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 into one of the above. ## Content Access to an online tool. A minimum of 100 distinct (unique) names must be supplied on your input file. The application's server will time-out if more than approximately 8000 names (including duplicate names) are supplied, so if your names list is longer than this, you will need to prepare multiple input files and run each one in turn. Input files should be less than 10MB. ## Quality, Representation and Bias Due to a stipulation from one of the upstream data suppliers, the software adds some "noise" to the results, perturbating the count values by a small amount, mimicking the inherent uncertainty and inaccuracy in predicting an ethnicity solely from a name. This does mean that running the software repeatedly on the same set of names will produce slightly different numbers each time. A normal distribution is applied to the size of the perturbation, for each name. The Coefficient of Variation (CV) of the "noise" perturbation diminishes for larger datasets. Only rarely will the perturbation significantly change the result. In the "Perturbation Examples" technical report below, the results of runs of two names lists - a small one and a large one, are show. Each are run 5 times, and the average and standard deviation is calculated. For low count results (less than 10), which are masked with an asterisk, a result of 3 is assumed for the SUM, but no result is assumed for the average and SD calculation. The unclassified count is not subject to perturbation.
Technical Report: Ethnicity Estimator Terms & Conditions
Technical Report: Scale-based Evaluation of Ethnicity Estimator and Onomap3
Paper: Kandt J, Longley PA (2018) Ethnicity estimation using family naming practices. PLOS ONE 13(8): e0201774.
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 scales previously unavailable – all while maintaining the highest standards of data protection and ethical research practice. ## Content The data are provided in CSV format. Additional resources, including a detailed glossary of terms, descriptive statistics and pen portraits are also available for download. This dataset applies a small-area estimation approach to model the geographic distribution of online gambling behaviours across GB. Regional-level customer counts drawn from circa 1.2 million accounts in 2022 were used to create LAD level estimates using decile-ranked estimates of gambling penetration profiles and local population data from the 2021/2022 Census. Market share adjustments, benchmarked to national prevalence rates derived from the Gambling Survey for Great Britain (GSGB) are used to ensure consistency of estimates with known patterns of gambling participation online. The resulting estimates provide neighbourhood-level counts of adults segmented by 13 classifications of online gambling behaviours. Full methodological details, including descriptions of input features, will be provided in a forthcoming academic paper. ## Quality, Representation and Bias The GB2C dataset is based on anonymised behavioural records from a single major British gambling operator, covering online gambling activity throughout the 2022 calendar year. While this operator is among the largest in the market with broad national reach, the dataset captures only a partial view of total gambling engagement across GB. Cross-operator activity, land-based gambling and online lottery participation are not observed, potentially leading to underestimation of some individuals’ total gambling behaviour. Our implicit assumption is that these effects are uniform between different gambling behaviours. To enhance representativeness, estimates were triangulated with national survey benchmarks from the GSGB, helping to align prevalence and ensure even coverage across GB regions. However, survey estimates are subject to response biases (e.g., recall and interviewer/interviewee interaction effects), which may propagate into small-area estimates. These limitations should be considered when interpreting the data in applications where they are relevant.
Great Britain Gambling Behaviours Classification (GB2C) (LAD Geography)
Data: Great Britain Gambling Behaviours Classification (GB2C) (LAD Geography)
Technical Report: Glossary of Terms, Statistics, Pen Portraits
Variable Dictionary: Classification Codes and Names
Related Record: Great Britain Gambling Behaviours Classification (GB2C) (LSOA Geography)
Flyer: Great Britain Gambling Behaviours Classification
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 the bottom of this page. ## Quality, Representation and Bias The record level data have been aggregated to create an annual dataset. The data covers England and Wales only, with a total of 9 years of data. The individual level records contain each CCJ recorded, whilst the LSOA level dataset may suffer from some missing observations in some years, though with overall very good coverage. Please note: after the release of the 2021 Census, the 2023 dataset has been aggregated to 2021 LSOA geography, whilst the prior years are at 2011 LSOA geography.
County Court Judgments (CCJs) (Individual Records and LSOA Geography)
Related Record: County Court Judgments (CCJs)
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.
Related Record: Retail Type, Vacancy and Address Data
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 twelve months.
External Website: The Meddin Bike-Sharing World Map
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.
External Website: FUSION project: multimodal transport user data
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.
This provides a lookup file for the codes, names, colours (as used in GeoDS Mapmaker) and Pen Portraits (descriptions) for LOAC 2011.
Variable Dictionary: Code, Name, Colour and Pen Portrait Lookup
Flyer: London Output Area Classification 2011
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.
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.
Data: London Output Area Classification 2011
This report is the same file that is contained within the Geodata Pack zip file, it is presented separately here for convenience.
Technical Report: London Output Area Classification (LOAC) Final Report
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 customer surveying firm on behalf of MaPS, online or by post. The survey includes quota/screening questions at the beginning to ensure a broadly representative sample of the population across the UK is included. Each respondent is assigned a weighting value which, when applied, should result in a survey that reflects the demographics of the UK. ## Special Stipulation MaPS requires a disclaimer on publications using the data, that the publication does not necessarily represent its views. The following text is recommended: “Disclaimer: the views and recommendations in this report are those of the organisation publishing this report and its author(s) and do not necessarily represent those of the Money and Pensions Service whose data was used to produce it.”
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 “Small Local Centres”. The different results from the hierarchical classifications of the retail centres are assigned different maximum walk/drive times, these are used with the road network to delineate the catchments. These times are listed in the Variable Dictionary: Catchment Durations file below. __Retail Centre Indicators__ Aggregate indicators are generated for the largest retail centres in the UK - those with an underlying retail count of at least 50 units. Indicators for these retail centres are developed from multiple data sources, focusing on their composition, diversity, vacancy, material deprivation and e-resilience, which are useful to better understand the characteristics and economic performance of the retail centres in the UK. A series of indicators have been developed based on openly available data, and are available for download below. Additional indicators are available as a safeguarded data product (see the link in the resource section below) with information on shop vacancies, shop types, composition and diversity. The product contains indicators at the retail centre level, which are split into three domains: diversity, E- Resilience and deprivation. 1. Diversity – a variable summarising key differences in terms of diversity between centres, representing an aggregate ‘clone town’ score (index) based on the ‘Clone Town Britain methodology’. 2. E-Resilience – series of variables summarising the relationship of retail centres to online shopping, including the index of supply vulnerability, online exposure index, and an E-Resilience index (created by the other two indices). 3. Deprivation – two variables summarising the relative deprivation of their associated drive and walking time catchments, measured by combining national deprivation indices (Index of Multiple Deprivation) and retail catchment data (drive time catchment and walking time catchment). ## Quality, Representation and Bias Retail agglomerations are built on a series of heuristic rules based on spatial density and size using openly available point retail locations. Point locations of retail specific units from the Valuation Office Agency (VOA) and OpenStreetMap (OSM) were used as the basis from which clusters of retail spaces were delineated. In Scotland and Northern Ireland, where VOA data is not available, OSM data forms the sole foundation of retail spaces. In England and Wales, VOA point data form the foundation which is then supplemented by OSM retail land use space where coverage may be missing. Retail centre catchments are delineated by varied drive/walk time along the road network from the centroid retail centres across the UK. The retail centres are based on the Retail Centre Boundaries dataset. Retail centre catchments are delineated only for those retail centres not classified as ‘Small Local Centres’ - the most common type. Similarly, the indicators do not cover all 6,423 centres. To ensure security, indicators were developed for those retail centres not classified as ‘Small Local Centres’ and containing over 50 LDC units (to exclude areas not surveyed by LDC). In addition, indicators were not developed for any centres in Northern Ireland (due to a lack of LDC data) and for one retail centre in Manchester due to an incorrect boundary. NA values are given for retail parks and shopping centres for variables in the diversity domain of variables, as such indicators would not prove useful in these instances.
Retail Centre Boundaries and Open Indicators
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.
Two retail catchment geo packages including walking time and driving time catchment data created by road network.
Data: Retail Centre Catchments (Driving and Walking time)
Variable Dictionary: Catchment Durations
Variable Dictionary: Retail Centres Classification Hierarchy
Source Code: Github repository (Retail Centre Boundaries)
The geographies are identical for those in the main geography files on this dataset page, but for a subset of retail centres.
Data: Retail Centre Typology (2022) (GeoPackage format)
Related Record: Retail Centre Indicators
Technical Report: Retail Centre Typology (2022) User Guide and Pen Portraits
Lookup tables between retail centres and common administrative geographies (Output Areas OA11CD and Workplace Zones WZ11CD)
Data: Retail Centre Administrative Geography Mappings
Variable Dictionary: Retail Centre Typology (2022) Lookup
Source Code: Github repository (Retail Centre Indicators)
Variable Dictionary: Columns and Sources
Related Record: 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).
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 of the above, but using number codes instead, this is more compact to manage and process but requires a lookup file to convert to full text. * codebook – a description of each data variable including data label, response code and response labels * weighting reference sheets - A lookup file to indicate which type of weighting is applied to each column * data user guide - A guide to the dataset and instruction on applying weights. The first field in each data file is a unique identifier. While these are individual records, no personally identifying data are included. There are 9 files included in total: (4 data files (CSV format), 2 "codebook" datamaps/references (XLSX format), 2 weighting reference sheets (XLSX format) and 1 user guide (DOCX format)). The largest data file is 1.48GB. The files are delivered in a 227MB zip file. The data is also available in SPSS format (SAV files) if you specifically request it in this format. ## Quality, Representation and Bias The Financial Lives Survey uses random probability sampling to recruit respondents to a largely online survey, with a smaller number of interviews conducted over the phone, in order to include in the sample those without internet access and to increase the number of participants aged 70 and over. More details, including a full description of the content, survey methodology, please see the reports (technical and general) via the external links below. ## Version History * 1.0 - Initial release (2017 survey) * 2.0 - 2020 survey added * 3.0 - 2022 survey added, along with 2017/2020/2022 tracker files. The earlier full survey microdata is no longer available. * 3.1 - The tracker (2017/2020/2022) files were modified in June 2024 to correct an issue with the weightings, and also an incorrectly truncated file. * 3.2 - Minor updates to the files were made in May 2025, to remove a small number of irrelevant data fields. * 3.3 - The data is now available in SPSS-format files to successful applicants, upon request. * 4.0 - 2024 survey added in October 2025, along with 2017/2020/2022 tracker files. * 4.1 - Minor updates to the files were made in December 2025, to remove a small number of irrelevant data fields. * 4.2 - Reissued in April 2026 with some missing weight values added in.
