By Daniel A. Griffith
This quantity compiles geostatistical and spatial autoregressive facts analyses regarding georeferenced socioeconomic, normal assets, agricultural, toxins, and epidemiological variables. Benchmark analyses are through 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, local scientists, utilized spatial statisticians, and spatial scientists from throughout disciplines.
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Additional resources for A Casebook for Spatial Statistical Data Analysis: A Compilation of Analyses of Different Thematic Data Sets
2. Those displaying an approximately linear relationship are DFRM (density of farms), DCD (density of cultivated land), and DFAM (density of farm families). Those exhibiting apparent nonlinear relationships are DMLK (density of milk production), DSGR (density of sugarcane production), DCF (density of coffee production), and DBN (density of banana/plantain production). DTB (density of tobacco production) seems to display nonconstant variance, as is shown by the varying spread of points along the vertical axis for different selected points along the horizontal axis.
Therefore, in this context, indexing the nature and degree of spatial autocorrelation becomes an exercise in missing variables diagnostic analysis. Missing variables are overlooked for a variety of reasons, including ignorance, an inability to measure them, or a lack of resources for collecting data on them. Fortunately, inferential and descriptive impacts of this specification error for georeferenced data often can be minimized by unknowingly capturing the effects of missing variables through the introduction of spatial dependency parameters into a mean response specification (Cressie 1991, 436).
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A Casebook for Spatial Statistical Data Analysis: A Compilation of Analyses of Different Thematic Data Sets by Daniel A. Griffith