On measures of dissimilarity between point patterns: Classification based on prototypes and multidimensional scaling

Jorge Mateu*, Frederic P. Schoenberg, David M. Diez, Jonatan A. González, Weipeng Lu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

This paper presents a collection of dissimilarity measures to describe and then classify spatial point patterns when multiple replicates of different types are available for analysis. In particular, we consider a range of distances including the spike-time distance and its variants, as well as cluster-based distances and dissimilarity measures based on classical statistical summaries of point patterns. We review and explore, in the form of a tutorial, their uses, and their pros and cons. These distances are then used to summarize and describe collections of repeated realizations of point patterns via prototypes and multidimensional scaling. We also show a simulation study to evaluate the performance of multidimensional scaling with two types of selected distances. Finally, a multivariate spatial point pattern of a natural plant community is analyzed through various of these measures of dissimilarity.

Original languageEnglish (US)
Pages (from-to)340-358
Number of pages19
JournalBiometrical Journal
Volume57
Issue number2
DOIs
StatePublished - Mar 1 2015

Bibliographical note

Publisher Copyright:
© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords

  • Classification
  • K-function
  • Multidimensional scaling
  • Point patterns
  • Prototypes
  • Spike-time distance

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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