I have a point shapefile (let's say A) with pollution measures (let's say Y). My hypothesis is that pollution at theses locations is primarily driven by nearby industrial units and not by activities at the locations in A. I have the locations of the factories in another shapefile (let's say B) along with a host of explanatory variables (let's say X1, X2, X3).
I want to model the pollution measure Y as a function of X1, X2, X3, and the distance of the industrial unites from the points of measurements. I was planning on using a Geographically Weighted Regression (GWR), but am not certain whether it is the right choice.
In ArcMap for example, the GWR tool expects one shapefile as input with both the dependent and explanatory variables.Thus, the assumption is that Y is driven by events at that point along with some influence of nearby locations. I looked into GWR in R also and the assumption is similar since it expects only one argument with
data. I know that in R I can force feed the explanatory variables and dependent variable to be from different files, like this:
gwr.sel(A$Y ~ B$X1 + B$X2 + B$X3 + B$dist, ...)
But I am not sure if this makes sense, primarily for 2 reasons:
- My dependant and explanatory variables are not in the same file. So, I may be violating a basic assumption of GWR.
distvariable only has the distance from the nearest industrial unit to the measurement location. Thus, I am loosing the effect of the industrial plants which are close to the measured location, but not the closest.
Thus, my question is what other alternative do I have to GWR that will help me model this phenomenon?