Assessing the impacts of gridded population model choice on degree of urbanisation metrics
Assessing the impacts of gridded population model choice on degree of urbanisation metrics
Defining urban and rural areas is crucial for assessing the accessibility of services and opportunities that impact people worldwide. The Degree of Urbanisation framework, endorsed by the UN Statistical Commission, primarily uses population grids to classify areas through a harmonised, population-centric approach, enabling international comparisons. However, variations in the distribution of population counts at the grid-cell level across different population datasets can significantly influence the resulting patterns. We applied the Degree of Urbanisation to 16 countries in Africa and the Caribbean, using four population grids to evaluate these effects. It shows that differences primarily occur in the classification of urban cluster. On average, 27.5 % of the population falls into mixed classes, with 17.5 % in mixed rural and urban cluster areas and 7.8 % in mixed urban cluster and urban centre areas. Population grids that only model populations within satellite-detected settlements show limited disagreement, with mixed rural and urban cluster population classifications decreasing by 5.6 percentage points and mixed urban cluster and urban centre populations by 1.4. Population modelling approaches that distribute populations more broadly, including outside of detected built-up areas, substantially reduce settlement identifications, resulting in 42.3 % fewer urban centres and 66.2 % fewer dense urban clusters than the average across the study countries. Our analyses highlight the potential sensitivity of Degree of Urbanisation to gridded population modelling assumptions and provide guidance on its implementation.
Degree of urbanisation, Gridded population data, Sensitivity analysis, Settlement classification, Urbanisation metrics
Zhang, Wen Bin
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Woods, Dorothea
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Olowe, Iyanuloluwa Deborah
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Schiavina, Marcello
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Fang, Weixuan
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Hornby, Graeme
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Bondarenko, Maksym
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Maes, Joachim
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Dijkstra, Lewis
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Tatem, Andrew J.
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Sorichetta, Alessandro
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21 July 2025
Zhang, Wen Bin
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Woods, Dorothea
2a542d84-18c1-48d5-b039-ebba67562006
Olowe, Iyanuloluwa Deborah
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Schiavina, Marcello
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Fang, Weixuan
1acb263c-e420-4e4c-b029-2628a12c8c66
Hornby, Graeme
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Bondarenko, Maksym
1cbea387-2a42-4061-9713-bbfdf4d11226
Maes, Joachim
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Dijkstra, Lewis
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Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Sorichetta, Alessandro
20014c19-5ac4-4417-9f7c-4b65f336989e
Zhang, Wen Bin, Woods, Dorothea, Olowe, Iyanuloluwa Deborah, Schiavina, Marcello, Fang, Weixuan, Hornby, Graeme, Bondarenko, Maksym, Maes, Joachim, Dijkstra, Lewis, Tatem, Andrew J. and Sorichetta, Alessandro
(2025)
Assessing the impacts of gridded population model choice on degree of urbanisation metrics.
Cities, 166, [106293].
(doi:10.1016/j.cities.2025.106293).
Abstract
Defining urban and rural areas is crucial for assessing the accessibility of services and opportunities that impact people worldwide. The Degree of Urbanisation framework, endorsed by the UN Statistical Commission, primarily uses population grids to classify areas through a harmonised, population-centric approach, enabling international comparisons. However, variations in the distribution of population counts at the grid-cell level across different population datasets can significantly influence the resulting patterns. We applied the Degree of Urbanisation to 16 countries in Africa and the Caribbean, using four population grids to evaluate these effects. It shows that differences primarily occur in the classification of urban cluster. On average, 27.5 % of the population falls into mixed classes, with 17.5 % in mixed rural and urban cluster areas and 7.8 % in mixed urban cluster and urban centre areas. Population grids that only model populations within satellite-detected settlements show limited disagreement, with mixed rural and urban cluster population classifications decreasing by 5.6 percentage points and mixed urban cluster and urban centre populations by 1.4. Population modelling approaches that distribute populations more broadly, including outside of detected built-up areas, substantially reduce settlement identifications, resulting in 42.3 % fewer urban centres and 66.2 % fewer dense urban clusters than the average across the study countries. Our analyses highlight the potential sensitivity of Degree of Urbanisation to gridded population modelling assumptions and provide guidance on its implementation.
Text
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Accepted/In Press date: 14 July 2025
e-pub ahead of print date: 21 July 2025
Published date: 21 July 2025
Keywords:
Degree of urbanisation, Gridded population data, Sensitivity analysis, Settlement classification, Urbanisation metrics
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Local EPrints ID: 503554
URI: http://eprints.soton.ac.uk/id/eprint/503554
ISSN: 0264-2751
PURE UUID: c61a9c50-0774-4b05-81c5-9e4a40a2c3ea
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Date deposited: 05 Aug 2025 16:39
Last modified: 10 Oct 2025 02:10
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Contributors
Author:
Wen Bin Zhang
Author:
Iyanuloluwa Deborah Olowe
Author:
Marcello Schiavina
Author:
Weixuan Fang
Author:
Joachim Maes
Author:
Lewis Dijkstra
Author:
Alessandro Sorichetta
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