An application of geographically weighted poisson regression

In fitting regression models with spatial data, it is often assumed that the relationships between the response variable and explanatory variables are the same throughout the study area (i.e., the processes being modelled are stationary over space). This may be a reasonable assumption, but should not be accepted without further analysis. Geographically weighted regression (GWR) is a technique for investigating the validity of this assumption and is used to examine the presence of spatial non-stationarity. It allows relationships between a response variable and the explanatory variables to vary over space. Most studies in GWR to date have focussed on the case where the response variable is continuous and is assumed to follow a normal distribution. However, in many regression models, this is not the case. Here, the concept of geographical weighting is applied to Poisson regression, where the response variable represents a count and takes the form of any non-negative integer