West Nile virus (WNV) is an emerging infectious disease that has widespread implications for public health practitioners across the world. Within a few years of its arrival in the United States the virus had spread across the North American continent. This research focuses on the development of a spatially explicit GIS-based predictive epidemiological model based on suitable environmental factors. We examined eleven commonly mapped environmental factors using both ordinary least squares regression (OLS) and geographically weighted regression (GWR). The GWR model was utilized to ascertain the impact of environmental factors on WNV risk patterns without the confounding effects of spatial non-stationarity that exist between place and health. It identifies the important underlying environmental factors related to suitable mosquito habitat conditions to make meaningful and spatially explicit predictions. Our model represents a multi-criteria decision analysis approach to create disease risk maps under data sparse situations. The best fitting model with an adjusted R2 of 0.71 revealed a strong association between WNV infection risk and a subset of environmental risk factors including road density, stream density, and land surface temperature. This research also postulates that understanding the underlying place characteristics and population composition for the occurrence of WNV infection is important for …