External Website: FCA - FLS 2024 Technical Report
External Website: FCA - FLS 2024 Questionnaire
External Website: FCA - Financial Lives survey
External Website: FCA - Questionnaires, Aggregated Data Tables and Reports
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 of a small number of users, based on their approximate location, demographics and spending habit. The spatial granularity is Output Area level. Researchers interested in the dataset can also request Lower Layer Super Output Areas (LSOA) and Middle Layer Super Output Areas (MSOA) granularities, where transactions amounts are grouped by geography, day of the transaction, Salad Money transaction category, subcategory and type. The data is available in CSV, Parquet. For detailed description of the columns contained within the data, see the Variable Dictionary, which can be downloaded from the bottom of this page. ## Quality, Representation and Bias The dataset provides sufficient detailed information about a sample of UK key workers living in financial precarity who applied for a loan through Salad Money. It can still be useful to detect spatial heterogeneity and neighbourhood geodemographics patterns in open banking data transactions.
Salad Money Open Banking Transaction Data
Related Record: Salad Money Daily Transaction Volumes and Values
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 all individual residential properties in the specified time period. The underpinning data consists of addresses contained in the GeoDS Linked Consumer Registers (LCRs), with their provenance outlined in two papers (see below). Consumer and administrative data were acquired directly or indirectly from multiple providers without warranties about accuracy or coverage, consistent with industry practices. Rigorous internal and external validation procedures were developed to render diverse data formats consistent and establish the provenance of consolidated registers. Known shortcomings in the data and an overall assessment of quality are detailed in peer-reviewed research papers. Discrepancies in the quality of counts among the different countries of the UK may exist, given that estimates rely on successful linkage (‘matching’) of properties recorded in the address database and properties captured in the LCRs. Matching success rates vary among individual countries, with match rates in England and Wales generally exceeding those in Northern Ireland and Scotland. The number of active properties in the latter two countries might therefore be underestimated.
Residential Property Counts (LSOA Geography)
Related Record: Residential Property Counts
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
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
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.
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/._
Classification of Multidimensional Open Data of Urban Morphology (MODUM)
Cluster labels per Output Area Code in CSV format
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.
A pdf describing the cluster profiles, including pen-portraits and attribute plots for every MODUM typology.
A ZIP file containing an ESRI shapefile version of the classification using 2011 OA boundaries for England and Wales.
Units are z-scores - standard deviation.
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 limited to a regional transport provider and contains only concessionary card holders and smart card holders. Only a small portion of data are associated with non-concessionary smart card holders.
Regional Transport Provider - Customer and Ticket Sales Data
Related Record: Regional Transport Provider - Travel Demand Data
This is a summary of one of the 247 CSV files supplied as part of the fourth tranche of data, covering 2021-2024.
Data Summary: Customer (Concessionary) Details
Data Summary: Tickets/Journeys (Concessionary)
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 and Conferences. Long-running/repeating events, such as exhibitions and visual art, which were ongoing before 1 January 2022, have their start schedule date snapped to this day. The last 10 days worth of data contain considerably more performances than for previous days. This is because, due to the volume of entries, historic film listings information from major cinema chains is not retained for longer.
External Website: Data Thistle Default Feed Specification
External Website: Data Thistle Impact Feed Specification
External Website: Data Thistle Publishing API
Venues, with location and addressing information.
Performances are events taking place as part of a schedule, in a particular place (venue).
External Website: Data Thistle Event listings - what's on
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 reports for each year. Data summaries and variable descriptions are supplied with the data itself.
GambleAware Treatment and Support Survey Data
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 temporal differences in some variables. - The COVID-19 pandemic severely impacted method of travel to workplace data across all nations. - Variable definitions are not always directly equivalent across countries. For example, Scotland's qualification levels reflect a different education system. The Table Notes document these differences in detail.
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)
Unified Cenus data tables in parquet format
Unified Census data tables in csv format
List of table titles and notes on any features of interest in the harmonisation process.
Related Record: Output Area Classification (2021/2)
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.
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’ 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 2008-2014 and 2019 values assigned to 2015-2020) to cover the entire period of the LCRs. The RMD Indexes were then created by subtracting the percentile score of each LCR mover’s origin neighbourhood from that of their destination, with positive values indicating moves to less deprived neighbourhoods. Mean differences between these values were calculated for (a) movers to each different destination neighbourhood, (b) moves from each different origin neighbourhood. These data allow researchers to whether incoming residents likely originate from more or less deprived neighbourhoods, perhaps indicating neighbourhood gentrification or relative obsolescence, each over an extended period. The annual estimates and attribution of precise neighbourhood origins and destinations extends what is available from decennial census data. In addition, researchers can investigate which neighbourhoods are platforms for moves to less or more deprived neighbourhoods, indicating neighbourhood roles in facilitating social mobility. LCR estimates are triangulated with census statistics and can be used to update them during intercensal periods. 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 underpinning data are the GeoDS Linked Consumer Registers (LCRs), the provenance of which is set out in two papers (see links below). 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 were developed in order to render the diverse data formats consistent and to establish the provenance of the consolidated registers. Known shortcomings in the data and over-all assessment of quality are set out in the peer-reviewed research papers. In addition to establishing consistency of address referencing, the research papers document the completeness of the data. In terms of coverage, the LCRs tend to under-estimate LSOA adult population sizes relative to UK mid-year population estimates for 2003-2020. The research papers describe procedures developed by GeoDS to fill in known gaps where possible. Data for Northern Ireland are estimated to be less complete because of specific administrative procedures and legislative requirements. Additional UK-wide issues are created by second-home owners and students. The other underpinning data (i.e., IMDs) are from each country of the UK. These datasets address the same general concept and use the same approach, although there is variability in precise domains, weighting and geographies. To make them as comparable and consistent as possible, percentile score and 2020 UK LSOA geographies (identical to 2011 UK LSOA geographies) were used to harmonise results. Whilst collaborating value added data resellers have attempted to compile address lists that have full and accurate coverage, the research papers identify systematic biases akin to those found in similar data sources. The primary data source over the period is the public version of UK Electoral Registers, the coverage of which has been in decline since the advent of opt out provisions in 2002. Post 2002 LCRs are understood to under-represent adults drawn from ethnic minorities and those resident in rented accommodation. GeoDS research using a names-based tool to infer ethnicity, developed in collaboration with the Office for National Statistics (ONS) has quantified the slight over-representation of White British adults. Approximately 84% of LCR individuals were classed as White British, compared to 81% self-identifying as such in the 2011 UK Census. Research also identified that areas with higher proportion of adults in rented accommodation had the greatest under-representation within cities. The counts of individuals in the original LCR data fluctuate according to data supplier in addition to actual population size changes. As such, meta data describing the annual distribution of population counts across LSOA that have been used in calculating RMD are made available. RMD may be out of line with census counts and users should consult census statistics if they have concerns.
Residential Mobility and Deprivation (RMD) Index (LSOA Geography)
Technical Report: Research Ready Smart Data
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–
Data Summary: RMD for Flows Out of LSOAs
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)
Related Record: Residential Mobility and Deprivation (RMD) Index (LAD Geography)
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.
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
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.
Contextual Note: Frequently Asked Questions
Including pen portraits (cluster names and descriptions).
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 cannot supply it to any commercial organisation as such where there is possibility of commercial gain from its use. We additionally cannot supply it to local authorities or related entities. ## Quality, Representation and Bias The Consilium dataset offers broad coverage, with LP status and macro-level data available for all 346 LPAs in England and Wales. Housing trajectory data is provided for 251 LPAs, five-year housing supply site data for 156 LPAs, and brownfield site data for 317 LPAs. Variations in reporting practices across LPAs may result in gaps or inconsistencies. This dataset serves as a vital tool for planners, analysts, and policymakers seeking to address housing challenges and opportunities in England and Wales.
External Website: Brownfield Land Registers
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 (LP) preparation is to identify housing needs and requirements and identify available land for housing. The LP requirement is adopted in the LP, alongside a corresponding housing trajectory, to illustrate the requirement will be met throughout the plan period. The 5-year supply is published annually and assesses whether the authority will meet the LP requirement, plus appropriate buffer and adjustment for historical under-delivery, over the next 5-year period. Once a plan has been adopted longer than 5 years ago, the 5-year supply is calculated against the LHN figure instead of the LP requirement. The authority should also publish the recorded dwelling completions, usually published annually within its Authority Monitoring Report. Please note that the Consilium data supplied here may not have been published in this chronological sequence. Consilium data provides the latest data for each of the existing data categories. This may mean, for example, that if an Authorities has an adopted LP requirement, and has since updated its LHN position - perhaps because it is beginning a review of its LP - then the LHN in the Consilium data extract will not be the housing need figure on which that LP requirement was originally based. ###5. Does the Consilium dataset cover all the Local Authorities in England & Wales? The macro-level and LP status data are supplied for 346 Local Authorities in England & Wales. The five-year housing supply site data is for 156 Local Authorities in England & Wales, containing data for 29,547 housing sites, along with trajectory site data for 251 Local Authorities in England & Wales, containing data for 47,174 housing sites. Consilium do not provide any micro-data (housing sites) for 72 of the Local Authorities in England & Wales. Edge Analytics operate a process of continually updating Consilium, in order to ensure that the resource contains the latest available data. However, not all Local Authorities publish or provide housing site data to cover both their 5-year supply and trajectory figures. Given this issue, the Research Analysts at Edge Analytics are continually processing and researching the housing site data. This approach ensures that the Consilium database records the latest published housing site data, and that additional research is provided, where it is available. ###6. What is the situation for brownfield site data? Edge Analytics collect the published Brownfield Land Register data from the Local Authority websites. It is unform and consistent across all authorities, noting this data covers England only. ###7. What is meant by “year and quarter” in the Consilium datasets? Edge Analytics record the year and quarter in which they collect any site data. ###8. How does Consilium calculate the figures for housing completions? Historical housing completions are found in three categories of the Consilium macro-level data. These are: (1) Council Net Completions – the housing completions published by the Councils in their planning policy documents; (2) DLUHC Net Additional Dwellings (Live Table 122) – the additional dwellings published by the DLUHC for each Authority in England. These include changes in the size of the Authorities’ dwelling stocks, due to new builds, conversions (e.g. houses to flats), changes of use (e.g. office to housing) and demolitions; (3) WG Housing Stock – The changes in dwelling stock estimates published by the Welsh Government for each Authority in Wales. These figures record the annual changes to the Welsh dwelling stock through new build completions plus any gains or losses through conversions and demolitions. These data items may not align, due to the different ways in which the Authorities report their completions data in LP documentation, and the formats in which they report the data to DLUHC.
Contextual Note: Frequently Asked Questions about the Consilium Datasets
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 bespoke rigorous address matching and name matching methodologies, it is still possible that same cases may not have matched. Thus the number of unique addresses is slightly overestimated. Addresses are recorded as address lines (in a separate table). This might make address matching to other data quite difficult as the number and composition of address lines varies by addresses, and between different versions of the data too. In addition, it is very difficult to determine the completeness of the data. The register has near complete coverage of the adult population for every year. However, for some addresses anonymised residents are imputed where a house sale is known to have occurred but no new households were detected (roughly 6% of addresses in 2016). In addition, the occupancy of households are brought forwards by a few years where no new data are obtained for an address. Data lags occur because individuals do not volunteer data every year. It is also possible that adults who reside in multiple addresses may have duplicate entries within the data. Whilst the data providers have attempted to compile registers which are both as complete and accurate as possible, there are data biases that should be considered. Firstly, the electoral register is known to sufficiently under-represent the following groups: the younger age groups, the non-white British population and those in rented accommodation. Further to this, research by the GeoDS using implied ethnic groups from names identified that there was a slight over-representation of White British names. Roughly 84% of individuals were classed as White British, compared to 81% from the 2011 Census for the UK. We have also identified that areas with higher proportion of adults in rented accommodation had the greatest underrepresentation within cities. One of the inputs into the Linked Consumer Registers, the Registers of Scotland, is available via [UBDC](https://www.ubdc.ac.uk/data-services/data-catalogue/housing-data/registers-of-scotland-data/).
