Hello,
I have a problem I've been trying to find a solution to, and I was hoping I could find some extra help or direction.
Scenario
I have 3 different point feature layers. Each layers represents a location of a mobile home park in the state of Pennsylvania. Each layer also has valuable attribute information. My goal is to have 1 comprehensive point feature layer that has no identical points and all of the attribute information from each layer.
My Issues. Each layer was assembled using a different method. Layer A has points created by using parcel centroids for the state. Layer B was assembled using address points (placed on the street the parcel connects too). Layer C assembly method is unknown. From looking at aerial imagery and comparing it to the other two layers, it seems as though the points are randomly plotted anywhere inside the parcel of the mobile home park.
Tools I've tried: Merge Append, Spatial Join.
More Issues. There is no identical field relating these three layers (No ID, ADDRESSES, ETC). Due to the position of the points 1 point might be closer to the parcel centroid of an entirely different mobile home park then it's matching mobile home park in another layer. This affects accuracy using a spatial join.
I'm attempting to find point features that aren't included across all three layers so I can have the most comprehensive layer. However, I can't use a buffer because the distance between matching mobile home parks between the three layers can range from 0 - unknown distance. There are over 2000 features in my dataset.
I'd appreciate any help or advice.
