How to Use the London Datastore for Urban Research

For urban researchers studying housing delivery and transport accessibility in London, the London Datastore (data.london.gov.uk) is the single most comprehensive curated repository of borough-level and city-wide data. Run by the GLA’s City Intelligence unit, it aggregates datasets from the GLA, Transport for London, the London boroughs, and national agencies such as the Office for National Statistics. This guide walks through the platform’s structure, highlights five essential datasets, and explains how to avoid common data pitfalls.

What the London Datastore covers – and what it does not

The Datastore covers 11 main categories: population, housing, transport, planning, environment, economy, health, education, crime, culture, and public realm. Each dataset includes a metadata page with update frequency, geographic resolution (borough, ward, or LSOA), methodology notes, and a download link (CSV or GeoJSON). Most files are available under the Open Government Licence, requiring only attribution.

However, the platform is a curated repository, not a live feed. Some datasets are updated annually, with a lag of several months. For example, the latest Public Transport Accessibility Levels (PTAL) data may not reflect recent service changes. Additionally, the Datastore does not contain raw building-by-building planning application records – those are held by individual borough planning portals. The Datastore provides only aggregated summaries by borough.

How to navigate the platform efficiently

To find a dataset, go to data.london.gov.uk and use the search bar with keywords such as “housing starts”, “cycling”, “PTAL”, or “population projections”. Alternatively, browse by category via the “Datasets” menu or filter by organisation (e.g., “GLA”, “Transport for London”). Each dataset page includes a description, update frequency, geographic coverage, and a download link.

For quick access to the most commonly used borough indicators, use the London Borough Data Explorer at data.london.gov.uk/datastore/borough-data-explorer. This simplified interface displays key metrics without requiring a full dataset download. The Datastore also offers an API for programmatic access; documentation is available on the site.

Five essential datasets for housing and transport research

The following table lists datasets that are especially useful for researchers analysing housing capacity and transport accessibility.

For example, the SHLAA dataset page (https://data.london.gov.uk/dataset/london-strategic-housing-land-availability-assessment) provides annual updates on estimated housing capacity, including site locations and size categories. Researchers should always check the methodology PDF attached to each dataset release.

Common pitfalls and how to avoid them

Three issues frequently trip up new users:

Data lag – The most recent year shown may be two years old. For example, 2023 population estimates were published in mid-2024. Always check the “Last updated” date on the dataset page.
2. Revision changes – The GLA regularly revises projections, especially for population and housing. The 2023-based population projections differ from the 2021-based ones. Use the version number and date in your citations.
3. Geographic aggregation – Most datasets are at borough level. For ward- or LSOA-level analysis, you need to combine Datastore data with ONS or borough-specific sources. The London Borough Data Explorer can help but does not provide raw ward-level downloads.

Practical tips for responsible use

Before using any dataset, read the “Data sources and methodology” section on the dataset page. This explains how the numbers were collected, what definitions were used, and any known limitations. For example, the PTAL methodology document clarifies that the index is calculated based on walking distance to stops and stations, not on actual service frequency.

Always cite the version you used, including the publication date. The suggested citation format is: “GLA City Intelligence, [dataset name], [year]”. If you combine multiple datasets, note the version numbers in your methodology appendix.

Next steps: start with a concrete dataset

If you are new to the London Datastore, start with the SHLAA dataset (linked above) or the PTAL dataset (https://data.london.gov.uk/dataset/public-transport-accessibility-levels). Download the CSV file, open it in a spreadsheet or QGIS, and explore the columns. For programming-based analysis, use the API to fetch the latest data directly.

After you have downloaded a dataset, cross-check a few figures with the original source – for example, compare PTAL scores with TfL’s own network maps, or check SHLAA site numbers against the relevant borough’s local plan. This practice will reveal any discrepancies and build your confidence in the data.

The London Datastore is a powerful starting point for urban research, but it is not the final word. Always verify critical numbers with the publishing agency (TfL, ONS, the relevant borough) and, for hyperlocal work, supplement with ward-level data from the London Borough Data Explorer or direct borough records.