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.
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
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.
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 which users should be aware of. Some premises have been given virtual inspections which can further affect date accuracy. These are noticeable between Q2 2020 and Q1 2021 where update activity recorded was reduced. The data supply date indicates the business was on the FSA register at that date. In some areas, premises disappear for periods and then can re-appear. As the premises have not shut, we cannot explain this. If a premise is closed for refurbishment, it gets removed from the supply, then re-added when it re-opens. This is likely due to re-inspection. Similarly, if a premise is taken over. If it is a new legal entity, it will be a change of ownership and may be allocated a new ID. The history of ratings as we supply is the complete set captured from the Food Standards Agency.
Food Hygiene Rating Scheme (FHRS) Ratings
Header and first two data lines for each data file in this product.
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 Variable Dictionary is supplied to successful applicants with the data itself. ## Quality, Representation and Bias The survey is high-quality and organised by a professional surveying firm on behalf of MaPS. The survey includes quota/screening questions at the beginning to ensure a broadly representative sample of the population across the UK is included. Each respondent is assigned a weighting value which, when applied, should result in a survey that reflects the demographics of the UK children. _Special Stipulation: MaPS requires a disclaimer on publications using the data, that the publication does not necessarily represent its views. The following text is recommended: “Disclaimer: the views and recommendations in this report are those of the organisation publishing this report and its author(s) and do not necessarily represent those of the Money and Pensions Service whose data was used to produce it.”_
MaPS Children and Young People's Financial Wellbeing Survey
Data Summary: Labels version (sample only)
Technical Report: Children and Young People Financial Wellbeing Survey 2022
External Website: UK Children and Young People’s Financial Wellbeing Survey: Financial Foundations
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, using these tests to enhance the precision and accuracy of their output.
Property Characteristics, Prices and Rents
Data Summary: Property Sale and Rent (Secure)
Data Summary: Property Characteristics (Secure)
Variable Dictionary: Property Sale and Rent (Secure)
Related Record: Property Characteristics, Prices and Rents (LSOA Geography)
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 2008-2014 and 2019 values assigned to 2015-2020) to cover the entire period of the LCRs. The RMD Indexes were then created by subtracting the percentile score of each LCR mover’s origin neighbourhood from that of their destination, with positive values indicating moves to less deprived neighbourhoods. Mean differences between these values were calculated for (a) movers to each different destination neighbourhood, (b) moves from each different origin neighbourhood and (c) moves within the same neighbourhood. These data allow researchers to whether incoming residents likely originate from more or less deprived neighbourhoods, perhaps indicating neighbourhood gentrification or relative obsolescence, each over an extended period. The annual estimates and attribution of precise neighbourhood origins and destinations extends what is available from decennial census data. In addition, researchers can investigate which neighbourhoods are platforms for moves to less or more deprived neighbourhoods, indicating neighbourhood roles in facilitating social mobility. LCR estimates are triangulated with census statistics and can be used to update them during intercensal periods. Note: Any '*' in the index means that the figures were suppressed to avoid disclosive issues (i.e., lower count). RMD figures are measured at a percentile scale. 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 underpinning data are the Linked Consumer Registers (LCRs), the provenance of which is set out in two papers linked below. Please see these papers for additional details about the data. 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 were developed in order to render the diverse data formats consistent and to establish the provenance of the consolidated registers. Known shortcomings in the data and over-all assessment of quality are set out in the peer-reviewed research papers. In addition to establishing consistency of address referencing, the research papers document the completeness of the data. In terms of coverage, the LCRs tend to under-estimate LSOA adult population sizes relative to UK mid-year population estimates for 2003-2020. The research papers describe procedures developed by GeoDS to fill in known gaps where possible. Data for Northern Ireland are estimated to be less complete because of specific administrative procedures and legislative requirements. Additional UK-wide issues are created by second-home owners and students. The other underpinning data (i.e., IMDs) are from each country of the UK. These datasets address the same general concept and use the same approach, although there is variability in precise domains, weighting and geographies. To make them as comparable and consistent as possible, percentile score and 2020 UK LSOA geographies (identical to 2011 UK LSOA geographies) were used to harmonise results. Whilst collaborating value added data resellers have attempted to compile address lists that have full and accurate coverage, the research papers identify systematic biases akin to those found in similar data sources. The primary data source over the period is the public version of UK Electoral Registers, the coverage of which has been in decline since the advent of opt out provisions in 2002. Post 2002 LCRs are understood to under-represent adults drawn from ethnic minorities and those resident in rented accommodation. Previous research using a names-based tool to infer ethnicity, developed in collaboration with the Office for National Statistics (ONS) has quantified the slight over-representation of White British adults. Approximately 84% of LCR individuals were classed as White British, compared to 81% self-identifying as such in the 2011 UK Census. Research also identified that areas with higher proportion of adults in rented accommodation had the greatest under-representation within cities. The counts of individuals in the original LCR data fluctuate according to data supplier in addition to actual population size changes. As such, meta data describing the annual distribution of population counts by median IMD change across LAD that have been used in calculating RMD are made available. RMD may be out of line with census counts and users should consult census statistics if they have concerns.
Residential Mobility and Deprivation (RMD) Index (LAD Geography)
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).
Related Record: Residential Mobility and Deprivation (RMD) Index (LSOA Geography)
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.
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 were developed in order to render the diverse data formats consistent and to establish the provenance of the consolidated registers. Known shortcomings in the data and over-all assessment of quality are set out in the peer-reviewed research papers. In addition to establishing consistency of address referencing, the research papers document the completeness of the data. In terms of coverage, the LCRs tend to under-estimate LSOA adult population sizes relative to UK mid-year population estimates for 2003-2020. The research papers describe procedures developed by GeoDS to fill in known gaps where possible. Data for Northern Ireland are estimated to be less complete because of specific administrative procedures and legislative requirements. Additional UK-wide issues are created by second-home owners and students. Whilst collaborating value added data resellers have attempted to compile address lists that have full and accurate coverage, the research papers identify systematic biases akin to those found in similar data sources. The primary data source over the period is the public version of UK Electoral Registers, the coverage of which has been in decline since the advent of opt out provisions in 2002. Post 2002 LCRs are understood to under-represent adults drawn from ethnic minorities and those resident in rented accommodation. GeoDS research using a names-based tool to infer ethnicity, developed in collaboration with the Office for National Statistics (ONS) has quantified the slight over-representation of White British adults. Approximately 84% of LCR individuals were classed as White British, compared to 81% self-identifying as such in the 2011 UK Census. Research also identified that areas with higher proportion of adults in rented accommodation had the greatest under-representation within cities. The counts of individuals in the original LCR data fluctuate according to data supplier in addition to actual population size changes. As such, meta data describing the annual distribution of population counts across LSOA that have been used in calculating DoRM are made available. DoRM may be out of line with census counts and users should consult census statistics if they have concerns.
Distance of Residential Moves (DORM) Index (LSOA Geography)
Related Record: Distance of Residential Moves (DORM) Index (LAD Geography)
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 2018 and 2020 data are not for the complete year - in both cases, only the first few months of the year are included. The 2011-18 data have a lower spatial resolution than the later data.
Speedchecker Broadband Internet Speed Tests
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.
Retail Centre Boundaries (Previous Versions)
Paper: Pavlis, M., Dolega, L. and Singleton, A. (2018), A Modified DBSCAN Clustering Method to Estimate Retail Center Extent. Geogr Anal, 50: 141-161.
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.
Data: Retail Centre Comparison Catchments (2017) (GeoPackage format)
Data: Typology and Centroids (2018) (Shapefile format)
GeoPackage (.gpkg) of retail boundaries across Great Britain.
Data: Boundaries (2017) (GeoPackage format)
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
GeoPackage (.gpkg) of retail centroid points across Great Britain.
Data: Centroids (2017) (Geopackage format)
Technical Report: Typology (2018) Pen Portraits
Data: Typology and Boundaries (2018) (Shapefile format)
Related Record: Retail Centre Boundaries and Open Indicators
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.]
Data: Retail Centre Convenience Catchments (2017) (Shapefile format)
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, and are not in an annual snapshot format. 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 be downloaded from the bottom of this page. For details on the Companies House data by Fusion Data Science, see the links below in the related sources. ## Quality, Representation and Bias The data should contain a complete listing of the UK’s companies, and therefore should be fully representative with no bias. Some data are self-supplied by the companies lists and quality can vary. The COVID-19 pandemic may have had an impact on the data, as there were a large number of dissolutions post-pandemic - these may have happened during the lockdowns but were not processed at the time. Some will also relate to rapidly changing economic circumstances following the pandemic.
External Website: Details of Companies House Data
Headers and first two data lines of each file.
External Website: Business Census Data Specification at Companies House (PDF)
For 2012, 2013 and 2024. Data summaries for intermediate years will be similar.
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 Approximation and Projection) to visualise high-dimensional embeddings in 2D. Visualisation and Communication: * Visualise clusters and subclusters using Kepler.gl for interactive mapping. * Explore cluster characteristics using summary statistics and index scores. * Export results to various formats (GeoPackage, Parquet) for use in GIS software. Cluster Naming with LLMs: * Use Large Language Models (LLMs) to generate descriptive names and summaries for clusters based on their characteristics.
Creating an Open Geodemographic Classification Using K-means Clustering in Python
Workshop slides presented at the Spatial Data Science Conference 2025. Includes a brief introduction to geodemographics and some suggested extentions to the tutorial.
Related Record: Creating a Geodemographic Classification Using K-means Clustering in R
Webpage version of the jupyter notebook.
External Website: Workshop Notebook Webpage
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 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, using these tests to enhance the precision and accuracy of their output.
Property Characteristics, Prices and Rents (LSOA Geography)
Related Record: Property Characteristics, Prices and Rents
Data Summary: Property Sale and Rent (Safeguarded)
Variable Dictionary: Property Sale and Rent (Safeguarded)
Data Summary: Property Characteristics (Safeguarded)
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.
Where and when the data are collected, a list of all variables, and the contextual characteristics of all Supergroups and Groups.
Technical Report: Data Sources, Variable List and Pen Portraits
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
The distribution summary of all variables related to the energy deprivation
The codes and classification labels/names lookup for each Supergroup and Group.
