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Cluster Pattern Detection in Spatial Data Based on Monte Carlo Inference
Author(s) -
Stoica Radu Stefan,
Gay Emilie,
Kretzschmar André
Publication year - 2007
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.200610326
Subject(s) - point process , cluster (spacecraft) , computer science , poisson distribution , inference , smoothing , monte carlo method , field (mathematics) , pairwise comparison , spatial analysis , statistical physics , data mining , algorithm , mathematics , statistics , artificial intelligence , physics , pure mathematics , programming language
Clusters in a data point field exhibit spatially specified regions in the observation window. The method proposed in this paper addresses the cluster detection problem from the perspective of detection of these spatial regions. These regions are supposed to be formed of overlapping random disks driven by a marked point process. The distribution of such a process has two components. The first is related to the location of the disks in the field of observation and is defined as an inhomogeneous Poisson process. The second one is related to the interaction between disks and is constructed by the superposition of an area‐interaction and a pairwise interaction processes. The model is applied on spatial data coming from animal epidemiology. The proposed method tackles several aspects related to cluster pattern detection: heterogeneity of data, smoothing effects, statistical descriptors, probability of cluster presence, testing for the cluster presence. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)