Binary Pattern Recognition in the Presence of Correlated Multiple Dependent Variables

Abstract

In binary spatial pattern recognition, there are many situations where the researcher could be interested in a number of dependent variables that are themselves correlated. For instance, different types of crime often coexist in the same area, or different species could share the same habitat. In cases like these, a natural correlation exists amongst the dependent variables of interest and is informative for spatial probability mapping. Weights of evidence (WE) modelling is a popular Bayesian probability method for binary pattern recognition, but it only deals with one single dependent variable at a time and ignores the correlation between the dependent variables. In this article, a multiple dependent variable weights of evidence (MDVWE) model will be developed. It will be shown that the new MDVWE model can be viewed as a restricted version of the conditional dependence-adjusted weights of evidence (CDAWE) model of Deng (Nat Resour Res 18(4):249–258, 2009). The MDVWE model is easy to program and implement. By means of a simulation study, it will be shown that the MDVWE model outperforms the traditional WE model both in terms of in-sample fit and out-of-sample prediction accuracy.