Geometric place footprints and the modifiable areal unit problem

When studying places in GIScience, we often analyse their geometric footprints. Sketch maps, for instance, can be used to capture sense of place, place identity, or place dependence. This paper examines the relationship between place footprints and the Modifiable Areal Unit Problem (MAUP). Unlike polygons such as postcode areas, place footprints inherently relate to the underlying places themselves. Considering the MAUP aspects of location, scale, shape, and aggregation, I argue that the MAUP remains relevant, as place footprints are stochastic candidates of geometric representations and thus inherently modifiable – not in relation to another process, but in a self-referential manner.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-12
DOI
https://doi.org/10.5281/zenodo.15202852
Primary Topic
Geographic Information Systems Studies
Type
article
Field-Weighted Citation Impact
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article

Geometric place footprints and the modifiable areal unit problem

Zenodo (CERN European Organization for Nuclear Research)
Geographic Information Systems Studies
article

Geometric place footprints and the modifiable areal unit problem

article en

Abstract

When studying places in GIScience, we often analyse their geometric footprints. Sketch maps, for instance, can be used to capture sense of place, place identity, or place dependence. This paper examines the relationship between place footprints and the Modifiable Areal Unit Problem (MAUP). Unlike polygons such as postcode areas, place footprints inherently relate to the underlying places themselves. Considering the MAUP aspects of location, scale, shape, and aggregation, I argue that the MAUP remains relevant, as place footprints are stochastic candidates of geometric representations and thus inherently modifiable – not in relation to another process, but in a self-referential manner.

Zenodo (CERN European Organization for Nuclear Research)
TU Dortmund University (DE)
Openalex Percentile: Top 50%
Geographic Information Systems Studies
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