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Natural Resources Research

, Volume 19, Issue 1, pp 33–44 | Cite as

A Spatially Autocorrelated Weights of Evidence Model

  • Minfeng DengEmail author
Article

Abstract

One of the most important features of spatial datasets is that they often exhibit spatial autocorrelation, where locational similarities are observed jointly with similarities in values. Both logistic regression (LR) modelling and weights of evidence (WE) modelling are methods commonly applied in binary pattern recognition. While a spatially autocorrelated variant of the LR model, the so-called autologistic regression (ALR) model, exists in the literature, a spatially autocorrelated variant of the WE model does not exist. In this paper, a spatially autocorrelated weights of evidence (SACWE) model will be proposed. It will be demonstrated that the new model contains the same amount of spatial information as does an ALR model, and it is easy to program and implement. Via a simulation study, it will be shown that, in the presence of spatial autocorrelation, both in terms of in-sample fit and out-of-sample predictions the SACWE model is on par with the ALR model, while significantly outperforming the conventional WE model.

Keywords

Logistic regression simulation autologistic regression spatial prediction 

Notes

Acknowledgment

The author thanks Jerry Jensen and the reviewers for their insightful comments.

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Copyright information

© International Association for Mathematical Geology 2009

Authors and Affiliations

  1. 1.Department of Econometrics and Business StatisticsMonash UniversityClaytonAustralia

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