What concept would I use to develop a spatial regression model for misaligned Insurance Claims and Policy data? For example, consider a situation where I have 1000 points that represent policies, and 100 points that represent claims. While some of the claims are located at the same spatial location as some of the policies, most are not. In addition, I have 20 predictor variables associated with the policy points, and 2 predictor variables associated with the claim points.
How can I develop a spatial regression where Y represents Claim $ amount, and X represents all of the predictor variables associated with the policies?
The image below is a toy example of this situation, where blue points represent policies and red triangles represent claims. Triangles either align perfectly with the circles, or they do not align with any circles. I'm assuming I have complete information about the policies (circles), so there are no missing circles.