Geostatistical Regression for Areal Data

Phaedon C. Kyriakidis1 and Nicholas N. Nagle2
1. Department of Geography University of California Santa Barbara, CA, U.S.A.
2. Department of Geography University of Tennessee Knoxville, TN, U.S.A.

1.phaedon@geog.ucsb.edu; 2.nnagle@utk.edu

Abstract: This paper presents a geostatistical approach for linear regression with areal data, applicable when such data are defined as aggregates of point-level attribute values within regular or irregular areal units (pixels or polygons), and when Gaussian assumptions are made for the regression model disturbances or errors. The distinctive features of the proposed geostatistical regression approach as compared to the more common spatial econometrics approach for linear regression involving lattice data are highlighted, and recommendations are provided for choosing between the two approaches.

Keywords: spatial aggregation; spatial autocorrelation; Kriging; spatial econometrics; Modifiable Area Unit Problem; Ecological Inference Problem

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