# How do I develop a spatial regression model when the Y and X points are partially misaligned? Do I have to use a grid?

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. • Questions about programs/coding are off-topic here, so I'd recommend that you edit this to focus on your statistical question. – mkt - Reinstate Monica Aug 20 '19 at 13:31 • Oh sorry! I'll try and fix it. – Kristaps Aug 20 '19 at 13:32 • what do you want to predict? – user31264 Aug 20 '19 at 13:38 • I want to predict Claim amount () at the claim points – Kristaps Aug 20 '19 at 13:40
• @mkt Gotcha - thank you mkt – Kristaps Aug 20 '19 at 14:13