Abstract
A class of multivariate nonparametric tests for spatial dependence, Multivariate Sequential Permutation Analyses (MSPA), is developed and applied to the analysis of spatial data. These tests allow the significance level (P value) of the spatial correlation to be computed for each lag class. MSPA is shown to be related to the variogram and other measures of spatial correlation. The interrelationships of these measures of spatial dependence are discussed and the measures are applied to synthetic and real data. The resulting plot of significance level vs. lag spacing, or P-gram, provides insight into the modeling of the semivariogram and the semimADogram. Although the test clearly rejects some models of correlation, the chief value of the test is to quantify the strength of spatial correlation, and to provide evidence that spatial correlation exists
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Walker, D.D., Loftis, J.C. & Mielke, Jr., P.W. Permutation Methods for Determining the Significance of Spatial Dependence. Mathematical Geology 29, 1011–1024 (1997). https://doi.org/10.1023/A:1022309619605
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DOI: https://doi.org/10.1023/A:1022309619605