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 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 The data represent aggregates calculated primarily from the Retail type, Vacancy and Address dataset, which contains near complete coverage of retailers across Great Britain. Coding of retail type (e.g. comparison) varies in consistency between different iterations (e.g. 2016), but have been recalculated and verified for these indicators. The indicators do not cover all centres as defined by GeoDS. To ensure security, indicators were developed for those retail centres not classified as ‘Small Local Centres’ and containing over 50 Green Street units (to exclude areas not surveyed by them). In addition, indicators were not developed for any centres in Northern Ireland (due to a lack of upstream data) and for one retail centre in Manchester due to an incorrect boundary. NA values are given for retail parks and shopping centres for variables in the diversity domain of variables, as such indicators would not prove useful in these instances.
Manual describing the indicator variables and the respective process for generating them.
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 LCR_EPC dataset, which establishes links between EPCs and the origin and destination addresses of individual movers within the LCR migration model. Only around 59% of residential properties across Great Britain have EPC records for the period between 2009 and 2023. This partial record coverage means that, for nearly half of the movers within this time period, EPC information cannot be linked to the LCR migration model, due to the EPC data being unavailable for either their previous or their current addresses. Furthermore, it is important to highlight that the representation of Scottish EPCs in the dataset is lower when compared to England and Wales. Consequently, movers with EPCs in the GeoDS LCR_EPC data in Scotland appear to be reduced, compared to England and Wales. For comprehensive details of the source and reliability of the GeoDS LCR_EPC dataset, please refer to the Contextual Note.
Residential Mobility and Energy Performance Certificate (RMEPC) Index (LSOA Geography)
Related Record: Residential Mobility and Energy Performance Certificates (RMEPC) Index (LAD Geography)
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 coverage, consistent with industry practice. Extensive internal and external validation procedures were developed in order to render the diverse data formats consistent and to establish the provenance of the consolidated registers. Known shortcomings in the data and over-all assessment of quality are set out in the peer-reviewed research papers. In addition to establishing consistency of address referencing, the research papers document the completeness of the data. In terms of coverage, the LCRs tend to under-estimate LSOA adult population sizes relative to UK mid-year population estimates for 2003-2020. The research papers describe procedures developed by the GeoDS to fill in known gaps where possible. Data for Northern Ireland are estimated to be less complete because of specific administrative procedures and legislative requirements. Additional UK-wide issues are created by second-home owners and students. Whilst collaborating value added data resellers have attempted to compile address lists that have full and accurate coverage, the research papers identify systematic biases akin to those found in similar data sources. The primary data source over the period is the public version of UK Electoral Registers, the coverage of which has been in decline since the advent of opt out provisions in 2002. Post 2002 LCRs are understood to under-represent adults drawn from ethnic minorities and those resident in rented accommodation. GeoDS research using a names-based tool to infer ethnicity, developed in collaboration with the Office for National Statistics (ONS) has quantified the slight over-representation of White British adults. Approximately 84% of LCR individuals were classed as White British, compared to 81% self-identifying as such in the 2011 UK Census. Research also identified that areas with higher proportion of adults in rented accommodation had the greatest under-representation within cities. The counts of individuals in the original LCR data fluctuate according to data supplier in addition to actual population size changes. As such, meta data describing the annual distribution of population counts by median distance across LAD that have been used in calculating DoRM are made available. DoRM may be out of line with census counts and users should consult census statistics if they have concerns.
Distance of Residential Moves (DORM) Index (LAD Geography)
Related Record: Distance of Residential Moves (DORM) Index (LSOA Geography)
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.
Data: Practical - Leveraging the CDRC API
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 to events recorded in a hospital setting. They do not include out-patient data, events recorded in primary care and data on self-rated health.
Local morbidity rates of Global Burden of Disease and alcohol-related conditions
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.
Related Record: NHS Hospital Admission Rates by Ethnic Group and other Characteristics
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*.
Financial Precarity Classification (FPC)
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
The codes and classification labels/names lookup.
The distribution summary of all variables related to the FPC.
A set of pen portraits describing the contextual characteristics of each classification group.
External Website: The geography of economic insecurity in Great Britain (data story)
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 often exhibit high levels of attribute incompleteness. Furthermore, there is a need for further research to validate the wider completeness and accuracy of our data product, particularly in relation to representation and bias. The data is licensed under the Community Database License Agreement – Permissive v2 (CDLA). ## Version History * 1.0 - Using the May 2024 upstream data. * 1.1 - Using the September 2024 upstream data.
Point of Interest data for the United Kingdom
Data: Point of Interest data for the United Kingdom
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.
External Website: Overture Maps Foundation
External Website: Geolytix supermarket retail points dataset
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 as entered into the Land Registry and similar. Sometimes, this information is recorded incorrectly, such as shared ownership properties being incorrectly entered as regular private sale with the price paid for a proportion of the property incorrectly showing as the full property value. Such errors are normally picked up with later updates of the Land Registry (and so updates to the HPSSA (House Prices for Small Statistical Areas) dataset) but these updates may not be reflected with the static copies hosted here.
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.
Data: Dwelling Age Band Counts (to 2015)
By LSOA11CD. Averaged across 12 months including quarter, for Q1 2020 and Q2 2021. As currently used in Mapmaker.
Data: Median House Prices (Q1 2020 and Q2 2021)
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).
Data: Dwelling Age Band Counts (to 2021)
Quarterly count of housing transactions per LSOA11CD.
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-dimensional embeddings into 2D for exploratory visualisation. * Assess cluster separation, stability, and overlap in embedding space. * Summarise cluster characteristics using counts, proportions, and centroids. Matching Building Characteristics to Describe the Clusters: * Integrate embeddings with administrative sources such as Energy Performance Certificates. * Derive and recode key attributes (e.g. built form, property type, age bands). * Construct propensity indices to understand how different building attributes concentrate within clusters. The Descriptive and Predictive Potential of Embeddings: * Compute cosine similarity measures to identify buildings most similar to chosen reference profiles (e.g. pre-1930 stock). * Build and evaluate a Random Forest model to predict construction age bands from embeddings. * Interpret precision, recall, F1, confusion matrices, and feature importance for multi-class prediction. * Critically assess what embeddings can and cannot reliably predict at 10m resolution. Visualisation and Communication: * Produce publication-ready plots, clustergrams, UMAP visualisations, and heatmaps. * Create interactive maps (e.g. Kepler.gl, GIS-ready GeoPackages) to explore spatial patterns. * Develop transparent, reproducible workflows suitable for policy, research, and applied urban analytics.
Using AlphaEarth Foundations Embeddings for Building Analysis
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.
Technical Report: Pen Portraits for the 2001 Output Area Classification
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.
Related Record: Output Area Classification (2011)
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 record] _In version 3.2 the following Pen Portraits changed their name: 4b1 changed from Generational Migrants to Migrant Families. 8b3 changed from Young Families and Neighbourhood Turnover to Young Family Flat Renters. Many pen portrait descriptions were also updated._
Group populations and index scores for each Supergroup, Group and Subgroup, for each local authority (LAD23).
Data: Group Populations and Index Scores for Local Authorities
Data: Classification geodata (Geopackage format)
Related Record: UK LSOA / DZ / SDZ Classification (2021/2 LSOAC)
Classification typology, glossary of variables, glossary of terms, distribution statistics and pen portraits.
Technical Report: Pen Portraits and Input Variable Listing
Data: Classification geodata (Shapefile format)
Source Code: UK Output Area Classification (UK OAC) - Final Version (GitHub Repository)
Technical Report: Local Authority Atlas (England A-D)
Technical Report: Local Authority Atlas (Wales)
Technical Report: Local Authority Atlas (England S-Z)
Technical Report: Upper Tier Local Authority Atlas (England M-Z)
Technical Report: Local Authority Atlas (England M-R)
Technical Report: Local Authority Atlas (England E-L)
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.
Technical Report: Upper Tier Local Authority Atlas (England A-L)
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 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 retail centres are developed consistently for the national extent of the U.S. In total there are 10,956 major agglomerations across the U.S. A characteristic-based classification is generated along with the delineated retail clusters, which represents the relative size and ranking of the retail space.
US Retail Centre Boundaries and Classification
Source Code: USRetailCentres (Github Repository)
Zipped GPKG geospatial data file. 2 files in this archive - US_RetailCentres.gpkg - __MACOSX/._US_RetailCentres.gpkg
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,
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.
Creating a Geodemographic Classification Using K-means Clustering in R
Data: Creating a Geodemographic Classification Using K-means Clustering in R
Related Record: Creating an Open Geodemographic Classification Using K-means Clustering in Python
Data: 2011 Output Area London (Shapefile)
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.
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
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.
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.
Advanced GIS Methods Training: AHAH and Multi-Dimensional Indices
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 standards of data protection and ethical research practice. ## Content The data are provided in CSV format. Additional resources, including a detailed glossary of terms, descriptive statistics and pen portraits are also available for download. This dataset applies a small-area estimation approach to model the geographic distribution of online gambling behaviours across GB. Regional-level customer counts drawn from circa 1.2 million accounts in 2022 were used to create LSOA level estimates using decile-ranked estimates of gambling penetration profiles and local population data from the 2021/2022 Census. Market share adjustments, benchmarked to national prevalence rates derived from the Gambling Survey for Great Britain (GSGB) are used to ensure consistency of estimates with known patterns of gambling participation online. The resulting estimates provide neighbourhood-level counts of adults segmented by 13 classifications of online gambling behaviours. Full methodological details, including descriptions of input features, will be provided in a forthcoming academic paper. ## Quality, Representation and Bias The GB2C dataset is based on anonymised behavioural records from a single major British gambling operator, covering online gambling activity throughout the 2022 calendar year. While this operator is among the largest in the market with broad national reach, the dataset captures only a partial view of total gambling engagement across GB. Cross-operator activity, land-based gambling and online lottery participation are not observed, potentially leading to underestimation of some individuals’ total gambling behaviour. Our implicit assumption is that these effects are uniform between different gambling behaviours. To enhance representativeness, estimates were triangulated with national survey benchmarks from the GSGB, helping to align prevalence and ensure even coverage across GB regions. However, survey estimates are subject to response biases (e.g., recall and interviewer/interviewee interaction effects), which may propagate into small-area estimates. These limitations should be considered when interpreting the data in applications where they are relevant.
Great Britain Gambling Behaviours Classification (GB2C) (LSOA Geography)
Related Record: Great Britain Gambling Behaviours Classification (GB2C) (LAD Geography)
## 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 smart meter rollout, there is a potential bias in the profile of the first adopters. These early users were more likely to be elderly individuals and families who were at home during the installation campaigns, which may skew the data towards this demographic. ## Usage Considerations This dataset provides detailed insights into energy consumption patterns but should be used with awareness of its biases, particularly in relation to geographic distribution and the early stage of the smart meter rollout. Additionally, suppressed data may affect the precision of certain analyses.
Domestic Energy Provider (Postcode Sector Geography)
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.
