By Daniel A. Griffith
This quantity compiles geostatistical and spatial autoregressive information analyses regarding georeferenced socioeconomic, ordinary assets, agricultural, pollutants, and epidemiological variables. Benchmark analyses are by way of analyses of available information units, emphasizing parallels among geostatistical and spatial autoregressive findings. either SAS and SPSS code are provided for implementation reasons. This informative casebook will serve geographers, nearby scientists, utilized spatial statisticians, and spatial scientists from throughout disciplines.
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Additional info for A Casebook for Spatial Statistical Data Analysis: A Compilation of Analyses of Different Thematic Data Sets
7. 39 Computer Code for Implementing Spatial Statistical Analyses Procedures for estimating spatial autoregressive or geostatistical models are not available in standard statistical software packages such as SAS, SPSS, and MINITAB, although recently MathSoft has included some of these functions in its S+ spatial statistics module, and SAS PROC GIS and PROC MIXED offer considerable potential. Users must find other specialty software packages when undertaking these types of analyses. Levine (1996) reviews some of the dedicated spatial autoregressive packages as well as a few other selected packages that relate to GIS interests.
Spatial autocorrelation) and formation contained in and | 9 denote the pure attribute in- these variables. Let pXy denote the product moment correlation coefficient measuring the linear relationship between variates rXY denote the corresponding sample correlation coefficient, and and ^y, px and py respectively denote the spatial autocorrelation latent in variables X and Y. 2), and that the values distributed over these maps constitute representative samples. Relatively speaking, as rXY - 1, Xj and yj become increasingly similar (subject to translation and scale adjustments), whereas as rXY ^ 1, Xj and (l)xyj become increasingly similar; as rXY - 0, xi and yi display a haphazard association.
Multivariate Spatial Association Two primary approaches have been taken to multivariate spatial autoregressive analysis. The first strategy reformulates univariate statistics of spatial autocorrelation, usually in terms of space-time data series (Griffith 1981a; Cliff and Ord 1981b). The second tactic formulates multivariate spatial autoregressive models (Streitberg 1979; Mardia 1988). The two multivariate techniques receiving the most attention are discriminant function components analysis (Switzer and Green (Mardia 1984).
A Casebook for Spatial Statistical Data Analysis: A Compilation of Analyses of Different Thematic Data Sets by Daniel A. Griffith