk/dataset/access-to-healthy-assets-hazards-ahah

External Website: Live air quality map and visual downloading tool
type: Distribution
format: HTML
issued: 2024-12-05T13:10:08.062250
modified: 2025-04-11T13:04:27.336523
access URL: http://www.jonnyhigham.co.uk/AIRQUALITY/

Data: Aeternum Pollution Sensors Q4 2024
type: Distribution
format: ZIP
issued: 2025-05-06T11:33:31.376364
modified: 2025-05-06T11:33:46.172046
access URL: https://data.geods.ac.uk/dataset/a36045c8-fd6a-43da-b282-b42afa2b8c08/resource/b8b751b4-532d-4137-a318-8131490e4f94/download/data_aeternum_2024q4.zip
byte size: 8089991
media type: application/zip

Index of Multiple Deprivation (IMD)
type: Dataset
description: The Index of Multiple Deprivation (IMD) datasets are small area measures of relative deprivation across each of the constituent nations of the United Kingdom. Areas are ranked from the most deprived area (rank 1) to the least deprived area. Each nation publishes its data on its own data portal. Each nation measures deprivation in a slightly different way but the broad themes include income, employment, education, health, crime, barriers to housing and services, and the living environment. ## Content GeoDS has collected the datasets together and republished them here. The republished files are separate English, Welsh, Scottish, and Northern Irish IMD data, calculated for different years. The statistical unit areas used to provide indices of relative deprivation across the country are Lower layer Super Output Areas (LSOAs) [England, Wales], Data Zones [Scotland] and Super Output Areas or Wards [Northern Ireland]. We also provide a number of value added versions, including a consolidated "Harmonised" version which combines the four nations together (without reweighting), and a rebased English IMD just for London - the GeoDS London IMD 2019 (English IMD 2019 Domains rebased). This re-ranks the English IMD for just the LSOAs in London, and splits these into deciles. The attributes used in the ranking remain unchanged. The English IMD 2019 rebased version of the file is mapped on [on Trust for London's London Poverty Profile](https://www.trustforlondon.org.uk/data/index-multiple-deprivation-2019-rebased-london/). The Index has been published by GeoDS only and is not an official government statistic. Many of the IMD datasets included here have been mapped on GeoDS Mapmaker, see link below. The related links section below includes a number of additional resources such as published papers that use the GeoDS Harmonised Index. ## Quality, Representation and Bias As official government statistics, the quality of the indices is believed to be very high, and complete - covering e…
issued: 2024-12-17T10:26:14.318420
modified: 2025-06-05T08:36:06.746756
contact point: Organization
  fn: Oliver O'Brien
keyword: Crime
keyword: Education
keyword: Employment
keyword: Health
keyword: Housing
keyword: Income
keyword: Poverty
landing page: MHCLG; OCSI; NISRA; Scottish Government

Data: Welsh IMD (WIMD) 2005/2008/2011
type: Distribution
description: LSOA 2001 geography.
format: CSV
issued: 2024-12-17T10:33:03.924854
modified: 2025-05-05T23:17:07.438729
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/0b2f3305-8587-48db-a5ed-620eb2942db4/download/wimd2005_2008_2011.csv
byte size: 736602
media type: text/csv

Data Summary: Welsh IMD (WIMD) 2014/2019
type: Distribution
format: CSV
issued: 2024-12-17T10:35:34.331038
modified: 2025-05-05T23:17:07.439725
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/10521036-738b-4d0b-9df8-9c80d8c5aba4/download/data_summary_wimd_2014_2019.csv
byte size: 3449
media type: text/csv

Paper: Longley, P.A., van Dijk, J. & Lan, T. The geography of intergenerational social mobility in Britain. Nat Commun 12, 6050pp
type: Distribution
format: HTML
issued: 2024-12-17T10:36:17.427853
modified: 2025-05-07T12:43:40.598160
access URL: https://doi.org/10.1038/s41467-021-26185-z

Data Summary: English IMD 2010 (2011 LSOAs)/2015
type: Distribution
format: CSV
issued: 2024-12-17T10:34:29.192498
modified: 2025-05-05T23:17:07.439149
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/1e7fe049-1918-4bb8-b868-b6652d4c7422/download/data_summary_imd2010adj_2015.csv
byte size: 8320
media type: text/csv

Data Summary: GeoDS Harmonised IMD 2019
type: Distribution
format: CSV
issued: 2024-12-17T10:33:55.059764
modified: 2025-05-07T12:40:47.816211
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/1fae1e00-1c14-4f5f-9853-19697350780f/download/data_summary_harmonisedimd.csv
byte size: 714
media type: text/csv

Data Summary: Northern Irish MDM 2001
type: Distribution
format: CSV
issued: 2024-12-17T10:35:04.890468
modified: 2025-05-05T23:17:07.439430
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/2821e373-4c94-4b66-ab50-2ca694ccb064/download/data_summary_nimdm_2001.csv
byte size: 2099
media type: text/csv

Data: Scottish IMD (SIMD) 2016/2020
type: Distribution
description: DZ 2011 geography.
format: CSV
issued: 2024-12-17T10:31:09.255661
modified: 2025-05-05T23:17:07.438514
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/2d9b7d09-1497-4af1-b37b-613b29d6354b/download/simd2016_2020.csv
byte size: 3125558
media type: text/csv

Data Summary: Scottish IMD (SIMD) 2004/2006/2009/2012
type: Distribution
format: CSV
issued: 2024-12-17T10:35:21.195293
modified: 2025-05-05T23:17:07.439652
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/2fd926b2-8f90-49eb-8032-6f621854bbdd/download/data_summary_simd_2004_2006_2009_2012.csv
byte size: 8866
media type: text/csv

Data Summary: Northern Irish MDM 2005/2010/2017
type: Distribution
format: CSV
issued: 2024-12-17T10:34:52.253700
modified: 2025-05-05T23:17:07.439312
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/338698dc-b7bd-4502-bd4f-46a3f1b1193f/download/data_summary_nimdm_2005_2010_2017.csv
byte size: 17290
media type: text/csv

Data Summary: English IMD 2019
type: Distribution
format: CSV
issued: 2024-12-17T10:34:20.149166
modified: 2025-05-05T23:17:07.439077
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/552c5a11-cd82-4ac8-8406-c097c299d095/download/data_summary_imd2019.csv
byte size: 4511
media type: text/csv

Variable Dictionary: GeoDS Harmonised IMD 2019
type: Distribution
format: CSV
issued: 2024-12-17T10:33:30.707241
modified: 2025-05-07T12:40:23.620401
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/5728c4c4-46bf-40c7-84df-7e92efa4cb6f/download/variable_dictionary_harmonisedimd.csv
byte size: 798
media type: text/csv

Data: English IMD 2019
type: Distribution
description: LSOA 2011 geography. For definitions of the inputs, consult the official website at https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019 - in particular “File 2” and “File 3”.
format: ZIP
issued: 2024-12-17T10:28:36.314399
modified: 2025-05-05T23:17:07.438151
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/5e8f6c8a-e552-479b-a041-00658890eb10/download/english-imd-2019.zip
byte size: 19784699
media type: application/zip

Data: English IMD 2004/2007/2010
type: Distribution
description: LSOA 2001 geography.
format: CSV
issued: 2024-12-17T10:29:44.530087
modified: 2025-05-05T23:17:07.438298
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/6232c48b-ac82-49ac-a77c-0693a68b33e4/download/imd_eng_2004_2007_2010_lsoa01.csv
byte size: 9993150
media type: text/csv

Data: Welsh IMD (WIMD) 2014/2019
type: Distribution
description: LSOA 2011 geography.
format: CSV
issued: 2024-12-17T10:32:36.047849
modified: 2025-05-05T23:17:07.438658
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/6adf895b-ba9b-4e32-bc16-a5c7b2e03661/download/wimd2014_2019.csv
byte size: 361660
media type: text/csv

Contextual Note
type: Distribution
description: Remarks, corrections and source footnotes from the original publishers of the various IMD datasets. ###SIMD 2016 Error in the working age population In a previous version of this file we included incorrect working age population figures. This only affects the variable 'Working_age_population', now called 'Working_age_population_Revised'. SIMD16 ranks are not affected. For the calculation of the SIMD16 ranking, correct working age population figures were used. ###SIMD 2020 The Scottish IMD 2020 was reissued by its publisher to correct a data error with the income data for a small number of areas. We have republished this amended (v2) version here. ###WIMD 2005 Author: Social Justice Statistics The indicators used for the income domain were: - Income Support claimants (and their children and partners), including pension credit - income-based Jobseeker's Allowance - Working Families' Tax Credit - Disabled Person's Tax Credit - National Asylum Support Service supported asylum seekers in receipt of subsistence only and accomondation support The indicators used for the employment domain were: - claimants of unemployment related benefits - claimants of Incapacity Benefit - Severe Disablement Allowance (for women under 60 and men under 65) - participants on New Deal for Young People and Intensity Activity Period (for New Deal 25+) Notes on the income and employment domains: 1 Number of deprived individuals. 2 Percentage of the at risk population, i.e. percentage of total population for income domain and percentage of working age population for employment domain. 3 The employment domain was revised on 22 November 2005 to include the claimant count indicator. Indicators used (with notes) for the geographical access to services domain: 1 Access to food shop (percentage within 10 minutes) 2 Access to GP (percentage within 15 minutes) 3 Access to primary school (percentage within 15 minutes) 4 Access to secondary school (percentage within 30 minutes) 5 Access to NHS dentist (per…
format: TXT
issued: 2024-12-17T10:36:00.613660
modified: 2025-05-08T15:21:24.328223
byte size: 5467

Data: Northern Irish MDM 2005/2010/2017
type: Distribution
description: SOA 2001/2011 geography.
format: CSV
issued: 2024-12-17T10:30:16.244362
modified: 2025-05-05T23:17:07.438369
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/7fd5658c-7c75-46dc-b308-ca03d3ac97c8/download/nimdm_2005_2010_2017.csv
byte size: 611308
media type: text/csv

Map: Mapmaker
type: Distribution
format: HTML
issued: 2025-06-05T08:36:06.783053
modified: 2025-06-05T08:36:06.755114
access URL: https://mapmaker.geods.ac.uk/#/index-of-multiple-deprivation

Data Summary: Scottish IMD (SIMD) 2016/2020
type: Distribution
format: CSV
issued: 2024-12-17T10:35:12.876923
modified: 2025-05-05T23:17:07.439554
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/90a7b7b7-81e5-4fa5-a594-76dda255e14d/download/data_summary_simd_2016_2020.csv
byte size: 8161
media type: text/csv

External Website: Trust for London - English Index of Multiple Deprivation, rebased for London
type: Distribution
format: HTML
issued: 2024-12-17T10:36:44.633429
modified: 2025-05-07T12:52:51.200583
access URL: https://trustforlondon.org.uk/data/index-multiple-deprivation-2019-rebased-london/

Data Summary: Welsh IMD (WIMD) 2005/2008/2011
type: Distribution
format: CSV
issued: 2024-12-17T10:35:45.773989
modified: 2025-05-05T23:17:07.439795
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/9c952ac5-dd58-4aa4-8c89-3e923936be9b/download/data_summary_wimd_2005_2008_2011.csv
byte size: 8379
media type: text/csv

Variable Dictionary: English IMD 2019
type: Distribution
format: CSV
issued: 2024-12-17T10:33:41.335761
modified: 2025-05-07T12:40:33.961260
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/9d9df9a1-3772-4140-8ffb-98565357168d/download/variable_dictionary_imd2019.csv
byte size: 5161
media type: text/csv

Data Summary: GeoDS London IMD 2019
type: Distribution
format: CSV
issued: 2024-12-17T10:34:09.898050
modified: 2025-05-07T12:42:56.314352
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/a2c7319f-04cd-4991-b421-5cc73ca3ae5f/download/data_summary_londonimd.csv
byte size: 2291
media type: text/csv

Data: GeoDS London IMD 2019
type: Distribution
description: A rebased version of the English IMD 2019. Overall index, domains and components. LSOA 2011 geography. Note that IDAOPI and IDACI are separate indexes of the domain components and not used directly in IMD.
format: CSV
issued: 2024-12-17T10:27:24.072154
modified: 2025-05-07T12:39:30.544259
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/b567aee5-b55f-4434-a084-2c76677f85e2/download/c1_english_imd_2019_rebased_for_london.csv
byte size: 520694
media type: text/csv

Data: Scottish IMD (SIMD) 2004/2006/2009/2012
type: Distribution
description: DZ 2001 geography.
format: CSV
issued: 2024-12-17T10:31:43.417134
modified: 2025-05-05T23:17:07.438586
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/cda86036-45ba-47a8-9acf-810936ffd335/download/simd_2004_2006_2009_2012.csv
byte size: 3397420
media type: text/csv

Data: English IMD 2000
type: Distribution
description: Ward 2000 geography.
format: XLS
issued: 2025-05-12T16:55:39.245252
modified: 2025-05-12T17:08:09.011871
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/d5bc07a9-3f2a-4487-bd43-7babe222cc82/download/131294.xls
byte size: 13146624
media type: application/vnd.ms-excel

Data: English IMD 2010 (2011 LSOAs)/2015
type: Distribution
description: LSOA 2011 geography. The 2010 data is created by GeoDS by inferring from the IMD 2010 (LSOA 2001 boundaries) source.
format: CSV
issued: 2024-12-17T10:29:12.715488
modified: 2025-05-07T12:40:11.696831
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/d859aa2a-8562-48a7-a18e-ec02548ee2c8/download/imd2010adj_2015.csv
byte size: 10288840
media type: text/csv

Data: Northern Irish MDM 2001
type: Distribution
description: Ward 1991 geography.
format: CSV
issued: 2024-12-17T10:30:39.427147
modified: 2025-05-05T23:17:07.438440
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/d90c130c-24a1-4b02-ba2d-d0b0f7eee49f/download/nimdm_2001.csv
byte size: 62198
media type: text/csv

Data Summary: English IMD 2004/2007/2010
type: Distribution
format: CSV
issued: 2024-12-17T10:34:40.915489
modified: 2025-05-05T23:17:07.439227
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/ecf778a5-80ed-4aec-a07c-0a1eba95f72a/download/data_summary_imd_2004_2007_2010.csv
byte size: 6013
media type: text/csv

Paper: Longley, P., Lan, T. & van Dijk, J. Geography, ethnicity, genealogy and inter-generational social inequality in Great Britain
type: Distribution
format: HTML
issued: 2024-12-17T10:36:34.920305
modified: 2025-05-07T12:43:54.428520
access URL: https://doi.org/10.1111/tran.12622

Data: GeoDS Harmonised IMD 2019
type: Distribution
description: Simple (non-reweighted) combination of the English IMD 2019, Welsh IMD 2019, Northern Irish MDM 2017 and the Scottish IMD 2020 deciles. For each country, 10% of areas fall in each decile. Each country's IMD is calculated according to slightly different criteria, and ranked within the country only, so deciles cannot be compared between countries. The shortcomings of this approach are acknowledged, nevertheless the approximation is still useful for some other GeoDS products which use it, e.g. handling cross-border moves in GeoDS Residential Mobility and Deprivation (RMD). Local authority districts are LAD19CD.
format: CSV
issued: 2024-12-17T10:26:57.456626
modified: 2025-05-07T12:39:07.465695
access URL: https://data.geods.ac.uk/dataset/a4486903-ece5-41a9-b100-5f432b9bc7ff/resource/f7f4a18a-5d59-4bec-92ca-266dd5570b24/download/uk_imd2019.csv
byte size: 1794493
media type: text/csv

West Midlands Accessibility and Travel Passes
type: Dataset
description: The West Midlands Accessibility and Travel Passes datasets are based around the boarding and travel associated to smartcards in the West Midlands Combined Authority (WMCA) and linked to the English National Concessionary Travel Scheme (ENCTS). All datasets are anonymised and contain information on the Lower Super Output Area level (LSOA). ## Content The 9 datasets are available for download below and contain the following information: * Accessibility estimates on Saturdays and Tuesdays to selected POIs (retail centres, supermarkets, clinics, GPs, hospitals, railway stations) based on bus timetables and the ITN road network at OA level 2010-2016. * Historic and forecast count of individuals in each LSOA eligible for ENCTS concessionary travel passes in the West Midlands. * Frequency of boardings per month by ENCTS pass holders (age 60+ and disabled) in the West Midlands between 2010 and 2016. * Imputed and actual monthly origin-destination flows of ENCTS passengers between LSOAs between January 2014 and August 2016. * Proportion of eligible ENCTS registered population in each LSOA assigned to each of six travel behaviour clusters. ## Quality, Representation and Bias The data only covers public transport usage in the West Midlands by smartcard users, and so has a significant demographic bias. Smartcard users did not account for many journeys in that time - concessionary travel is overweighted in the data because such users generally required smartcards at the time, unlike commercial users. The data is supplied by the official transport authority for the area and so is of high quality. The flow counts do not represent all journeys and are limited to a specific service provider.
issued: 2024-12-16T13:56:36.628739
modified: 2025-05-08T15:21:38.318890
version info: 1.0
contact point: Organization
  fn: Oliver O'Brien
keyword: Bus
landing page: West Midlands Combined Authority (WMCA)

Data: West Midlands ENCTS Bus Origin-Destination Flows (Actual, 81%)
type: Distribution
description: Monthly origin-destination flows of ENCTS passengers between LSOAs between January 2014 and August 2016. The data report the frequency of journeys between LSOAs where it was possible to determine both the journey origin and journey destination. These counts do not represent all journeys and are limited to a specific service provider.
format: CSV
issued: 2024-12-16T14:00:22.789889
modified: 2025-05-07T12:38:29.839137
access URL: https://data.geods.ac.uk/dataset/a620dffa-3b7f-46b7-820a-794cdf3a2082/resource/2664260c-dab0-42a9-a553-bb82aa4d0136/download/mp_lsoa_avl_10_or_more_81_percent.csv
byte size: 3982983
media type: text/csv

Data: West Midlands ENCTS Traveller Clusters Distribution
type: Distribution
description: Proportion of eligible ENCTS registered population in each LSOA assigned to each of six travel behaviour clusters.
format: CSV
issued: 2024-12-16T14:00:45.962515
modified: 2025-05-08T15:21:38.325135
access URL: https://data.geods.ac.uk/dataset/a620dffa-3b7f-46b7-820a-794cdf3a2082/resource/27b989b4-dd05-4a76-84cc-cd7ff0443764/download/lsoaclusterpropslong.csv
byte size: 291976
media type: text/csv

Data: West Midlands ENCTS Eligible Population (Forecast)
type: Distribution
description: Count of individuals in each LSOA eligible for ENCTS concessionary travel passes in the West Midlands. The frequencies accont for planned increases in the age of eligibility. - 2016 to 2041 based on ONS population forecasts. ESRC Project Code: ES/P010741/1
format: CSV
issued: 2024-12-16T13:58:51.353402
modified: 2025-05-07T12:37:53.096985
access URL: https://data.geods.ac.uk/dataset/a620dffa-3b7f-46b7-820a-794cdf3a2082/resource/4cad1c4b-cdb1-4234-9616-7398f6ec6435/download/elegible_pop_wm_forecast.csv
byte size: 4657
media type: text/csv

Data: West Midlands ENCTS boarding by LSOA (Age 60+)
type: Distribution
description: Frequency of boardings per month by ENCTS pass holders in the West Midlands between 2010 and 2016. LSOA is based on passengers home LSOA rather than Boarding or Alighting location.
format: CSV
issued: 2024-12-16T13:59:10.188934
modified: 2025-05-07T12:38:02.363186
access URL: https://data.geods.ac.uk/dataset/a620dffa-3b7f-46b7-820a-794cdf3a2082/resource/590fdd1f-69a5-4cc6-b707-6f1a07ca87f3/download/trendslsoa60pl.csv
byte size: 5716038
media type: text/csv

https://data.geods.ac.uk/dataset/a620dffa-3b7f-46b7-820a-794cdf3a2082/resource/656e4c91-1fdb-4798-9c20-25db96ad13e5
type: Distribution
format: HTML
issued: 2024-12-16T14:02:00.135549
modified: 2025-05-05T23:17:00.956248
title: Paper: Jens Kandt, Alistair Leak, Examining inclusive mobility through smartcard data: What shall we make of senior citizens' declining bus patronage in the West Midlands?, Journal of Transport Geography, Volume 79, 2019, 102474, ISSN 0966-6923
access URL: https://doi.org/10.1016/j.jtrangeo.2019.102474

External Website: Inclusive and Healthy Mobility: Understanding Trends in Concessionary Travel in the West Midlands
type: Distribution
format: HTML
issued: 2024-12-16T14:02:18.494464
modified: 2025-05-05T23:17:00.956314
access URL: https://www.ucl.ac.uk/bartlett/casa/research/completed-projects/inclusive-and-healthy-mobility-understanding-trends-concessionary-travel

Data: West Midlands ENCTS Eligible Population (Historic)
type: Distribution
description: Count of individuals in each LSOA eligible for ENCTS concessionary travel passes in the West Midlands. The frequencies accont for planned increases in the age of eligibility. - 2010 to 2016 based on population estimates. ESRC Project Code: ES/P010741/1
format: CSV
issued: 2024-12-16T13:58:31.139074
modified: 2025-05-07T12:37:43.751339
access URL: https://data.geods.ac.uk/dataset/a620dffa-3b7f-46b7-820a-794cdf3a2082/resource/6ad9013f-7f98-479d-98c6-0778e0dc9eef/download/eligible_pop_wm.csv
byte size: 292018
media type: text/csv

Data Summary
type: Distribution
format: CSV
issued: 2024-12-16T14:01:07.952358
modified: 2025-05-05T23:17:00.956112
access URL: https://data.geods.ac.uk/dataset/a620dffa-3b7f-46b7-820a-794cdf3a2082/resource/86d307bf-4f74-4935-b479-59a47bdccbe0/download/data_summary_westmidsaccess.csv
byte size: 10377
media type: text/csv

Data: West Midlands Bus Based Accessibility (Tuesday)
type: Distribution
description: Accessibility estimates to selected POIs (retail centres, supermarkets, clinics, GPs, hospitals, railway stations) based on bus timetables and the ITN road network at OA level 2010-2016. Travel time is the mean minimum time to reach each of the specifiec POIs across a 24 hour period calculated at 30 minute intervals. Based on a Tuesday. The tables also include the percentage change in mean travel time from the value 12 months previous, where this is available. ESRC Project Code: ES/P010741/1
format: CSV
issued: 2024-12-16T13:58:08.452438
modified: 2025-05-07T12:37:33.636758
access URL: https://data.geods.ac.uk/dataset/a620dffa-3b7f-46b7-820a-794cdf3a2082/resource/94ffa0ed-211b-40e8-a810-b6eb18af1a37/download/accessibility_data_tue_incl_change.csv
byte size: 27590717
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-12-16T14:01:17.572163
modified: 2025-05-05T23:17:00.956181
access URL: https://data.geods.ac.uk/dataset/a620dffa-3b7f-46b7-820a-794cdf3a2082/resource/e520e775-abdc-4966-a145-2fc1fde15075/download/variable_dictionary_westmidsaccess.csv
byte size: 2555
media type: text/csv

Data: West Midlands ENCTS boarding by LSOA (Disabled)
type: Distribution
description: Frequency of boardings per month by ENCTS pass holders in the West Midlands between 2010 and 2016. LSOA is based on passengers home LSOA rather than Boarding or Alighting location.
format: CSV
issued: 2024-12-16T13:59:31.513523
modified: 2025-05-07T12:38:10.531046
access URL: https://data.geods.ac.uk/dataset/a620dffa-3b7f-46b7-820a-794cdf3a2082/resource/f4871259-8f36-432b-9f2d-74ef6d08fd4e/download/trendslsoadisab.csv
byte size: 4386989
media type: text/csv

Data: West Midlands ENCTS Bus Origin-Destination Flows (Imputed, 87%)
type: Distribution
description: Monthly origin-destination flows of ENCTS passengers between LSOAs between January 2014 and August 2016. The data report the frequency of journeys between LSOAs where it was possible to determine both the journey origin and journey destination. These counts do not represent all journeys and are limited to a specific service provider.
format: CSV
issued: 2024-12-16T13:59:51.891243
modified: 2025-05-07T12:38:21.279001
access URL: https://data.geods.ac.uk/dataset/a620dffa-3b7f-46b7-820a-794cdf3a2082/resource/fb89f29b-d87e-498a-b99d-eb409607f9d8/download/ihm_mp_lsoa_imp_10_or_more_87_percent.csv
byte size: 12484954
media type: text/csv