Advanced GIS Methods Training: Retail Centres and Catchment Areas
Data: Data for Retail-Catchment-Training
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 would have hampered data linkage when making the index. For instance, we estimate that 100,000 surnames are misspelled/recorded slightly differently in each register. In addition, addresses (particularly flats) can be recorded differently. Addresses are recorded as address lines. This makes address matching to other data quite difficult as the number and composition of address lines varies by addresses, and between different versions of the data too. Lastly, it is very difficult to determine the completeness of the data. It is possible that deceased adults are not removed immediately and often not until new individuals are registered at their address. This means that RMI values in the last few years (2018-25) should be interpreted with caution given the delay in the change of registered addresses for individuals. Generally, town centres are underrepresented though. The earlier consumer registers (2003-12) and later electoral registers (2018-20) tended to under represent the adult population relative to mid-year population estimates. However, efforts were made by GeoDS to fill in any gaps where possible. The data for Northern Ireland is also estimated to be less complete due to distinctive administrative procedures in the region. It is also possible that adults who reside in multiple addresses may have duplicate entries within the data. Whilst the data providers have attempted to compile registers which are both as complete and accurate as possible, there are data biases that should be considered. First, the electoral register (up to 2025) is known to sufficiently under-represent the following groups: the younger age groups, the non-white British population and those in rented accommodation. Second, the counts in the original data also fluctuated relative to the population growth. GeoDS research subsequently attempted to fill in the gaps and reweight changes based on additional data sources. The counts of households in the original LCR data fluctuate according to data supplier in addition to actual population size changes. RMI may be out of line with census counts and users should consult census statistics if they have concerns. This is particularly pertinent to the last few years (2018-25), especially 2019, in addition to areas such as Braintree. The coverage and completeness of the LCR which have been used to create these data has gradually degraded in recent years, which may have resulted in a slight decline in the quality these derived data.
Data Summary: Residential Mobility Index by LAD23
Data: Residential Mobility Index LAD 1997-2025
Data Summary: Residential Mobility Index by LSOA11
Data: Residential Mobility Index LSOA 1997-2025
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 by email once you have made your initial application and received the form. Applicants would need to sign a non-disclosure agreement before accessing this detail, and such as request will significantly increase the time for data delivery. You can, of course, make a full application for the data without first receiving this collection/ coverage metadata. ## Content These data are provided at 1km x 1km OSGB Grid cells. Activity counts of 1-10 devices are masked and replaced by “*” in the database, as low counts present potentially identifiable information. 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 Excellent quality and coverage for major towns and cities. The data may be less complete for smaller settlements or more rural areas. Data are subject to suppression of potentially disclosive low counts as detailed above. Huq collects data from a varying mix of apps, the identities of which are commercially sensitive. Apps may be added to or deleted from the secure and summary data products over time. This, along with increasing national coverage and mobile phone uptake, results in general increases in apparent activity over the period covered by the data. The dataset would benefit from comparison with population estimates (e.g. census data) to investigate coverage issues. 2016 data have the highest percentage of suppressed counts, and data suppression generally decreases over time, particularly in metropolitan (Met) areas. Data suppression levels in metropolitan areas generally fall below 50% by 2020.
External Website: OS Grid Description from Digimap
Related Record: Linked Consumer Registers
Related Record: Local Data Company - SmartStreetSensor Footfall Data
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 interest. Travel times are calculated using the Valhalla open-source routing engine with OpenStreetMap road network data, assuming free-flow traffic conditions. Please see the Technical Methodology Report for comprehensive documentation of the data sources and index construction methodology. For detailed description of all 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 data are compiled from multiple authoritative sources selected for quality and coverage. Health service locations are derived from official NHS administrative datasets. Air quality data uses DEFRA's validated modelled surfaces. Greenspace metrics are derived from European Space Agency Sentinel-2 satellite imagery processed via Google Earth Engine. Accessibility calculations achieve near-complete coverage, with fewer than 0.02% of postcodes returning missing routes (primarily in remote island locations). At the LSOA/Data Zone level, no areas have complete missing values for any indicator. Users should note that: - Travel times assume private vehicle access with free-flow traffic conditions - Retail environment data is subject to commercial classification schemes - Temporal alignment varies across indicators (health services: late 2025; air quality: 2024; greenspace imagery: April-September 2024) ## Version History Version 5 was released in February 2026 and is the current release. It it is the first release to use the 2022 Scottish Data Zone census boundaries. It re-added an Active Greenspace indicator and used the Valhalla routing engine. Version 4 was released in late 2024 and used 2021 England/Wales LSOA census boundaries and 2011 Scottish Data Zone census boundaries. Version 3 was released in late 2022 and (like Version 2 and 1) used the 2011 LSOA/DZ census boundaries. Version 2 was released in 2017 and revised the methodology from the original Version 1 (2016) release. Previous versions are available from the "Previous versions" record linked below.
Access to Healthy Assets & Hazards (AHAH)
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.)
Data: Geofile for AHAH Overall Index, Domains and Components (v5)
Flyer: Access to Healthy Assets & Hazards
Note that this repository contains code starting from Version 3 (See 'releases').
Related Record: Previous versions of AHAH
Data: AHAH Overall Index, Domains and Components (v5)
Includes Data Sources and Indicator Components List.
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.
Related Record: Geolytix aggregated in-app location dataset
Related Record: HUQ aggregated in-app location dataset
Related Record: SmartStreetSensor Footfall Data
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.
County Court Judgments (CCJs) (MSOA Geography)
Related Record: County Court Judgments (CCJs) (Individual Records and LSOA Geography)
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.
All the data files are supplied with these columns.
Locations, names and location photographs for the newly deployed sensors. Report as of November 2023.
Technical Report: Air Quality Sensor Deployment for Bus Route 10
Data Summary: Sensor locations, names and data date ranges
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.
Data: Aeternum Pollution Sensors Q1 2024
Data: Aeternum Pollution Sensors Q1 2025
Data: Aeternum Pollution Sensors Q3 2024
Data: Aeternum Pollution Sensors Q2 2024
Related Record: Access to Healthy Assets and Hazards
External Website: Live air quality map and visual downloading tool
Data: Aeternum Pollution Sensors Q4 2024
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 every small statistical unit area of the United Kingdom at regular intervals. Each home nation tunes its version of the index to meet local needs, resulting in different emphasises (and so biases) for each nation.
Data Summary: Welsh IMD (WIMD) 2014/2019
Paper: Longley, P.A., van Dijk, J. & Lan, T. The geography of intergenerational social mobility in Britain. Nat Commun 12, 6050pp
Data Summary: English IMD 2010 (2011 LSOAs)/2015
Data Summary: Scottish IMD (SIMD) 2004/2006/2009/2012
Data Summary: Northern Irish MDM 2005/2010/2017
Variable Dictionary: GeoDS Harmonised IMD 2019
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”.
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 (percentage within 20 minutes) 6 Access to post office (percentage within 15 minutes) 7 Access to public library (percentage within 15 minutes) 8 Access to leisure centre (percentage within 20 minutes) Indicators used (with notes) for the education, skills, and training domain: 1 Key stage 2, average point score (2004) 2 Key stage 3, average point score (2004) 3 Key stage 4, average point score (2004) 4 Secondary school absence rates (2004) 5 Proportion of 17 and 18 year olds not entering full-time further or higher education (2004) 6 Proportion of adults with low or no qualifications (2001) Notes on Overall domain ranks: 1 The employment domain was revised on 22 November 2005 to include the claimant count indicator and this affected the overall Index values. These figures replace those released on 30 September 2005. 2 The employment domain was revised on 22 November 2005 to include the claimant count indicator. These figures replace those released on 30 September 2005. ###WIMD 2008 Welsh Index of Multiple Deprivation 2008 (revised 20/12/2011, 29/03/2011 and 13/07/2010) Author: SJ&E, Welsh Assembly Government During production of the 2011 edition of WIMD, an error was discovered in the WIMD 2008 income domain. This was because the dependent children of claimants of income-related Department of Work and Pensions (DWP) benefits were erroneously omitted from the indicator in 2008. The 2008 income indicators were corrected and re-published on StatsWales in November 2011. Indicator values were on average over 20 per cent higher than the originally published value. Changes to the income domain and overall index ranks were made both to StatsWales and to the WIMD 2008 Summary Report on 20 December 2011. In July 2010, an error was discovered in the physical environment domain of WIMD 2008. As a result, the LSOA scores and ranks in the physical environment domain were revised on 13th July 2010. The change in LSOA ranking in the physical environment domain caused small changes to the overall WIMD scores and ranks, and these were also revised on the same date. In March 2011, an error was discovered in the community safety domain of WIMD 2008. As a result, the LSOA scores and ranks in the community safety domain were revised on 29th March 2011. The change in LSOA ranking in the community safety domain caused small changes to the overall WIMD scores and ranks, and these were also revised on the same date. In March 2011, an error was discovered in the community safety domain of WIMD 2008: Child Index. As a result, the LSOA scores and ranks in the community safety domain were revised on 29 March 2011 The change in LSOA ranking in the community safety domain caused small changes to the overall WIMD: Child Index scores and ranks, and the burglary indicator. These were revised on the same date. In July, 2010, an error was discovered in the physical environment domain of WIMD 2008: Child Index. As a result, the LSOA scores and ranks in the physical environment domain were revised on 13th July 2010. The change in LSOA ranking in the physical environment domain caused small changes to the overall WIMD: Child Index scores and ranks, and these were revised on the same date. ###WIMD 2011 No notes. ###WIMD 2014 (r) Data in these columns were revised on 12 August 2015 following provision of revised data by the Department for Work and Pensions (DWP). ###WIMD 2019 No notes.
Data Summary: Scottish IMD (SIMD) 2016/2020
External Website: Trust for London - English Index of Multiple Deprivation, rebased for London
Data Summary: Welsh IMD (WIMD) 2005/2008/2011
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.
LSOA 2011 geography. The 2010 data is created by GeoDS by inferring from the IMD 2010 (LSOA 2001 boundaries) source.
Data: English IMD 2010 (2011 LSOAs)/2015
Data Summary: English IMD 2004/2007/2010
Paper: Longley, P., Lan, T. & van Dijk, J. Geography, ethnicity, genealogy and inter-generational social inequality in Great Britain
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.
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.
West Midlands Accessibility and Travel Passes
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.
Data: West Midlands ENCTS Bus Origin-Destination Flows (Actual, 81%)
Proportion of eligible ENCTS registered population in each LSOA assigned to each of six travel behaviour clusters.
Data: West Midlands ENCTS Traveller Clusters Distribution
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
Data: West Midlands ENCTS Eligible Population (Forecast)
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.
