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On measures of dissimilarity between point patterns: Classification based on prototypes and multidimensional scaling
Author(s) -
Mateu Jorge,
Schoenberg Frederic P.,
Diez David M.,
González Jonatan A.,
Lu Weipeng
Publication year - 2015
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201300150
Subject(s) - multidimensional scaling , point (geometry) , point process , multivariate statistics , scaling , range (aeronautics) , computer science , data mining , statistics , pattern recognition (psychology) , cluster (spacecraft) , cluster analysis , point pattern analysis , mathematics , artificial intelligence , common spatial pattern , materials science , geometry , composite material , programming language
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.