Data: West Midlands Bus Based Accessibility (Saturday)
type: Distribution
description: Accessibility estimates to selected POIs (retail centres, supermarkets, clinics, GPs, hospitals, railway stations) based on bus timetables and the ITN road network at OA level 2010-2016. Travel time is the mean minimum time to reach each of the specifiec POIs across a 24 hour period calculated at 30 minute intervals. Based on a Saturday. The tables also include the percentage change in mean travel time from the value 12 months previous, where this is available. ESRC Project Code: ES/P010741/1
format: CSV
issued: 2024-12-16T13:57:17.874582
modified: 2025-05-07T12:37:25.694961
access URL: https://data.geods.ac.uk/dataset/a620dffa-3b7f-46b7-820a-794cdf3a2082/resource/fc47b21c-e31c-46cf-b0b3-8803c68c015e/download/accessibility_data_sat_incl_change.csv
byte size: 26911236
media type: text/csv

London aggregated in-app Origin-Destination Flows
type: Dataset
description: The aggregated origin–destination (OD) flow dataset provides a detailed view of human mobility patterns across Greater London in 2019. It is derived from individual-level mobile phone location data, capturing movements between MSOAs over the course of the day. Each flow represents the number of individuals moving from one MSOA to another within a two-hour threshold, allowing researchers to explore both the volume and direction of interactions between neighbourhoods. By aggregating individual trajectories to the MSOA level and removing low-count flows, the dataset maintains strict privacy and disclosure control, making it suitable for research and policy applications. This temporal and spatial granularity enables the study of commuting patterns, local activity, and broader urban connectivity, providing insights into how the functional roles of areas shift throughout the day. The dataset is particularly valuable for applications in transport planning, urban analytics, geodemographics and social research, as it moves beyond static residential or workplace data to capture the dynamic interactions that shape city life. ## Content The dataset is provided in CSV format and contains aggregated origin–destination flows between MSOAs in Greater London. Each row represents the sum of flows from an origin MSOA to a destination MSOA during a specified hour of the day. The dataset includes the following fields: origin MSOA, destination MSOA, hour of day, and the total number of flows. Flows of less than ten have been removed to ensure disclosure control. The data are organised to allow analysis of interactions at an hourly level across all MSOAs, enabling comparisons between areas, examination of peak movement times, and exploration of connectivity patterns within the city. The structured format supports straightforward integration with other spatial or demographic datasets for research purposes. ## Quality, Representation and Bias The dataset provides a consistent spatial repres…
issued: 2025-11-05T17:25:44.159010
modified: 2025-11-24T13:58:52.832673
contact point: Organization
  fn: Maurizio Gibin
keyword: Mobility
keyword: Population
keyword: Smart Data
landing page: Huq

Data: Origin-Destination Flows by MSOA
type: Distribution
format: CSV
issued: 2025-11-24T13:43:58.142468
modified: 2025-11-24T13:58:52.837179
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/a819c4f3-c961-47d5-a09b-78fdb9897876/resource/30286e2a-6795-43e6-97b6-a752eb7413e9/download/interactions_by_msoa_pair_and_time_2019_directional.csv
byte size: 5250172
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2025-11-05T17:30:12.375649
modified: 2025-11-05T17:30:14.437909
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/a819c4f3-c961-47d5-a09b-78fdb9897876/resource/353bca7b-fbc4-4afc-a6e0-a32147e5df85/download/interactions_by_msoa_pair_and_time_2019_directional_variable_dictionary.csv
byte size: 221
media type: text/csv

https://data.geods.ac.uk/dataset/a819c4f3-c961-47d5-a09b-78fdb9897876/resource/99db19c3-3bab-4e7f-b032-03d8e511c156
type: Distribution
format: HTML
issued: 2025-11-24T13:58:52.847342
modified: 2025-11-24T13:58:52.837537
title: Paper: Mavrogeni, M., van Dijk, J. & Longley, P. (2025) Understanding place-to-place interactions using flow patterns derived from in-app mobile phone location data. The Geographical Journal, 191, e70033. Available from: https://doi.org/10.1111/geoj.70033
access URL: https://doi.org/10.1111/geoj.70033

Data Summary
type: Distribution
format: CSV
issued: 2025-11-05T17:26:02.359575
modified: 2025-11-05T17:26:03.910342
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/a819c4f3-c961-47d5-a09b-78fdb9897876/resource/acd721bb-4e7b-443b-8c44-7996bcafcdf1/download/interactions_by_msoa_pair_and_time_2019_directional_data_summary.csv
byte size: 381
media type: text/csv

Small Area UK Vegetation Indices
type: Dataset
description: These data provide Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Fractional Vegetation Cover (FVC) statistics for each 2021 Lower Layer Super Output Area (LSOA) in England and Wales; 2021 Super Data Zone in Northern Ireland; and 2022 Data Zone in Scotland. These data have utility for a range of applications related to the measurement of exposure to green space, and are also a composite component of our Access to Healthy Assets and Hazards indicator. Vegetation indices are derived from [Sentinel-2 Level-2A Surface Reflectance](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED) imagery processed via Google Earth Engine. For each year (2021–2025), images are filtered to the growing season (April–October) and pre-filtered to exclude scenes with greater than 40% cloud cover. Remaining cloud and cloud shadow pixels are masked using the Scene Classification Layer (SCL), excluding classes 3 (cloud shadow), 8 (medium probability cloud), 9 (high probability cloud), and 10 (thin cirrus). A median composite is then generated from all valid pixels at 10m resolution. ## Content The data is supplied as CSV files, one for each year, attached to this record. The following indices are computed for each pixel: * `NDVI (Normalised Difference Vegetation Index)`: $$\text{NDVI} = \frac{\text{NIR} - \text{Red}}{\text{NIR} + \text{Red}}$$ * `EVI (Enhanced Vegetation Index)`: $$\text{EVI} = 2.5 \times \frac{\text{NIR} - \text{Red}}{\text{NIR} + 6 \times \text{Red} - 7.5 \times \text{Blue} + 1}$$ * `FVC (Fractional Vegetation Cover)`: $$\text{FVC} = \left( \frac{\text{NDVI} - \text{NDVI}_{\text{soil}}}{\text{NDVI}_{\text{veg}} - \text{NDVI}_{\text{soil}}} \right)^2$$ where NDVI soil = 0.2 and NDVI veg = 0.86, with output values clamped to [0, 1]. Recommendations for use: - Use median statistics (e.g., `NDVI_median`) to mitigate the effects of outliers and noise - Use `FVC` for determining proportional vegetation cover…
issued: 2025-01-16T10:31:30.798283
modified: 2026-01-15T14:08:37.089575
contact point: Organization
  fn: Alex Singleton
keyword: Green
keyword: Land Cover
keyword: Satellite
landing page: ESA Sentinel-2

External Website: Small Area UK Vegetation Indices (2025 EOX-based version)
type: Distribution
format: HTML
issued: 2025-05-06T15:06:07.576388
modified: 2026-01-12T13:44:19.957353
access URL: https://github.com/GeographicDataService/ndvi/tree/main/datarecord

Data: Small Area UK Vegetation Indices (Summer 2022)
type: Distribution
format: CSV
issued: 2026-01-07T14:41:47.123758
modified: 2026-01-07T14:41:48.458234
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/a8ac0e42-0dcd-46f0-9f59-902d0e48fa42/resource/6adc9692-a109-4e84-b000-ba8c5128f55d/download/vegetation_indices_2022.csv
byte size: 14849278
media type: text/csv

Data: Small Area UK Vegetation Indices (Summer 2023)
type: Distribution
format: CSV
issued: 2026-01-07T14:46:14.508187
modified: 2026-01-07T14:46:16.068133
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/a8ac0e42-0dcd-46f0-9f59-902d0e48fa42/resource/7213a70a-2a1e-4442-8adb-29d3f8dff060/download/vegetation_indices_2023.csv
byte size: 14830146
media type: text/csv

Data: Small Area UK Vegetation Indices (Summer 2021)
type: Distribution
format: CSV
issued: 2025-01-16T10:58:52.880795
modified: 2026-01-07T14:40:30.787161
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/a8ac0e42-0dcd-46f0-9f59-902d0e48fa42/resource/81595c7a-2ceb-43a6-bd3c-077dfa182572/download/vegetation_indices_2021.csv
byte size: 14962454
media type: text/csv

Data Summary
type: Distribution
description: Based on the 2025 data file.
format: CSV
issued: 2025-01-16T11:02:29.466787
modified: 2026-01-07T14:52:19.718216
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/a8ac0e42-0dcd-46f0-9f59-902d0e48fa42/resource/8458839e-9f36-4eeb-8855-e08299d0acbe/download/data_summary_vegetation_indices_2025.csv
byte size: 2721
media type: text/csv

Data: Small Area UK Vegetation Indices (Summer 2024)
type: Distribution
format: CSV
issued: 2026-01-07T14:46:46.950436
modified: 2026-01-07T14:46:48.476655
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/a8ac0e42-0dcd-46f0-9f59-902d0e48fa42/resource/8ae8e852-52cf-4164-bad2-4e54ad02bb25/download/vegetation_indices_2024.csv
byte size: 14814643
media type: text/csv

Data: Small Area UK Vegetation Indices (Summer 2025)
type: Distribution
format: CSV
issued: 2026-01-07T14:47:37.487251
modified: 2026-01-07T14:47:38.966091
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/a8ac0e42-0dcd-46f0-9f59-902d0e48fa42/resource/c8afc1b6-2186-422a-9dce-7b83f4d9ba4c/download/vegetation_indices_2025.csv
byte size: 14883654
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2025-01-16T10:59:10.685749
modified: 2026-01-07T15:05:39.280492
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/a8ac0e42-0dcd-46f0-9f59-902d0e48fa42/resource/f7becf1f-77f1-4e42-b9d7-0fa839d86ea8/download/ndvi_variable_dictionary.csv
byte size: 1327
media type: text/csv

London Workplace Zone Classification
type: Dataset
description: The London Workplace Zone Classification (LWZC) is a geodemographic classification based on various data sources including the 2011 UK census. It provides insight into the variegated workplace functions and nature of employment across Greater London by segmenting London’s 8,154 workplace zones into categories based on 92 characteristics to broadly define each zone’s employment structure, its employer and employee characteristics, commuting accessibility and residential context. The broader context attests to distinctive patterns within the capital that are not apparent when using the nationwide COWZ-UK (Classification of Workplace Zones) classification of the London area. The LWZC sorts workplace zones into five groups and further sub-groups, identifying whether each zone principally offers residential, city-focused, infrastructure-based, or integrating and independent services, or whether it offers metropolitan destinations for high streets, retail or leisure. ## Content The data are available for download at the bottom of this page. Also available are a look up for subgroups and descriptions of each cluster group (pen portraits). ## Quality, Representation and Bias There is an accompanying paper for this data product which details these issues in full. Input to the classification are mostly workplace zone 2011 census data, so are bounded by the usual operational quality / representation and bias of a national census. Additional consumer data included in the model include attributes from Green Street, with these detailed on our Retail Type, Vacancy and Address record.
issued: 2024-12-16T18:30:50.225564
modified: 2026-04-28T11:11:49.515400
contact point: Organization
  fn: Dr Maurizio Gibin
keyword: Classification
keyword: Workforce
landing page: Office for National Statistics

Data Summary
type: Distribution
format: CSV
issued: 2024-12-16T18:38:38.733654
modified: 2025-05-08T15:21:27.071211
access URL: https://data.geods.ac.uk/dataset/abb4e682-6a0e-4e23-91d5-5421ab23da7c/resource/2fb33faf-1307-4387-93dc-e9c778b27a64/download/data_summary_lwzc17_finalclusterlookupwithsubgroupswithsupression.csv
byte size: 261
media type: text/csv

Related Record: Retail Type, Vacancy and Address Data
type: Distribution
format: HTML
issued: 2026-04-15T13:16:33.138372
modified: 2026-04-28T11:11:49.523948
access URL: https://data.geods.ac.uk/dataset/local-data-company-retail-type-vacancy-and-address-data

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2024-12-16T18:39:20.578475
modified: 2025-05-05T23:23:27.913660
access URL: https://data.geods.ac.uk/dataset/abb4e682-6a0e-4e23-91d5-5421ab23da7c/resource/61d76eee-6f41-4a92-9c5d-f9cffff00305/download/variable_dictionary_lwzc17.csv
byte size: 108
media type: text/csv

Variable Dictionary: Pen Portraits
type: Distribution
description: Descriptions of each cluster group in LWZC 2017.
format: CSV
issued: 2024-12-16T18:33:48.111900
modified: 2025-05-05T23:23:27.912635
access URL: https://data.geods.ac.uk/dataset/abb4e682-6a0e-4e23-91d5-5421ab23da7c/resource/9f396155-4e00-4a95-ae03-c46e79450fff/download/variable_dictionary_lwzc17_lookup.csv
byte size: 6341
media type: text/csv

External Website: GLA Intelligence London Workplace Zone Classification
type: Distribution
format: HTML
issued: 2025-05-07T12:05:09.918398
modified: 2025-05-07T12:06:12.078766
access URL: https://data.london.gov.uk/dataset/london-workplace-zone-classification

Flyer: London Workplace Zone Classification
type: Distribution
description: A flyer for LWZC
format: PDF
issued: 2024-12-16T18:37:47.263071
modified: 2025-05-05T23:23:27.912911
access URL: https://data.geods.ac.uk/dataset/abb4e682-6a0e-4e23-91d5-5421ab23da7c/resource/ad3a984c-202d-40d9-a137-bba992cbee88/download/1.9.4-lwzc.pdf
byte size: 319772
media type: application/pdf

External Website: Classification of Workplace Zones (COWZ-UK)
type: Distribution
format: HTML
issued: 2025-05-07T12:06:12.085757
modified: 2025-05-07T13:26:10.949209
access URL: https://www.ons.gov.uk/methodology/geography/geographicalproducts/areaclassifications/2011workplacebasedareaclassification/classificationofworkplacezonesfortheukmethodologyandvariables

Data: Final Cluster Lookup with Sub Groups with Suppression
type: Distribution
description: The LWZC geodemographic classification result for each workplace zone in London.
format: CSV
issued: 2024-12-16T18:32:45.821596
modified: 2025-05-05T23:23:27.912343
access URL: https://data.geods.ac.uk/dataset/abb4e682-6a0e-4e23-91d5-5421ab23da7c/resource/ebceb898-42fe-49a1-bf94-bc86a73eee2b/download/finalclusterlookupwithsubgroupswithsupression.csv
byte size: 130486
media type: text/csv

Small Area Estimates of Attitudes to COVID-19
type: Dataset
description: More Metrics Ltd has modelled publicly available parliamentary petition data (as submitted to the UK Government and Parliament petitions website) to provide small area estimates of attitudes to COVID-19 expressed through specific parliamentary petition themes and topics relating to the pandemic. The modelled data are provided at Lower Layer Super Output Area (LSOA) or equivalent scale (2021 England/Wales, 2011 Scotland Data Zone (DZ), 2011 N.I. Super Output Area (SOA)) level, allowing for fine-grained understanding of the attitudes of people to COVID-19 issues. The available data includes 65 petition topics generated from over 9 million signatures grouped into 21 Attitudes to COVID-19 themes (using a combination of clustering and topic narrative). Population rates for each theme are disaggregated to LSOA or equivalent level for all parts of the UK. These provide estimates of the likelihood of a resident (aged 16 and over) living in each zone who are signing petitions for each theme. Additional datasets provide insight into the characteristics of individuals most likely to sign petitions based on a combination of their health status, broad age band and household composition. The themes, clustered from the relevant petitions, are: Keep Schools Closed, Support Key Workers, Prohibit employers testing staff, Open up football, Better Maternity leave, Support Zoos and animal boarding, Vaccine Choice, Open up Gyms, Lockdown the UK, Support housing and low paid, Close Universities and support students, Review Exam Grade process, Support Self Employed, Support the Arts, Support Directors, Public inquiry into Government, Long Covid Research, Prioritise Vaccines, Repeal the Coronavirus Act, Open Up Golf, and Public Inquiry into Vaccine and Other impacts. ## Content * CatPredictorsByLSOA: For each LSOA or equivalent, this contains scores for the following demographics: Pop16plus, OnePerson66plus, OnePersonOther, Couple, LoneParent, FamilyWithChildren, Other, YoungerGood, Younger…
issued: 2024-11-29T14:18:23.078615
modified: 2025-06-05T21:27:10.084859
contact point: Organization
  fn: Oliver O'Brien
keyword: COVID-19
keyword: Health
keyword: Human Health
keyword: Public Health
keyword: Small Area Estimates
landing page: More Metrics, UK Government and Parliament

Contextual Note: Detailed Analysis of Quality, Representation and Bias
type: Distribution
description: We regard the source petition data used for this analysis to be of high quality. The data is collated and published by the House of Commons Petitions Committee on their website. From the information published there, steps are taken to ensure that British and UK citizens can only sign a petition once . More Metrics use publicly available data published by ONS at Output Area (OA) and LSOA for their disaggregation process. This is mainly data collected at the decennial census (2011 and 2021) and this source data is also regarded as being of the highest quality. The small area estimates (at LSOA level) derived by More Metrics using disaggregation are obtained using established proprietary techniques developed over a number of years. Researchers using this data should find that it provides useful insight when used alongside other data sources (e.g. market research data, bio bank data etc). It is however important to note that the nature of the disaggregation process and the fact that More Metrics does not have access to any data on the individuals who have signed petitions means that we are not able to provide statistical analysis on the accuracy of our small area estimates. Therefore, the suitability and usefulness of our data is a question for the researcher to consider when assessed alongside other data sources available to them. The provided data covers the whole of the UK at LSOA level. The LSOA21 coding is used for England and Wales and LSOA11 coding is used for Scotland and Northern Ireland. The availability of census data used for the disaggregation was limited in Scotland and Northern Ireland to census 2011. In England and Wales census data for 2011 and 2021 was used. The nature of petition data means that it is potentially subject to bias. Steps are taken by More Metrics to identify and, where possible, to mitigate bias. There are three sources of bias to consider: Local skew. Some petitions can have a very local skew (e.g. “save our hospital”) making them unsu…
format: TXT
issued: 2024-11-29T14:19:33.216000
modified: 2025-05-05T23:15:12.981517

External Website: Petitions - UK Government and Parliament
type: Distribution
format: HTML
issued: 2024-11-29T14:23:08.894041
modified: 2025-05-07T13:27:59.566846
access URL: https://petition.parliament.uk/

Data Summary
type: Distribution
format: CSV
issued: 2024-11-29T14:18:49.593549
modified: 2025-05-05T23:15:12.981711
access URL: https://data.geods.ac.uk/dataset/ac6e668b-fdd7-43c8-9390-edbb9354ab31/resource/50109a83-d770-47dd-9d10-9ba6d8f51c63/download/data_summary_mm.csv
byte size: 9591
media type: text/csv

External Website: More Metrics
type: Distribution
format: HTML
issued: 2024-11-29T14:23:22.961529
modified: 2025-05-08T15:21:25.855482
access URL: https://www.moremetrics.co.uk/

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-29T14:18:38.221939
modified: 2025-05-05T23:15:12.981631
access URL: https://data.geods.ac.uk/dataset/ac6e668b-fdd7-43c8-9390-edbb9354ab31/resource/d4541bd7-12df-4adf-b12f-57c4bfb29c49/download/variable_dictionary_mm.csv
byte size: 2076
media type: text/csv

Modelled Ethnicity Proportions (LAD Geography)
type: Dataset
description: These data combine historical electoral roll and linked consumer register data (on surnames, forenames and locations) from 1997 onwards, with an aggregated metric derived from ONS data which lists the most frequently selected second-level ethnicity category for the most common forenames and surnames. The data are aggregated to Local Authority Districts (LAD) as defined in 2023. They are supplied as Open data, and can be downloaded at the bottom of this page - there is a zip file containing the data, for each Local Authority year. For more spatially detailed data (at Lower Super Output Area level), these are available on application, please see the Related Content link below for further details. Users of this dataset should be mindful that these data concern ethnicity categories and not one showing migration, citizenship, nationality or country of origin. These data were derived as part of an ESRC-funded project 'Ethnicity Estimator' - Virtual Microdata Laboratory project number: 0000013; and comprise a diagnostic table resulting from the application of a bespokealgorithm. The aggregate data were provided by the ONS within the Virtual Microdata Laboratory (VML). ## Content The data is available as CSV files, one for each of the ethnicity groups. Each row contains the LAD ID, followed by the proportion of the population that is believed to be of that ethnicity (based on surname analysis) rounded to the nearest 0.5%. To create the data a slight aggregation on the second-level ethnicity categories is carried out. We then aggregate by 2023 LAD. Category populations less than 5 are set to 0. The results are then divided by the total population and rounded to the nearest 0.005 (i.e. 0.5%). A value of 0 indicates there is no measurable total population for this LAD and year combination. These values generally only occur for the first few years and in only a small number of LAD areas. Totals may not add up to 1.000 (100%) because of rounding, but also because of an Unknown e…
issued: 2024-11-28T14:41:43.136153
modified: 2025-12-01T18:06:39.222528
version info: 4.0
contact point: Organization
  fn: Maurizio Gibin
keyword: Consumer Register
keyword: Electoral Roll
keyword: Ethnicity
landing page: ONS; GeoDS Linked Consumer Register

Technical Report: Metadata and Validation for Modelled Ethnicity Proportions
type: Distribution
format: PDF
issued: 2024-11-28T14:53:07.236022
modified: 2025-05-05T22:58:00.682379
access URL: https://data.geods.ac.uk/dataset/b11f206a-8256-4937-b724-26477c4d617c/resource/1b2f970a-35fd-465d-8e28-65a5b6eec9e5/download/ee-oe3_comparison.pdf
byte size: 2339311
media type: application/pdf

Data Summary: MEP 2025 LAD23 Geography
type: Distribution
format: CSV
issued: 2024-11-28T14:49:48.271109
modified: 2025-12-01T17:16:49.005996
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/b11f206a-8256-4937-b724-26477c4d617c/resource/22d30649-7d43-4581-b9cf-e5c70c7b0521/download/mep_oslaua_2025_data_summary.csv
byte size: 764
media type: text/csv

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T14:53:31.501074
modified: 2025-12-01T17:20:46.680238
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/b11f206a-8256-4937-b724-26477c4d617c/resource/26dd3ba8-75ab-4cd6-bc45-b8dcda117198/download/variable_dictionary_mep_lad.csv
byte size: 1165
media type: text/csv

Data: GeoDS Modelled Ethnicity Proportions LAD23 for 1997-2025
type: Distribution
format: ZIP
issued: 2024-11-28T14:47:39.608907
modified: 2025-12-01T17:16:47.877597
access URL: https://data.geods.ac.uk/dataset/b11f206a-8256-4937-b724-26477c4d617c/resource/2927f429-78b3-48ee-a189-2aeea29e2230/download/mep_oslaua_lad23_2025.zip
byte size: 104802
media type: application/zip

Map: Mapmaker
type: Distribution
format: HTML
issued: 2024-11-28T16:04:08.142228
modified: 2025-12-01T16:06:31.035697
access URL: https://mapmaker.geods.ac.uk/#/modelled-ethnicity-proportions

Related Record: Modelled Ethnicity Proportions (LSOA Geography)
type: Distribution
format: HTML
issued: 2025-12-01T18:06:19.587181
modified: 2025-12-01T18:06:39.226000
access URL: https://data.geods.ac.uk/dataset/modelled-ethnicity-proportions-lsoa-geography

London OAC
type: Dataset
description: The London Output Area Classification (LOAC) is a geodemographic that summarises the built and population characteristics of all 2021 Output Areas within Greater London. The region is organised into a hierarchical typology composed of 7 Supergroups and 16 Groups. The classification was created from the 2021 Census data and was a partnership between the Geographic Data Service and the Greater London Authority. ## Content The data is available for download below. Also available are a relevant shapefile and geopackage. A lookup for comparison between groups is also provided, as well as Borough profiles, cluster descriptions and a technical manual that details data input, cluster analysis and description, as well as end user consultation. An older edition of LOAC, based on the 2011 Census data, is available via the Related Record link below. ## Quality, Representation and Bias A journal article accompanies LOAC which provides a thorough evaluation. All data used for this classification are sourced from the 2021 Census so bounded by the usual operational quality / representation and bias of a national census. The geodemographic classification created presents a best effort of the authors to represent the characteristics of the population and geographic context of London, however, there are decisions made during the classification process that guide these representations. For a full overview of these decisions and their rationale, see the published paper.
issued: 2024-12-16T17:28:23.917519
modified: 2026-03-26T15:47:51.446596
contact point: Organization
  fn: Dr Maurizio Gibin
keyword: London
keyword: Output Area Classification
landing page: Office for National Statistics