Data: West Midlands ENCTS boarding by LSOA (Age 60+)
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
External Website: Inclusive and Healthy Mobility: Understanding Trends in Concessionary Travel in the West Midlands
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
Data: West Midlands ENCTS Eligible Population (Historic)
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
Data: West Midlands Bus Based Accessibility (Tuesday)
Data: West Midlands ENCTS boarding by LSOA (Disabled)
Data: West Midlands ENCTS Bus Origin-Destination Flows (Imputed, 87%)
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
Data: West Midlands Bus Based Accessibility (Saturday)
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 representation of mobility patterns across Greater London, but several factors affect its quality and coverage. Location data are derived from a self-selecting sample of mobile app users, so there may be socio-demographic biases in the representation of the population. Certain groups, such as children, the elderly, individuals without smartphones, or those with non-standard work patterns (e.g., shift workers, gig economy workers), may be under-represented. Conversely, areas with high smartphone activity, such as commercial or retail zones, may be over-represented, potentially exaggerating flow volumes. OD flows reflect sequential movements between detected activity locations but cannot fully distinguish interim stops from final destinations. While the data capture major commuting and activity patterns, they may not fully reflect the complexity of individual routines, such as multiple short visits, dispersed work locations, or irregular mobility. A two-hour cut-off is applied to define a flow, which balances capturing meaningful movements and excluding unrealistic gaps, but may omit longer legitimate trips or flows from slower-moving populations. Lastly, flows are aggregated to MSOAs to mitigate GPS inaccuracies and preserve privacy, but this introduces potential modifiable areal unit problems (MAUP). Aggregation may obscure finer-grained spatial patterns, and alternative representations (e.g., hexagonal grids or interaction-based zones) could reveal different connectivity structures. Despite the considerations outlined above, which primarily serve to guide the correct interpretation and use of the data, the dataset remains a highly valuable resource. It preserves the spatial and temporal granularity of origin–destination movements better than standard aggregated products, enabling analysis of hourly flows, connectivity patterns, and urban interactions. While it does not capture the entire population, the dataset provides a robust and privacy-safe approximation of mobility trends, filling a critical gap in available data products by offering detailed origin–destination flows derived directly from individual-level mobility data. This makes it particularly useful for research, policy analysis, and urban planning applications.
London aggregated in-app Origin-Destination Flows
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
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 across zones - Use `NDVI` or `EVI` values for characterising "greenness" intensity - The ratio of `valid_pixels_sum` to `total_pixels_sum` provides an indication of data quality and cloud contamination for each zone Full details of the data pipeline used to generate these statistics will be published shortly on a Github repository at https://github.com/GeographicDataService/. ## Quality, Representation and Bias These averaged measures are developed primarily as indicators of greenness for social science and public health applications. Because input data are filtered to the growing season and combined into a median composite (which may blend observations from different dates within that period), these data may not be suitable for phenology-based studies requiring precise temporal information. The pixel count statistics are exported separately from the vegetation statistics in your code — you may want to join these before final distribution, or document that they're in separate files. A previous "2025" version of the our Small Area UK Vegetation Indices, released in 2025 and based on summer 2023 data from EOX and ESA Sentinel-2, has now been archived and replaced by this version which includes annual files from 2021 onwards. For research reproducibility purposes you can access this previous version, along with the previous record description, at the link listed below.
External Website: Small Area UK Vegetation Indices (2025 EOX-based version)
Data: Small Area UK Vegetation Indices (Summer 2022)
Data: Small Area UK Vegetation Indices (Summer 2023)
Data: Small Area UK Vegetation Indices (Summer 2021)
Data: Small Area UK Vegetation Indices (Summer 2024)
Data: Small Area UK Vegetation Indices (Summer 2025)
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.
Descriptions of each cluster group in LWZC 2017.
External Website: GLA Intelligence London Workplace Zone Classification
Flyer: London Workplace Zone Classification
External Website: Classification of Workplace Zones (COWZ-UK)
The LWZC geodemographic classification result for each workplace zone in London.
Data: Final Cluster Lookup with Sub Groups with Suppression
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, YoungerNotGood, MidageGood, MidageNotGood, OlderGood, OlderNotGood * PetitionClusterModelBetas: For each petition cluster and demographic group except Pop16plus, + an Intercept for each cluster, it contains a betahat, SE, tval and NumModels value. NumModels is always 500. * PetitionClusterModelValues_ByLSOA: For each LSOA and cluster, it contains a Value, a Value Geo and a Value Cat. * PetitionToClusterMapping: This is an Excel spreadsheet containing the following tabs: (a) A Read me containing various useful notes. (b) Petition to cluster (with links to the petition, its name, cluster code, Government department, URL and created/opened/closed data). (c) Signatures by Westminster Parliamentary Constituency (Pcon); For each cluster, the number of signatures by parliamentary constituency. (d) Petition map: A multidimensional analysis of the clusters, showing their similarities across five dimensions. * COVID Petitions Feb 2024: A PDF outlining the project and techniques used by the data provider to assemble the dataset. 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 Additional information about the data are available in a contextual note. This note can be viewed via the link at the bottom of this page.
Small Area Estimates of Attitudes to COVID-19
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 unsuitable for inclusion in our UK wide analysis. These petitions need to be identified and dropped from the analysis at the outset. National / Regional level bias. This is particularly important in the case of Covid 19 because the progression of the pandemic and its response varied considerably across the UK. This is most obviously the case at a national level, but also applies regionally with local tiering introduced, and with some elected mayors having a high profile (e.g. Andy Burnham in Manchester). All our chosen petitions are subject to this type of bias and mitigation is taken at parliamentary constituency level to account for this. Geo-demographic bias. Parliamentary petitions are signed “on-line” and therefore the level of access to the internet and a suitable electronic device will impact on signature rates. This is likely to introduce systematic bias into the data based on factors such as age, household income etc. This bias cannot be mitigated at parliamentary constituency level and needs to be identified and accounted for by the researcher (if required). Options for doing this include profiling at LSOA level using other CDRC datasets to re-weight the results appropriately. The actions taken by More Metrics at parliamentary constituency level to mitigate the effect of bias are as follows: Petition counts are standardised by Region in England and by Country outside of England. Standardisation involves dividing petition counts at parliamentary constituency level by the national / regional average and multiplying by the UK average. Petitions with local bias are identified (after standardisation) by only selecting a subset of petitions at Parliamentary Constituency level that have an “unambiguous” set of (Spearman rank) correlations to every other chosen petition. The process used for this involves positioning each petition in five-dimensional “petition space” with the co-ordinates chosen so that the Euclidian distances between pairs of petitions is as close as possible to 1-r (where r is the corelation coefficient of the pairing). Petitions with total positioning errors above a threshold are dropped one-by-one until a set of petitions remain that have well-defined positions in petition space relative to every other chosen petition. The position co-ordinates also provide the necessary data needed to cluster petition topics into themes based on a combination of nearest neighbour distances and petition narratives.
Contextual Note: Detailed Analysis of Quality, Representation and Bias
External Website: Petitions - UK Government and Parliament
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 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/journal.pone.0201774 - the model used is EE-A6, on a deterministic (not probabilistic) basis. 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 data are synthetically modelled, based on the most common ethnicities stated for particular surnames (regardless of location) in England/Wales. It is not actual measured data for the populations. Because this particular source is only from England/Wales, we would expect marginally less accurate results from Scotland/Northern Ireland. The underpinning data are the 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 were developed in order to render the diverse data formats consistent and to establish the provenance of the consolidated registers. Known shortcomings in the data and over-all assessment of quality are set out in the peer-reviewed research papers. In addition to establishing consistency of address referencing, the research papers document the completeness of the data. In terms of coverage, the LCRs tend to under estimate LSOA adult population sizes relative to UK mid-year population estimates for 2003-2020. The research papers describe procedures developed by GeoDS to fill in known gaps where possible. Data for Northern Ireland are estimated to be less complete because of specific administrative procedures and legislative requirements. Additional UK-wide issues are created by second-home owners and students. The counts of individuals in the original LCR data fluctuate according to data supplier in addition to actual population size changes. As such, meta data describing the annual distribution of population counts across LAD that have been used in calculating Modelled Ethnicity Proportions are made available. Modelled Ethnicity Proportions may be out of line with census counts and users should consult census statistics if they have concerns. ## Data Sources * ONS Census 2011 - most common ethnicities by forename, most common ethnicities by surname. Single composition result for all of England/Wales. Used for more common forenames and surnames. * Onomap (various sources, e.g. phonebooks) - forename/surname pairs used in the Onomap model, from which some ethnicity based groupings are identified. Global coverage. Data typically from 2000-12 with some more recent data. Used for less common forenames and surnames only. * GeoDS Linked Consumer Register - names and addresses of individual people in households, every year from 1997 to 2025. ## Version History * 1.0 (August 2018) - Based on the LCR v1 (1997 to 2016) * 2.0 (January 2021) - Based on the LCR v2 (1997 to 2019) * 3.0 (September 2023) - Based on the LCR v3 (1997 to 2023, with LAD21 geography) * 3.1 (May 2024) - Added LAD23 geography. * 4.0 (December 2025) - Based on the LCR v4 (1997 to 2025, with LAD23 geography)
Modelled Ethnicity Proportions (LAD Geography)
Technical Report: Metadata and Validation for Modelled Ethnicity Proportions
Data: GeoDS Modelled Ethnicity Proportions LAD23 for 1997-2025
Related Record: Modelled Ethnicity Proportions (LSOA Geography)
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.
An overview of the methods used to create LOAC
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
A lookup between 2021 Output Area Code, Supergroup and Group
LOAC appended to OA and OA with built-up areas only showing
Supergroup and Group labels and descriptions
Data: LOAC GIS Files (Geopackage format)
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.
Salad Money Daily Transaction Volumes and Values
Related Record: Salad Money Open Banking Transaction Data
External Website: Salad Money - How it works
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 georeferenced CSV files, can be split into two broad categories: * The preventable hospitalisation admission by ethnic group data are age- and sex-standardised hospital admission rates per 100,000 population by ethnic group and English Local Authority District. Data are for acute ambulatory care sensitive conditions (ACSC). Data for acute, chronic and vaccine-preventable cases are combined. ACSC admissions are regarded as preventable admissions and have higher associations with under-5, elderly, deprived, and minority ethnic group patients. These data are provided for a single time period, 2009 to 2014. There is one row for each ethnic group. * The hospital admission for major disease categories data are age- and sex-standardised hospital admission rates per 100,000 population by ethnic group and English Local Authority District. Data are for 27 Level 1 (all major) disease categories as defined by the Global Burden of Disease (GBD). This data has been provided for two time periods, 1999-2004 and 2009- 2014, with one file for each period. Each file has one row for each combination of local authority district, ethnic group, disease category and ethnicity coding type. ## Quality, Representation and Bias 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. As a limitation, it should be noted that ethnicity is a complex concept encompassing biological, cultural, and subjective aspects. Variation in prediction success of name-based ethnicity classification can therefore arise for different reasons including individuals’ sense of belonging and resulting choice of ethnic group, socio-cultural naming and name-change practices, distinctiveness of names across ethnic groups, and the extent to which the name-based classification covers different origins at a given time point, e.g. when later waves of immigration have widened the range of diasporic names in the host country since the creation of the software. We used denominator data from Census 2011 as the most complete dataset on the ethnicity of the residential population in England. The census contains self-reported ethnicity. HES draws on the central NHS patient register with self-reported ethnicity. The proportion of patients without ethnicity record was 38.8% in 1999/00-2003/04 and 9.7% in 2009/10-2013/14. The dataset contain admission estimates by ethnic group as recorded by NHS as well as three different ethnicity classifications enhanced with names-based ethnicity. Full details about the completeness of ethnicity records and the prediction success of the EE software for different ethnic groups over time and across regions can be found in the PDF report attached to this data record.
