Summary
Contents
Subject index
The widespread use of Geographical Information Systems (GIS) has significantly increased the demand for knowledge about spatial analytical techniques across a range of disciplines. As growing numbers of researchers realize they are dealing with spatial data, the demand for specialized statistical and mathematical methods designed to deal with spatial data is undergoing a rapid increase. Responding to this demand, The SAGE Handbook of Spatial Analysis is a comprehensive and authoritative discussion of issues and techniques in the field of spatial data analysis.
Spatial Regression
Spatial Regression
Introduction
Spatial regression deals with the specification, estimation, and diagnostic checking of regression models that incorporate spatial effects. Two broad classes of spatial effects may be distinguished, referred to as spatial dependence and spatial heterogeneity (Anselin, 1988b). In this chapter, attention will be limited to the former, since spatial heterogeneity is addressed in Chapter 13, on Geographically Weighted Regression. The focus will be on ways to incorporate spatial correlation structures into a linear regression model, and the implications of this for estimation and specification testing.
Early interest in the statistical implications of estimating spatial regression models dates back to the pioneering results of the statistician Whittle (1954), followed by other by now classic papers in statistics, such as Besag (1974) and Ord (1975), and ...
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