Variable Dictionary: Lookups
type: Distribution
format: CSV
issued: 2024-12-16T17:48:28.647599
modified: 2025-05-05T23:15:56.459783
access URL: https://data.geods.ac.uk/dataset/b14aa4aa-5fb3-4367-802b-d5ec4c6c60d8/resource/0a35b370-229a-49f7-b343-fa221ecd0b43/download/variable_dictionary_loac21_lookups.csv
byte size: 15412
media type: text/csv

Map: Mapmaker
type: Distribution
format: HTML
issued: 2025-06-05T08:37:06.352142
modified: 2025-08-21T10:19:18.263522
access URL: https://mapmaker.geods.ac.uk/#/london-oac

Tool: LOAC Postcode Search
type: Distribution
format: HTML
issued: 2024-12-16T17:46:27.032425
modified: 2025-05-08T15:24:28.126051
access URL: https://apps.cdrc.ac.uk/loac/LOACSearch

Technical Report: Method Overview
type: Distribution
description: An overview of the methods used to create LOAC
format: PDF
issued: 2024-12-16T17:44:08.320168
modified: 2025-05-05T23:15:56.459358
access URL: https://data.geods.ac.uk/dataset/b14aa4aa-5fb3-4367-802b-d5ec4c6c60d8/resource/6badf96a-5cb1-4b78-ba84-5ece4727dc2d/download/loac_technical_manual.pdf
byte size: 1387060
media type: application/pdf

https://data.geods.ac.uk/dataset/b14aa4aa-5fb3-4367-802b-d5ec4c6c60d8/resource/7fb58010-909c-44cc-b86a-41b45d2978a3
type: Distribution
format: HTML
issued: 2025-08-21T10:19:18.275223
modified: 2025-08-21T10:20:40.977255
title: Paper: Singleton, A. D., & Longley, P. A. (2024). Classifying and mapping residential structure through the London Output Area Classification. Environment and Planning B: Urban Analytics and City Science, 51(5), 1153-1164. doi: 10.1177/23998083241242913
access URL: https://doi.org/10.1177/23998083241242913

Flyer: London OAC
type: Distribution
format: PDF
issued: 2026-02-02T14:27:55.419656
modified: 2026-03-26T15:47:51.450329
access URL: https://data.geods.ac.uk/dataset/b14aa4aa-5fb3-4367-802b-d5ec4c6c60d8/resource/8b564ce6-d5ff-4179-8457-8ae434654d59/download/loac-2026-web.pdf
byte size: 1858574
media type: application/pdf

Data: LOAC Lookup Table
type: Distribution
description: A lookup between 2021 Output Area Code, Supergroup and Group
format: CSV
issued: 2024-12-16T17:36:21.847407
modified: 2025-05-05T23:15:56.459124
access URL: https://data.geods.ac.uk/dataset/b14aa4aa-5fb3-4367-802b-d5ec4c6c60d8/resource/96c8c79b-ee74-41e7-a47c-8ddbc91233d1/download/loac_groups.csv
byte size: 395543
media type: text/csv

Data: All Files
type: Distribution
format: ZIP
issued: 2024-12-16T17:35:07.606896
modified: 2025-05-05T23:15:56.459010
access URL: https://data.geods.ac.uk/dataset/b14aa4aa-5fb3-4367-802b-d5ec4c6c60d8/resource/98743276-e7e7-44f9-ab4d-639a274aea54/download/loac2021_allfiles_0.zip
byte size: 25218591
media type: application/zip

Data: LOAC GIS Files (Shapefile format)
type: Distribution
description: LOAC appended to OA and OA with built-up areas only showing
format: ZIP
issued: 2024-12-16T17:45:48.022774
modified: 2025-05-05T23:15:56.459504
access URL: https://data.geods.ac.uk/dataset/b14aa4aa-5fb3-4367-802b-d5ec4c6c60d8/resource/bb4f7acb-f380-43a0-b3ab-12f1d8eb2e4e/download/loac_oa_shapefiles.zip
byte size: 8029647
media type: application/zip

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2024-12-16T17:47:41.864051
modified: 2025-05-05T23:15:56.459715
access URL: https://data.geods.ac.uk/dataset/b14aa4aa-5fb3-4367-802b-d5ec4c6c60d8/resource/bd7335c2-d457-41e3-8363-3c574e3c618b/download/variable_description_loac21.csv
byte size: 225
media type: text/csv

Technical Report: Borough Profiles
type: Distribution
description: LOAC maps and cluster distributions
format: PDF
issued: 2024-12-16T17:36:59.733645
modified: 2025-05-05T23:15:56.459203
access URL: https://data.geods.ac.uk/dataset/b14aa4aa-5fb3-4367-802b-d5ec4c6c60d8/resource/c35256c3-df0e-4a44-940b-46b6aa874652/download/loac_borough_profiles_0.pdf
byte size: 6721932
media type: application/pdf

Technical Report: Cluster Descriptions
type: Distribution
description: Supergroup and Group labels and descriptions
format: PDF
issued: 2024-12-16T17:43:33.959483
modified: 2025-05-05T23:15:56.459282
access URL: https://data.geods.ac.uk/dataset/b14aa4aa-5fb3-4367-802b-d5ec4c6c60d8/resource/c78cfb7f-5daa-4eb4-8445-e82a83609c07/download/loac_cluster_descriptions.pdf
byte size: 1000569
media type: application/pdf

Related Record: London OAC (2011)
type: Distribution
format: HTML
issued: 2025-08-21T10:20:40.990052
modified: 2026-02-02T14:27:55.413524
access URL: https://data.geods.ac.uk/dataset/london-oac-2011

Data: LOAC GIS Files (Geopackage format)
type: Distribution
description: LOAC appended to OA and OA with built-up areas only showing
format: ZIP
issued: 2024-12-16T17:44:53.291968
modified: 2025-05-05T23:15:56.459431
access URL: https://data.geods.ac.uk/dataset/b14aa4aa-5fb3-4367-802b-d5ec4c6c60d8/resource/cce96e1e-540f-4ca4-bbc6-a78d15379941/download/loac_oa_geopackages.zip
byte size: 9006989
media type: application/zip

Data Summary
type: Distribution
format: CSV
issued: 2024-12-16T17:46:51.346836
modified: 2025-05-05T23:15:56.459645
access URL: https://data.geods.ac.uk/dataset/b14aa4aa-5fb3-4367-802b-d5ec4c6c60d8/resource/ea3ed1cb-fe0b-4010-8d13-64a5f6af0022/download/data_summary_loac21.csv
byte size: 894
media type: text/csv

Salad Money Daily Transaction Volumes and Values
type: Dataset
description: The Salad Money Daily Transaction Volumes and Values data provide counts of transactions, customers, median spending categories, and dates of transaction by Salad Money customers. Salad Money provides loans to people living in financial precarity or low wage workers using Open Banking data instead of credit score. The loans are structured to be affordable and have fair terms, helping individuals access credit when needed without being burdened by high interest rates or hidden fees. The application process is quick and managed online through the Salad Money website. Open Banking Data transactions are stored for fourteen months prior the loan application date. Individual payment references (not provided) are reclassified by Salad Money for additional privacy. Data are fully anonymised to prevent identification and protects user privacy. Days with less than 10 Salad Money customers and 10 transactions by category, subcategory and transaction type, have been suppressed. These data are the safeguarded version of the secure Salad Money Open Banking Transaction Data dataset. ## Content These data comprise: total users by day, number of transactions and median amounts in GBP by: day, Salad Money category, Salad Money subcategory, and Salad Money transaction type. For detailed description of the columns contained within the data, see the Variable Dictionary. This file can be downloaded from the bottom of this page. ## Quality, Representation and Bias These data provides sufficient detailed information about a sample of UK key workers living in financial precarity who applied for a loan through Salad Money. They can still be useful to detect spatial heterogeneity and neighbourhood geodemographics patterns in Open Banking data transactions.
issued: 2024-11-29T14:41:05.343972
modified: 2026-03-02T15:14:22.231698
contact point: Organization
  fn: Maurizio Gibin
keyword: Cost Of Living
keyword: Ethical Lenders
keyword: Financial precarity
keyword: Loan
keyword: Open Banking
keyword: Spending
landing page: Salad Money Data

Variable Dictionary: Category Options
type: Distribution
format: CSV
issued: 2024-12-12T13:14:50.794090
modified: 2025-04-25T13:44:22.852842
access URL: https://data.geods.ac.uk/dataset/b155bf2d-01a4-4dd7-b6a6-d111a5843d6b/resource/60469b7d-0136-44c1-938f-06f57905fd8b/download/variable_dictionary_saladmoneycatoptions.csv
byte size: 2305
media type: text/csv

Related Record: Salad Money Open Banking Transaction Data
type: Distribution
format: HTML
issued: 2024-12-12T13:06:22.280915
modified: 2025-05-08T15:21:53.672768
access URL: https://data.geods.ac.uk/dataset/salad-money-open-banking-transaction-data

External Website: Salad Money - How it works
type: Distribution
format: HTML
issued: 2024-11-29T14:45:47.094393
modified: 2025-04-25T13:44:22.852542
access URL: https://www.saladmoney.co.uk/how-it-works

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2024-11-29T15:24:09.062928
modified: 2025-05-05T22:59:59.905164
access URL: https://data.geods.ac.uk/dataset/b155bf2d-01a4-4dd7-b6a6-d111a5843d6b/resource/e71caf76-a91f-40e0-9107-933c374a6a22/download/salad_money_safeguarded_median_transactions_variable_dictionary.csv
byte size: 875
media type: text/csv

Data Summary
type: Distribution
format: CSV
issued: 2025-05-13T15:46:48.308262
modified: 2025-05-13T15:46:59.682496
access URL: https://data.geods.ac.uk/dataset/b155bf2d-01a4-4dd7-b6a6-d111a5843d6b/resource/f6ab40f6-6e02-48d3-afee-f8a43da6972b/download/data_summary_salad_money_safeguarded_daily_transaction_volumes_and_values.c…
byte size: 602
media type: text/csv

NHS Hospital Admission Rates by Ethnic Group and other Characteristics
type: Dataset
description: These data show, for each local authority district, the hospital admission rates, split by ethnic group for major disease categories and for preventable admissions. The data are standardised by age and sex. Accurate recording of ethnicity in electronic healthcare records is important for the monitoring of health inequalities. Reducing inequalities in health has explicitly been part of the government agenda in the United Kingdom since 1997. Inequalities are associated with poverty and may be exacerbated for ethnic minorities due to discrimination, lack of health knowledge or other barriers in access to health services such as language. NHS-commissioned hospitals monitor their use by ethnic group in the national hospital admission records database, known as Hospital Episode Statistics (HES). Emergency hospital admissions are distressing for patients, associated with poorer long-term outcomes, and are costly to the healthcare system. Indicators for admissions considered preventable have been defined and known as ambulatory care sensitive conditions (ACSC) - acute, chronic, and vaccine-preventable combined. ACSC admissions have been associated with patients under the age 5 years, the elderly, deprivation, and ethnicity. NHS monitors ACSC in the general population and saw a 40% rise in 2001-2011. Studies in US, New Zealand, and Scotland have found higher risk of ACSC admission for many ethnic minorities compared to the White majority populations. Historically, ethnicity information was absent in Hospital Episode Statistics (HES) from a significant portion of records of patients who received inpatient care in England. To address the gap in the completeness of ethnicity data, GeoDS worked with NHS Digital to enable them to enhance HES data collected between 1999/00 and 2013/14 with name-based ethnicity imputation using the GeoDS Ethnicity Estimator tool (EE). The resulting aggregated statistics were supplied back to GeoDS. ## Content The dataset, available as 3 georeferenc…
issued: 2024-11-29T13:45:35.357284
modified: 2025-05-08T15:21:45.591541
version info: 1.0
contact point: Organization
  fn: Oliver O'Brien
keyword: NHS
landing page: NHS Digital

Related Record: Ethnicity Estimator
type: Distribution
description: Ethnicity Estimator Software
format: HTML
issued: 2024-11-29T13:47:57.038246
modified: 2025-05-06T16:10:46.485437
access URL: https://data.geods.ac.uk/dataset/ethnicity-estimator

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-29T13:53:24.099133
modified: 2025-05-05T22:57:03.815506
access URL: https://data.geods.ac.uk/dataset/b1b99060-8b13-4e08-8350-946df032cbb0/resource/483f075a-671a-4fca-ab74-8e3dae25ce98/download/variable_dictionary_hes.csv
byte size: 4027
media type: text/csv

Data Summary: HES by GBD for 1999-04
type: Distribution
format: CSV
issued: 2024-11-29T13:53:46.892475
modified: 2025-05-05T22:57:03.815584
access URL: https://data.geods.ac.uk/dataset/b1b99060-8b13-4e08-8350-946df032cbb0/resource/4a1b29a4-8a23-4df3-97fe-eee089ea5490/download/data_summary_hes_1999_gbd.csv
byte size: 1504
media type: text/csv

Data Summary: HES by GBD for 2009-14
type: Distribution
format: CSV
issued: 2024-11-29T13:53:58.601442
modified: 2025-05-05T22:57:03.815657
access URL: https://data.geods.ac.uk/dataset/b1b99060-8b13-4e08-8350-946df032cbb0/resource/7c76bfcf-286e-4048-982d-260c0891ecec/download/data_summary_hes_2009_gbd.csv
byte size: 1511
media type: text/csv

Data Summary: HES for ACSC (Preventable)
type: Distribution
format: CSV
issued: 2024-11-29T13:54:10.855168
modified: 2025-05-05T22:57:03.815728
access URL: https://data.geods.ac.uk/dataset/b1b99060-8b13-4e08-8350-946df032cbb0/resource/b72e506e-66e0-4c8f-84f7-ada3cf6780a4/download/data_summary_hes_preventable.csv
byte size: 5426
media type: text/csv

Related Record: Local morbidity rates of Global Burden of Disease and alcohol-related conditions
type: Distribution
format: HTML
issued: 2024-11-29T13:46:27.328825
modified: 2025-05-08T15:21:45.596540
access URL: https://data.geods.ac.uk/dataset/local-morbidity-rates-of-global-burden-of-disease-and-alcohol-related-conditions

Technical Report: Hospital Episode Statistics Ethnicity Data Products
type: Distribution
format: PDF
issued: 2024-11-29T13:53:13.099566
modified: 2025-05-05T22:57:03.815394
access URL: https://data.geods.ac.uk/dataset/b1b99060-8b13-4e08-8350-946df032cbb0/resource/e78ab8cf-1502-4d54-93ef-ab70f9c21693/download/cdrc_hes_ethnicity_data_documentation.pdf
byte size: 969718
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Retail Type, Vacancy and Address Data
type: Dataset
description: The Retail Type, Vacancy and Address Data files offer insights into the locations and characteristics of retail activity and vacancy across the UK. Held by GeoDS and supplied by Green Street (formerly LDC), the datasets contain details of retail locations in the UK, which have been surveyed by Green Street. The data file includes: - Business name and type (independent/chain) - Retail classification (Category, Subcategory inc. Vacancy) - Chain and holding company information - Full address (Unit, Building, Street, Town, Postcode, County, Region) - Geographic coordinates (Latitude, Longitude) - Business rates valuation (VOA) - Temporal information (Created Date, Closed Date, Status) We can also supply two additional types of data that can be linked to the data file, upon special request in your application: - Opening times - Photographs of the shopfronts If you are interested in local authority district level aggregated data, please see the Safeguarded record. Please note that this dataset cannot be accessed via our service by local authorities or organisations working with/for local authorities or associated entities such as Business Improvement Districts or Combined Authorities. ## Content This historical snapshot covers the period from 2015-01-01 to 2025-09-30 and contains all retail unit records with their created and (if applicable) closed dates, enabling temporal analysis of retail activity. For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. ## Earlier Data This data set covers the period 2015 to 2025-09-30. Data for 2014 is available as a separate point-in-time snapshot rather than the longitudinal format used in this dataset. This earlier snapshot uses a slightly different category classification scheme, so care should be taken when comparing across time periods. ## Quality, Repre…
issued: 2024-11-28T14:08:55.354134
modified: 2026-04-15T13:11:46.079278
version info: 2.0
contact point: Organization
  fn: Owen Goodwin
keyword: High Street
keyword: Retail
keyword: Retailer
keyword: Vacancy
landing page: Green Street (formerly LDC)

Related Record: Retail Type or Vacancy Classification
type: Distribution
format: HTML
issued: 2025-05-06T11:50:48.960218
modified: 2026-04-15T13:11:21.741996
access URL: https://data.geods.ac.uk/dataset/local-data-company-retail-type-or-vacancy-classification

Variable Dictionary
type: Distribution
format: CSV
issued: 2025-06-03T15:09:59.482438
modified: 2026-01-15T14:54:00.302824
checksum: Checksum
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Data Summary
type: Distribution
format: CSV
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modified: 2026-01-15T14:53:44.310705
checksum: Checksum
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Regional Transport Provider - Travel Demand Data
type: Dataset
description: These are the data from two bus travel demand surveys which were carried out by the data provider, in 2014 (3700 passengers, 26 questions) and 2022 (3900 passengers, 41 questions). These surveys ask bus passengers various questions, such as how long they would normally wait for a bus, where they are going, why they are doing the journey, and why they had chosen to use the bus service. Questions include (for 2022): Interview time/location, travelling within/outside region, travelling with child, main purpose, journey is outward or return, weekend use of buses, start and end location for journey, travel mode to/from bus stop, wait time, peak/off-peak user, extra time allowance, frequency of other modes of transport, ticket type, payment method, payment vendor type, reason for choosing bus, how long having been using buses, changes since COVID-19 pandemic, how journeys are planned and how passengers keeps themselves updated during journey, whether car is an option, driving licence held, availability of cars in household, use of smartphone, social networks used, demographics (age, occupation, ethnicity, gender, visible disability or frailty, number of people accompanying passenger. ## Content The anonymised individual responses (microdata). A copy of the questionnaire is included (for 2022) as well as a summary document containing various aggregations. ## Quality, Representation and Bias The dataset covers one region of the UK only, in central England. It is a relatively small sample, has not been demographically weighted and only covers bus travel.
issued: 2024-12-06T10:48:28.455870
modified: 2026-01-15T14:12:30.251753
version info: 1.0
contact point: Organization
  fn: Oliver O'Brien
keyword: Bus
keyword: Buses
keyword: Survey
landing page: Regional Transport Provider

Variable Dictionary: Regional Transport Provider (2014)
type: Distribution
format: CSV
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modified: 2025-05-05T23:00:21.569211
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Variable Dictionary: Regional Transport Provider (2022)
type: Distribution
format: CSV
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Related Record: Regional Transport Provider - Customer and Ticket Sales Data
type: Distribution
format: HTML
issued: 2024-12-06T10:49:55.061253
modified: 2025-05-06T14:43:43.437183
access URL: https://data.geods.ac.uk/dataset/regional-transport-provider-customer-and-ticket-sales-data

Data Summary: Regional Transport Provider (2022)
type: Distribution
format: CSV
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Data Summary: Regional Transport Provider (2014)
type: Distribution
format: CSV
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Advanced GIS Methods Training: IUC and K-Means Clustering
type: Dataset
description: This course presents the Internet User Classification (IUC) and Retail Centre Typologies (RCT) datasets and the methods used to create them, K-means clustering. K-means clustering is used to create many different data sets, including the geodemographic classification, OAC. We will discuss the IUC and RCT datasets, how and why they were created, and what they can be used for. We will also discuss and show you how to use the K-means clustering method to create your own classification, using IUC as an example. It is split into two parts, each with a video clip and a series of commands to work through: * Part 1: Internet User Classification (IUC) * Part 2: K-Means Clustering _You need some prior knowledge of R to get the most from this course. If you are new to R, we recommend you complete the Short Course on Using R as a GIS first._ After completing the material, you will: * Know what IUC is and what it can be used for * Be aware of how IUC was created * Understand some of its key strengths and weaknesses * Know how to use Internet User Classification (IUC) in RStudio * Know what K-means clustering is and what it can be used for * Be aware of how K-means clustering was used to create the IUC * Understand some of its key strengths and weaknesses * Know how to create your own custom clustering in RStudio To access the course, click on Download next to the 'Part 1: Internet User Classification (IUC) - Workbook' or 'Part 2: K-Means - Workbook' files below. It is recommended that you have the course material open in one window, and RStudio open in another window next to it, using either a big monitor, or two monitors. If you have any comments or feedback, please email us. This course is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International licence.
issued: 2024-11-28T13:57:08.290285
modified: 2025-05-08T15:21:06.100224
contact point: Organization
  fn: Alexander Singleton
keyword: Clustering
keyword: IUC
keyword: K-Means
keyword: R Software
keyword: Tutorial
landing page: ONS, Ofcom, BPS, GeoDS

Data: Part 2: K-Means Clustering - Workbook
type: Distribution
format: HTML
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Data: Part 1: Internet User Classification (IUC) - Workbook
type: Distribution
format: HTML
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An Introduction to Spatial Data Analysis and Visualisation in R
type: Dataset
description: This tutorial series is designed to provide an accessible introduction to techniques for handling, analysing and visualising spatial data in R. R is an open source software environment for statistical computing and graphics. It has a range of bespoke packages which provide additional functionality for handling spatial data and performing complex spatial analysis operations. The practical series uses open data which has been made readily available for each tutorial and demonstrates a range of techniques that are useful for social science research including multivariate analysis, mapping and spatial interpolation. The tutorials and their associated data are freely available, although users are required to register for an account on this website to access them. For any questions or concerns please see the contact information below.
issued: 2024-11-28T11:42:23.611328
modified: 2025-05-08T15:21:10.396085
contact point: Organization
  fn: James Cheshire
keyword: GIS
keyword: R Software
keyword: Spatial Data Analysis
keyword: Tutorial
landing page: ONS, GeoDS

Data: House Sales Shapefile
type: Distribution
format: ZIP
issued: 2024-11-28T11:54:53.246400
modified: 2025-05-07T11:56:48.366989
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Data: Practical 10: Geographically Weighted Regression in R
type: Distribution
description: Census Data: "Practical Data", OA Shapefile: "Camden OA 2011 Shapefile"
format: HTML
issued: 2024-11-28T11:53:54.729176
modified: 2025-05-07T11:56:06.555865
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Data: Practical 4: Finding Relationships in R
type: Distribution
description: Census Data: "Practical Data"
format: HTML
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Data: Practical 6: Mapping Point Data in R
type: Distribution
description: Census Data: "Practical Data", OA Shapefile: "Camden OA 2011 Shapefile", House Price Data: "Camden House Sales 2015"
format: HTML
issued: 2024-11-28T11:52:26.402485
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Data: Practical 3: Bivariate Plots in R
type: Distribution
description: Census Data: "Practical Data"
format: HTML
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Data: Practical 9: Measuring Spatial Autocorrelation in R
type: Distribution
description: Census Data: "Practical Data", OA Shapefile: "Camden OA 2011 Shapefile"
format: HTML
issued: 2024-11-28T11:53:36.168630
modified: 2025-05-07T11:55:56.024280
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Data: Practical Data
type: Distribution
format: CSV
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Data: Practical 1: An Introduction to R
type: Distribution
description: Data needed: 2011 Census Data Packs for Local Authority District: Camden (E09000007)
format: HTML
issued: 2024-11-28T11:50:36.999994
modified: 2025-05-07T11:53:53.305179
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Data: 2011 Census Data Packs for Local Authority District: Camden (E09000007)
type: Distribution
format: ZIP
issued: 2024-11-28T11:56:07.610717
modified: 2025-05-08T15:21:10.403097
access URL: https://data.geods.ac.uk/dataset/b85742f5-cfc8-4426-90cd-7bbdcae90b6d/resource/7bfcf18e-b75a-4176-9ef5-e2677976b4f7/download/sdavr_camden.zip
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Data: Practical 11: Interpolating Point Data in R
type: Distribution
description: OA Shapefile: "Camden OA 2011 Shapefile", House Sales Shapefile: "House Sales Shapefile"
format: HTML
issued: 2024-11-28T11:54:15.126535
modified: 2025-05-07T11:56:20.468968
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media type: text/html

Data: Camden OA 2011 Shapefile
type: Distribution
format: ZIP
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modified: 2025-05-07T11:57:19.078451
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Data: Camden House Sales 2015
type: Distribution
format: CSV
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modified: 2025-05-07T11:57:07.597537
access URL: https://data.geods.ac.uk/dataset/b85742f5-cfc8-4426-90cd-7bbdcae90b6d/resource/9ed9700f-dcc2-4027-ac91-92be11a19464/download/sdavr_camdenhousesales15.csv
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media type: text/csv

