Spatial Regression Analysis vs. Kriging Methods for Spatial Estimation

Gema Fernández-Avilés ()

International Advances in Economic Research, 2009, vol. 15, issue 1, 44-58

Abstract: Due to the rapid development of Geographic Information Systems (GIS) in recent years, spatial data analysis has received considerable attention and played an important role in social science. Although many standard statistical techniques are attractive in traditional data analysis, they cannot be implemented uncritically for spatial data. Generally, most of the studies in spatial data analysis can be divided into two branches: the model-driven approach and the data-driven approach. The main aim of this paper is the comparison of both approaches. To carry out such a task, crime rate data in Columbus (Ohio), coming from a well-known database, have been used. The main aim of this paper is to illustrate how spatial effects can be viewed as spatial econometric models, which assess the limitations of standard techniques in a spatial context, suggesting alternative methods to deal with this problem. An application to the crime rate in Columbus (Ohio) has been carried out. Copyright International Atlantic Economic Society 2009

Keywords: Weight matrix; Spatial correlation; Spatial econometrics; Econometric models; Autocorrelation; Kriging estimator; C10; C21; C40; E00 (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/s11294-008-9189-0

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