Statistical Methods for Bivariate Spatial Analysis in Marked Points: Examples in Spatial Epidemiology
Spatial and Spatio-temporal Epidemiology, Available online 15 June 2011
Marc Souris and Laurence Bichaud
“Highlights:
- We present methods to analyze spatial relationships between two marked point patterns.
- We develop statistical tests for bivariate spatial analysis.
- Tests use Monte-Carlo simulation to evaluate index variability under null hypothesis.
- We implement the tests in GIS SavGIS freeware.
“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.”