Data: Practical 7: Using R as a GIS
type: Distribution
description: Census Data: "Practical Data", OA Shapefile: "Camden OA 2011 Shapefile", House Sales Shapefile: "House Sales Shapefile"
format: HTML
issued: 2024-11-28T11:52:54.074562
modified: 2025-05-07T11:55:34.363441
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Data: Practical 12: Functions and Loops in R
type: Distribution
description: Census Data: "Practical Data", OA Shapefile: "Camden OA 2011 Shapefile"
format: HTML
issued: 2024-11-28T11:54:34.083517
modified: 2025-05-07T11:56:32.037080
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Data: Practical 2: Data exploration in R
type: Distribution
description: Census Data: "Practical Data"
format: HTML
issued: 2024-11-28T11:51:01.944062
modified: 2025-05-07T11:54:12.609011
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Data: Practical 5: Making maps in R
type: Distribution
description: Census Data: "Practical Data", OA Shapefile: "Camden OA 2011 Shapefile"
format: HTML
issued: 2024-11-28T11:52:03.244272
modified: 2025-05-07T11:55:00.464861
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byte size: 664544
media type: text/html

Data: Practical 8: Representing Densities in R
type: Distribution
description: OA Shapefile: "Camden OA 2011 Shapefile" House Sales Shapefile: "House Sales Shapefile"
format: HTML
issued: 2024-11-28T11:53:14.612334
modified: 2025-05-07T11:55:45.589258
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byte size: 460357
media type: text/html

Modelled Ethnicity Proportions (LSOA Geography)
type: Dataset
description: These data combine historical electoral roll and linked consumer register data (on surnames, forenames and locations) from 1997 onwards, with an aggregated metric derived from ONS data which lists the most frequently selected second-level ethnicity category for the most common forenames and surnames. The data are aggregated to Local Super Output Area (LSOA11CD) or equivalent scale. Users of these data should be mindful that they concern ethnicity categories, and not migration, citizenship, nationality, or country of origin. The roll/ registers have been linked together for data inference and cleaning, to provide population continuity and result in a smoother, higher quality temporal output. These data were derived as part of an ESRC-funded project 'Ethnicity Estimator' - Virtual Microdata Laboratory project number: 0000013; and comprise a diagnostic table resulting from the application of a bespokealgorithm. The aggregate data were provided by the ONS within the Virtual Microdata Laboratory (VML). ## Content The data is available as CSV files, one for each of the ethnicity groups. Each row contains the LSOA11CD, followed by the proportion of the population that is believed to be of that ethnicity (based on surname analysis) rounded to the nearest 0.5%. ## Methodology To create the data a slight aggregation on the second-level ethnicity categories is carried out. We then aggregate by LSOA11CD. Category populations less than 5 are set to 0. The results are then divided by the total population and rounded to the nearest 0.005 (i.e. 0.5%). A value of 0 indicates there is no measurable total population for this LSOA and year combination. These values generally only occur for the first few years and in only a small number of LSOA areas. Totals may not add up to 1.000 (100%) because of rounding, but also because of an Unknown ethnicity category which a small proportion of names are assigned to. Details of the model method can be found in this paper: https://doi.org/10.1371…
issued: 2024-11-28T15:07:17.693183
modified: 2025-12-01T18:27:25.820519
contact point: Organization
  fn: Oliver O'Brien
keyword: Consumer Register
keyword: Ethnicity
keyword: Migration
keyword: Population
landing page: ONS; GeoDS Linked Consumer Register

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T15:10:12.476626
modified: 2025-05-02T16:15:28.195986
access URL: https://data.geods.ac.uk/dataset/b8d30799-ddeb-4857-9724-8c06f6944065/resource/6b4259dc-668d-4342-b7d9-02695c92d737/download/data_dictionary_mep.csv
byte size: 1234
media type: text/csv

Data Summary
type: Distribution
format: CSV
issued: 2024-11-28T15:09:47.707568
modified: 2025-12-01T18:27:25.827098
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/b8d30799-ddeb-4857-9724-8c06f6944065/resource/9549d38b-b8c1-43ed-9398-47e5680538b4/download/mep_lsoa11_2025_data_summary.csv
byte size: 793
media type: text/csv

Related Record: Modelled Ethnicity Proportions (LAD Geography)
type: Distribution
format: HTML
issued: 2024-11-28T15:11:47.531467
modified: 2025-05-07T12:28:54.589940
access URL: https://data.geods.ac.uk/dataset/modelled-ethnicity-proportions-lad-geography

Technical Report: Metadata and Validation for Modelled Ethnicity Proportions
type: Distribution
format: PDF
issued: 2024-11-28T15:12:47.189107
modified: 2025-05-08T15:24:24.893505
access URL: https://data.geods.ac.uk/dataset/b11f206a-8256-4937-b724-26477c4d617c/resource/1b2f970a-35fd-465d-8e28-65a5b6eec9e5/download/ee-oe3_comparison.pdf
media type: application/pdf

Output Area Classification (2011)
type: Dataset
description: _This classification relates to the 2011 Census, and is specified for 2011 Output Areas. This classification has been superseded by a new edition based on 2021 Census data and geography and is available from the Related Record link below._ The 2011 Classification for Output Areas (2011 OAC) is a hierarchical geodemographic classification across the UK which identifies areas of the country with similar characteristics. The OAC is produced as a collaboration between the Office for National Statistics and University College London. The classification contains the following breakdown of Output Areas into different groups and sub-groups. ## Content The data is available for download from the bottom of this page. Also available are pen portraits, a lookup table and a flyer which provides additional information about this project. ## Quality, Representation and Bias A journal article accompanies 2011 OAC which provides a thorough evaluation. All data used for this classification are sourced from the 2011 Census so bounded by the usual operational quality / representation and bias of a national census. The geodemographic classification created presents a best effort of the authors to represent the characteristics of the population and geographic context of London, however, there are decisions made during the classification process that guide these representations. For a full overview of these decisions and their rationale, see the published paper.
issued: 2024-12-16T18:43:01.914558
modified: 2026-02-05T11:29:26.125727
version info: 2.0
contact point: Organization
  fn: Oliver O'Brien
keyword: Classification
keyword: Geodemographics
keyword: Output Area Classification
landing page: Office for National Statistics

External Website: ONS Area Classifications
type: Distribution
format: HTML
issued: 2024-12-17T11:54:45.674550
modified: 2025-05-06T15:25:04.231144
access URL: https://webarchive.nationalarchives.gov.uk/ukgwa/20160110080540/http://www.ons.gov.uk/ons/guide-method/geography/products/area-classifications/ns-area-classifications/ns-2011-area-classifications/inde…
media type: text/html

Data: Output Area Classification 2011 (Shapefile format)
type: Distribution
format: ZIP
issued: 2024-12-17T12:42:00.432220
modified: 2025-05-05T23:23:26.217167
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byte size: 675520161
media type: application/zip

Related Record: Output Area Classification (2001)
type: Distribution
format: HTML
issued: 2026-02-05T11:29:00.158659
modified: 2026-02-05T11:29:26.132647
access URL: https://data.geods.ac.uk/dataset/output-area-classification-2001

Flyer: Output Area Classification
type: Distribution
description: A flyer for the OAC dataset
format: PDF
issued: 2024-12-16T19:00:29.871453
modified: 2025-05-05T23:23:26.216735
access URL: https://data.geods.ac.uk/dataset/b9b3548b-6321-4018-9bef-36606b51d7ca/resource/6b7a83b4-ff2a-44bf-b87c-23f45a8b979c/download/1.11.4-oac.pdf
byte size: 311071
media type: application/pdf

Data Summary
type: Distribution
format: CSV
issued: 2024-12-16T19:01:10.105696
modified: 2025-05-05T23:23:26.216888
access URL: https://data.geods.ac.uk/dataset/b9b3548b-6321-4018-9bef-36606b51d7ca/resource/9c5fa07c-79b1-438f-b05d-72c18e9d697c/download/data_summary_oac11_data.csv
byte size: 12640
media type: text/csv

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2024-12-16T19:00:51.315459
modified: 2025-05-05T23:23:26.216813
access URL: https://data.geods.ac.uk/dataset/b9b3548b-6321-4018-9bef-36606b51d7ca/resource/ad6eb558-afd6-438c-b69d-7af8bdb814ea/download/variable_dictionary_oac11_columns.csv
byte size: 869
media type: text/csv

Map: Mapmaker
type: Distribution
format: HTML
issued: 2024-12-17T11:56:00.874634
modified: 2025-12-09T17:56:32.566491
access URL: https://mapmaker.geods.ac.uk/#/output-area-classification-2011

Variable Dictionary: Code, Name, Colour and Pen Portrait Lookup
type: Distribution
format: CSV
issued: 2024-12-16T18:59:38.748656
modified: 2025-05-05T23:23:26.216622
access URL: https://data.geods.ac.uk/dataset/b9b3548b-6321-4018-9bef-36606b51d7ca/resource/ba90fe39-8a97-412a-afa9-8a3da6c035ac/download/penportraits.csv
byte size: 51219
media type: text/csv

Related Record: Output Area Classification (2021/2)
type: Distribution
format: HTML
issued: 2024-12-17T11:54:36.362117
modified: 2026-02-05T11:29:00.144780
access URL: https://data.geods.ac.uk/dataset/output-area-classification-2021

Technical Report: Pen Portraits for the 2011 Output Area Classification
type: Distribution
format: PDF
issued: 2024-12-16T18:58:14.608900
modified: 2025-05-05T23:23:26.216512
access URL: https://data.geods.ac.uk/dataset/b9b3548b-6321-4018-9bef-36606b51d7ca/resource/efcb2f8d-f915-4561-a48c-12b0e4b84b60/download/penportraits.pdf
byte size: 332445
media type: application/pdf

Short Course on Using R as a GIS
type: Dataset
description: This is a quick overview of working with spatial data in R. You can complete it as either a stand alone course, or as a recap before moving on to more advanced work with spatial data in R. It is split into three sections, each with a short video clip and a series of commands to work through: * Part 1: What is R & how does it work? * Part 2: Mapping spatial data in R * Part 3: Working with Loops in R If you are new to R, expect this to take between 1 and 2 hours to work through. It is recommended that you take a break between each part. After completing this material, you will: * Be able to use R to read in CSV data * Be able to use R to read in spatial data * Know how to plot spatial data in R * Know how to customize colours and classifications * Understand how to use loops for multiple maps To access the course, click on Download next to the 'Short Course on Using R as a GIS - Workbook' file below. It is recommended that you have the course material open in one window, and RStudio open in another window next to it, using either a big monitor, or two monitors. If you have any comments or feedback, please email us. This course is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International licence.
issued: 2024-11-28T13:08:09.341487
modified: 2025-05-08T15:21:12.870336
contact point: Organization
  fn: Maurizio Gibin
keyword: GIS
keyword: R Software
keyword: Tutorial
landing page: ONS, Nick Bearman

Data: Short Course on Using R as a GIS - Workbook
type: Distribution
format: HTML
issued: 2024-11-28T13:08:21.763360
modified: 2025-05-08T15:21:12.874078
access URL: https://data.geods.ac.uk/dataset/bbf99e40-271a-4bb7-9283-6fb23a05b5c6/resource/1de2af65-3579-4880-b16f-9129931fcd8b/download/short-course-on-using-r-as-a-gis.html
byte size: 1659237
media type: text/html

London Night Workers Classification (LNWC)
type: Dataset
description: Approximately a quarter of London’s workforce is employed in the evening and night-time economy, working between the hours of 6pm and 6am. Compared to those people who work during the day, there is little public information on where they work, how they travel to work, and what amenities they use during work hours. The London Night Workers Classification (LNWC) is an innovative, specialised geodemographic to enable planners, researchers and policymakers to better understand the complex landscape of night work in London. The LNWC is an open geodemographic based on the night-working characteristics of all 2021 Lower layer Super Output Areas (LSOAs) within Greater London. Uniquely, the classification is built by combining official employment statistics with mobile phone mobility footfall data. Using these data, LSOAs were segmented into seven geographic clusters, each with its own distinctive night-working characteristics. ## Content The LNWC is constructed using mobile phone activity data (BT footfall data licensed through the GLA High Streets Data Service) and employment structure data (Directory of London Businesses, available through the London Datastore). The data are provided in CSV format at LSOA level for Greater London. Additional resources, including a variable dictionary and a technical report, are also available for download. ## Quality, Representation and Bias All processing steps and methodological details are documented in a peer-reviewed paper published in _Environment and Planning B: Urban Analytics and City Science_ as well as a technical report. Final cluster allocations and interpretations were shared for comment and validation with representatives from the GLA, TfL, local councils and businesses.
issued: 2025-12-10T15:41:35.189626
modified: 2026-03-27T13:45:24.187814
contact point: Organization
  fn: Cheshire, James
keyword: Data After Dark
keyword: Geodemographics
keyword: London
keyword: Night workers
keyword: Night-time economy
keyword: cluster analysis
landing page: UCL Social Data Institute, Geographic Data Service

Map: Mapmaker
type: Distribution
format: HTML
issued: 2026-03-04T10:14:48.416578
modified: 2026-03-04T10:16:03.803301
access URL: https://mapmaker.geods.ac.uk/

External Website: Data After Dark
type: Distribution
description: GeoDS website for the _Data After Dark_ transdisciplinary research collaboration, featuring all research outputs including the LNWC.
format: HTML
issued: 2025-12-18T11:45:36.482325
modified: 2025-12-18T11:50:19.936041
access URL: https://dataafterdark.org/

Variable Dictionary: Pen Portraits
type: Distribution
format: CSV
issued: 2025-12-12T13:05:01.141008
modified: 2025-12-18T11:20:05.970178
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/bde42d8f-2395-4010-989b-81c2bd2d75fd/resource/5b0ce6ba-b821-40c8-94cd-fbf278b6ffff/download/lnwc_variable_dictionary_pen_portaits.csv
byte size: 4824
media type: text/csv

Map: London Night Workers Classification Map Dashboard
type: Distribution
format: HTML
issued: 2025-12-18T10:56:52.625287
modified: 2026-03-26T15:21:49.501759
access URL: https://dataafterdark.org/data/london-night-workers-classification-map-dashboard.html
media type: text/html

Technical Report: Mapping Night Work
type: Distribution
description: Data paper on the London Night Workers Classification, published in December 2025 on [dataafterdark.org](https://dataafterdark.org/)
format: PDF
issued: 2025-12-12T13:04:05.707846
modified: 2026-03-04T10:14:48.397900
access URL: https://data.geods.ac.uk/dataset/bde42d8f-2395-4010-989b-81c2bd2d75fd/resource/9a633f5f-e704-4fa7-a164-54a85b37bf72/download/ucl-mapping-night-work-report.pdf
byte size: 2333949
media type: application/pdf

https://data.geods.ac.uk/dataset/bde42d8f-2395-4010-989b-81c2bd2d75fd/resource/9b13f8db-a220-4862-a6b0-dff61317599f
type: Distribution
format: HTML
issued: 2026-01-05T09:09:08.378419
modified: 2026-01-05T09:10:06.764520
title: Paper: Mavrogeni, M., Cheshire, J., Iliev, M., & van Dijk, J. (2025). Creating the London night-worker geodemographic classification. Environment and Planning B: Urban Analytics and City Science, 0(0).
access URL: https://doi.org/10.1177/23998083251410392

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2025-12-18T11:20:05.986980
modified: 2025-12-18T11:20:07.442106
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/bde42d8f-2395-4010-989b-81c2bd2d75fd/resource/9d80ab53-9919-4753-9238-9843c745cf86/download/lnwc_variable_dictionary_columns.csv
byte size: 151
media type: text/csv

Data Summary
type: Distribution
format: CSV
issued: 2025-12-18T11:22:13.755978
modified: 2025-12-18T11:22:15.149008
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/bde42d8f-2395-4010-989b-81c2bd2d75fd/resource/a4d983ca-194d-42ed-8778-e4e217c5ad46/download/lnwc_data_summary.csv
byte size: 319
media type: text/csv

Data: Classification
type: Distribution
format: CSV
issued: 2025-12-12T13:04:44.528186
modified: 2025-12-18T10:46:48.394873
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/bde42d8f-2395-4010-989b-81c2bd2d75fd/resource/cf0fd566-51f7-4d0b-9820-7b6386d1d813/download/london_night_workers_classification_data.csv
byte size: 114888
media type: text/csv

Local Data Spaces
type: Dataset
description: The Local Data Spaces (LDS) programme is a collaboration between the [Joint Biosecurity Centre (JBC)](https://www.gov.uk/government/groups/joint-biosecurity-centre), [Office for National Statistics (ONS)](https://www.adruk.org/about-us/our-partnership/office-for-national-statistics/), the [Ministry of Housing, Communities and Local Government (MHCLG)](https://www.gov.uk/government/organisations/ministry-of-housing-communities-and-local-government) and [ADR UK](http://www.adruk.org). The LDS programme used de-identified Covid-19 data from the national Test and Trace programme and also the ONS, to understand the spread of the pandemic in their areas and its impact on their communities. Four academic researchers from University of Liverpool, University of Leeds and UCL were funded to provide analysis on behalf of local authorities in need of analytical support. Among the deliverables of the project is a series of reports, for each Local Authority District in England, on different topics of interest during the COVID-19 pandemic: * Demographic Inequalities In Covid 19 * Economic Vulnerability * Ethnic Inequalities In Covid 19 * Excess Mortality * Geospatial Inequalities In Covid 19 * Human Mobility Report * Industry Densities * Occupational Inequalities * Population and Housing * Social Economy ## Content The reports, in html form and accessible offline, are available below. A zip file for each English region has been created, each file itself contains zip files for each local authority in that region. They have been created using data and boundaries from different years. Some Local Authority District might have a different code than the current one and might not contain all the reports listed above. Please note that the zip files are very large. (300MB-1.8GB) ## Quality, Representation and Bias This work was produced using statistical data from ONS. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretati…
issued: 2024-12-17T13:20:18.181082
modified: 2025-05-08T15:21:33.867701
contact point: Organization
  fn: Dr Maurizio Gibin
keyword: COVID-19
keyword: Demography
keyword: Economy
keyword: Health
keyword: Public Health
landing page: JBC, ONS, DLUHC, ADR

Data: Local authorities in South East England
type: Distribution
description: Medway, Bracknell Forest, West Berkshire, Reading, Slough, Windsor and Maidenhead, Wokingham, Milton Keynes, Brighton and Hove, Portsmouth, Southampton, Isle of Wight, Aylesbury Vale, Chiltern, South Bucks, Wycombe, Eastbourne, Hastings, Lewes, Rother, Wealden, Basingstoke and Deane, East Hampshire, Eastleigh, Fareham, Gosport, Hart, Havant, New Forest, Rushmoor, Test Valley, Winchester, Ashford, Canterbury, Dartford, Dover, Gravesham, Maidstone, Sevenoaks, Folkestone and Hythe, Swale, Thanet, Tonbridge and Malling, Tunbridge Wells, Cherwell, Oxford, South Oxfordshire, Vale of White Horse, West Oxfordshire, Elmbridge, Epsom and Ewell, Guildford, Mole Valley, Reigate and Banstead, Runnymede, Spelthorne, Surrey Heath, Tandridge, Waverley, Woking, Adur, Arun, Chichester, Crawley, Horsham, Mid Sussex, Worthing
format: ZIP
issued: 2024-12-17T14:19:52.834211
modified: 2025-05-05T23:23:23.995576
access URL: https://data.geods.ac.uk/dataset/be4c29ab-c06f-43be-b961-263cabc5206d/resource/166d8f3a-bd03-4fc7-b4d2-846cb317ba9c/download/local_data_spaces_south_east.zip
byte size: 1765831791
media type: application/zip

Data: Local authorities in East of England
type: Distribution
description: Peterborough, Luton, Southend-on-Sea, Thurrock, Bedford, Central Bedfordshire, Cambridge, East Cambridgeshire, Fenland, Huntingdonshire, South Cambridgeshire, Basildon, Braintree, Brentwood, Castle Point, Chelmsford, Colchester, Epping Forest, Harlow, Maldon, Rochford, Tendring, Uttlesford, Broxbourne, Dacorum, Hertsmere, North Hertfordshire, Three Rivers, Watford, Breckland, Broadland, Great Yarmouth, King's Lynn and West Norfolk, North Norfolk, Norwich, South Norfolk, Babergh, Ipswich, Mid Suffolk, St Albans, Welwyn Hatfield, East Hertfordshire, Stevenage, East Suffolk, West Suffolk
format: ZIP
issued: 2024-12-17T14:20:57.089453
modified: 2025-05-05T23:23:23.993804
access URL: https://data.geods.ac.uk/dataset/be4c29ab-c06f-43be-b961-263cabc5206d/resource/3127951e-2851-4542-8d9b-7c3eaa529045/download/local_data_spaces_east_of_england.zip
byte size: 1200205705
media type: application/zip

Data: London Boroughs
type: Distribution
description: City of London, Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea, Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, Tower Hamlets, Waltham Forest, Wandsworth, Westminster
format: ZIP
issued: 2024-12-17T14:29:39.854161
modified: 2025-05-05T23:23:23.994571
access URL: https://data.geods.ac.uk/dataset/be4c29ab-c06f-43be-b961-263cabc5206d/resource/40490e93-d173-434e-bca6-0a930496c156/download/local_data_spaces_london.zip
byte size: 1011143931
media type: application/zip

Source Code: Github
type: Distribution
format: HTML
issued: 2024-12-17T13:20:36.045400
modified: 2025-05-05T23:23:23.996931
access URL: https://github.com/ESRC-CDRC/LocalDataSpaces

Data: Local authorities in North East England
type: Distribution
description: Hartlepool, Middlesbrough, Redcar and Cleveland, Stockton-on-Tees, Darlington, County Durham, Northumberland, Newcastle upon Tyne, North Tyneside, South Tyneside, Sunderland, Gateshead
format: ZIP
issued: 2024-12-17T13:46:51.918323
modified: 2025-05-05T23:23:23.994899
access URL: https://data.geods.ac.uk/dataset/be4c29ab-c06f-43be-b961-263cabc5206d/resource/5392b88e-9b16-49f8-8b98-b7fe15cbb364/download/local_data_spaces_north_east.zip
byte size: 313357357
media type: application/zip

Data: Local authorities in Yorkshire and the Humber
type: Distribution
description: Kingston upon Hull (City of), East Riding of Yorkshire, North East Lincolnshire, North Lincolnshire, York, Craven, Hambleton, Harrogate, Richmondshire, Ryedale, Scarborough, Selby, Barnsley, Doncaster, Rotherham, Sheffield, Bradford, Calderdale, Kirklees, Leeds, Wakefield
format: ZIP
issued: 2024-12-17T14:12:01.591134
modified: 2025-05-05T23:23:23.996626
access URL: https://data.geods.ac.uk/dataset/be4c29ab-c06f-43be-b961-263cabc5206d/resource/74b13107-220a-4e4c-b22c-ea7f5c02c2b1/download/local_data_spaces_yorkshire_and_the_humber.zip
byte size: 581917978
media type: application/zip

Data: Local authorities in East Midlands
type: Distribution
description: Derby, Leicester, Rutland, Nottingham, Amber Valley, Bolsover, Chesterfield, Derbyshire Dales, Erewash, High Peak, North East Derbyshire, South Derbyshire, Blaby, Charnwood, Harborough, Hinckley and Bosworth, Melton, North West Leicestershire, Oadby and Wigston, Boston, East Lindsey, Lincoln, North Kesteven, South Holland, South Kesteven, West Lindsey, Corby, Daventry, East Northamptonshire, Kettering, Northampton, South Northamptonshire, Wellingborough, Ashfield, Bassetlaw, Broxtowe, Gedling, Mansfield, Newark and Sherwood, Rushcliffe
format: ZIP
issued: 2024-12-17T14:18:37.238816
modified: 2025-05-05T23:23:23.994228
access URL: https://data.geods.ac.uk/dataset/be4c29ab-c06f-43be-b961-263cabc5206d/resource/9a5db4fe-7faa-4c8a-ae20-ef743b0f0be7/download/local_data_spaces_east_midlands.zip
byte size: 1070702798
media type: application/zip

Data: Local authorities in South West England
type: Distribution
description: Bath and North East Somerset, Bristol (City of), North Somerset, South Gloucestershire, Plymouth, Torbay, Swindon, Cornwall, Isles of Scilly, Wiltshire, Bournemouth Christchurch and Poole, Dorset, East Devon, Exeter, Mid Devon, North Devon, South Hams, Teignbridge, Torridge, West Devon, East Dorset, North Dorset, Purbeck, West Dorset, Weymouth and Portland, Cheltenham, Cotswold, Forest of Dean, Gloucester, Stroud, Tewkesbury, Mendip, Sedgemoor, South Somerset, Somerset West and Taunton
format: ZIP
issued: 2024-12-17T14:23:58.510582
modified: 2025-05-05T23:23:23.995964
access URL: https://data.geods.ac.uk/dataset/be4c29ab-c06f-43be-b961-263cabc5206d/resource/a22e0f8f-34a0-4bd8-adb9-99d5cc19bb40/download/local_data_spaces_south_west.zip
byte size: 798938630
media type: application/zip

