Download A Casebook for Spatial Statistical Data Analysis: A by Daniel A. Griffith PDF

By Daniel A. Griffith

This quantity compiles geostatistical and spatial autoregressive info analyses related to georeferenced socioeconomic, average assets, agricultural, pollutants, and epidemiological variables. Benchmark analyses are by way of analyses of available facts 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

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Becomes v/n 0, regardless of n; there is no information left to conduct a difference of means test! As spatial autocorrelation approaches -1, the effective degrees of freedom increases beyond n. And, as spatial autocorrelation approaches 0, the effective degrees of freedom approaches n. , geographic interpolation) when undertaking geostatistical analyses. In other words, given a set of georeferenced information, there is a better than "equally likely" chance that neighboring numerical values can be used to predict each other.

Inflation of such correlation coefficients can increase detected multicollinearity in the regressor variables composing matrix X, further aggravating calculation error introduced by the matrix inversion (XrX)'1 used to compute regression coefficients (Griffith and Amrhein 1997, 97101). This notion is demonstrated empirically in Griffith (1988a, 214-18), where shifts in the principal components structure of a set of Buffalo crime data variables that are attributable to spatial autocorrelation are explored.

2. Two hypothetical georeferenced variables. Left: geographic distribution of X. Right: geographic distribution of Y. 20 A Casebook for Spatial Statistical Data Analysis would be a gradient of increasing values moving from the southeast corner (observation #16) to the northwest comer (observation #1); such a pattern tends to maximize the positive spatial autocorrelation that is detected. Of course, this gradient could incline in the opposite direction (values increasing from the northwest to the southeast corner), in a 90° rotation of this pattern (values increasing from the southwest to the northeast corner), or in a 270° rotation of this pattern (values increasing from the northeast to the southwest corner).

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