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

By Daniel A. Griffith

ISBN-10: 0195109589

ISBN-13: 9780195109580

This quantity compiles geostatistical and spatial autoregressive information analyses concerning georeferenced socioeconomic, average assets, agricultural, toxins, and epidemiological variables. Benchmark analyses are by means of analyses of on hand 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

Example text

6). 8), when variables X and Y are normally distributed and 0 spatial autocorrelation prevails, is pxvAn ~ 0? which is consistent with the univariate MC expectation because pxx = pYY = 1 variable is perfectly positively correlated with itself). , a index is capturing both attribute and locational information. Its variance, based upon all possible permutations of values over the map, has been derived by Mantel (1967). Unfortunately, the form of its sampling distribution currently is unknown. 2. Extensions to More Than Two Georeferenced Variables: Multicollinearity and Multivariate Spatial Autocorrelation Presently spatial autocorrelation complications in pairs of variables are both quite difficult to handle and poorly understood.

These functions then can be used to predict unknown attribute values for unsampled areas from the data collected for sampled locations; that is, kriging. Some semivariogram models have been shown to be valid and/or useful, namely, the linear, exponential, circular, spherical, Gaussian, Bessel, power and wave/hole; they routinely appear throughout the geostatistics literature. 3 have been fitted to the Wolfcamp aquifer data. 5 —r-. 3. Semivariogram plot of piezometric-head pressure for Cressie's Wolfcamp aquifer data, with selected semivariogram model curves superimposed.

3. 2. This connection becomes more apparent when rewriting MC as a ratio of regression coefficients. The numerator regression coefficient is obtained with the following procedure: Step 1: center the variable under study, X, yielding (X - lTXl/n); Step 2; compute C(X - lTXl/n); Step 3: using a no-intercept regression model, regress C(X - lTXl/n) on (X - lTXl/n). Denote the resulting regression coefficient with bxcx. Essentially this is the slope of the regression line for a MC scatterplot, where the vertical axis is labeled with C(X - lrXl/n) and the horizontal axis is labeled with (X - lTXl/n).

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A Casebook for Spatial Statistical Data Analysis: A Compilation of Analyses of Different Thematic Data Sets by Daniel A. Griffith

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