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AGILE: GIScience Series Open-access proceedings of the Association of Geographic Information Laboratories in Europe
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Articles | Volume 3
AGILE GIScience Ser., 3, 61, 2022
https://doi.org/10.5194/agile-giss-3-61-2022
AGILE GIScience Ser., 3, 61, 2022
https://doi.org/10.5194/agile-giss-3-61-2022
 
11 Jun 2022
11 Jun 2022

A regionalization method filtering out small-scale spatial fluctuations

Lucas Spierenburg1, Sander van Cranenburgh2, and Oded Cats1 Lucas Spierenburg et al.
  • 1Transport and Planning, Delft University of Technology, Delft, The Netherlands
  • 2Transport and Logistics group, Department of Engineering Systems and Services, Delft University of Technology, Delft, The Netherlands

Keywords: regionalization, agglomerative clustering, regional science

Abstract. Regionalization is the process of aggregating contiguous spatial units to form areas that are homogeneous with respect to one or a set of variables. It is useful when studying spatial phenomena or when designing region-based policies, as it allows to unravel the latent spatial structure of a dataset. However, this task is challenging when small-scale fluctuations in the data interfere with the phenomenon of interest. In such circumstances, regionalization techniques are prone to overfitting small-scale fluctuations, and producing erratic regions. This paper presents a regionalization method robust to small-scale variations that is particularly relevant when handling demographic data. Fluctuations are filtered out using a weighted spatial average before applying agglomerative clustering. The method is tested against a conventional agglomerative clustering approach on a fine-resolution demographic dataset, for a set of indicators quantifying: the ability to identify large-scale spatial patterns, the homogeneity of the regions produced, and the spatial regularity of these regions. These indicators have been computed for the two methods for a number of clusters ranging from 2 to 101, and results show that the proposed approach performs better than conventional agglomerative clustering more than 90% of the time at identifying large-scale patterns, and produces more regular regions 96% of the time.

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