A scale-sensitive approach for comparing and classifying point patterns. Issue 2 (3rd May 2020)

• Record Type: Journal Article
• Authors: Sadahiro, Yukio; Liu, Yan
• Is Part Of: Journal of spatial science. Volume 65:Issue 2(2020)
• Page Start: 281
• Page End: 306
• Publication Date: 2020-05-03
• Subjects: Comparison and classification -- spatial scale -- similarity -- kernel smoothing -- point patterns; Geographic information systems -- Periodicals; Cartography -- Periodicals; Surveying -- Periodicals; Geodesy -- Periodicals; Photogrammetry -- Periodicals; Cartography; Geodesy; Geographic information systems; Photogrammetry; Surveying; Periodicals; 526.05
• DOI: 10.1080/14498596.2018.1492466
• Languages: English
• ISSNs: 1449-8596

Abstract

ABSTRACT: This paper proposes a novel method for comparing and classifying point patterns. The method explicitly considers the spatial scale since the evaluation of similarity depends on the scale at which one compares point patterns. Three functions were proposed, each of which represents the similarity between point patterns. A single measure was introduced to summarize the overall similarity between patterns. This single measure was used to classify point patterns into groups according to their spatial patterns. The validity of the method was tested through numerical experiments as well as the analysis of travel transaction data in Southeast Queensland, Australia.