gla-data

Shared tools for loading, transforming, and visualising the GLA datasets used in City Data analysis.

Installation

Global install (CLI)

To install gla-data globally so the gla-data CLI is available anywhere in your terminal, use uv tool:

uv tool install gla-data

Normally pixi global install would be the preferred approach, but it does not yet support PyPI packages — so uv tool is the right choice for now.

If you don't have uv installed, you can install it via pixi:

pixi global install uv

pip

pip install gla-data

pixi

pixi add gla-data

Pixi pulls geospatial dependencies (geopandas, pyproj, etc.) from conda-forge, which provides reliable pre-built binaries across all platforms and Python versions.

Usage

Python API

import gla_data

# Load 2021 LSOA boundaries for London
lsoa_gdf = gla_data.load_boundaries(geography='lsoa', year=2021)

# Load census dwelling counts
dwellings_2021 = gla_data.load_census_dwellings(year=2021)
dwellings_2011 = gla_data.load_census_dwellings(year=2011)

# Crosswalk 2011 dwelling counts onto 2021 LSOA boundaries
dwellings_2011_xw = gla_data.crosswalk(
    dwellings_2011,
    from_year=2011,
    to_year=2021,
    value_cols=['dwellings_total'],
)

# Load an OA → LSOA geography lookup
lookup = gla_data.load_geography_lookup(year=2021, smallest_geography='oa')

# Aggregate OA-level data up to LSOA level
lsoa_totals = gla_data.aggregate(
    df,
    from_geography='oa',
    to_geography='lsoa',
    value_cols=['count'],
    year=2021,
)

CLI

The package installs a gla-data command. Run catalogue to list all available datasets:

gla-data catalogue

This prints a table of every dataset, the function used to load it, a description, and its access tier (public/S3).

Development

git clone <repo-url>
cd gla-data
pixi install

pixi run test       # run tests
pixi run lint       # check code style
pixi run format     # auto-format
pixi run typecheck  # type checking

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gla_data-0.1.3.tar.gz (141.1 kB view details)

Uploaded Jul 6, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

gla_data-0.1.3-py3-none-any.whl (17.4 kB view details)

Uploaded Jul 6, 2026 Python 3

File details

Details for the file gla_data-0.1.3.tar.gz.

File metadata

Download URL: gla_data-0.1.3.tar.gz
Upload date: Jul 6, 2026
Size: 141.1 kB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for gla_data-0.1.3.tar.gz
Algorithm	Hash digest
SHA256	`fbe08d15c46b038673d32307d23fe0525bec413796c720e01c21d364d162891e`
MD5	`3aa863e7066da07aeef40322ae7eed16`
BLAKE2b-256	`06b580d910aee75a7c6b3159f5083aee2f12fef20ffd5e57eb17a7e496b532fc`

See more details on using hashes here.

Provenance

The following attestation bundles were made for gla_data-0.1.3.tar.gz:

Publisher: publish.yml on Greater-London-Authority/gla-data

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Statement:
- Statement type: https://in-toto.io/Statement/v1
- Predicate type: https://docs.pypi.org/attestations/publish/v1
- Subject name: gla_data-0.1.3.tar.gz
- Subject digest: fbe08d15c46b038673d32307d23fe0525bec413796c720e01c21d364d162891e
- Sigstore transparency entry: 2084768776
- Sigstore integration time: Jul 6, 2026
Source repository:
- Permalink: Greater-London-Authority/gla-data@e02cc062f6aaa2e201829b5d2d041722f9c48da4
- Branch / Tag: refs/tags/v0.1.3
- Owner: https://github.com/Greater-London-Authority
- Access: private
Publication detail:
- Token Issuer: https://token.actions.githubusercontent.com
- Runner Environment: github-hosted
- Publication workflow: publish.yml@e02cc062f6aaa2e201829b5d2d041722f9c48da4
- Trigger Event: push