The Analysis of Spatial Association by Use of Distance Statistics

Introduced in this paper is a family of statistics, G, that can be used as a measure of spatial association in a number of circumstances. The basic statistic is derived, its properties are identified, and its advantages explained. Several of the G statistics make it possible to evaluate the spatial association of a variable within a specified distance of a single point. A comparison is made between a general G statistic and Moran’s I for similar hypothetical and empirical conditions. The empirical work includes studies of sudden infant death syndrome by county in North Carolina and dwelling unit prices in metropolitan San Diego by zip-code districts. Results indicate that G statistics should be used in conjunction with I in order to identify characteristics of patterns not revealed by the I statistic alone and, specifically, the G i and G i ∗ statistics enable us to detect local “pockets” of dependence that may not show up when using global statistics.

[1]  E. J. G. Pitman,et al.  The “closest” estimates of statistical parameters , 1937, Mathematical Proceedings of the Cambridge Philosophical Society.

[2]  W. Hoeffding A Combinatorial Central Limit Theorem , 1951 .

[3]  N. Mantel The detection of disease clustering and a generalized regression approach. , 1967, Cancer research.

[4]  David A. Pierce,et al.  Least squares estimation in the regression model with autoregressive-moving average errors , 1971 .

[5]  R. Reyment,et al.  Statistics and Data Analysis in Geology. , 1988 .

[6]  I. Gibson Statistics and Data Analysis in Geology , 1976, Mineralogical Magazine.

[7]  B. Ripley Modelling Spatial Patterns , 1977 .

[8]  L. Hubert Generalized proximity function comparisons , 1978 .

[9]  Lawrence Hubert,et al.  Matching models in the analysis of cross-classifications , 1979 .

[10]  Arthur Getis,et al.  Interaction Modeling Using Second-Order Analysis , 1984 .

[11]  Wilpen L. Gorr,et al.  An adaptive filter for estimating spatially-varying parameters: application to modeling police hours spent in response to calls for service , 1986 .

[12]  J. Franklin,et al.  Second-Order Neighborhood Analysis of Mapped Point Patterns , 1987 .

[13]  Luc Anselin,et al.  Do spatial effects really matter in regression analysis , 2005 .

[14]  L. Anselin Spatial Econometrics: Methods and Models , 1988 .

[15]  Giuseppe Arbia,et al.  Spatial Data Configuration in Statistical Analysis of Regional Economic and Related Problems , 1989 .

[16]  N. Cressie,et al.  Spatial Modeling of Regional Variables , 1993 .

[17]  Noel A Cressie,et al.  Spatial data-analysis of regional counts , 1989 .

[18]  Arthur Getis,et al.  Spatial Interaction and Spatial Autocorrelation: A Cross-Product Approach , 1991 .

[19]  Reginald G. Golledge,et al.  Generalized Procedures for Evaluating Spatial Autocorrelation , 2010 .