Statistical methods for bivariate spatial analysis in marked points. Examples in spatial epidemiology

Abstract

This article presents methods to analyze global spatial relationships between two variables in two different sets of fixed points. Analysis of spatial relationships between two phenomena is of great interest in health geography and epidemiology, especially to highlight competing interest between phenomena or evidence of a common environmental factor. Our general approach extends the Moran and Pearson indices to the bivariate case in two different sets of points. The case where the variables are Boolean is treated separately through methods using nearest neighbors distances. All tests use Monte-Carlo simulations to estimate their probability distributions, with options to distinguish spatial and no spatial correlation in the special case of identical sets analysis. Implementation in a Geographic Information System (SavGIS) and real examples are used to illustrate these spatial indices and methods in epidemiology.