External Website: ADR UK-funded Local Data Spaces wins ONS Research Excellence Award
type: Distribution
format: HTML
issued: 2024-12-17T13:20:53.654518
modified: 2025-05-05T23:23:23.997232
access URL: https://www.adruk.org/news-publications/news-blogs/adr-uk-funded-local-data-spaces-wins-ons-research-excellence-award-467/

Data: Local authorities in North West England
type: Distribution
description: Halton, Warrington, Blackburn with Darwen, Blackpool, Cheshire East, Cheshire West and Chester, Allerdale, Barrow-in-Furness, Carlisle, Copeland, Eden, South Lakeland, Burnley, Chorley, Fylde, Hyndburn, Lancaster, Pendle, Preston, Ribble Valley, Rossendale, South Ribble, West Lancashire, Wyre, Bolton, Bury, Manchester, Oldham, Rochdale, Salford, Stockport, Tameside, Trafford, Wigan, Knowsley, Liverpool, St. Helens, Sefton, Wirral
format: ZIP
issued: 2024-12-17T14:21:55.053798
modified: 2025-05-05T23:23:23.995238
access URL: https://data.geods.ac.uk/dataset/be4c29ab-c06f-43be-b961-263cabc5206d/resource/f2c86473-d28e-4ada-a243-fd3c47596c2c/download/local_data_spaces_north_west.zip
byte size: 1070338430
media type: application/zip

Data: Local authorities in West Midlands
type: Distribution
description: Herefordshire (County of) Telford and Wrekin Stoke-on-Trent Shropshire Cannock Chase East Staffordshire Lichfield Newcastle-under-Lyme South Staffordshire Stafford Staffordshire Moorlands Tamworth North Warwickshire Nuneaton and Bedworth Rugby Stratford-on-Avon Warwick Bromsgrove Malvern Hills Redditch Worcester Wychavon Wyre Forest Birmingham Coventry Dudley Sandwell Solihull Walsall Wolverhampton
format: ZIP
issued: 2024-12-17T14:26:50.838600
modified: 2025-05-05T23:23:23.996313
access URL: https://data.geods.ac.uk/dataset/be4c29ab-c06f-43be-b961-263cabc5206d/resource/f634318f-702f-4075-860d-83d3eaf72b7e/download/local_data_spaces_west_midlands.zip
byte size: 797334108
media type: application/zip

COVID-19 Lockdown High Street Resilience Classification
type: Dataset
description: This dataset, developed by GeoDS using data from Green Street (formerly LDC), assesses the resilience of high streets in Great Britain following government-enforced COVID-19 pandemic restrictions from March 2020 to August 2021. It provides detailed, spatially granular resilience indicators, allowing researchers to examine retail characteristics at the high street level across Great Britain. ## Content The dataset contains aggregated indicators for British high streets as defined by the retail centre boundaries (comprising 15 or more retail locations) and the Ordnance Survey’s 2019 high street definition. High street classifications were drawn from seven types of retail centres (e.g., district centres, major town centres) and exclude retail parks and out-of-town shopping centres. For a detailed explanation of columns and dataset structure, refer to the Variable Dictionary and Data Summary, available for download in the resource section below. ## Quality, Representation and Bias The dataset aggregates data primarily from the Green Street Retail Type, Vacancy, and Address dataset, which offers near-complete coverage of retailers across Great Britain. Retail type coding has been recalculated and verified for consistency. However: * Indicators are not available for all 6,423 centres in the GeoDS retail centre boundaries dataset. * Retail parks, out-of-town centres, and centres with fewer than 15 Green Street-tracked units were excluded to ensure data security and protect commercial sensitivity. * High street data for Northern Ireland is not included due to a lack of Green Street data.
issued: 2024-11-29T09:29:27.504104
modified: 2026-04-15T13:14:50.926390
contact point: Organization
  fn: Oliver O'Brien
keyword: COVID
keyword: COVID-19
keyword: High Street
keyword: Resilience
landing page: Green Street (formerly LDC), GeoDS

Data Summary
type: Distribution
format: CSV
issued: 2024-11-29T09:29:54.265989
modified: 2025-05-05T23:13:28.456653
access URL: https://data.geods.ac.uk/dataset/bf37e597-e729-47f8-934a-df0e37e56fea/resource/3ac0088b-3fc6-4d23-b942-d9836ce0f0c1/download/data_summary_covidhsr.csv
byte size: 330
media type: text/csv

Related Record: Retail Centre Boundaries
type: Distribution
format: HTML
issued: 2024-11-29T09:35:31.627789
modified: 2025-05-07T13:50:55.560541
access URL: https://data.geods.ac.uk/dataset/retail-centre-boundaries-and-open-indicators

Related Record: Retail Type, Vacancy and Address Data
type: Distribution
format: HTML
issued: 2024-11-29T09:36:04.756044
modified: 2026-04-15T13:14:50.933477
access URL: https://data.geods.ac.uk/dataset/local-data-company-retail-type-vacancy-and-address-data

Paper: Hill, A., Cheshire, J. An Investigation of the Impact and Resilience of British High Streets Following the COVID-19 Lockdown Restrictions. Appl. Spatial Analysis (2022).
type: Distribution
format: HTML
issued: 2024-11-29T09:30:40.645943
modified: 2025-05-05T23:13:28.456734
access URL: https://doi.org/10.1007/s12061-022-09494-8

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-29T09:29:41.233204
modified: 2025-05-05T23:13:28.456544
access URL: https://data.geods.ac.uk/dataset/bf37e597-e729-47f8-934a-df0e37e56fea/resource/d9370080-b8eb-4b89-afde-0df768abceea/download/variable_dictionary_covidhsr.csv
byte size: 1238
media type: text/csv

GB MSOA / IZ Classification (2021/2 MSOAC)
type: Dataset
description: This is an aggregation of UK 2021/2 Classification of Output Areas (2021/2 UK OAC) which is a hierarchical geodemographic classification which identifies neighbourhoods across the UK that share similar characteristics. The taxonomy classifies Output Areas (OAs) into 8 different Supergroups, along with 21 nested Groups and 52 nested Subgroups. Each MSOA (Middle layer Super Output Area, England and Wales) or Intermediate Zone (Scotland) is made up of smaller OAs. We assign an OAC Subgroup to each larger aggregation by tallying the total populations for each Output Area falling into each OAC Subgroup. The higher-level aggregation is then assigned to whichever Subgroup that accounts for the largest share of its population. This approach enables 2021/2 MSOAC to share the familiar taxonomy of the widely-used 2021/2 UK OAC, and uses the research finding that more than three quarters (65%) of MSOA (or equivalent) zones host populations from eleven or fewer of the 52 Subgroups, and a large majority of zones from different Subgroups are part of the same Group. We anticipate that most MSOAC users will conduct analysis at the Group or Supergroup level. ## Content The data are supplied in Parquet format. Additional information about these data, including the detailed typology, glossary of variables and terms, and distributional statistics, is available for download below, along with classification codes and labels. ## Quality, Representation and Bias Built using official government statistical estimates, the quality of the underpinning data is understood to be very high, and offers coverage of every UK neighbourhood. The classification methodology has been published and subjected to independent peer review. We provide additional documentation of the ways in which differences in Census variables differs amongst UK nations. The Census in Scotland was delayed from 2021 until 2022, and small area outputs from Northern Ireland's Census were not available at the time of production of …
issued: 2025-09-04T21:59:27.716746
modified: 2026-02-10T13:16:35.744805
version info: 1.0
contact point: Organization
  fn: Alex Singleton
keyword: Census
keyword: Geodemographics
keyword: OAC
landing page: ONS, Scottish Government

Variable Dictionary: Classification Codes and Names
type: Distribution
format: CSV
issued: 2025-09-04T22:01:52.509503
modified: 2025-09-04T22:01:57.519006
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/c20d048c-34e9-4778-8fc1-30c3b248e87d/resource/1a63e2c2-f850-483a-97bb-a77be645a361/download/classification_codes_and_names_ukoac_msoa_iz_oac.csv
byte size: 7702
media type: text/csv

Technical Report: Classification Typology and Pen Portraits
type: Distribution
description: While this report mainly refers to UK-OAC, the pen portraits and the updated technical note refers to MSOAC as well.
format: PDF
issued: 2025-12-08T14:11:31.025672
modified: 2025-12-09T17:57:04.564479
access URL: https://data.geods.ac.uk/dataset/c20d048c-34e9-4778-8fc1-30c3b248e87d/resource/44fe2eaf-a688-45cf-a9c0-ad8878bdd077/download/pen_portraits_ukoac.pdf
byte size: 11258922
media type: application/pdf

Source Code: Aggregate ONS Output Area Classification (OAC) for GB MSOA/IZ (GitHub repository)
type: Distribution
format: HTML
issued: 2026-02-10T13:15:54.566221
modified: 2026-02-10T13:16:35.748651
access URL: https://github.com/GeographicDataService/MSOA_IZ_Area_Classification

https://data.geods.ac.uk/dataset/c20d048c-34e9-4778-8fc1-30c3b248e87d/resource/65e9d2ab-1519-46cd-adbe-beb6deff0552
type: Distribution
format: HTML
issued: 2025-09-04T22:03:03.741540
modified: 2025-09-04T22:03:22.744597
title: Paper: Wyszomierski, J., Longley, P.A., Singleton, A.D., Gale, C. & O’Brien, O. (2023) A neighbourhood Output Area Classification from the 2021 and 2022 UK censuses. The Geographical Journal, 00, 1–20.HTML
access URL: https://doi.org/10.1111/geoj.12550

Data: 2021/2 MSOAC
type: Distribution
format: CSV
issued: 2025-12-08T14:09:35.470520
modified: 2025-12-08T14:10:13.017546
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/c20d048c-34e9-4778-8fc1-30c3b248e87d/resource/901948ef-dd6f-468f-b62f-3dd065c3b63f/download/msoa-iz-lookup.csv
byte size: 232188
media type: text/csv

Data Summary
type: Distribution
format: CSV
issued: 2025-09-04T22:02:21.023011
modified: 2025-09-04T22:02:25.930721
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/c20d048c-34e9-4778-8fc1-30c3b248e87d/resource/9245704a-d403-41f9-897b-a26dba5b3aba/download/data_summary_msoa_iz_oac.csv
byte size: 305
media type: text/csv

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2025-09-04T22:02:07.988848
modified: 2025-09-04T22:02:09.749656
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/c20d048c-34e9-4778-8fc1-30c3b248e87d/resource/b0c47100-b63b-4acd-964e-128e50790ee7/download/variable_dictionary_msoa_iz_oac.csv
byte size: 179
media type: text/csv

Related Record: Output Area Classification (2021/2)
type: Distribution
format: HTML
issued: 2025-09-04T22:03:22.754234
modified: 2026-02-10T13:15:54.556243
access URL: https://data.geods.ac.uk/dataset/output-area-classification-2021

Related Record: UK LSOA / DZ / SDZ Classification (2021/2 LSOAC)
type: Distribution
format: HTML
issued: 2025-09-04T22:25:02.101817
modified: 2025-12-08T14:09:35.464356
access URL: https://data.geods.ac.uk/dataset/lsoac

The Ageing in Place Classification (AiPC)
type: Dataset
description: The Ageing in Place Classification (AiPC) is the first-of-its-kind bespoke geodemographic classification in England targeting the population aged 50 years old and older. The AiPC classifies the population aged 50 years old and older in England into distinctive groups. The classification is integrates multiple high quality data sources including the ONS 2011 Census, British Population Survey (BPS), Access to Healthy Assets & Hazards (AHAH), NHS English Prescribing Data (EPD), and Registered Patient (RP) data. ## Content The geodemographic consists of two tiers. Tier 1, the Supergroup, contains five clusters providing the most generic descriptions of the older population (aged 50 and over) and their living environments. Tier 2, the Groups, further differentiates within the five clusters of Tier 1 giving an additional 13 clusters. These data are available at LSOA11CD level. Data can be downloaded from the bottom of this page, as the file ‘Data: Ageing in place classification’. For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. ## Quality, Representation and Bias The classification is based on multiple high quality data sources including ONS 2011 Census, British Population Survey (BPS), Access to Healthy Assets & Hazards (AHAH), NHS English Prescribing Data (EPD), Registered Patient (RP). A clustering k-means algorithm is used to group the different variables in Supergroup and Group. The classification covers all LSOAs in England.
issued: 2024-12-16T12:59:18.668339
modified: 2026-01-09T12:22:36.067020
contact point: Organization
  fn: Dr Les Dolega
keyword: Age
keyword: Classification
keyword: Population

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2024-12-16T13:04:00.715509
modified: 2025-05-08T07:59:26.370626
access URL: https://data.geods.ac.uk/dataset/ca0ec782-30f8-45f0-964b-c9aee9b4463d/resource/182ae812-0578-49b0-8719-6170f33f8866/download/variable_dictionary_aipc_columns.csv
byte size: 220
media type: text/csv

Data: AiPC (Shapefile format)
type: Distribution
description: Zipped .shp file of the AiPC for England. Created particularly for ArcGIS users. 4 files in this archive - aipc_geo.dbf - aipc_geo.prj - aipc_geo.shp - aipc_geo.shx
format: ZIP
issued: 2024-12-16T13:02:43.737755
modified: 2025-05-08T07:59:16.191906
access URL: https://data.geods.ac.uk/dataset/ca0ec782-30f8-45f0-964b-c9aee9b4463d/resource/67a63a84-7dad-434a-9202-5f752913ae57/download/aipc_geo.shp.zip
byte size: 4593213
media type: application/zip

Variable Dictionary: Lookups
type: Distribution
format: CSV
issued: 2024-12-16T13:04:16.451166
modified: 2025-05-08T07:59:36.433318
access URL: https://data.geods.ac.uk/dataset/ca0ec782-30f8-45f0-964b-c9aee9b4463d/resource/764f7753-9f12-475d-9cbd-79573992c894/download/variable_dictionary_aipc_lookups.csv
byte size: 13899
media type: text/csv

Map: Mapmaker
type: Distribution
format: HTML
issued: 2025-09-05T13:13:52.856732
modified: 2025-12-18T11:11:03.273197
access URL: https://mapmaker.geods.ac.uk/#/ageing-in-place-classification

Data Summary
type: Distribution
format: CSV
issued: 2024-12-16T13:03:36.956626
modified: 2025-05-08T07:59:20.933374
access URL: https://data.geods.ac.uk/dataset/ca0ec782-30f8-45f0-964b-c9aee9b4463d/resource/88aa0932-b29c-4ebb-932b-daf7194338b4/download/data_summary_aipc.csv
byte size: 558
media type: text/csv

Variable Dictionary: Sources
type: Distribution
format: CSV
issued: 2024-12-16T13:04:45.425925
modified: 2025-05-08T07:59:31.590852
access URL: https://data.geods.ac.uk/dataset/ca0ec782-30f8-45f0-964b-c9aee9b4463d/resource/bbf65d17-aa59-40e4-93e4-79d5b3df76b3/download/variable_dictionary_aipc_sources.csv
byte size: 3215
media type: text/csv

Data: AiPC (GeoPackage format)
type: Distribution
description: GeoPackage (zipped .gpkg) file of the AiPC for England.
format: ZIP
issued: 2024-12-16T13:01:58.785823
modified: 2025-05-08T07:59:10.891009
access URL: https://data.geods.ac.uk/dataset/ca0ec782-30f8-45f0-964b-c9aee9b4463d/resource/c810de24-e87c-4805-99a8-8be16c073398/download/aipc_geo.gpkg_.zip
byte size: 5534136
media type: application/zip

Data: Ageing in Place Classification (CSV format)
type: Distribution
description: The Classification data for 32,844 LSOAs in England.
format: CSV
issued: 2024-12-16T13:01:26.190918
modified: 2025-05-08T07:59:05.499378
access URL: https://data.geods.ac.uk/dataset/ca0ec782-30f8-45f0-964b-c9aee9b4463d/resource/d960aab6-d0b1-4333-b956-b5aa7769fe5a/download/aipc.csv
byte size: 3342343
media type: text/csv

Technical Report: Classifying the Older Population
type: Distribution
description: Includes Pen Portraits and Supergroup Profiles (Z-scores). This research is funded by Nuffield Foundation; grant ref: WEL/44091. The Nuffield Foundation is an independent charitable trust with a mission to advance social well-being. It funds research that informs social policy, primarily in Education, Welfare, and Justice. It also funds student programmes that provide opportunities for young people to develop skills in quantitative and scientific methods. The Nuffield Foundation is the founder and co-founder of the Nuffield Council on Bioethics and the Ada Lovelace Institute. The Foundation has funded this project, but the views expressed are those of the authors and not necessarily the Foundation. Visit www.nuffieldfoundation.org
format: PDF
issued: 2024-12-16T13:03:18.327325
modified: 2026-01-09T12:22:36.074422
access URL: https://data.geods.ac.uk/dataset/ca0ec782-30f8-45f0-964b-c9aee9b4463d/resource/e29428d3-84f7-446d-b595-d88661b56824/download/mpo-nuffield-aipc-report-final.pdf
byte size: 8272864
media type: application/pdf

MaPS Debt Need Survey
type: Dataset
description: The Money and Pensions Service (MaPS) Debt Need Survey was an annual panel-based survey. It looked at attitudes towards debt advice and interactions with providers, how people feel about their household finances and money management in general, arrears on credit commitments and bills, use of high-cost credit, and adverse events/impacts (financial and personal). Multiple panels were used, ensuring a broad cross section of the population was surveyed, including socially deprived communities. The Debt Need Survey has been superseded by MoneyView, which is also available as a product here at GeoDS. The survey questions and answers are grouped into the following sections: * Basic demographic questions for quota/screening (age, sex, location, ethnicity) * Over-indebtedness and advice seeking * Method of advice seeking, service used and response received * Need for debt advice (please see an explanation of this measure, at https://moneyandpensionsservice.org.uk/wp-content/uploads/2022/02/need-for-debt-advice-explanation-february-2022.pdf) * Impact of COVID on personal finances * Life events (work, money, health, family) * Demographics (e.g. education, tenure, household composition, employment status, social grade, marital status, income, religion, sexual orientation, health) There are more than 1000 fields/variables in the surveys, with different groups of fields enumerated based on a participant’s response to early questions. Please see the variable description CSV for a summary of the questions asked. ## Content We hold the anonymised individual records containing the responses from each survey participant. The four editions of the survey (2020 to 2023) are broadly similar in terms of question content making them suitable for comparisons, while evolving slightly to reflect changes in society. For example, the 2022 survey included new questions relating to Buy Now Pay Later products. The survey answers are available as, for each year, two record-level CSVs (one with codes…
issued: 2024-11-28T14:56:16.164206
modified: 2025-09-17T13:04:36.844507
version info: 1.0
contact point: Organization
  fn: Oliver O'Brien
keyword: Debt
landing page: Money and Pensions Service

Related Record: MaPS MoneyView Survey
type: Distribution
format: HTML
issued: 2025-09-17T13:03:24.893743
modified: 2025-09-17T13:04:36.851517
access URL: https://data.geods.ac.uk/dataset/maps-moneyview-survey

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T14:56:50.991031
modified: 2025-05-08T15:24:26.957485
access URL: https://data.geods.ac.uk/dataset/cae9e82b-9017-4d99-bcea-760b42ca96e2/resource/f1a926be-6225-41ec-ab35-4c47b7239f1c/download/maps_dns_variable_descriptions.csv
byte size: 4344
media type: text/csv

Geodemographic Classification of Interactivity (GCI)
type: Dataset
description: Neighbourhoods are more than just where people live – they are spaces of movement, interaction and access. Traditional geodemographic classifications, such as the UK Output Area Classification (OAC), provide valuable but static portraits of residents at night-time addresses. They cannot show how neighbourhoods actually function during the day or how people connect to them. The GCI dataset provides the first geodemographic classification of neighbourhood interactivity. Uniquely, it integrates dynamic mobility, connectivity and accessibility indicators with conventional Census-based socio-economic measures. This creates a richer picture of how places in Greater London operate over the 24-hour day. The classification is built at OA level for Greater London using: 1. Census 2021 variables on age, household composition, tenure, occupation and health. 2. Connectivity and footfall metrics derived from anonymised, GPS-based origin–destination flows and diurnal activity patterns recorded between 2016 and 2019 (approximately 380,000 mobile users and 21 million journeys). 3. Service accessibility measures based on multimodal travel times to jobs, schools, hospitals and supermarkets (OpenStreetMap + GTFS). Using these data, OAs were segmented into seven distinct clusters of neighbourhood types. Each captures a unique combination of socio-economic composition, daytime/night-time activity, transport connectivity and service access. Full pen portraits are available in the technical report, and interactive maps of the clusters are available through Mapmaker. This classification extends what is possible using Census data alone. By linking georeferenced, anonymised mobility records to neighbourhood characteristics, the GCI captures how neighbourhoods function and who they serve, rather than relying solely on where residents sleep at night. This independent, ethically approved GeoDS Research Ready Data product demonstrates a practical framework for integrating dynamic indicators into …
issued: 2025-11-05T16:47:40.061927
modified: 2025-12-12T08:20:55.585686
contact point: Organization
  fn: Maurizio Gibin
keyword: Mobile phone data
keyword: Mobility
keyword: Population
keyword: Smart Data
landing page: Huq, ONS (Census 2021), OpenStreetMap, GTFS transit feeds

Technical Report: Pen Portraits and Input Variable Listing
type: Distribution
format: PDF
issued: 2025-11-05T16:55:58.487768
modified: 2025-11-24T13:46:22.023113
access URL: https://data.geods.ac.uk/dataset/d0f533ff-61f0-4a46-ab21-14f99e0193b9/resource/062918da-5239-46aa-8868-91b48b364d12/download/gci_technical_report.pdf
byte size: 388589
media type: application/pdf

Map: Mapmaker (Interactivity Metrics)
type: Distribution
format: HTML
issued: 2025-11-24T13:46:22.042596
modified: 2025-11-24T13:46:35.943594
access URL: https://mapmaker.geods.ac.uk/#/interactivity-metrics

Data: Variable Means
type: Distribution
format: CSV
issued: 2025-11-05T16:57:29.412729
modified: 2025-11-24T13:44:06.542230
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/d0f533ff-61f0-4a46-ab21-14f99e0193b9/resource/685246ba-f0c2-412c-8838-3ab2a90ad22f/download/gci_variable_means.csv
byte size: 5906
media type: text/csv

Data: Classification
type: Distribution
format: CSV
issued: 2025-11-05T16:49:48.741167
modified: 2025-11-24T13:44:23.463462
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/d0f533ff-61f0-4a46-ab21-14f99e0193b9/resource/753d25c1-ffd5-446c-af29-0b33559df224/download/geodemographic_classification_of_interactivity_gci.csv
byte size: 1320612
media type: text/csv

Data Summary
type: Distribution
format: CSV
issued: 2025-11-05T17:19:31.788511
modified: 2025-11-05T17:19:33.634289
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/d0f533ff-61f0-4a46-ab21-14f99e0193b9/resource/7f285bcf-531a-4055-ac4a-47fb542e1cc7/download/geodemographic_classification_of_interactivity_gci_data_summary.csv
byte size: 538
media type: text/csv

Variable Dictionary: Classification Codes and Names
type: Distribution
format: CSV
issued: 2025-11-05T16:52:56.300986
modified: 2025-11-05T16:52:57.619417
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/d0f533ff-61f0-4a46-ab21-14f99e0193b9/resource/801d08be-b4bf-45ec-a0ac-846f017d0e53/download/gci_classiifcation_codes_and_names.csv
byte size: 1607
media type: text/csv

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2025-11-05T17:19:14.166366
modified: 2025-11-24T13:45:16.484373
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/d0f533ff-61f0-4a46-ab21-14f99e0193b9/resource/ae210e25-8533-49e9-92df-95fab69f827b/download/geodemographic_classification_of_interactivity_gci_variable_dictionary.csv
byte size: 312
media type: text/csv

Map: Mapmaker
type: Distribution
format: HTML
issued: 2025-11-06T12:37:43.392747
modified: 2025-11-24T13:43:43.164641
access URL: https://mapmaker.geods.ac.uk/#/interactivity

