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Addressing bias in the use of buffers for focal and geographically weighted analyses.

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Title: Addressing bias in the use of buffers for focal and geographically weighted analyses.
Authors: Huck, J. J.1 (AUTHOR) jonathan.huck@manchester.ac.uk, Dennis, M.1 (AUTHOR), Labib, S. M.2 (AUTHOR)
Source: International Journal of Geographical Information Science. Jun2025, Vol. 39 Issue 6, p1183-1202. 20p.
Subjects: Research personnel, Characteristic functions, Neighborhoods
Abstract: Focal analyses (also known as 'buffer' or 'neighbourhood' analyses) seek to characterise a location based on its surroundings and are commonplace in GIS applications across many fields and disciplines. However, the implicit assumptions made by researchers in these analyses result in an unintended bias towards the periphery of the focal window, which we term Focal Area Bias (FAB). FAB can have a substantial impact upon the resulting values, and in the most extreme cases can result in paradoxical outcomes. Where geographical weighting functions are used, the interaction between the weighting function and FAB means that it will not have the expected effect, leading to the misinterpretation of results. This research characterises the issue of FAB, before presenting a corrective function to remove it. The efficacy of the proposed corrective function and the spatial characteristics of FAB are then evaluated to demonstrate the importance of this issue. We recommend that researchers and practitioners should consider the impact of FAB when undertaking focal analysis and make use of the corrective functions presented here to remove this issue, particularly where geographical weighting is desired. [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.)
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  Data: Addressing bias in the use of buffers for focal and geographically weighted analyses.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Geographical+Information+Science%22">International Journal of Geographical Information Science</searchLink>. Jun2025, Vol. 39 Issue 6, p1183-1202. 20p.
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  Data: Focal analyses (also known as 'buffer' or 'neighbourhood' analyses) seek to characterise a location based on its surroundings and are commonplace in GIS applications across many fields and disciplines. However, the implicit assumptions made by researchers in these analyses result in an unintended bias towards the periphery of the focal window, which we term Focal Area Bias (FAB). FAB can have a substantial impact upon the resulting values, and in the most extreme cases can result in paradoxical outcomes. Where geographical weighting functions are used, the interaction between the weighting function and FAB means that it will not have the expected effect, leading to the misinterpretation of results. This research characterises the issue of FAB, before presenting a corrective function to remove it. The efficacy of the proposed corrective function and the spatial characteristics of FAB are then evaluated to demonstrate the importance of this issue. We recommend that researchers and practitioners should consider the impact of FAB when undertaking focal analysis and make use of the corrective functions presented here to remove this issue, particularly where geographical weighting is desired. [ABSTRACT FROM AUTHOR]
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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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      – Type: doi
        Value: 10.1080/13658816.2024.2440048
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      – Code: eng
        Text: English
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        PageCount: 20
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        Type: general
      – SubjectFull: Characteristic functions
        Type: general
      – SubjectFull: Neighborhoods
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      – TitleFull: Addressing bias in the use of buffers for focal and geographically weighted analyses.
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              Text: Jun2025
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