A stochastic approach to integerize floating-point estimates in gridded population mapping
A stochastic approach to integerize floating-point estimates in gridded population mapping
Gridded population datasets are increasingly relied upon for spatial planning, resource allocation, and disaster response due to their flexible integration with other spatial data layers. These datasets are typically produced by disaggregating population counts from administrative units into grid cells, yielding non-integer values that preserve overall counts. However, floating-point cell values are often difficult for users to interpret, and standard rounding approaches may introduce aggregation errors at administrative levels that affect planning decisions. Here, we present a stochastic integerisation method that preserves total population and demographic proportions, and compare it with existing approaches. The method separates the value of each cell into integer and decimal parts, and probabilistically allocates the residual based on decimal magnitudes. Applying the method to gridded population data shows that it effectively reduces unrealistic population predictions in uninhabited areas. The results also demonstrate that the new integerisation method can effectively convert floating-point population estimates into integers while preserving both spatial distribution and demographic proportions, such as age-sex structures. These findings highlight the performance of the proposed integerisation method to generate reliable gridded population distribution datasets across diverse contexts that are easier to interpret, particularly for areas with sparse populations or complex geometries of underlying administrative units.
Gridded population datasets, floating-point value, integer, policymaking, stochastic
Zhang, Wen-Bin
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Sorichetta, Alessandro
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Frye, Charlie
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Tejedor-Garavito, Natalia
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Fang, Weixuan
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Cihan, Duygu
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Woods, Dorothea
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Yetman, Gregory
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Hilton, Jason
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Tatem, Andrew J.
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Bondarenko, Maksym
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1 October 2025
Zhang, Wen-Bin
a4ab325c-e9cb-4369-959b-25a3320bb4e3
Sorichetta, Alessandro
c80d941b-a3f5-4a6d-9a19-e3eeba84443c
Frye, Charlie
c6f01001-35a3-40ca-8838-87d4388cf965
Tejedor-Garavito, Natalia
26fd242c-c882-4210-a74d-af2bb6753ee3
Fang, Weixuan
1acb263c-e420-4e4c-b029-2628a12c8c66
Cihan, Duygu
6562410f-2de3-4ca0-a86f-2bf612a6e9e8
Woods, Dorothea
2a542d84-18c1-48d5-b039-ebba67562006
Yetman, Gregory
502f5c7d-0576-4c88-99a4-e8c771844257
Hilton, Jason
da31e515-1e34-4e9f-846d-633176bb3931
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Bondarenko, Maksym
1cbea387-2a42-4061-9713-bbfdf4d11226
Zhang, Wen-Bin, Sorichetta, Alessandro, Frye, Charlie, Tejedor-Garavito, Natalia, Fang, Weixuan, Cihan, Duygu, Woods, Dorothea, Yetman, Gregory, Hilton, Jason, Tatem, Andrew J. and Bondarenko, Maksym
(2025)
A stochastic approach to integerize floating-point estimates in gridded population mapping.
International Journal of Geographical Information Science.
(doi:10.1080/13658816.2025.2568721).
Abstract
Gridded population datasets are increasingly relied upon for spatial planning, resource allocation, and disaster response due to their flexible integration with other spatial data layers. These datasets are typically produced by disaggregating population counts from administrative units into grid cells, yielding non-integer values that preserve overall counts. However, floating-point cell values are often difficult for users to interpret, and standard rounding approaches may introduce aggregation errors at administrative levels that affect planning decisions. Here, we present a stochastic integerisation method that preserves total population and demographic proportions, and compare it with existing approaches. The method separates the value of each cell into integer and decimal parts, and probabilistically allocates the residual based on decimal magnitudes. Applying the method to gridded population data shows that it effectively reduces unrealistic population predictions in uninhabited areas. The results also demonstrate that the new integerisation method can effectively convert floating-point population estimates into integers while preserving both spatial distribution and demographic proportions, such as age-sex structures. These findings highlight the performance of the proposed integerisation method to generate reliable gridded population distribution datasets across diverse contexts that are easier to interpret, particularly for areas with sparse populations or complex geometries of underlying administrative units.
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Accepted/In Press date: 25 September 2025
Published date: 1 October 2025
Additional Information:
Publisher Copyright:
© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Keywords:
Gridded population datasets, floating-point value, integer, policymaking, stochastic
Identifiers
Local EPrints ID: 506592
URI: http://eprints.soton.ac.uk/id/eprint/506592
ISSN: 1365-8816
PURE UUID: 0a7def3c-8319-4762-9da2-12aae849fe27
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Date deposited: 11 Nov 2025 17:58
Last modified: 12 Nov 2025 03:05
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Author:
Wen-Bin Zhang
Author:
Charlie Frye
Author:
Weixuan Fang
Author:
Duygu Cihan
Author:
Gregory Yetman
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