A Local Indicator of Multivariate Spatial Association: Extending Geary's c

2018/08/13 by Luc Anselin · 232 citations
Agricultural and Biological Sciences · Economics, Econometrics and Finance · Environmental Science · Mathematics · #Computer science #Context (archaeology) #Econometrics #Geography #Interpretation (philosophy) #Mathematics #Multivariate analysis #Multivariate statistics #Permutation (music) #Scan statistic #Sensory Analysis and Statistical Methods #Soil Geostatistics and Mapping #Spatial and Panel Data Analysis #Statistic #Statistics #Univariate

paper · doi:10.1111/gean.12164

published in Geographical Analysis 51(2), 133-150 (Wiley)

openalex publication_date 2018/08/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

This paper extends the application of the Local Geary c statistic to a multivariate context. The statistic is conceptualized as a weighted distance in multivariate attribute space between an observation and its geographical neighbors. Inference is based on a conditional permutation approach. The interpretation of significant univariate Local Geary statistics is clarified and the differences with a multivariate case outlined. An empirical illustration uses Guerry's classic data on moral statistics in 1830s France.

Citations

• Local Spatial Autocorrelation Statistics: Distributional Issues and an Application
• Controlling the False Discovery Rate: A New Application to Account for Multiple and Dependent Tests in Local Statistics of Spatial Association
• Local Forms of Spatial Analysis
• A Generalized Randomization Approach to Local Measures of Spatial Association
• Local Indicators of Spatial Association—LISA
• The Analysis of Spatial Association by Use of Distance Statistics
• GeoDa: An Introduction to Spatial Data Analysis
• Constructing the Spatial Weights Matrix Using a Local Statistic

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Related

• Tobler’s Law in a Multivariate World
• The Analysis of Spatial Association by Use of Distance Statistics
• Local Spatial Autocorrelation Statistics: Distributional Issues and an Application
• A.-M. Guerry’s Moral Statistics of France: Challenges for Multivariable Spatial Analysis
• Comparison of Moran's I and Geary's c in Multivariate Spatial Pattern Analysis