Regression Analysis of Spatial Data

Abstract

Practitioners of regional science often are engaged in statistical analysis of regional data samples collected with reference to points in space. Examples are cross-sectional observations on county-level income, employment or payroll, cross-sectional observations from a group of neighboring states in a region, and firm-level employment or payroll where we know the firm address or an approximate location based on a postal code. Ignoring the spatial configuration of sample observations in regression analysis has been found to produce residuals that vary systematically over space, a phenomenon known as spatial autocorrelation. This paper illustrates how to incorporate spatial information in regression relationships that exhibit spatial auto-correlation. I argue that a simple contiguity matrix provides a unified approach that works with cross-sectional continuous linear relationships, as well as with binary and censored dependent variable problems and autoregressive time series relationships.

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