Geographically Weighted Regression

I finally found out how to do geographically weighted regression! This allows us to look at local changes in a statistical relationship. I tried out the technique by regressing the natural log of arable rent against the natural log of wheat yield. The coefficient on wheat yield will give us the elasticity: the percentage that arable rent changes for a certain percentage of wheat yield. Elasticity is a commonly-used measurement in economics....often used for price and demand. So if a 100% percentage decrease in price for some item caused the demand for that item to double (go up 100%) , we could say that the elasticity was one, or unitary.  I have used elasticity to try to show how much the landlord keeps from the rent. Here is a map indicating the coefficient for the natural log of wheat yield, or the elasticity. To the west, the elasticity is lowest, but rises gradually as we move to the east, towards London. Just why we should see this very clear trend is highly interesting. Any ideas?