Topological data analysis for geographical information science using persistent homology. Issue 3 (4th March 2023)

• Record Type: Journal Article
• Authors: Corcoran, Padraig; Jones, Christopher B.
• Is Part Of: International journal of geographical information science. Volume 37:Issue 3(2023)
• Page Start: 712
• Page End: 745
• Publication Date: 2023-03-04
• Subjects: Geographical information science -- topological data analysis -- persistent homology; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2022.2155654
• Languages: English
• ISSNs: 1365-8816

Abstract

Abstract: 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.