Classification of Events Using Local Pair Correlation Functions for Spatial Point Patterns

Jonatan A. González (), Francisco J. Rodríguez-Cortés (), Elvira Romano () and Jorge Mateu ()
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Jonatan A. González: University Jaume I
Francisco J. Rodríguez-Cortés: Universidad Nacional de Colombia
Elvira Romano: Universitá della Campania “Luigi Vanvitelli”
Jorge Mateu: University Jaume I

Journal of Agricultural, Biological and Environmental Statistics, 2021, vol. 26, issue 4, No 2, 538-559

Abstract: Abstract Spatial point pattern analysis usually concerns identifying features in an observation window where there is also noise. This identification traditionally begins with studying the second-order properties of the point pattern, and it may be done locally by using local second-order characteristics (LISA). Some properties of this local structure solve the problem of classification into feature and clutter points. This paper proposes an estimator for local pair correlation LISA functions, discusses some of its properties and considers a particular distance to measure dissimilarities. Two classification procedures to separate feature from clutter points are described. One of them adopts multidimensional scaling and support vector machines, and the other employs bagged clustering. Simulations demonstrate the performance of the method, and it is applied to a dataset concerning earthquakes in a seismic nest located in Colombia.

Keywords: Bagged clustering; Bucaramanga nest; Local indicator of spatial association; Multidimensional scaling; Pair correlation function; Spatial point process; Support Vector Machine (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s13253-021-00455-1

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