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A Local Model for Multivariate Analysis: Extending Wartenberg’s Multivariate Spatial Correlation
Author(s) -
Lin Jie
Publication year - 2020
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/gean.12196
Subject(s) - multivariate statistics , spatial analysis , principal component analysis , multivariate analysis , correlation , computer science , spatial correlation , autocorrelation , statistics , data mining , artificial intelligence , mathematics , geometry
Local analysis can provide specific information about individual observations that is often useful in understanding nonstationary interactions among variables. This paper extends the application of Wartenberg’s Multivariate Spatial Correlation (MSC) method to a local setting. The original MSC can be considered as an adaptation of Principal Component Analysis for spatial effects with respect to spatial autocorrelation. The extended MSC method described in this paper, however, further incorporates another spatial effect, spatial heterogeneity, by the addition of geographic weights in standardizing the data and in calculating the spatial association weight matrix. The extension allows more local analysis and facilitates additional visualization of the results. The geographically weighted MSC is illustrated and justified using the classic dataset collected by André‐Michel Guerry on moral statistics in 1830s France.