NHS Hospital Admission Rates by Ethnic Group and other Characteristics
Data Summary: HES for ACSC (Preventable)
Related Record: Local morbidity rates of Global Burden of Disease and alcohol-related conditions
Technical Report: Hospital Episode Statistics Ethnicity Data Products
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, 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. The survey representation varies overtime with the most recent years being the most complete, therefore additional caution should be taken with temporal analysis.
Related Record: Retail Type or Vacancy Classification
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.
Regional Transport Provider - Travel Demand Data
Variable Dictionary: Regional Transport Provider (2014)
Variable Dictionary: Regional Transport Provider (2022)
Related Record: Regional Transport Provider - Customer and Ticket Sales Data
Data Summary: Regional Transport Provider (2022)
Data Summary: Regional Transport Provider (2014)
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.
Advanced GIS Methods Training: IUC and K-Means Clustering
Data: Part 2: K-Means Clustering - Workbook
Data: Part 1: Internet User Classification (IUC) - Workbook
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.
An Introduction to Spatial Data Analysis and Visualisation in R
Census Data: "Practical Data", OA Shapefile: "Camden OA 2011 Shapefile"
Data: Practical 10: Geographically Weighted Regression in R
Data: Practical 4: Finding Relationships in R
Census Data: "Practical Data", OA Shapefile: "Camden OA 2011 Shapefile", House Price Data: "Camden House Sales 2015"
Data: Practical 6: Mapping Point Data in R
Data: Practical 9: Measuring Spatial Autocorrelation in R
Data needed: 2011 Census Data Packs for Local Authority District: Camden (E09000007)
Data: 2011 Census Data Packs for Local Authority District: Camden (E09000007)
OA Shapefile: "Camden OA 2011 Shapefile", House Sales Shapefile: "House Sales Shapefile"
Data: Practical 11: Interpolating Point Data in R
Census Data: "Practical Data", OA Shapefile: "Camden OA 2011 Shapefile", House Sales Shapefile: "House Sales Shapefile"
Data: Practical 12: Functions and Loops in R
Data: Practical 2: Data exploration in R
OA Shapefile: "Camden OA 2011 Shapefile" House Sales Shapefile: "House Sales Shapefile"
Data: Practical 8: Representing Densities in R
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/journal.pone.0201774 - the model used is EE-A6, on a deterministic (not probabilistic) basis. 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 data are synthetically modelled, based on the most common ethnicities stated for particular surnames (regardless of location) in England/Wales. It is not actual measured data for the populations. Because this particular source is only from England/Wales, we would expect marginally less accurate results from Scotland. The underpinning data are the 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 were developed in order to render the diverse data formats consistent and to establish the provenance of the consolidated registers. Known shortcomings in the data and over-all assessment of quality are set out in the peer-reviewed research papers. In addition to establishing consistency of address referencing, the research papers document the completeness of the data. In terms of coverage, the LCRs tend to under estimate LSOA adult population sizes relative to UK mid-year population estimates for 2003-2020. The research papers describe procedures developed by the GeoDS to fill in known gaps where possible. The counts of individuals in the original LCR data fluctuate according to data supplier in addition to actual population size changes. As such, meta data describing the annual distribution of population counts across LSOA that have been used in calculating Modelled Ethnicity Proportions are made available. Modelled Ethnicity Proportions may be out of line with census counts and users should consult census statistics if they have concerns. ## Data Sources * ONS Census 2011 - most common ethnicities by forename, most common ethnicities by surname. Single composition result for all of England/Wales. Used for more common forenames and surnames. * Onomap (various sources, e.g. phonebooks) - forename/surname pairs used in the Onomap model, from which some ethnicity based groupings are identified. Global coverage. Data typically from 2000-12 with some more recent data. Used for less common forenames and surnames only. * GeoDS Linked Consumer Register - names and addresses of individual people in households, every year from 1997 to 2025.
Modelled Ethnicity Proportions (LSOA Geography)
Related Record: Modelled Ethnicity Proportions (LAD Geography)
_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.
External Website: ONS Area Classifications
Data: Output Area Classification 2011 (Shapefile format)
Related Record: Output Area Classification (2001)
Technical Report: Pen Portraits for the 2011 Output Area Classification
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.
Data: Short Course on Using R as a GIS - Workbook
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.
London Night Workers Classification (LNWC)
GeoDS website for the _Data After Dark_ transdisciplinary research collaboration, featuring all research outputs including the LNWC.
Map: London Night Workers Classification Map Dashboard
Data paper on the London Night Workers Classification, published in December 2025 on [dataafterdark.org](https://dataafterdark.org/)
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).
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 interpretation or analysis of the statistical data. This work uses research datasets which may not exactly reproduce National Statistics aggregates.
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
Data: Local authorities in South East England
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
Data: Local authorities in East of England
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
Hartlepool, Middlesbrough, Redcar and Cleveland, Stockton-on-Tees, Darlington, County Durham, Northumberland, Newcastle upon Tyne, North Tyneside, South Tyneside, Sunderland, Gateshead
Data: Local authorities in North East England
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
Data: Local authorities in Yorkshire and the Humber
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
Data: Local authorities in East Midlands
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
Data: Local authorities in South West England
External Website: ADR UK-funded Local Data Spaces wins ONS Research Excellence Award
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
Data: Local authorities in North West England
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
Data: Local authorities in West Midlands
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.
COVID-19 Lockdown High Street Resilience Classification
Related Record: Retail Centre Boundaries
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).
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 the classification, which was originally released for England and Wales only. Now updated, 2021/2 UK OAC is constructed entirely from census data, from 2021 for England, Northern Ireland and Wales, and from 2022 for Scotland. 2021/2 GB MSOAC summarises general population characteristics and built environment attributes and are designed for wide use in research for policy. The Wyszomierski et al (2023) paper provides a full rationale and documentation of the work undertaken.
GB MSOA / IZ Classification (2021/2 MSOAC)
While this report mainly refers to UK-OAC, the pen portraits and the updated technical note refers to MSOAC as well.
Technical Report: Classification Typology and Pen Portraits
Source Code: Aggregate ONS Output Area Classification (OAC) for GB MSOA/IZ (GitHub repository)
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
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.
The Ageing in Place Classification (AiPC)
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
GeoPackage (zipped .gpkg) file of the AiPC for England.
The Classification data for 32,844 LSOAs in England.
Data: Ageing in Place Classification (CSV format)
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
Technical Report: Classifying the Older Population
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 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 adult population. Please note the spatial unit supplied is UK region. 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 survey is high-quality and organised by a consumer research firm on behalf of MaPS, accessing several user panels. The survey includes quota/screening questions at the beginning to ensure a broadly representative sample of the population across the UK is included. Each respondent is assigned a weighting value which, when applied, should result in a survey that reflects the demographics of the UK. _N.B. The Debt Need Survey is sometimes referred to as the Debt Needs Survey._
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 geodemographic classification. It enables planners, researchers and policy-makers to explore accessibility gaps, daytime population flows and the night-time economy at a much finer scale than before – all while maintaining the highest standards of data protection and ethical research practice. ## Content The data are provided in CSV format at OA level for Greater London. Additional resources, including a detailed glossary of variables, cluster descriptions and a technical report are also available for download. This dataset applies K-Means clustering with decile-scaled, input variables (52 indicators across two domains) to produce stable and interpretable cluster assignments. All processing steps and methodological details are documented in the accompanying technical report. ## Quality, Representation and Bias The GCI consists of variables based on anonymised GPS-derived movement records from a sample of London residents between 2016–2019. While the sample is generally representative, it covers only a proportion of total movements and excludes some groups (e.g., those without smartphones). Cross-app or out-of-London movements are not observed, which may lead to underestimation of some flows. Nonetheless, the main patterns of movement and connectivity across London are well captured, making the dataset suitable for developing a robust classification. Further methodological details and validation results are provided in the accompanying technical report.
Geodemographic Classification of Interactivity (GCI)
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 outputs from Northern Ireland's Census were not available at the time of production of the classification, which was originally released for England and Wales only. Now updated, 2021/2 UK OAC is constructed entirely from census data, from 2021 for England, Northern Ireland and Wales, and from 2022 for Scotland. 2021/2 UK LSOAC summarises general population characteristics and built environment attributes and are designed for wide use in research for policy. The Wyszomierski et al (2023) paper provides a full rationale and documentation of the work undertaken. ## Version History * 1.0 - For Census 2011, constructed by ONS * 2.0 - For Census 2021/2, based on UK OAC 2021/2
UK LSOA / DZ / SDZ Classification (2021/2 LSOAC)
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.
While this report mainly refers to UK-OAC, the pen portraits and the updated technical note refers to LSOAC as well.
Related Record: GB MSOA / IZ Classification (2021/2 MSOAC)
Source Code: Aggregate ONS Output Area Classification (OAC) for UK LSOA/DZ/SDZ (Github repository)
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 provided in the Technical Report on linking and validating, below. *(1) RMEPC move out* The RMEPC move out datasets 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 LAD. Two RMEPC data products have been published. The first one, named 'rmepc_laua21_mo', aggregates data for the entire time period between 2009 and 2023, while the second one, 'rmepc_laua21_mo_threeyears', provides data for three-year intervals within the same time period. *(2) RMEPC move into* The RMEPC move into datasets provide average and median change of energy performance rating, potential energy performance rating and total floor area for the residences of individuals moving into each LAD in GB. Similar to the previously mentioned move-out datasets, there are two RMEPC move-in products. The first one, named ‘rmepc_laua21_mi', aggregates data for the entire time period between 2009 and 2023, while the second one, named rmepc_laua21_mi_threeyears', provides data for three-year intervals within the same time frame. *(3) RMEPC move within* The RMEPC move within datasets provide the average and median changes of energy performance rating, potential energy performance rating and total floor area for the residences of individuals moving within LADs in GB. Two RMEPC move within products are available. The first, labelled 'rmepc_laua21_wi', is data for the entire time period spanning from 2009 to 2023. The second, named 'rmepc_laua21_mi_threeyears', offers data at three-year intervals within the same timeframe. ## Quality, Representation and Bias The underpinning data comprise the GeoDS LCR_EPC dataset, which establishes links between Energy Performance Certificates (EPCs) and the origin and destination addresses of individual movers within the LCR migration model. Only around 59% of residential properties across GB have EPC records for the period between 2009 and 2023. This partial record coverage means that, for nearly half of the movers within this time period, EPC information cannot be linked to the LCR migration model, due to the EPC data being unavailable for either their previous or their current addresses. Furthermore, it is important to highlight that the representation of Scottish EPCs in the dataset is lower when compared to England and Wales. Consequently, the number of movers with EPCs in the GeoDS LCR_EPC data is reduced in Scotland, compared to England and Wales. For comprehensive details on the source and reliability of the GeoDS LCR_EPC dataset, please refer to the Technical Report on linking and validating, below.
