I am working on a project were we receive point data from multiple households in multiple clusters. I want to flag clusters where the minimum distance between households is very small.
Using the "Calculate Distance Band From Neighbor Count" tool I am getting very close to what I need. However, I need some adjustments to the tool and am hoping for some insight on how to make these.
1. I want the distance to be calculated where the cluster IDs match (some sort of group by query?) 2. I want the output to be in a table form that can be exported to our server (rather than the results window)
Hoping these ideas might be helpful: 1) Most tools work on a selection set, so you could do a query to select features with the same ID and then summarize distances. 2) If there are lots of clustered IDs and you don't know ahead of time what the values will be, you can create a model to loop through all of the unique values in a field (the ID field) and process them. You would use the Field Value iterator in Model Builder. 3) Near table might provide the summary information you need. 4) If you run the Near tool to add a field with the distance to each feature's nearest neighbor, you could then run Summary Statistics, group by ID (case field) and compute the average, minimum, range, etc. statistics for each cluster (in other words, summarize on the Distance field created when you ran Near).
Perhaps one of these solutions will work. Hope so. Best wishes! Lauren Scott Esri
I was hoping you could help me out as well. I have data points (addresses) that I have entered in from Excel and used the geocode address function to add them to my basemap. I have changed all the projections to my local NAD83(meters) State Plane from WGS84.
The problem I am having is whenever I perform the near or near table functions my output is always in decimal degrees. My map units at the bottom right are in meters as well as all the other layers since I have changed the projections yet it continues to display in decimal degrees. I need a true linear measurement for my analysis.
Any tips you may have would be greatly appreciated (as well as any one else)