By Daniel A. Griffith
This quantity compiles geostatistical and spatial autoregressive information analyses concerning georeferenced socioeconomic, usual assets, agricultural, pollutants, and epidemiological variables. Benchmark analyses are through analyses of on hand facts units, emphasizing parallels among geostatistical and spatial autoregressive findings. either SAS and SPSS code are offered for implementation reasons. This informative casebook will serve geographers, neighborhood 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
As distances between locations increase toward the maximum distance possible (that is, the distance separating the two geographically most extreme points), fewer and fewer attribute value pairs are available to contribute to computing Y(h). This decrease in N(h) usually leads to the spread of Y(h) increasing as these lag distances increase. One complication of this spread in Y(h) values leads to a poorer fit (larger RSSE) of any selected model. Such values in the semivariogram plot can be discarded because, again, they contribute only slightly (or not at all) to describing spatial dependency in the data set.
This expression reduces to the n- 2 bivariate regression result reported in Cliff and Ord (1981, 202). If the Xj are random variables, too, then E(MCy) = —5—, and --—reduces to ——. if * n-l n-2 n-l p = 1, which is the constant mean case, it again reduces to —l—. Meanwhile, n- 1 Switzer and Green (1984) extend this general idea to a battery of attribute variables. 8)—a matrix of z-scores, say Z, is constructed from the original raw data battery of georeferenced variables. 9) EEcyCn-l) i=i M The diagonal entries of this matrix are the individual univariate MC values.
Addressing issues of attribute normality and variance homogeneity is deferred to Chapter 2; the more restrictive issues of induced tessellation heterogeneity and anisotropy will be introduced in this chapter. 1). 4. 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).
A Casebook for Spatial Statistical Data Analysis: A Compilation of Analyses of Different Thematic Data Sets by Daniel A. Griffith