Spatial data tend to exhibit characteristics that negatively impact the statistical properties of quantitative models, decreasing their reliability: spatial analytical models are designed to mitigate these negative effects. The most crucial properties of spatial data are spatial dependence (near things tend to be more similar than distant things) and non-stationarity (inconstant variability of phenomena across space) [18] . Two broad categories of spatial analytical models include spatially autoregressive (SAR) methods, which deal with spatial dependence [19] , and geographically weighted (GWR) methods, which deal with spatial non-stationarity [20] .