Residential Mobility and Energy Performance Certificate (RMEPC) Index (LAD Geography)
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.
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).
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.
Data: RMEPC-into Index LAD (three years)
Related Record: Residential Mobility and Energy Performance Certificate (RMEPC) Index (LSOA Geography)
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).
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.
Data: RMEPC-within Index LAD (three years)
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).
Technical Report: Linking LCR and EPC data and Validating RMEPC with Census data
Files used by courses at the University of Liverpool and other institutions.
Data: Output Area Classification 2011 for Liverpool City Region (E47000004)
Data: Index of Multiple Deprivation (IMD) 2010/15/19 for Liverpool City Region (E47000004)
Data: Index of Multiple Deprivation (IMD) 2010/15/19 for Liverpool (E08000012)
Data: Census Residential Data Pack 2011 for Liverpool City Region (E47000004)
Data: Output Area Classification 2011 for Liverpool (E08000012)
Data: Internet User Classification 2018 for Liverpool City Region (E47000004)
Data: Internet User Classification 2018 for Liverpool (E08000012)
Data: Mid Year Population Estimates 2013-18 for Liverpool (E08000012)
Data: Census Workplace Data Pack 2011 for Liverpool (E08000012)
Data: Census Workplace Data Pack 2011 for Liverpool City Region (E47000004)
Data: Access to Healthy Assets and Hazards (AHAH) v2 Data Pack for Liverpool (E08000012)
Data: Census Residential Data Pack 2011 for Liverpool (E08000012)
Related Record: Geographic Data Science in Python
Data: Mid Year Population Estimates 2013-18 for Liverpool City Region (E47000004)
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.
Combined 2016-2022 file (used for all the Mapmaker maps).
Data: Broadband Speed and Availability Averaged by OA for 2016 to 2022
Data: Broadband Coverage (Availability) Averaged by OA for 2018
External Website: Connected Nations and infrastructure reports
Data: Broadband Speed Averaged by OA for 2016
Data: Broadband Performance (Speed) Averaged by OA for 2018
External Website: Connected Nations 2021: data downloads
Data: Broadband Speed Averaged by OA for 2017
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 dataset has changed significantly in terms of variables included, particularly regarding durables and Internet behaviour. It now includes 145 (compared to 153) variables. However, since e.g. ethnicity is now combined into 1 variable (instead of 17), a direct variable comparison is not possible. ## Quality, Representation and Bias Samples are based on the postcode district level, using Geodemographic models for half the sample, while the other half is sampled in under-weighted profiles to increase the probability of representative selections. The team of Interviewers are given quotas for Gender, Age, Working Status and Social Grade according to the Census statistics. The final process is to ensure, via the interview process, that no respondent is interviewed twice, over time. This methodology tries to ensure the sampling of an accurate cross-section of the British Population, and as the same methodology is used every week, it tries to ensure that trends will be equally accurate over time. To reduce final bias, the survey includes a weighting system (specifically, by means of the Rim Weighting method). The weights are based on the Census mid-year estimates, and checks against other available population profiles such as Age, Gender, Region, Home Tenure, and Social Grade. The dataset is quite complete, although caution should be exercised as there are a number of chain and follow-up questions in the survey which are not always applicable, hence coded as missing values (e.g. “NA”, “NULL”). However, there is a “Not Asked” code for e.g. online shopping when the individual was previously replied with no access to the internet. Furthermore, some questions have various answers that are not always usable, such as “No answer”, “Refused”, “Don’t know” etc., depending on the question. As such, the missing values reported on the data profile table below may not be entirely accurate, however it tries to be as comprehensive as possible. Care should also be taken when linking other geographic data. For the 2008 – 2014 BPS data, LSOA coverage in GB is 64%, meaning 64% of all LSOAs (or Data Zones in Scotland) have at least 1 individual who has taken part in the survey. The average ratio is 13.28 responders by LSOA. _This dataset is a spatially generalised version of the Secure British Population Survey dataset, also available on this site._
British Population Survey (LSOA Geography)
Related Record: British Population Survey
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 indirectly from multiple providers without warranties about accuracy or coverage, consistent with industry practices. Rigorous internal and external validation procedures were developed to render diverse data formats consistent and establish the provenance of consolidated registers. Known shortcomings in the data and an overall assessment of quality are detailed in peer-reviewed research papers. Discrepancies in the quality of counts among the different countries of the UK may exist, given that estimates rely on successful linkage (‘matching’) of properties recorded in the address database and properties captured in the LCRs. Matching success rates vary among individual countries, with match rates in England and Wales generally exceeding those in Northern Ireland and Scotland. The number of active properties in the latter two countries might therefore be underestimated.
Related Record: Residential Property Counts (LSOA Geography)
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.
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.
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.
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 - [ ].
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.
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 surveying firm on behalf of MaPS, online or face-to-face. The survey includes quota/screening questions at the beginning to ensure a broadly representative sample of the population across the UK is included. Each respondent is assigned a weighting value which, when applied, should result in a survey that reflects the demographics of the UK. ##Special Stipulation MaPS requires a disclaimer on publications using the data, that the publication does not necessarily represent its views. The following text is recommended: “Disclaimer: the views and recommendations in this report are those of the organisation publishing this report and its author(s) and do not necessarily represent those of the Money and Pensions Service whose data was used to produce it.”
External Website: MaPS MoneyView 2025 - Technical Report
Related Record: MaPS Financial Wellbeing Survey
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.
Geolytix aggregated in-app location dataset
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).
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.
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.
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 times. For each departure time, travel times are calculated for every minute within a 30-minute window, with the median value reported (see publication for full parameter details). In the second step, the travel-time matrices are linked to our Retail Type, Vacancy, and Address dataset. Four retail subcategories were included to provide a broad and realistic representation of food retail availability in London — Supermarkets, Grocers, Greengrocers & Fruitsellers, and Convenience Stores. Using outputs from _r5r_ and the _accessibility_ package, travel times to the nearest hexagon containing at least one open food store are calculated for each departure time, with store availability determined dynamically based on opening hours. ## Quality, Representation and Bias To mitigate boundary effects, food retail outlets located within 2,000 metres of the Greater London boundary were also included. The final dataset comprises 8,337 outlets, representing the most comprehensive analysis of food accessibility in London to date. It should be noted, however, that the analysis relies on a single accessibility indicator—travel time to the nearest open outlet. This measure does not account for the diversity, quality, or density of available food retail options. The objective was to produce an easily interpretable and policy-ready indicator, rather than more complex measures such as gravity-based accessibility models. The dataset is based on March 2024 data, using GTFS schedules from 1–2 March and the March snapshot of Green Street data. Acknowledging that accessibility varies by day and season, a specific day was selected rather than using averaged conditions. While the dataset reliably captures general accessibility patterns, differences between days, for example those driven by store operating hours, should be acknowledged. Missing values (NA) are assigned to hexagons that are either inaccessible (i.e., contain no road network) or have travel times exceeding 60 minutes, which is our maximum travel-time threshold.
London Nighttime Access to Food Retail Options
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).
Data: London Nighttime Access to Food Retail Options (CSV Format)
Map: London Nighttime Access to Food Retail map dashboard
External Website: Locating London's night-time food deserts (data story)
Data: London Nighttime Access to Food Retail Options (Geopackage Format)
Related Record: London Night Workers Classification (LNWC)
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: The process of transforming probe requests into footfall estimates involves assumptions that may lead to overcounting or undercounting in specific scenarios. * Geographical Representation: The dataset is heavily skewed toward Greater London, with one-third of sensor locations situated in this region. Consequently, national-level aggregated metrics may disproportionately reflect patterns in London. * Temporal Coverage: Early stages of data collection, before July 2016, included fewer sensors (approximately 200), mostly located in London, further amplifying initial geographical biases. * Device Misclassification: Sensors cannot distinguish between mobile devices and other Wi-Fi-enabled devices (e.g., printers or routers), potentially inflating counts. * City-Level Distribution: The highest sensor concentration is in cities like London (381 locations), followed by Edinburgh (46) and Manchester (32). In contrast, smaller towns often have one or two sensors, limiting granularity in those areas.
Related Record: Ultra Wide Band Footfall Data
Contextual Note: Production of cleaned footfall estimates for safeguarded dataset
A list of towns/cities and the number of sensors in each that included in the SmartStreetSensor data.
Data Summary: Location Counts by Urban Area
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.
Access to Healthy Assets & Hazards (AHAH) (Previous Versions)
Technical Report: Data Sources for AHAH v4
Data: AHAH v3 Index, Domains and Components
Data: AHAH v1 Index, Domains and Components
Data: AHAH v4 Index, Domains and Components
Data: AHAH v2 Index, Domains and Components
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.
A CSV table describing the classification of every LSOA.
A PDF file providing the pen portraits of every cluster, including plots of cluster centres and a brief summary of the methodology.
An ZIP archive containing a Standard ESRI Shapefile of Custom Generalised LSOAs in GB, EPSG:27700.
Socio-Economic Indices per Cluster Group
Technical Report: IUC 2018 Cluster Census Indices
Mean attribute values (z-scores) describing cluster centres.
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 significantly in terms of variables included, particularly regarding durables and Internet behaviour. It now includes 145 (compared to 153) variables. However, since e.g. ethnicity is now combined into 1 variable (instead of 17), a direct variable comparison is not possible. ## Quality, Representation and Bias Samples are based on the postcode district level, using Geodemographic models for half the sample, while the other half is sampled in under-weighted profiles to increase the probability of representative selections. The team of Interviewers are given quotas for Gender, Age, Working Status and Social Grade according to the Census statistics. The final process is to ensure, via the interview process, that no respondent is interviewed twice, over time. This methodology tries to ensure the sampling of an accurate cross-section of the British Population, and as the same methodology is used every week, it tries to ensure that trends will be equally accurate over time. To reduce final bias, the survey includes a weighting system (specifically, by means of the Rim Weighting method). The weights are based on the Census mid-year estimates, and checks against other available population profiles such as Age, Gender, Region, Home Tenure, and Social Grade. The dataset is quite complete, although caution should be exercised as there are a number of chain and follow-up questions in the survey which are not always applicable, hence coded as missing values (e.g. “NA”, “NULL”). However, there is a “Not Asked” code for e.g. online shopping when the individual was previously replied with no access to the internet. Furthermore, some questions have various answers that are not always usable, such as “No answer”, “Refused”, “Don’t know” etc., depending on the question. As such, the missing values reported on the data profile table below may not be entirely accurate, however it tries to be as comprehensive as possible. Care should also be taken when linking other geographic data. For the 2008 – 2014 BPS data, LSOA coverage in GB is 64%, meaning 64% of all LSOAs (or Data Zones in Scotland) have at least 1 individual who has taken part in the survey. The average ratio is 13.28 responders by LSOA.
Related Record: British Population Survey (LSOA Geography)