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A stochastic approach to integerize floating-point estimates in gridded population mapping.

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Title: A stochastic approach to integerize floating-point estimates in gridded population mapping.
Authors: Zhang, Wen-Bin1 (AUTHOR) wb.zhang@soton.ac.uk, Sorichetta, Alessandro2 (AUTHOR), Frye, Charlie3 (AUTHOR), Tejedor-Garavito, Natalia1 (AUTHOR), Fang, Weixuan1 (AUTHOR), Cihan, Duygu1 (AUTHOR), Woods, Dorothea1 (AUTHOR), Yetman, Gregory4 (AUTHOR), Hilton, Jason5 (AUTHOR), Tatem, Andrew J.1 (AUTHOR), Bondarenko, Maksym1 (AUTHOR)
Source: International Journal of Geographical Information Science. Jun2026, Vol. 40 Issue 6, p1601-1617. 17p.
Subjects: Floating-point arithmetic, Resource allocation, Population statistics
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. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 193689714
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A stochastic approach to integerize floating-point estimates in gridded population mapping.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Wen-Bin%22">Zhang, Wen-Bin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wb.zhang@soton.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Sorichetta%2C+Alessandro%22">Sorichetta, Alessandro</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Frye%2C+Charlie%22">Frye, Charlie</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tejedor-Garavito%2C+Natalia%22">Tejedor-Garavito, Natalia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fang%2C+Weixuan%22">Fang, Weixuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cihan%2C+Duygu%22">Cihan, Duygu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Woods%2C+Dorothea%22">Woods, Dorothea</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yetman%2C+Gregory%22">Yetman, Gregory</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hilton%2C+Jason%22">Hilton, Jason</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tatem%2C+Andrew+J%2E%22">Tatem, Andrew J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bondarenko%2C+Maksym%22">Bondarenko, Maksym</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Geographical+Information+Science%22">International Journal of Geographical Information Science</searchLink>. Jun2026, Vol. 40 Issue 6, p1601-1617. 17p.
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  Data: <searchLink fieldCode="DE" term="%22Floating-point+arithmetic%22">Floating-point arithmetic</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Population+statistics%22">Population statistics</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/13658816.2025.2568721
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      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 1601
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      – SubjectFull: Floating-point arithmetic
        Type: general
      – SubjectFull: Resource allocation
        Type: general
      – SubjectFull: Population statistics
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      – TitleFull: A stochastic approach to integerize floating-point estimates in gridded population mapping.
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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