UK LSOA / DZ / SDZ Classification (2021/2 LSOAC)
type: Dataset
description: This is an aggregation of UK 2021/2 Classification of Output Areas (2021/2 UK OAC) which is a hierarchical geodemographic classification which identifies neighbourhoods across the UK that share similar characteristics. The taxonomy classifies Output Areas (OAs) into 8 different Supergroups, along with 21 nested Groups and 52 nested Subgroups. Each LSOA (Lower layer Super Output Area, England and Wales), Data Zone (Scotland) or Super Data Zone (Northern Ireland) is made up of smaller OAs (England, Scotland and Wales) or Data Zones (Northern Ireland). We assign an OAC Subgroup to each larger aggregation by tallying the total populations for each Output Area falling into each OAC Subgroup. The higher-level aggregation is then assigned to whichever Subgroup that accounts for the largest share of its population. This approach enables 2021/2 LSOAC to share the familiar taxonomy of the widely-used 2021/2 UK OAC, and uses the research finding that more than three quarters (77%) of LSOA (or equivalent) zones host populations from four or fewer of the 52 Subgroups, and a large majority of zones from different Subgroups are part of the same Group. We anticipate that most LSOAC users will conduct analysis at the Group or Supergroup level. ## Content The data are supplied in CSV format. Additional information about these data, including the detailed typology, glossary of variables and terms, and distributional statistics, is available for download below, along with classification codes and labels. ## Quality, Representation and Bias Built using official government statistical estimates, the quality of the underpinning data is understood to be very high, and offers coverage of every UK neighbourhood. The classification methodology has been published and subjected to independent peer review. We provide additional documentation of the ways in which differences in Census variables differs amongst UK nations. The Census in Scotland was delayed from 2021 until 2022, and small area out…
issued: 2025-05-12T16:35:13.992604
modified: 2026-02-09T13:18:26.827170
version info: 2.0
contact point: Organization
  fn: Singleton, Alex
keyword: Census
keyword: Geodemographics
keyword: OAC
landing page: ONS, NISRA, Scottish Government

https://data.geods.ac.uk/dataset/d257ab6a-2353-4aec-b6f7-2c3dbc359766/resource/1797054e-c8e6-46f0-96ad-8a5650b9078e
type: Distribution
format: HTML
issued: 2025-05-12T16:46:23.453211
modified: 2025-08-22T17:20:33.599671
title: Paper: Wyszomierski, J., Longley, P.A., Singleton, A.D., Gale, C. & O’Brien, O. (2023) A neighbourhood Output Area Classification from the 2021 and 2022 UK censuses. The Geographical Journal, 00, 1–20.
access URL: https://doi.org/10.1111/geoj.12550

Related Record: Output Area Classification (2021/2)
type: Distribution
format: HTML
issued: 2025-05-12T16:45:26.014212
modified: 2026-02-09T13:18:26.832593
access URL: https://data.geods.ac.uk/dataset/output-area-classification-2021

Technical Report: Classification Typology and Pen Portraits
type: Distribution
description: While this report mainly refers to UK-OAC, the pen portraits and the updated technical note refers to LSOAC as well.
format: PDF
issued: 2025-05-12T16:36:08.134334
modified: 2025-05-12T16:36:34.826658
access URL: https://data.geods.ac.uk/dataset/d257ab6a-2353-4aec-b6f7-2c3dbc359766/resource/40966f17-c033-4b69-a82c-6fbc572a4897/download/pen_portraits_ukoac_0.pdf
byte size: 11258922
media type: application/pdf

Variable Dictionary: Classification Codes and Names
type: Distribution
format: CSV
issued: 2025-05-12T16:37:04.570071
modified: 2025-08-23T07:21:31.147850
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/d257ab6a-2353-4aec-b6f7-2c3dbc359766/resource/8f2e48a0-7900-47b5-b41e-c7fd8e303c3d/download/classification_codes_and_names_ukoac_lsoac.csv
byte size: 6244
media type: text/csv

Related Record: GB MSOA / IZ Classification (2021/2 MSOAC)
type: Distribution
format: HTML
issued: 2025-09-04T22:23:31.008666
modified: 2025-12-09T17:56:50.526906
access URL: https://data.geods.ac.uk/dataset/msoac

Data: 2021/2 LSOAC
type: Distribution
format: CSV
issued: 2025-12-08T14:03:01.870442
modified: 2025-12-08T14:03:03.256687
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/d257ab6a-2353-4aec-b6f7-2c3dbc359766/resource/92de7c1f-07a8-440c-8793-203fe61d3005/download/lsoa_dz_sdz_lookup.csv
byte size: 834407
media type: text/csv

Variable Dictionary: Columns
type: Distribution
format: CSV
issued: 2025-05-12T16:36:55.185087
modified: 2025-05-12T16:37:04.565155
access URL: https://data.geods.ac.uk/dataset/d257ab6a-2353-4aec-b6f7-2c3dbc359766/resource/b72857d9-ca37-4bdc-a72b-c8d850fc636f/download/variable_dictionary_ukoac21_columns_0-1.csv
byte size: 206
media type: text/csv

Data Summary
type: Distribution
format: CSV
issued: 2025-05-12T16:36:34.832096
modified: 2025-05-12T16:36:55.179971
access URL: https://data.geods.ac.uk/dataset/d257ab6a-2353-4aec-b6f7-2c3dbc359766/resource/c8b2096a-2aaf-4f63-8bf9-b70f49912e06/download/data_summary_lsoa_dz_sdz_lookup.csv
byte size: 362
media type: text/csv

Source Code: Aggregate ONS Output Area Classification (OAC) for UK LSOA/DZ/SDZ (Github repository)
type: Distribution
format: HTML
issued: 2026-02-09T13:18:26.840458
modified: 2026-02-09T13:18:26.832747
access URL: https://github.com/GeographicDataService/LSOA_DZ_SDZ_Area_Classification

Residential Mobility and Energy Performance Certificate (RMEPC) Index (LAD Geography)
type: Dataset
description: Residential Mobility and Energy Performance Certificate (RMEPC) data are compiled through the linkage of Linked Consumer Registers (LCRs), and Domestic Energy Performance Certificates (EPCs) at a property level. The data provide average and median changes of energy performance rating, potential energy performance rating, and total floor area of residential move out, into, and within each Local Authority District (LAD) level in Great Britain (GB). These metrics are aggregated for the entire period from 2009 to 2023 and further segmented into three-year intervals within this timeframe. The data allow researchers to explore changes in residential mobility at the local scale, along with changes in housing energy and space. Both the average and median changes in energy performance rating, potential energy performance rating, and total floor area of the residences of movers are provided for adult individuals moving out of, moving into (which includes both moving into and moving within), for each GB LAD. These datasets offer insights into both the overall changes in a property's EPC during migration throughout the entire time period, and provide more detailed information at three-year intervals. The data undergoes annual updates. ## Content The RMEPC is delivered as a selection of six datasets, divided into three categories, which are labelled as 'move out' (mo), 'move into' (mi), and 'move within' (wi) for each LAD in GB. For each type of mover (e.g., move out), one dataset provides statistics for the entire time period between 2009 and 2023, while the other dataset provides statistics for every three-year segment within the same period. Statistics for units with fewer than five movers are marked with an asterisk (*). Field level metadata are provided at the end of this profile document. These six datasets are aggregated results obtained from linking our Linked Consumer Registers (LCRs) and Domestic EPC data. A brief context of the linkage between LCR and EPC data is prov…
issued: 2024-11-28T16:30:29.244794
modified: 2025-08-12T13:27:21.499927
contact point: Organization
  fn: Maurizio Gibin
keyword: Energy Efficiency
keyword: Floorspace
keyword: Housing
keyword: Population
keyword: mobility
landing page: EPCs, GeoDS Linked Consumer Registers

Data Summary: RMEPC (LA) Within flows
type: Distribution
description: Across full time-series.
format: CSV
issued: 2024-11-28T16:40:59.468558
modified: 2025-05-05T23:23:42.200881
access URL: https://data.geods.ac.uk/dataset/d87719dd-a449-43cb-85fe-95b9082d5e3e/resource/102535b9-eb52-4f9f-9b51-033b79c09832/download/data_summary_rmepc_la_within.csv
byte size: 733
media type: text/csv

Data: RMEPC-out Index LAD (three years)
type: Distribution
description: The data provides the average and median changes in energy performance ratings, potential energy performance ratings, and total floor area of residential move-outs in each Local Authority District (LAD) at three-year intervals between 2009 and 2023.
format: CSV
issued: 2024-11-28T16:34:37.670141
modified: 2025-05-06T16:03:31.513968
access URL: https://data.geods.ac.uk/dataset/d87719dd-a449-43cb-85fe-95b9082d5e3e/resource/226dfa8e-435d-41fc-a7b4-a0149acb68ca/download/rmepc_laua21_mo_threeyears.csv
byte size: 78918
media type: text/csv

Data Summary: RMEPC (LA) Inflows
type: Distribution
description: Across the full time-series.
format: CSV
issued: 2024-11-28T16:39:36.039407
modified: 2025-05-05T23:23:42.200745
access URL: https://data.geods.ac.uk/dataset/d87719dd-a449-43cb-85fe-95b9082d5e3e/resource/2294583f-82f7-4745-965e-8901c50575e0/download/data_summary_rmepc_la_in.csv
byte size: 733
media type: text/csv

Data: RMEPC-within Index LAD
type: Distribution
description: The data provides average and median changes of energy performance rating, potential energy performance rating, and total floor area of residential move within each Local Authority District (LAD).
format: CSV
issued: 2024-11-28T16:32:27.925524
modified: 2025-05-06T16:03:02.725840
access URL: https://data.geods.ac.uk/dataset/d87719dd-a449-43cb-85fe-95b9082d5e3e/resource/337c9bf4-2853-4a91-aacc-e29c05fd747a/download/rmepc_laua21_wi.csv
byte size: 13655
media type: text/csv

Data: RMEPC-into Index LAD (three years)
type: Distribution
description: The data provides average and median changes in energy performance rating, potential energy performance rating, and total floor area for residential move-into each Local Authority District (LAD) at three-year intervals between 2009 and 2023.
format: CSV
issued: 2024-11-28T16:34:01.921142
modified: 2025-05-06T16:03:11.529300
access URL: https://data.geods.ac.uk/dataset/d87719dd-a449-43cb-85fe-95b9082d5e3e/resource/43adf8b9-ac6a-4e13-8271-515540348c6e/download/rmepc_laua21_mi_threeyears.csv
byte size: 78545
media type: text/csv

Map: Mapmaker
type: Distribution
format: HTML
issued: 2024-11-28T16:41:28.524271
modified: 2025-08-12T13:27:21.507967
access URL: https://mapmaker.geods.ac.uk/

Related Record: Residential Mobility and Energy Performance Certificate (RMEPC) Index (LSOA Geography)
type: Distribution
format: HTML
issued: 2025-08-12T13:27:21.521475
modified: 2025-08-12T13:27:21.508121
access URL: https://data.geods.ac.uk/dataset/residential-mobility-and-energy-performance-certificate-rmepc-index-lsoa-geography

Data: RMEPC-into Index LAD
type: Distribution
description: The data provides average and median changes of energy performance rating, potential energy performance rating, and total floor area of residential move into each Local Authority District (LAD).
format: CSV
issued: 2024-11-28T16:31:26.488100
modified: 2025-05-06T16:02:46.245479
access URL: https://data.geods.ac.uk/dataset/d87719dd-a449-43cb-85fe-95b9082d5e3e/resource/9377a278-ddb3-4dad-a281-c24b6bbf0585/download/rmepc_laua21_mi.csv
byte size: 13926
media type: text/csv

Data Summary: RMEPC (LA) Outflows
type: Distribution
description: Across the full time-series.
format: CSV
issued: 2024-11-28T16:40:12.778906
modified: 2025-05-05T23:23:42.200814
access URL: https://data.geods.ac.uk/dataset/d87719dd-a449-43cb-85fe-95b9082d5e3e/resource/95c897a0-961b-46ba-9a1a-e1624ddb090e/download/data_summary_rmepc_la_out.csv
byte size: 738
media type: text/csv

Data: RMEPC-within Index LAD (three years)
type: Distribution
description: The data provide average and median changes of energy performance rating, potential energy performance rating, and total floor area of residential move within each Local Authority District (LAD) at three-year intervals between 2009 and 2023.
format: CSV
issued: 2024-11-28T16:37:16.583017
modified: 2025-05-07T12:24:38.479089
access URL: https://data.geods.ac.uk/dataset/d87719dd-a449-43cb-85fe-95b9082d5e3e/resource/a27f0931-de59-49d9-ab53-cc7bd011fa1b/download/rmepc_laua21_wi_threeyears.csv
byte size: 96981
media type: text/csv

Technical Report: Research Ready Smart Data
type: Distribution
format: PDF
issued: 2024-11-28T16:38:12.672198
modified: 2025-05-05T23:23:42.200605
access URL: https://data.geods.ac.uk/dataset/49ec5cbe-8c22-4ae0-bd1b-7fb2e1df72cd/resource/d0d431f2-9070-42f9-93e9-c8088b5456c5/download/lcr_technical_report_2023.pdf
media type: application/pdf

Data: RMEPC-out Index LAD
type: Distribution
description: The data provides average and median changes of energy performance rating, potential energy performance rating, and total floor area of residential move out each Local Authority District (LAD).
format: CSV
issued: 2024-11-28T16:32:03.056584
modified: 2025-05-06T16:02:53.501617
access URL: https://data.geods.ac.uk/dataset/d87719dd-a449-43cb-85fe-95b9082d5e3e/resource/d5c45ed3-6191-4391-b857-17e22369c58c/download/rmepc_laua21_mo.csv
byte size: 14142
media type: text/csv

Technical Report: Linking LCR and EPC data and Validating RMEPC with Census data
type: Distribution
format: PDF
issued: 2024-11-28T16:39:06.113960
modified: 2025-05-08T15:21:32.445668
access URL: https://data.geods.ac.uk/dataset/d87719dd-a449-43cb-85fe-95b9082d5e3e/resource/fcf49083-3037-4196-acab-4a4cf43987ab/download/rmepc_la_contextualnote.pdf
byte size: 1231263
media type: application/pdf

Teaching Materials
type: Dataset
description: Files used by courses at the University of Liverpool and other institutions.
issued: 2025-07-16T09:00:25.614128
modified: 2025-11-25T11:13:49.836561
version info: 1.0
contact point: Organization
  fn: Oliver O'Brien
keyword: Tutorial
keyword: course
keyword: courses
keyword: teaching
landing page: ONS, GeoDS, UK Government

Data: Output Area Classification 2011 for Liverpool City Region (E47000004)
type: Distribution
format: ZIP
issued: 2025-07-16T09:59:27.716948
modified: 2025-07-16T09:59:39.175770
access URL: https://data.geods.ac.uk/dataset/d8b0349b-a810-4451-8dae-a657cff04549/resource/11472243-abe8-4138-baeb-dba7610321fa/download/output_area_classification_2011_e47000004.zip
byte size: 12364984
media type: application/zip

Data: Index of Multiple Deprivation (IMD) 2010/15/19 for Liverpool City Region (E47000004)
type: Distribution
format: ZIP
issued: 2025-07-16T09:50:04.827933
modified: 2025-07-16T09:50:24.090635
access URL: https://data.geods.ac.uk/dataset/d8b0349b-a810-4451-8dae-a657cff04549/resource/51bc729e-6e43-49d3-b406-b17166466c6f/download/index_of_multiple_deprivation_imd_e47000004.zip
byte size: 608078
media type: application/zip

Data: Index of Multiple Deprivation (IMD) 2010/15/19 for Liverpool (E08000012)
type: Distribution
format: ZIP
issued: 2025-07-16T09:00:59.132504
modified: 2025-07-16T10:28:41.910284
access URL: https://data.geods.ac.uk/dataset/d8b0349b-a810-4451-8dae-a657cff04549/resource/639783e3-1a7a-4c2b-b1ac-07b9ae5f6a0a/download/index_of_multiple_deprivation_imd_e08000012.zip
byte size: 179240
media type: application/zip

Data: Census Residential Data Pack 2011 for Liverpool City Region (E47000004)
type: Distribution
format: ZIP
issued: 2025-07-16T09:48:03.655030
modified: 2025-07-16T09:51:13.808359
access URL: https://data.geods.ac.uk/dataset/d8b0349b-a810-4451-8dae-a657cff04549/resource/6449cef6-89c5-4533-bc77-84d372471101/download/census_residential_data_pack_2011_e47000004.zip
byte size: 7484672
media type: application/zip

Data: Output Area Classification 2011 for Liverpool (E08000012)
type: Distribution
format: ZIP
issued: 2025-07-16T09:58:53.872846
modified: 2025-07-16T10:34:41.177991
access URL: https://data.geods.ac.uk/dataset/d8b0349b-a810-4451-8dae-a657cff04549/resource/780363ac-e9b8-468e-bd9c-b9240c04c3d2/download/output_area_classification_2011_e08000012.zip
byte size: 3515512
media type: application/zip

Data: Internet User Classification 2018 for Liverpool City Region (E47000004)
type: Distribution
format: ZIP
issued: 2025-07-16T09:57:29.151779
modified: 2025-07-16T09:57:52.971140
access URL: https://data.geods.ac.uk/dataset/d8b0349b-a810-4451-8dae-a657cff04549/resource/816a03d3-cfb5-4155-912e-c51c956bc2b3/download/internet_user_classification_e47000004.zip
byte size: 511289
media type: application/zip

Data: Internet User Classification 2018 for Liverpool (E08000012)
type: Distribution
format: ZIP
issued: 2025-07-16T09:52:17.112968
modified: 2025-07-16T10:29:10.082241
access URL: https://data.geods.ac.uk/dataset/d8b0349b-a810-4451-8dae-a657cff04549/resource/9751f6c7-f6b4-4633-a53c-b540189a8bc3/download/internet_user_classification_e08000012.zip
byte size: 126043
media type: application/zip

Data: Mid Year Population Estimates 2013-18 for Liverpool (E08000012)
type: Distribution
format: ZIP
issued: 2025-07-16T09:57:52.984424
modified: 2025-07-16T10:29:18.986625
access URL: https://data.geods.ac.uk/dataset/d8b0349b-a810-4451-8dae-a657cff04549/resource/9bb71e9c-5f82-4d34-9d56-c71a6baaac48/download/mid_year_population_estimates_e08000012.zip
byte size: 1725026
media type: application/zip

Data: Census Workplace Data Pack 2011 for Liverpool (E08000012)
type: Distribution
format: ZIP
issued: 2025-07-16T09:48:46.952345
modified: 2025-07-16T10:28:58.422063
access URL: https://data.geods.ac.uk/dataset/d8b0349b-a810-4451-8dae-a657cff04549/resource/9d839c7c-87c9-4dc4-a5c8-c64abf220f91/download/census_workplace_data_pack_2011_e08000012.zip
byte size: 1928064
media type: application/zip

Data: Census Workplace Data Pack 2011 for Liverpool City Region (E47000004)
type: Distribution
format: ZIP
issued: 2025-07-16T09:49:30.102398
modified: 2025-07-16T09:51:30.021003
access URL: https://data.geods.ac.uk/dataset/d8b0349b-a810-4451-8dae-a657cff04549/resource/adf3b921-b81a-4bdb-aae8-63017e7ee839/download/census_workplace_data_pack_2011_e47000004.zip
byte size: 6994991
media type: application/zip

Data: Access to Healthy Assets and Hazards (AHAH) v2 Data Pack for Liverpool (E08000012)
type: Distribution
format: ZIP
issued: 2025-07-16T09:01:40.159592
modified: 2025-07-16T10:28:32.511811
access URL: https://data.geods.ac.uk/dataset/d8b0349b-a810-4451-8dae-a657cff04549/resource/b27e3172-450a-4f91-b500-bed40f9d2cc9/download/access_to_healthy_assets_and_hazards_ahah_e08000012.zip
byte size: 1374847
media type: application/zip

Data: Census Residential Data Pack 2011 for Liverpool (E08000012)
type: Distribution
format: ZIP
issued: 2025-07-16T09:01:27.705463
modified: 2025-07-16T10:28:50.071720
access URL: https://data.geods.ac.uk/dataset/d8b0349b-a810-4451-8dae-a657cff04549/resource/bfc8ecd7-4063-46d5-b9b4-cd137938a865/download/census_residential_data_pack_2011_e08000012.zip
byte size: 2133997
media type: application/zip

Related Record: Geographic Data Science in Python
type: Distribution
format: HTML
issued: 2025-07-16T10:34:41.196007
modified: 2025-07-16T10:34:41.178329
access URL: https://data.geods.ac.uk/dataset/geographic-data-science-in-python

Data: Mid Year Population Estimates 2013-18 for Liverpool City Region (E47000004)
type: Distribution
format: ZIP
issued: 2025-07-16T09:58:25.635695
modified: 2025-07-16T10:00:30.494748
access URL: https://data.geods.ac.uk/dataset/d8b0349b-a810-4451-8dae-a657cff04549/resource/f5b6f767-c1c4-4912-a6f1-b5b1288646d3/download/mid_year_population_estimates_e47000004.zip
byte size: 6304520
media type: application/zip

Broadband Speed
type: Dataset
description: This data includes the average fixed-line broadband speed by output area, based on 2016-2022 data released by Ofcom. The source data file is linked below. For 2016 and 2017, GeoDS aggregated the supplied postcode data by output areas as defined by the 2011 Census (OA11CD) and the smallest areas for which extents are openly available. For 2018-2021, Ofcom now itself aggregates by OA11CD, on its Connected Nations data downloads website. For 2022, some data was aggregated by Ofcom by OA21 and some by OA11CD. We have allocated the OA21 to OA11 geographies on a best-fit basis to allow for continued comparison. ## Content Data files are available for download below. They include measures of data coverage and performance. Please also see the variable dictionaries for guidance on the column meanings, as well as data summaries for overviews of the data. These are also available for download below. ## Quality, Representation and Bias The data are compiled by a national regulator and are believed to therefore be high quality. Their process of estimating what the residential/non-residential dwelling populations should be, and matching it to actual data supplied by network operators, can lead to a loss of precision where the numbers don't align well. Note that the 2022 best-fit comparison necessarily involves a slight loss in quality due to the best-fit allocation process, which can be on a many-to-many population basis for a small number of areas which have see recent rapid population changes.
issued: 2024-12-06T08:37:35.912122
modified: 2025-06-05T08:26:42.839549
contact point: Organization
  fn: Oliver O'Brien
keyword: Broadband
keyword: Digital Inclusion
keyword: Internet
landing page: Ofcom, ONS

Variable Dictionary: Single-Year Files
type: Distribution
format: CSV
issued: 2024-12-06T08:41:10.708251
modified: 2025-05-02T14:01:57.643829
access URL: https://data.geods.ac.uk/dataset/db1eb7e7-5ae0-469d-9908-3c5d380dd334/resource/0a3314a9-a2d4-42b6-9db4-1f0b325517fb/download/variable_dictionary_broadband_singleyearfiles.csv
byte size: 5825
media type: text/csv

Data: Broadband Speed and Availability Averaged by OA for 2016 to 2022
type: Distribution
description: Combined 2016-2022 file (used for all the Mapmaker maps).
format: CSV
issued: 2024-12-06T08:40:57.391050
modified: 2025-06-05T08:26:42.844658
access URL: https://data.geods.ac.uk/dataset/db1eb7e7-5ae0-469d-9908-3c5d380dd334/resource/1759022c-884f-4eb9-9f7f-05710a0e6e86/download/bba_all22.csv
byte size: 25303485
media type: text/csv

Data: Broadband Coverage (Availability) Averaged by OA for 2018
type: Distribution
format: CSV
issued: 2024-12-06T08:40:11.454907
modified: 2025-05-02T14:01:57.643122
access URL: https://data.geods.ac.uk/dataset/db1eb7e7-5ae0-469d-9908-3c5d380dd334/resource/23c96e79-6cd0-4b62-b163-09bd2934a662/download/201809_fixed_oa11_coverage_r01.csv
byte size: 21505485
media type: text/csv

Data Summary: 2017
type: Distribution
format: CSV
issued: 2024-12-06T08:41:48.901976
modified: 2025-05-02T14:01:57.643682
access URL: https://data.geods.ac.uk/dataset/db1eb7e7-5ae0-469d-9908-3c5d380dd334/resource/25f44520-9962-49be-8703-fae09341a346/download/data_summary_broadband2017.csv
byte size: 3410
media type: text/csv

Data Summary: Combined Years
type: Distribution
format: CSV
issued: 2024-12-06T08:42:24.871906
modified: 2025-05-02T14:01:57.643465
access URL: https://data.geods.ac.uk/dataset/db1eb7e7-5ae0-469d-9908-3c5d380dd334/resource/3ef9cff2-e042-4a38-8f21-ccdf085a43ea/download/data_summary_broadbandcombined.csv
byte size: 2515
media type: text/csv

External Website: Connected Nations and infrastructure reports
type: Distribution
format: HTML
issued: 2024-12-06T09:49:58.435056
modified: 2025-05-08T15:24:17.271716
access URL: https://www.ofcom.org.uk/research-and-data/multi-sector-research/infrastructure-research

