The incorporation of spatially dependent variables in a machine learning model can greatly improve the model’s performance. These features can include, but not limited to: the spatial lag (neighborhood average) of a variable counts of neighboring features most common category nearby spatial embedding via principle coordinate analysis Deriving spatial features These kinds of spatial variables are dependent upon the features nearby them. To calculate these variable one needs to have a concept...
