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Topological data analysis for geographical information science using persistent homology

Corcoran, Padraig ORCID: and Jones, Christopher ORCID: 2023. Topological data analysis for geographical information science using persistent homology. International Journal of Geographical Information Science 37 (3) , pp. 712-745. 10.1080/13658816.2022.2155654
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Topological Data Analysis (TDA) is an emerging field of research which considers the application of topology to data analysis. Recently, these methods have been successfully applied to research problems in the field of Geographical Information Science (GIS) and there is much potential for future applications. In this article, we provide an introduction to the fundamentals of TDA for GIS researchers and practitioners and highlight specific benefits that TDA methods provide relative to some conventional methods. We focus on the method of persistent homology which is the most commonly used TDA method. We describe how persistent homology can be applied to data types commonly encountered in the GIScience domain, namely sets of points, networks and sequences of images. We also describe the application of persistent homology to two specific GIS problems, which are the point pattern analysis of UK city pubs and the analysis of UK rainfall radar imagery. In each case we stress the specific benefits of TDA methods that include, for example, generating an output signature in a form that can be subject to subsequent analyses; identification of void regions in point patterns; and providing a relatively simple method to track objects in spatio-temporal images

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Taylor and Francis Group
ISSN: 1365-8824
Date of First Compliant Deposit: 5 December 2022
Date of Acceptance: 2 December 2022
Last Modified: 10 Nov 2023 15:33

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