Data: Broadband Speed Averaged by OA for 2016
type: Distribution
format: CSV
issued: 2024-12-06T08:39:20.623823
modified: 2025-05-02T14:01:57.643320
access URL: https://data.geods.ac.uk/dataset/db1eb7e7-5ae0-469d-9908-3c5d380dd334/resource/87d5186e-badd-43bb-8ee4-8b15fbe25f20/download/bband16oa.csv
byte size: 82323347
media type: text/csv

Data: Broadband Performance (Speed) Averaged by OA for 2018
type: Distribution
format: CSV
issued: 2024-12-06T08:40:31.547006
modified: 2025-05-02T14:01:57.643235
access URL: https://data.geods.ac.uk/dataset/db1eb7e7-5ae0-469d-9908-3c5d380dd334/resource/8f23e5e4-13ad-417e-9f13-28626251de31/download/201805_fixed_oa11_performance_r02.csv
byte size: 25625415
media type: text/csv

External Website: Connected Nations 2021: data downloads
type: Distribution
format: HTML
issued: 2024-12-06T09:49:37.123166
modified: 2025-05-07T13:15:57.075610
access URL: https://www.ofcom.org.uk/phones-and-broadband/coverage-and-speeds/connected-nations-2021-data-downloads

Data Summary: 2016
type: Distribution
format: CSV
issued: 2024-12-06T08:41:38.622738
modified: 2025-05-02T14:01:57.643752
access URL: https://data.geods.ac.uk/dataset/db1eb7e7-5ae0-469d-9908-3c5d380dd334/resource/bc510a5e-1206-4ce7-90b8-3abc58860683/download/data_summary_broadband2016.csv
byte size: 3489
media type: text/csv

Data Summary: 2018 Speed
type: Distribution
format: CSV
issued: 2024-12-06T08:42:12.896286
modified: 2025-05-02T14:01:57.643612
access URL: https://data.geods.ac.uk/dataset/db1eb7e7-5ae0-469d-9908-3c5d380dd334/resource/bcd34f64-9e80-4457-9246-1a92083ce1d8/download/data_summary_broadband2018speed.csv
byte size: 3996
media type: text/csv

Map: Mapmaker
type: Distribution
format: HTML
issued: 2025-06-05T08:26:42.858774
modified: 2025-06-05T08:26:42.845726
access URL: https://mapmaker.geods.ac.uk/#/broadband-speed

Data Summary: 2018 Availability
type: Distribution
format: CSV
issued: 2024-12-06T08:42:00.034755
modified: 2025-05-02T14:01:57.643536
access URL: https://data.geods.ac.uk/dataset/db1eb7e7-5ae0-469d-9908-3c5d380dd334/resource/cfb39bb3-9687-4cb8-b43e-0587a88b4b0a/download/data_summary_broadband2018availability.csv
byte size: 2982
media type: text/csv

Data: Broadband Speed Averaged by OA for 2017
type: Distribution
format: CSV
issued: 2024-12-06T08:39:52.262468
modified: 2025-05-02T14:01:57.643394
access URL: https://data.geods.ac.uk/dataset/db1eb7e7-5ae0-469d-9908-3c5d380dd334/resource/d91053d8-088f-4951-9274-dfaf1d976069/download/bband17oa.csv
byte size: 53412589
media type: text/csv

Variable Dictionary: Multi-Year File
type: Distribution
format: CSV
issued: 2024-12-06T08:41:22.786628
modified: 2025-05-02T14:01:57.643899
access URL: https://data.geods.ac.uk/dataset/db1eb7e7-5ae0-469d-9908-3c5d380dd334/resource/ec8a6950-8a1b-4eda-bac7-d77387702adf/download/variable_dictionary_broadband_multiyear.csv
byte size: 2214
media type: text/csv

British Population Survey (LSOA Geography)
type: Dataset
description: The British Population Survey (BPS) was a regularly run survey with monthly face-to-face historical data from 2008 to 2015. It aimed to capture the socio-economic and consumer characteristics of the population of Great Britain. Data includes 6000-8000 records a month covering demographics, economics, shopping preferences, durables, media, and internet use. The BPS is conducted in the homes of all respondents. Interviews are conducted via Computer Assisted Personal Interview (CAPI). ## Content The following variables are present in the data: * Family: Gender, Age Group, Numeric age, Lifestage, Ethnic Origin, Marital Status, Parent of children, Parental Status, Child Maintenance, Number in household, Presence of children in household, No. of children in household, Age of children in household * Geography: Country, Standard Region 4, Standard Region 11, Urban/Rural, Postcode Area (City), Lower Level Super Output Area * Economics: Social Grade, Qualification level, Working status of respondent, household income, chief income earner (CIE), working status of CIE, Home tenure, main shopper, main supermarket, debit card/s, credit card/s. * Media: daily newspaper, Sunday newspaper, ITV station most watched. Durables: no. of cars in household, TV, Satellite TV, Cable TV, Freeview, Freesat, Landline telephone, simple mobile phone, web mobile phone, video, DVD recorder, DVD player, personal computer, laptop PC, tablet PC, games console, MP3, DAB radio, DIG camera (ex phone). * Internet access: internet access – frequency, internet access – method, cable broadband, ADSL broadband, other broadband, non broadband, internet access – history. Internet use: emails, info-requests, info-products, purchases – not groceries, grocery shopping, bank a/c & finances, job search, play games online, online gaming for money, download music, download movies, download/stream TV, online dating, VOIP, social networks/blogs, other. * Date: Year and month. * Survey: ID, Weight. Note that the 2015 BPS…
issued: 2024-12-06T08:31:57.722667
modified: 2026-01-15T14:31:19.059707
contact point: Organization
  fn: Dr Maurizio Gibin
keyword: Durables
keyword: Internet Use
keyword: Marketing
keyword: Media
keyword: Population
keyword: Survey
landing page: DataTalk Ltd

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-12-06T08:32:31.998797
modified: 2025-05-05T23:27:50.360033
access URL: https://data.geods.ac.uk/dataset/dfa50987-5f2e-4f3f-8770-0bae933db7f1/resource/10667f93-3e2a-4847-8923-aef0f0cff175/download/bps_safeguarded_variable_dictionary.csv
byte size: 28175
media type: text/csv

Related Record: British Population Survey
type: Distribution
format: HTML
issued: 2024-12-06T10:56:33.672434
modified: 2025-05-08T15:24:16.056954
access URL: https://data.geods.ac.uk/dataset/british-population-survey

Data Summary
type: Distribution
format: CSV
issued: 2024-12-06T08:32:18.443835
modified: 2025-05-05T23:27:50.359905
access URL: https://data.geods.ac.uk/dataset/dfa50987-5f2e-4f3f-8770-0bae933db7f1/resource/45ceb40b-ee4d-4ae2-8c6f-37b983d0c40c/download/bps_data_summary.csv
byte size: 415
media type: text/csv

Residential Property Counts
type: Dataset
description: Residential Property Counts data provide yearly small area estimates (1997-2022) of geolocated 'active' residential properties in the UK. Acquiring historical lifecycle information about individual properties in the UK poses challenges, as most data providers primarily focus on monitoring 'active' properties for facilitating mail and package deliveries around the country. ## Content The Residential Property Counts are created by tracing the names and addresses of more than one billion individuals dating back to 1997 (LCRs: Linked Consumer Registers) to calibrate property lifecycle information within an authoritative geolocated address and property dataset. The Residential Property Counts data allow researchers to take a temporal perspective (limited to the years pertaining to 1997-2022) on, for instance, the geography and development of the residential housing stock in the UK. Please note that for access to these data, evidence of a research licence for the Ordnance Survey’s Address Base Premium product is required when applying to access this Secure dataset. No such restrictions apply to the Safeguarded version of these data which have been aggregated to LSOA geography with small counts masked. Please see the separate data record (linked below) for the Safeguarded version. For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. ## Quality, Representation and Bias As the data are compiled through the integration of data from various organisations, data products, and providers, it is unlikely to contain 100% of all individual residential properties in the specified time period. The underpinning data consist of addresses contained within the Linked Consumer Registers (LCRs), with their provenance outlined in two papers (see below). Consumer and administrative data were acquired directly or ind…
issued: 2024-11-28T14:14:22.431276
modified: 2025-05-08T15:21:43.075604
version info: 1.0
contact point: Organization
  fn: Justin van Dijk
keyword: Churn
keyword: Mobility
keyword: Property
keyword: Residential
landing page: GeoDS Linked Consumer Registers

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T14:17:18.870158
modified: 2025-05-05T23:15:00.581393
access URL: https://data.geods.ac.uk/dataset/dfb078e6-969c-4c18-9826-3965b6700e84/resource/049b2d36-839a-435b-a8fb-f941a798fda4/download/variable_dictionary_rpc_secure.csv
byte size: 689
media type: text/csv

Related Record: Residential Property Counts (LSOA Geography)
type: Distribution
format: HTML
issued: 2025-05-06T15:57:23.244025
modified: 2025-05-08T15:21:43.080107
access URL: https://data.geods.ac.uk/dataset/residential-property-counts-lsoa

Data Summary
type: Distribution
format: CSV
issued: 2024-11-28T14:17:49.152410
modified: 2025-05-05T23:15:00.581770
access URL: https://data.geods.ac.uk/dataset/dfb078e6-969c-4c18-9826-3965b6700e84/resource/c5908b10-6142-45ea-9872-5bdf634fd7f0/download/data_summary_rpc_secure.csv
byte size: 433
media type: text/csv

https://data.geods.ac.uk/dataset/dfb078e6-969c-4c18-9826-3965b6700e84/resource/e51c5b87-bd17-4e87-9f9a-6adb51f6ea7d
type: Distribution
format: HTML
issued: 2024-11-28T14:56:49.311239
modified: 2025-05-05T23:15:00.582104
title: Paper: Lansley G, Li W, Longley P A 2019. Creating a linked consumer register for granular demographic analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society) DOI:10.1111/rssa.12476
access URL: https://discovery.ucl.ac.uk/id/eprint/10078650/

https://data.geods.ac.uk/dataset/dfb078e6-969c-4c18-9826-3965b6700e84/resource/e8e58fc5-9eb0-4dfe-95ab-e222cbd6a8f5
type: Distribution
format: HTML
issued: 2024-11-28T14:57:28.865164
modified: 2025-05-05T23:15:00.582263
title: Paper: Van Dijk J, Lansley G, Longley P A 2021. Using linked consumer registers to estimate residential moves in the United Kingdom. Journal of the Royal Statistical Society Series A (Statistics in Society). DOI:10.1111/rssa.12713
access URL: https://discovery.ucl.ac.uk/id/eprint/10128043/

https://data.geods.ac.uk/dataset/dfb078e6-969c-4c18-9826-3965b6700e84/resource/fa51f9d7-8d42-40d9-a6f5-ab9cd57f2ee1
type: Distribution
format: HTML
issued: 2024-11-28T14:56:14.307523
modified: 2025-05-06T15:57:23.229032
title: Paper: Van Dijk, J., Todd, J. and Lan, T. (2024) ‘Leveraging digital footprints data for accurate estimation of the residential housing stock in the United Kingdom, 1997–2022’, Annals of GIS, pp. 1–16.
access URL: https://doi.org/10.1080/19475683.2024.2360206

PDV Consumer Lifestyle Surveys
type: Dataset
description: The PDV Consumer Lifestyle Surveys contain two consumer datasets managed and supplied by PDV Ltd, called DLG and MyOffers (MO). Their principal use is as consumer lifestyle data products for data-driven marketing campaigns in the UK. GeoDS holds a copy of these products. They are made available as anonymised and spatially generalised records within our secure infrastructure only for bona fide research purposes. These surveys consider a wide spectrum of consumer topics alongside a variety of socio-economic characteristics. ## Content The combined datasets comprise of over 10 million records, each with hundreds of variables (some are blank for many respondents). Data from 2001-2023 are available. Most records were created or updated between 2008 and 2021. The data has been spatially generalised to Lower Layer Super Output Areas (LSOAs). For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. ## Quality, Representation and Bias The DLG dataset has a higher completion rate than the MO dataset, for most variables. Both PDV datasets under-represent the younger population due to the nature of the data collection method. The DLG dataset has a significant over-representation of the population above 35 years old, and the MO dataset shows similar over-representation of the population between 30 and 60. The DLG dataset shows under-representation of the never-married population and one person households and an over-representation towards households with have more than 3 persons. The MO dataset highly over-represents females and the never married/single population.
issued: 2024-11-29T18:03:13.292418
modified: 2026-03-26T15:48:43.243829
contact point: Organization
  fn: Maurizio Gibin
keyword: survey
landing page: PDV Ltd

Data Summary: DLG (Partial)
type: Distribution
description: This a data summary for a sample of the rows in the DLG part of the dataset. A full data summary is supplied with the data.
format: CSV
issued: 2024-11-29T18:12:12.601258
modified: 2025-05-05T23:15:22.996573
access URL: https://data.geods.ac.uk/dataset/dfb72a02-1715-4b9f-ad89-1711f453e818/resource/1bed5332-4f5a-4874-95d7-b88c68c4eac3/download/data_summary_pdv_dlg_partial.csv
byte size: 1082
media type: text/csv

Technical Report: PDV Data Analysis
type: Distribution
format: PDF
issued: 2024-11-29T18:13:28.147715
modified: 2025-05-05T23:15:22.996837
access URL: https://data.geods.ac.uk/dataset/dfb72a02-1715-4b9f-ad89-1711f453e818/resource/23d85821-d38e-4ccb-a321-c32e97b58741/download/pdv_data_analysis_report.pdf
byte size: 3346112
media type: application/pdf

Flyer: PDV Consumer Lifestyle Surveys
type: Distribution
format: PDF
issued: 2026-02-02T14:29:04.800552
modified: 2026-03-26T15:48:43.246977
access URL: https://data.geods.ac.uk/dataset/dfb72a02-1715-4b9f-ad89-1711f453e818/resource/6e28e8a2-e5f8-4251-985c-421a578e5bdc/download/pdv-2026-web.pdf
byte size: 1792129
media type: application/pdf

Variable Dictionary: DLG (grouped)
type: Distribution
description: This is the the variable dictionary for the DLG part of the dataset. The MO part of the dataset is similar. Columns with Yes/No responses to various types within a category have been collapsed to a single row in the dictionary, these are shown with square brackets - [ ].
format: CSV
issued: 2024-11-29T18:12:56.168830
modified: 2025-06-13T14:37:41.972678
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/dfb72a02-1715-4b9f-ad89-1711f453e818/resource/ce69a493-4041-4ad5-bfa7-bef43ed6ddaf/download/variable_dictionary_pdv_dlg_grouped.csv
byte size: 5652
media type: text/csv

Data Summary: MO (Partial)
type: Distribution
description: This a data summary for a sample of the rows in the MO part of the dataset. A full data summary is supplied with the data.
format: CSV
issued: 2024-11-29T18:12:35.263173
modified: 2025-05-05T23:15:22.996680
access URL: https://data.geods.ac.uk/dataset/dfb72a02-1715-4b9f-ad89-1711f453e818/resource/fc6a6ba7-a103-431d-a51f-a63de1c554a3/download/data_summary_pdv_mo_partial.csv
byte size: 737
media type: text/csv

MaPS MoneyView Survey
type: Dataset
description: The MaPS MoneyView survey is a nationally representative survey of over 12,000 adults living in the UK. It offers a unique insight into the population’s financial situation and their current feelings towards their finances. The survey was commissioned by Money and Pensions Service (MaPS). The survey features a core section asked every year, modules that change each year, and demographics. * Core section: Financial inclusion, wellbeing and confidence, Need for debt advice, Bills and credit, Savings and financial resilience, Pensions and retirement, Life satisfaction. * Modules (2024 fieldwork): Life events – usage of money information, guidance and advice, Retirement planning, Planning for Later Life (66+), Material deprivation, Financial inclusion. * Demographics and classification questions: Income, Demographics (e.g. ethnicity, religion, disability, education), Location (LSOA/equivalent for each UK nation). It is an amalgamation of two surveys previously run by Money and Pensions Service (MaPS) which are retained on GeoDS Data and can also be applied for via their own record pages (see listing at bottom of this page): the Adult Financial Wellbeing Survey and the Debt Need Survey. Key questions from these surveys appear in MoneyView. More detail on the survey is available on the MoneyView section of the MaPS website. Reports based on 2024’s interviews were first published in 2025. As a result, they are referred to “MoneyView 2025”. ##Content GeoDS holds the anonymised individual records containing the responses from each survey participant. Access to this data is available through the GeoDS Data service. The survey answers are available as two record-level CSVs (one with codes and one with labels). There is also a code to a label lookup file, a questionnaire copy and a technical report. The record-level is also available in SPSS format (a .sav file). ##Quality, Representation and Bias The survey is a high quality survey organised by a professional customer surveyin…
issued: 2025-09-17T12:23:05.923430
modified: 2026-01-15T14:11:09.361349
version info: 1.0
contact point: Organization
  fn: Oliver O'Brien
keyword: Debt
keyword: Financial
keyword: Financial Wellness
keyword: Microdata
keyword: Survey
keyword: Wellness
landing page: Money and Pensions Service

External Website: MaPS MoneyView 2025 - Technical Report
type: Distribution
format: HTML
issued: 2025-11-05T16:23:27.754117
modified: 2025-11-12T21:52:49.585158
access URL: https://maps.org.uk/en/publications/research/2025/moneyview-2025-technical-report

External Website: MaPS MoneyView 2025
type: Distribution
format: HTML
issued: 2025-09-17T13:06:40.530941
modified: 2025-11-12T21:52:49.585086
access URL: https://maps.org.uk/en/publications/moneyview

Related Record: MaPS Debt Need Survey
type: Distribution
format: HTML
issued: 2025-09-17T12:24:20.873114
modified: 2025-09-17T12:24:21.176296
access URL: https://data.geods.ac.uk/dataset/maps-debt-need-survey

Related Record: MaPS Financial Wellbeing Survey
type: Distribution
format: HTML
issued: 2025-09-17T12:23:34.425801
modified: 2025-09-17T12:24:20.859783
access URL: https://data.geods.ac.uk/dataset/maps-financial-wellbeing-survey

Geolytix aggregated in-app location dataset
type: Dataset
description: The Geolytix Aggregated App location data have been collected and supplied by Geolytix Ltd. for the time period of August 2021 to July 2022. The data contain hourly aggregated activity counts derived from geolocated mobile-phone app data across Great Britain. Mobile phone applications seek user’s consent for recording and storing the mobile device’s GPS location when the app is in use. Footfall and activity proxy counts are derived from these locations as the sum of distinct devices per H3 resolution 11 hexagon grid cell per day. Each hexagon grid cell has an average edge length of 280 meters . Footfall activity count data can provide research value as an estimate of activity levels within local areas, particularly in heavily used urban spaces. They may be linked to workplace zones or geodemographic classifications, for example, to better understand the functioning of local areas. Activity counts not reliant on fixed footfall sensors are valuable in ascertaining activity levels in retail and other locations. ## Content For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page. ## Quality, Representation and Bias These data have excellent quality and coverage for major towns and cities. The data may be less complete for smaller settlements or more rural areas. Geolytix’s upstream data providers collect data from a varying mix of apps, the identities of which are commercially sensitive and not revealed . Apps supplying data may not be consistent over time. This, along with fluctuations in national coverage and changing mobile phone app uptake, results in variations in apparent activity over the period covered by the data. When utilising these data, they would benefit from triangulation with population estimates (e.g. census data) to investigate coverage issues.
issued: 2024-11-28T19:20:01.446269
modified: 2025-05-30T15:41:14.250930
contact point: Organization
  fn: Dr Maurizio Gibin
keyword: Activities
keyword: Consumer Counts
keyword: Devices
keyword: Footfall
keyword: High Street
keyword: Mobile phone data
keyword: Mobility
landing page: Geolytix

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-11-28T19:22:36.999874
modified: 2025-05-05T23:12:52.527477
access URL: https://data.geods.ac.uk/dataset/e39073bb-918a-479a-a36e-524a8b363fd4/resource/01886d07-da2d-40a1-bdab-554ad81798c2/download/geolytix_secure_variable_dictionary.csv
byte size: 186
media type: text/csv

Paper: Ballantyne, P., Singleton, A. & Dolega, L. Using unstable data from mobile phone applications to examine recent trajectories of retail centre recovery. Urban Info 1, 21 (2022).
type: Distribution
format: HTML
issued: 2024-11-28T19:25:21.848875
modified: 2025-05-05T23:12:52.527553
access URL: https://doi.org/10.1007/s44212-022-00022-0

External Website: H3 Geography
type: Distribution
format: HTML
issued: 2024-11-28T19:25:58.809805
modified: 2025-05-07T13:31:36.906831
access URL: https://h3geo.org/

Data Summary
type: Distribution
format: CSV
issued: 2024-11-28T19:20:30.514555
modified: 2025-05-05T23:12:52.527358
access URL: https://data.geods.ac.uk/dataset/e39073bb-918a-479a-a36e-524a8b363fd4/resource/f5239278-7809-4772-bc4e-320b442d64b9/download/geolytix_secure_data_summary.csv
byte size: 4113
media type: text/csv

BT Historical Subscribers Lists
type: Dataset
description: This dataset provides a historical record of telephone subscribers for three cities in Great Britain, digitised from printed British telephone directories published between 1881 and 1951 at decadal intervals. It includes individual- and household-level entries capturing name, address, exchange, and number. The source directories were digitised using OCR, followed by rule-based cleaning and structuring to extract tabular data. ## Content The dataset is available in CSV format with each record corresponding to an individual subscriber and column reporting, the forename initial, the surname, address, number and the geographical coordinates of the address. Commercial subscribers are also included. Accompanying documentation explains the structure and scope of the data, offers a glossary of terms, and presents classification codes, metadata, and statistical summaries that support interpretation and reuse. ## Quality, Representation and Bias Data quality varies by year and region due to differences in printing conventions, OCR performance, and changes in publication standards over time. Early years are often sparser and more urban-focused, with rural coverage improving post-World War I. OCR errors and inconsistencies in address formatting present challenges, particularly for automated geocoding. The methodology includes procedures to minimise such errors, though residual issues may affect a small proportion of records. As with all historical sources, certain demographic groups (e.g. women, minorities, renters) may be underrepresented.
issued: 2025-05-21T12:34:23.996583
modified: 2025-10-07T11:31:10.865755
contact point: Organization
  fn: Maurizio Gibin
keyword: BT Archives
keyword: Historical Geography
keyword: Phone directories
keyword: Population
landing page: BT, GeoDS

Variable Dictionary
type: Distribution
format: CSV
issued: 2025-05-21T12:35:24.252448
modified: 2025-10-06T17:02:42.378039
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/e437d1c7-70d2-4048-aad5-a7155c5acbe6/resource/708ac98d-3065-4a05-95e9-aaad60fb8677/download/bt_secure_variable_dictionary.csv
byte size: 1809
media type: text/csv

Paper: Tanu, N., Gibin, M., Hu, D., & Longley, P. A. (2024). A century of telephony: digital capture of British telephone directories, 1880–1984. Annals of GIS.
type: Distribution
format: HTML
issued: 2025-05-21T12:38:56.811227
modified: 2025-05-23T10:50:17.527247
access URL: https://discovery.ucl.ac.uk/id/eprint/10190133/1/Longley_A%20century%20of%20telephony%20%20digital%20capture%20of%20British%20telephone%20directories%20%201880-1984.pdf
media type: application/pdf

Data Summary
type: Distribution
format: CSV
issued: 2025-05-21T12:34:52.180703
modified: 2025-10-06T17:02:18.058445
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/e437d1c7-70d2-4048-aad5-a7155c5acbe6/resource/ed7400ba-0840-4ee7-ae5c-b8709b87b044/download/bt_secure_data_summary.csv
byte size: 8951
media type: text/csv

London Nighttime Access to Food Retail Options
type: Dataset
description: The London Nighttime Access to Food Retail Options dataset was developed as part of the _Data After Dark_ research project. The study aimed, first, to map access to food retail options during nighttime hours and, second, to integrate this information with the spatio-temporal distribution of night workers in order to identify underserved areas at a granular scale. This release provides the accessibility (i.e., supply) layer, which integrates open-source public transport schedule data with information on the locations and opening hours of food retail outlets supplied by Green Street (formerly LDC), which are also available through GeoDS. The dataset can be further utilised in academic research and policy analysis to assess access to essential services during nighttime hours in London. ## Content The dataset is provided in GeoPackage (GPKG) format and reports travel times (in minutes) to the nearest open food retail outlet across Greater London, measured on a granular hexagonal grid (350-metre edge-to-edge). Accessibility is measured over a continuous 24-hour period, from 6:00 a.m. Friday to 6:00 a.m. Saturday (March 2024). Daytime accessibility (6:00 a.m.–6:00 p.m.), included for comparative purposes, is reported in 3-hour intervals, while nighttime accessibility (6:00 p.m.–6:00 a.m. the following day) is reported at hourly intervals. The data are provided using a bespoke hexagonal grid created by BT, since the distribution of nighttime workers was released at that spatial level. The first methodological step involved computing travel-time matrices between the centroids of all hexagons for each departure time using _r5r_, an R package for rapid realistic routing on multimodal transport networks. The routing incorporates GTFS and street network data, drawing on two GTFS feeds: Transport for London services accessed via the Bus Open Data Service and rail services from the National Rail Data Portal. Door-to-door travel times account for walking, waiting, and transfer tim…
issued: 2026-01-28T09:39:27.355148
modified: 2026-04-15T13:17:29.869542
contact point: Organization
  fn: Iliev, Michał
keyword: Data After Dark
keyword: London
keyword: food accessibility
keyword: night workers
keyword: night-time economy
landing page: Green Street, Department for Transport, National Rail

