A regionalization method filtering out small-scale spatial fluctuations

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.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: A regionalization method filtering out small-scale spatial fluctuations ; volume:3 ; year:2022 ; pages:1-6 ; extent:6 ; AGILE: GIScience series ; 3 (2022), 1-6 (gesamt 6)

Classification and Topics

• Classification: Geografie, Reisen

Contributors, Places and Time

• Creator: Spierenburg, Lucas ; van Cranenburgh, Sander ; Cats, Oded

Further information

• DOI: 10.5194/agile-giss-3-61-2022
• URN: urn:nbn:de:101:1-2022061605243273189996
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:26 AM CEST