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Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem

BACKGROUND: All analyses of spatially aggregated data are vulnerable to the modifiable areal unit problem (MAUP), which describes the sensitivity of analytical results to the arbitrary choice of spatial aggregation unit at which data are measured. The MAUP is a serious problem endemic to analyses of...

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Autores principales: Tuson, M., Yap, M., Kok, M. R., Murray, K., Turlach, B., Whyatt, D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6437958/
https://www.ncbi.nlm.nih.gov/pubmed/30917821
http://dx.doi.org/10.1186/s12942-019-0170-3
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author Tuson, M.
Yap, M.
Kok, M. R.
Murray, K.
Turlach, B.
Whyatt, D.
author_facet Tuson, M.
Yap, M.
Kok, M. R.
Murray, K.
Turlach, B.
Whyatt, D.
author_sort Tuson, M.
collection PubMed
description BACKGROUND: All analyses of spatially aggregated data are vulnerable to the modifiable areal unit problem (MAUP), which describes the sensitivity of analytical results to the arbitrary choice of spatial aggregation unit at which data are measured. The MAUP is a serious problem endemic to analyses of spatially aggregated data in all scientific disciplines. However, the impact of the MAUP is rarely considered, perhaps partly because it is still widely considered to be unsolvable. RESULTS: It was originally understood that a solution to the MAUP should constitute a comprehensive statistical framework describing the regularities in estimates of association observed at different combinations of spatial scale and zonation. Additionally, it has been debated how such a solution should incorporate the geographical characteristics of areal units (e.g. shape, size, and configuration), and in particular whether this can be achieved in a purely mathematical framework (i.e. independent of areal units). We argue that the consideration of areal units must form part of a solution to the MAUP, since the MAUP only manifests in their presence. Thus, we present a theoretical and statistical framework that incorporates the characteristics of areal units by combining estimates obtained from different scales and zonations. We show that associations estimated at scales larger than a minimal geographical unit of analysis are systematically biased from a true minimal-level effect, with different zonations generating uniquely biased estimates. Therefore, it is fundamentally erroneous to infer conclusions based on data that are spatially aggregated beyond the minimal level. Instead, researchers should measure and display information, estimate effects, and infer conclusions at the smallest possible meaningful geographical scale. The framework we develop facilitates this. CONCLUSIONS: The proposed framework represents a new minimum standard in the estimation of associations using spatially aggregated data, and a reference point against which previous findings and misconceptions related to the MAUP can be understood. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12942-019-0170-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-64379582019-04-08 Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem Tuson, M. Yap, M. Kok, M. R. Murray, K. Turlach, B. Whyatt, D. Int J Health Geogr Methodology BACKGROUND: All analyses of spatially aggregated data are vulnerable to the modifiable areal unit problem (MAUP), which describes the sensitivity of analytical results to the arbitrary choice of spatial aggregation unit at which data are measured. The MAUP is a serious problem endemic to analyses of spatially aggregated data in all scientific disciplines. However, the impact of the MAUP is rarely considered, perhaps partly because it is still widely considered to be unsolvable. RESULTS: It was originally understood that a solution to the MAUP should constitute a comprehensive statistical framework describing the regularities in estimates of association observed at different combinations of spatial scale and zonation. Additionally, it has been debated how such a solution should incorporate the geographical characteristics of areal units (e.g. shape, size, and configuration), and in particular whether this can be achieved in a purely mathematical framework (i.e. independent of areal units). We argue that the consideration of areal units must form part of a solution to the MAUP, since the MAUP only manifests in their presence. Thus, we present a theoretical and statistical framework that incorporates the characteristics of areal units by combining estimates obtained from different scales and zonations. We show that associations estimated at scales larger than a minimal geographical unit of analysis are systematically biased from a true minimal-level effect, with different zonations generating uniquely biased estimates. Therefore, it is fundamentally erroneous to infer conclusions based on data that are spatially aggregated beyond the minimal level. Instead, researchers should measure and display information, estimate effects, and infer conclusions at the smallest possible meaningful geographical scale. The framework we develop facilitates this. CONCLUSIONS: The proposed framework represents a new minimum standard in the estimation of associations using spatially aggregated data, and a reference point against which previous findings and misconceptions related to the MAUP can be understood. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12942-019-0170-3) contains supplementary material, which is available to authorized users. BioMed Central 2019-03-27 /pmc/articles/PMC6437958/ /pubmed/30917821 http://dx.doi.org/10.1186/s12942-019-0170-3 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology
Tuson, M.
Yap, M.
Kok, M. R.
Murray, K.
Turlach, B.
Whyatt, D.
Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem
title Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem
title_full Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem
title_fullStr Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem
title_full_unstemmed Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem
title_short Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem
title_sort incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6437958/
https://www.ncbi.nlm.nih.gov/pubmed/30917821
http://dx.doi.org/10.1186/s12942-019-0170-3
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