Paper: Iliev, M., Cheshire, J., & Law, S. (2026). Revealing the geography of food (in)accessibility for nighttime workers in the Greater London Area. Urban Studies, 0(0).
type: Distribution
format: HTML
issued: 2026-03-10T15:55:51.421132
modified: 2026-03-10T16:12:25.651624
access URL: https://journals.sagepub.com/doi/full/10.1177/00420980261422967

Data: London Nighttime Access to Food Retail Options (CSV Format)
type: Distribution
format: CSV
issued: 2026-02-03T16:20:15.819741
modified: 2026-03-10T16:12:51.472723
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/e65b4e9f-2e35-4589-8be0-897884466aca/resource/21bc1b97-7c10-4cf9-a511-22fb8f6be52b/download/food_travel_time_allhex.csv
byte size: 746488
media type: text/csv

Data Summary
type: Distribution
format: CSV
issued: 2026-03-10T15:00:21.326207
modified: 2026-03-10T16:12:27.489246
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/e65b4e9f-2e35-4589-8be0-897884466aca/resource/2da2c67b-ec1f-4424-b7ff-9d153518d01e/download/hex_food_data_summary.csv
byte size: 1872
media type: text/csv

Map: London Nighttime Access to Food Retail map dashboard
type: Distribution
format: HTML
issued: 2026-03-16T11:22:22.238547
modified: 2026-03-16T11:24:37.746892
access URL: https://dataafterdark.org/data/london-nighttime-access-to-food-retail-map-dashboard.html
media type: text/html

External Website: Locating London's night-time food deserts (data story)
type: Distribution
format: HTML
issued: 2026-03-16T11:24:37.754403
modified: 2026-03-16T11:24:56.753776
access URL: https://dataafterdark.org/stories/locating-londons-night-time-food-deserts.html
media type: text/html

External Website: Data After Dark
type: Distribution
format: HTML
issued: 2026-01-28T10:00:51.782954
modified: 2026-01-28T10:01:22.818877
access URL: https://dataafterdark.org/

Data: London Nighttime Access to Food Retail Options (Geopackage Format)
type: Distribution
format: Geopackage
issued: 2026-02-03T16:23:58.653640
modified: 2026-03-10T16:12:50.188365
access URL: https://data.geods.ac.uk/dataset/e65b4e9f-2e35-4589-8be0-897884466aca/resource/925bc6f8-1773-46c5-b429-bbe3331aeebc/download/hex_foodretail_accessibility.gpkg
byte size: 4308992
media type: application/geopackage+sqlite3

Related Record: London Night Workers Classification (LNWC)
type: Distribution
format: HTML
issued: 2026-01-28T09:59:30.510916
modified: 2026-01-28T10:00:51.776737
access URL: https://data.geods.ac.uk/dataset/london-night-workers-classification-lnwc

Related Record: Retail Type, Vacancy and Address Data
type: Distribution
format: HTML
issued: 2026-03-06T17:11:41.709959
modified: 2026-04-15T13:12:27.940773
access URL: https://data.geods.ac.uk/dataset/local-data-company-retail-type-vacancy-and-address-data

Variable Dictionary
type: Distribution
format: CSV
issued: 2026-03-10T16:13:06.189512
modified: 2026-03-10T16:13:07.423760
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/e65b4e9f-2e35-4589-8be0-897884466aca/resource/f68b3539-fd05-44ea-922b-349e52355a3b/download/hex_food_variable_dictionary.csv
byte size: 202
media type: text/csv

SmartStreetSensor Footfall Data
type: Dataset
description: An aggregated level footfall sensor data is a derived product from raw footfall data, producing five-minute footfall counts. The raw data was passive WiFi signal probing from a sensor network across Great Britain between 2015 and 2020. The data are used as a proxy for estimating footfall at retail locations. The dataset includes details about the location of the sensors (description as well as latitude, longitude, height, depth, installation dates) and cleaned five-minute interval footfall estimates which include timestamps, locations, adjusted and unadjusted footfall counts. A complete description of this dataset can be found below in the Data and Resources section. ## Content The dataset includes information from 1151 sensor locations across 107 cities in Great Britain, identified by addresses including building numbers, street names, and unit postcodes. Data spans from July 2015 to September 2020, aggregated into five-minute intervals. * Rows: Over 20 million records are across approximately 67 monthly files. * Columns: The dataset comprises 22 variables distributed across 71 files. ## Quality, Representation and Bias The quality and representation of the SmartStreetSensor Footfall dataset are influenced by the methodologies employed and the inherent biases associated with its collection process: 1. Sensor Range: The signal strength and sensor range are variable, influenced by environmental conditions and technical specifications. This variability introduces inconsistencies in coverage. 2. Probing Frequency: Devices probe for Wi-Fi signals at differing frequencies based on manufacturer, operating system, and usage state, affecting the detection consistency. 3. MAC Address Collisions: A minor percentage (0.01%) of MAC addresses are reported by multiple devices due to MAC randomization techniques, adding complexity to data cleaning. 4. Human Error: Sensor power disconnections and operational disruptions result in occasional data gaps. 5. Postprocessing Assumptions:…
issued: 2024-12-17T11:27:11.973810
modified: 2026-04-15T13:06:01.844440
contact point: Organization
  fn: Maurizio Gibin
keyword: Footfall
keyword: Location
keyword: Mobile
keyword: Network
keyword: Sensor
keyword: Wifi
landing page: Green Street (formerly LDC)

Data Summary: Location Metadata
type: Distribution
format: CSV
issued: 2025-07-30T16:28:53.649844
modified: 2025-07-30T16:28:55.200765
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/ea1a60ee-f501-47d6-98ea-63e0f55eba87/resource/7542ffd0-6232-4100-8da3-028422cf4375/download/data_summary_sss_secure_locations.csv
byte size: 1252
media type: text/csv

Related Record: Ultra Wide Band Footfall Data
type: Distribution
format: HTML
issued: 2024-12-17T11:34:14.579244
modified: 2026-04-15T13:06:01.849659
access URL: https://data.geods.ac.uk/dataset/local-data-company-ultra-wide-band-footfall-data

Related Record: HUQ aggregated in-app location dataset
type: Distribution
format: HTML
issued: 2024-12-17T11:33:47.963532
modified: 2025-05-07T09:40:38.788953
access URL: https://data.geods.ac.uk/dataset/huq-aggregated-in-app-location-dataset

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-12-17T11:32:55.214804
modified: 2025-03-17T13:39:59.168160
access URL: https://data.geods.ac.uk/dataset/ea1a60ee-f501-47d6-98ea-63e0f55eba87/resource/a62ebb3c-1ef5-4881-891d-4e2380bae432/download/sss_footfall_research_agggregated_variable_dictionary.csv
byte size: 1488
media type: text/csv

Contextual Note: Production of cleaned footfall estimates for safeguarded dataset
type: Distribution
format: TXT
issued: 2025-05-07T09:35:39.650542
modified: 2025-05-07T09:35:51.589298
access URL: https://data.geods.ac.uk/dataset/ea1a60ee-f501-47d6-98ea-63e0f55eba87/resource/b5295f81-2f6b-4e25-924c-4ecc36d9823a/download/contextualnote.txt
byte size: 2215
media type: text/plain

Related Record: Geolytix aggregated in-app location dataset
type: Distribution
format: HTML
issued: 2024-12-17T11:34:06.188550
modified: 2025-05-07T09:40:47.309126
access URL: https://data.geods.ac.uk/dataset/geolytix-aggregated-in-app-location-dataset

Data Summary: Observations
type: Distribution
format: CSV
issued: 2024-12-17T11:32:46.277286
modified: 2025-07-30T16:28:53.635038
access URL: https://data.geods.ac.uk/dataset/ea1a60ee-f501-47d6-98ea-63e0f55eba87/resource/cb02fd77-b339-4618-83a1-9ba1a92583f2/download/sss_footfall_research_agggregated_data_summary.csv
byte size: 448
media type: text/csv

Data Summary: Location Counts by Urban Area
type: Distribution
description: A list of towns/cities and the number of sensors in each that included in the SmartStreetSensor data.
format: CSV
issued: 2025-07-30T16:24:53.089595
modified: 2025-07-30T16:24:55.107400
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/ea1a60ee-f501-47d6-98ea-63e0f55eba87/resource/fd15ba72-93b5-4a92-a42a-7c9842e8b3f2/download/data_summary_sss_secure_locationlist.csv
byte size: 1413
media type: text/csv

Access to Healthy Assets & Hazards (AHAH) (Previous Versions)
type: Dataset
description: This page contains older versions of the AHAH data product. The most recent version can be viewed at a link below. AHAH (the index of ‘Access to Health Assets and Hazards’) is a multi-dimensional index developed by GeoDS for Great Britain measuring how ‘healthy’ neighbourhoods are. The AHAH index combines indicators under four different domains of accessibility: * Retail environment (access to fast food outlets, pubs, tobacconists, gambling outlets), * Health services (access to GPs, hospitals, pharmacies, dentists, leisure services), * Physical environment (Blue Space, Green Space - Passive, Green space - active), and * Air quality (Nitrogen Dioxide, Particulate Matter 10, Sulphur Dioxide) (domain added ## Content Measurement data and deciles for the overall index, 3 or 4 domains and 14 or 15 inputs are produced for Lower Level Super Output Areas (LSOAs) for England and Wales, and Data Zones (DZ) for Scotland. Column headings have been standardised across all versions. As a result there is some variation between the column headings defined in the source code and in the datasets. Please consult the relevant Variable Dictionary for full details. ## Quality, Representation and Bias The data are from multiple data sources. Each were selected following review since they presented the best quality or low bias data sources. We checked individual data sources for bias using local checks, however we did not find any issues. All external datasets are validated by their data producer as well. ## Version History Version 5 (on a different record) was updated including the most up-to-date data (released ealy 2026). AHAH Version 4 (released 2024), Version 3 (released 2022), Version 2 (released 2017) and Version 1 (released 2016) are here.
issued: 2024-12-05T15:24:16.010574
modified: 2026-02-04T16:57:13.723130
version info: 1.0 to 4.0
contact point: Organization
  fn: Alex Singleton
keyword: Hazard
keyword: Health
keyword: Healthy
landing page: LDC, OSM, NHS

Technical Report: Data Sources for AHAH v4
type: Distribution
format: PDF
issued: 2026-02-04T16:56:27.577564
modified: 2026-02-04T16:56:52.490740
access URL: https://data.geods.ac.uk/dataset/ee10823e-4f07-433c-9e1c-c46dd5ffed0d/resource/1b16e1b3-c9b7-4f7c-81f7-0bcb87273466/download/ahah_datasources.pdf
byte size: 109199
media type: application/pdf

Data: AHAH v3 Index, Domains and Components
type: Distribution
format: CSV
issued: 2024-12-05T15:27:37.968045
modified: 2025-05-02T13:19:50.111250
access URL: https://data.geods.ac.uk/dataset/ee10823e-4f07-433c-9e1c-c46dd5ffed0d/resource/29db81aa-6e3e-44e0-bdc2-c19415e16e37/download/ahah_v3_fulldata.csv
byte size: 15812346
media type: text/csv

Data Summary: AHAH v4
type: Distribution
format: CSV
issued: 2026-02-04T14:20:13.388538
modified: 2026-02-04T14:20:14.528472
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/ee10823e-4f07-433c-9e1c-c46dd5ffed0d/resource/313f45d4-f9b6-4bea-b5c9-1f8d101e8aac/download/summary_ahah_v4.csv
byte size: 5780
media type: text/csv

Data: AHAH v1 Index, Domains and Components
type: Distribution
format: CSV
issued: 2024-12-05T15:27:15.767187
modified: 2025-05-02T13:19:50.111449
access URL: https://data.geods.ac.uk/dataset/ee10823e-4f07-433c-9e1c-c46dd5ffed0d/resource/4c2603ac-79ff-44b5-a578-7128f762bdf1/download/ahah_v1_fulldata.csv
byte size: 10551174
media type: text/csv

Data: AHAH v4 Index, Domains and Components
type: Distribution
format: CSV
issued: 2026-02-04T14:19:19.061954
modified: 2026-02-04T14:19:22.304721
checksum: Checksum
access URL: https://data.geods.ac.uk/dataset/ee10823e-4f07-433c-9e1c-c46dd5ffed0d/resource/84cd15aa-6b70-4192-9109-b5b963932660/download/ahah_v4.csv
byte size: 13318377
media type: text/csv

Source Code: AHAH (GitHub Repository)
type: Distribution
description: Note that this repository contains code starting from Version 3 (See 'releases').
format: HTML
issued: 2024-12-06T10:51:46.337633
modified: 2025-05-02T13:19:50.111815
access URL: https://github.com/ESRC-CDRC/ahah/

Variable Dictionary: AHAH v1-v4
type: Distribution
format: CSV
issued: 2024-12-05T15:26:51.289122
modified: 2026-02-04T16:57:13.727593
access URL: https://data.geods.ac.uk/dataset/ee10823e-4f07-433c-9e1c-c46dd5ffed0d/resource/a7a2d638-dd25-44df-88df-31890740bab2/download/variabledictionary_ahah.csv
byte size: 2748
media type: text/csv

Paper: Daras, K., Green, M.A., Davies, A. et al. Open data on health-related neighbourhood features in Great Britain. Sci Data 6, 107 (2019). (Note this is for v1 which used a different methodology.)
type: Distribution
format: HTML
issued: 2024-12-06T10:53:30.223942
modified: 2025-05-02T13:19:50.111909
access URL: https://doi.org/10.1038/s41597-019-0114-6

Data Summary: AHAH v3
type: Distribution
format: CSV
issued: 2024-12-05T15:26:00.023723
modified: 2025-05-02T13:19:50.111524
access URL: https://data.geods.ac.uk/dataset/ee10823e-4f07-433c-9e1c-c46dd5ffed0d/resource/ba0da2aa-9bb1-4640-8e93-eda93aa8032f/download/data_summary_ahah_v3_fulldata.csv
byte size: 5595
media type: text/csv

Data Summary: AHAH v2
type: Distribution
format: CSV
issued: 2024-12-05T15:26:16.299904
modified: 2025-05-02T13:19:50.111598
access URL: https://data.geods.ac.uk/dataset/ee10823e-4f07-433c-9e1c-c46dd5ffed0d/resource/e0b5f668-446e-4806-9696-1124a27d7915/download/data_summary_ahah_v2_fulldata.csv
byte size: 4589
media type: text/csv

Data Summary: AHAH v1
type: Distribution
format: CSV
issued: 2024-12-05T15:26:37.562125
modified: 2025-05-02T13:19:50.111670
access URL: https://data.geods.ac.uk/dataset/ee10823e-4f07-433c-9e1c-c46dd5ffed0d/resource/e3253291-0364-4cee-9a13-beca0724bdf4/download/data_summary_ahah_v1_fulldata.csv
byte size: 3539
media type: text/csv

Data: AHAH v2 Index, Domains and Components
type: Distribution
format: CSV
issued: 2024-12-05T15:27:56.563848
modified: 2025-05-02T13:19:50.111369
access URL: https://data.geods.ac.uk/dataset/ee10823e-4f07-433c-9e1c-c46dd5ffed0d/resource/e3dddb3f-b143-4ea0-b6c9-17f7cfc1b47f/download/ahah_v2_fulldata.csv
byte size: 13265937
media type: text/csv

Related Record: AHAH v5
type: Distribution
format: HTML
issued: 2024-12-06T10:54:02.536567
modified: 2026-02-04T14:20:57.335093
access URL: https://data.geods.ac.uk/dataset/access-to-healthy-assets-hazards-ahah

Internet User Classification
type: Dataset
description: The IUC is a bespoke geodemographic classification that describes how people living in different parts of Great Britain interact with the Internet. It provides aggregate population profiles of Internet use and engagement at the Lower Super Output Area (LSOA) and Data Zone (DZ) level (2011 Census Geography). It has been developed in order to update and expand to the previous (2014) IUC and as such includes wider and more comprehensive data, built from a range of consumer, survey and open data collected by the GeoDS. ## Content The data are available for download at the bottom of this page. Also available are a user guide, which provides additional insight about the methodology and data used, and a shapefile for mapping. For detailed descriptions of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can also be downloaded from the bottom of this page. ## Quality, Representation and Bias For full details and evaluation of inputs, there is an accompanying published peer reviewed journal article. Data used in the classification were sourced from consumer purchasing records, surveys, and open data (e.g., broadband speeds from Ofcom). Small Area Estimation was used to extrapolate from representative surveys to the national extent. Consumer data including in the classification have representation that reflects the overarching customer distribution. Internal and external evaluations were conducted on the created typology, including linking the IUC to Census response rates, demonstrating its utility in identifying disparities in online engagement and informing targeted interventions.
issued: 2024-12-17T10:41:33.879307
modified: 2025-05-07T13:21:06.534231
contact point: Organization
  fn: Prof Alex Singleton
keyword: Demographics
keyword: Internet
keyword: Online
keyword: Survey
landing page: Ofcom; BPS; ONS

Data: IUC 2018 (CSV)
type: Distribution
description: A CSV table describing the classification of every LSOA.
format: CSV
issued: 2024-12-17T10:44:40.385119
modified: 2025-05-05T23:17:10.542156
access URL: https://data.geods.ac.uk/dataset/fd655eca-53f7-4f87-b82c-eefc2b70db8a/resource/2bf6060d-9087-4412-b3f4-9f13352a0adc/download/iuc2018.csv
byte size: 2348585
media type: text/csv

Flyer: Internet User Classification
type: Distribution
format: PDF
issued: 2024-12-17T10:45:00.558230
modified: 2025-05-05T23:17:10.542230
access URL: https://data.geods.ac.uk/dataset/fd655eca-53f7-4f87-b82c-eefc2b70db8a/resource/2da6d965-7de8-45a6-aa58-650022f374f7/download/1.4.4-iuc.pdf
byte size: 263954
media type: application/pdf

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-12-17T10:45:13.351932
modified: 2025-05-05T23:17:10.542311
access URL: https://data.geods.ac.uk/dataset/fd655eca-53f7-4f87-b82c-eefc2b70db8a/resource/5d517531-b118-4aea-96c2-d1d74e4defc2/download/variable_dictionary_iuc18.csv
byte size: 167
media type: text/csv

Technical Report: IUC 2018 User Guide
type: Distribution
description: A PDF file providing the pen portraits of every cluster, including plots of cluster centres and a brief summary of the methodology.
format: PDF
issued: 2024-12-17T10:42:23.555357
modified: 2025-05-05T23:17:10.541795
access URL: https://data.geods.ac.uk/dataset/fd655eca-53f7-4f87-b82c-eefc2b70db8a/resource/67d5e2c0-f31b-4301-9158-ca960ec487aa/download/iuc2018userguide.pdf
byte size: 357873
media type: application/pdf

Data Summary
type: Distribution
format: CSV
issued: 2024-12-17T10:45:27.658659
modified: 2025-05-05T23:17:10.542387
access URL: https://data.geods.ac.uk/dataset/fd655eca-53f7-4f87-b82c-eefc2b70db8a/resource/7e0efcea-a28c-4377-a66c-68ebdb305f98/download/data_summary_iuc2018.csv
byte size: 482
media type: text/csv

Data: IUC 2018 (Shapefile format)
type: Distribution
description: An ZIP archive containing a Standard ESRI Shapefile of Custom Generalised LSOAs in GB, EPSG:27700.
format: ZIP
issued: 2024-12-17T10:44:11.682683
modified: 2025-05-05T23:17:10.542078
access URL: https://data.geods.ac.uk/dataset/fd655eca-53f7-4f87-b82c-eefc2b70db8a/resource/9e1020f0-1900-4da1-9ca4-8b5399815ded/download/internet-user-classification.zip
byte size: 55052027
media type: application/zip

Technical Report: IUC 2018 Cluster Census Indices
type: Distribution
description: Socio-Economic Indices per Cluster Group
format: PDF
issued: 2024-12-17T10:42:02.322790
modified: 2025-05-05T23:17:10.541589
access URL: https://data.geods.ac.uk/dataset/fd655eca-53f7-4f87-b82c-eefc2b70db8a/resource/b25cd310-90b5-41c0-aa12-60d2f80d7514/download/iuc2018clustercensusindices.pdf
byte size: 141786
media type: application/pdf

Data: IUC 2018 Cluster Centres
type: Distribution
description: Mean attribute values (z-scores) describing cluster centres.
format: CSV
issued: 2024-12-17T10:42:44.215046
modified: 2025-05-05T23:17:10.541969
access URL: https://data.geods.ac.uk/dataset/fd655eca-53f7-4f87-b82c-eefc2b70db8a/resource/fe6fd9c3-8c21-4311-b055-3975ec87a36a/download/iuc2018clustercentres.csv
byte size: 4235
media type: text/csv

British Population Survey
type: Dataset
description: The British Population Survey (BPS) was a regularly run survey with monthly face-to-face historical data from 2008 to 2015. It aimed to capture the socio-economic and consumer characteristics of the population of Great Britain. Data includes 6000-8000 records a month covering demographics, economics, shopping preferences, durables, media, and internet use. The BPS is conducted in the homes of all respondents. Interviews are conducted via Computer Assisted Personal Interview (CAPI). ## Content The following variables are present in the data: * Family: Gender, Age Group, Numeric age, Lifestage, Ethnic Origin, Marital Status, Parent of children, Parental Status, Child Maintenance, Number in household, Presence of children in household, No. of children in household, Age of children in household * Geography: Country, Standard Region 4, Standard Region 11, Urban/Rural, Postcode Area, Unit Postcode * Economics: Social Grade, Qualification level, Working status of respondent, household income, chief income earner (CIE), working status of CIE, Home tenure, main shopper, main supermarket, debit card/s, credit card/s. * Media: daily newspaper, Sunday newspaper, ITV station most watched. Durables: no. of cars in household, TV, Satellite TV, Cable TV, Freeview, Freesat, Landline telephone, simple mobile phone, web mobile phone, video, DVD recorder, DVD player, personal computer, laptop PC, tablet PC, games console, MP3, DAB radio, DIG camera (ex phone). * Internet access: internet access – frequency, internet access – method, cable broadband, ADSL broadband, other broadband, non broadband, internet access – history. Internet use: emails, info-requests, info-products, purchases – not groceries, grocery shopping, bank a/c & finances, job search, play games online, online gaming for money, download music, download movies, download/stream TV, online dating, VOIP, social networks/blogs, other. * Date: Year and month. * Survey: ID, Weight. Note that the 2015 BPS dataset has changed si…
issued: 2024-12-05T15:46:54.146349
modified: 2026-01-15T14:30:37.814873
contact point: Organization
  fn: Maurizio Gibin
keyword: Geodemographic
keyword: Internet Use
keyword: Supermarket
keyword: Survey
landing page: DataTalk Ltd

Related Record: British Population Survey (LSOA Geography)
type: Distribution
format: HTML
issued: 2025-05-06T16:10:38.043858
modified: 2025-05-08T15:24:31.317971
access URL: https://data.geods.ac.uk/dataset/british-population-survey-lsoa-geography

Variable Dictionary
type: Distribution
format: CSV
issued: 2024-12-05T15:47:26.263585
modified: 2025-05-06T16:03:31.108545
access URL: https://data.geods.ac.uk/dataset/fea04bc5-6a04-4ace-9c64-8eef83eb1621/resource/3d6fe32d-5a32-4c14-976c-e530c3cf5710/download/bps_secure_variable_dictionary.csv
byte size: 29656
media type: text/csv

Data Summary
type: Distribution
format: CSV
issued: 2024-12-05T15:47:12.668463
modified: 2025-05-06T16:03:31.108433
access URL: https://data.geods.ac.uk/dataset/fea04bc5-6a04-4ace-9c64-8eef83eb1621/resource/9c896c5d-911a-4b8e-855c-6d9609a92cf2/download/bps_data_summary.csv
byte size: 415
media type: text/csv

GeoDS
type: Agent