Statistical Models for Spatial Data: Some Linkages and Communalities

Chapter
First Online:
Part of the Advances in Geographic Information Science book series (AGIS, volume 1)

Abstract

Introductory mathematical statistics textbooks discuss topics such as the sample variance by invoking the assumption of independent and identically distributed (iid). In other words, in terms of second moments, of the n 2 possible covariations for a set of n observations, the independence assumption posits that n(n – 1) of these covariations have an expected value of 0, leaving only the n individual observation variance terms for analysis. This independence assumption is for convenience, historically making mathematical statistical theory tractable. But it is an arcane specification that fails to provide an acceptable approximation to reality in many contexts.

Keywords

Spatial Autocorrelation Geostatistical Model Semivariogram Model Positive Spatial Autocorrelation Georeferenced Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgment

Contributions made to the development of this chapter by Dr. Larry Layne, University of New Mexico, are greatly appreciated.

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Copyright information

© Springer Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.University of Texas, Dallas School of Economic, Political & Policy SciencesRichardsonUSA
  2. 2.George Mason University, School of Public PolicyRotterdamNetherlands

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