This may look a bit familiar but I am not sure about a few things.
I want to investigate the possible relationship between crime incidents and some environmental variables such as the:
Distance to roads, distance to residential areas, land use and population density. Note that the Dependent variable is a count/discrete data (crime incident points) and all the explanatory variables are rasters and continuous variables (land use is categorical, which I intend to include using the dummy variable route).
1. I intend to convert all the rasters including the land use raster to points. Is this really okay?
2. Can I use the raw crime data (without aggregating to a polygon such as fishnet, hexagon, etc) in Geographically Weighted Regression (GWR) to assess the relationship as explained above. Or must I aggregate into some kind of polygon for it to work?
3. I was also looking at Forest-based classification... what do you think?
Kindly offer your thoughts, please.
