Carnell, E.; Tomlinson, S.

Urban-Rural Classification of UK gridded population at 1 km resolution for 2021 based on Census 2021/2022 and Land Cover Map 2021

https://doi.org/10.5285/0d6995a5-5007-4145-b685-d149dcb24c83
Download/Access

By accessing or using this dataset, you agree to the terms of the relevant licence agreement(s). You will ensure that this dataset is cited in any publication that describes research in which the data have been used.


This dataset is available under the terms of the Open Government Licence

This dataset contains gridded population estimates for the UK at 1 km x 1 km resolution, disaggregated by urban and rural classification, based on the 2021 UK census (2022 for Scotland) and Land Cover Map 2021 input data. It is related to the UK gridded population dataset, sharing the same Output Area-level census inputs, Land Cover Map 2021 land-use classes, and 1 km British National Grid (OSGB36) output framework. This dataset separates the gridded estimates into urban and rural populations, based on the rural/urban classification of the source Output Areas.

In addition to this simple, binary urban/rural split, the dataset retains more detailed classifications, where available. England & Wales uses a 6-fold classification (Urban, Larger Rural and Smaller Rural, each split by accessibility to a larger town or city). Scotland an 8-fold classification that further distinguishes Accessible, Remote and Very Remote areas by drive time to the nearest substantial settlement. Northern Ireland has no official Output Area/Data Zone-level rural-urban classification, so only the binary Urban/Rural split (derived from NISRA's 2015 Settlement Development Limits) is available there.

Each 1 km grid cell also includes the underlying proportion of urban and rural Output Areas, allowing the population estimates to be compared to the area coverage. Population totals at each processing step were checked against official 2021/2022 Census totals, so that the summed urban and rural (and intermediate category) grids reproduce the official Census population count for the UK in 2021/2022.
Publication date: 2026-09-16

Format

TIFF

Spatial information

Study area
Spatial representation type
Raster
Spatial reference system
OSGB 1936 / British National Grid
Spatial resolution
1000 metres

Temporal information

Temporal extent
2021-01-01    to    2021-12-31

Provenance & quality

Census data from the Office of National Statistics, National Records for Scotland and Northern Ireland Statistics and Research Agency have been downloaded as CSV/Excel files.
Land Cover Map 2021 raster data has been obtained from UKCEH.
Output Area (/Data Zone) geography data (Extent of the Realm), were obtained for England & Wales, Northern Ireland, and Scotland.
The processing steps are detailed in the Supporting Documentation.
To ensure consistency of the final product, total population numbers were compared, at each processing step, with the official 2021/2022 Census totals. As a result, the final output file is consistent with the Census total population for the UK in 2021.

Licensing and constraints

This dataset is available under the terms of the Open Government Licence

Cite this dataset as:
Carnell, E.; Tomlinson, S. (2026). Urban-Rural Classification of UK gridded population at 1 km resolution for 2021 based on Census 2021/2022 and Land Cover Map 2021. NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/0d6995a5-5007-4145-b685-d149dcb24c83

Correspondence/contact details

Edward Carnell
UK Centre for Ecology & Hydrology
 enquiries@ceh.ac.uk

Authors

Carnell, E.
UK Centre for Ecology & Hydrology
Tomlinson, S.
UK Centre for Ecology & Hydrology

Other contacts

Publisher
NERC EDS Environmental Information Data Centre
 info@eidc.ac.uk
Rights holder
UK Centre for Ecology & Hydrology
Custodian
NERC EDS Environmental Information Data Centre
 info@eidc.ac.uk

Additional metadata

Topic categories
health
INSPIRE theme
Population Distribution - Demography
Keywords
Land cover , Land use , Modelling , population density
Funding
Natural Environment Research Council Award: NE/Y006208/1