I'm working on a project where, essentially, I'm measuring threat levels based on geospatial patterns. I have different groups with higher or lower threat levels based on the distance from their location. These distances are fixed between different groups, all are roughly 0-30 miles, 31-90 miles, 91-270 miles, etc.

I have a data set that lists these groups and the threat levels based on those distances. It basically looks like:

Threat Assessment Chart

There will be multiple examples of each group, and I know how to create the points and buffers off the points, but this is where my current skill set fails me and I need help.

  1. I need to assign values to each groups buffer area.
  2. I need to identify areas where the buffers overlap, and create a high threat level.

Ideally, the outcome would look something like this, where the points are a various groups locations, the buffer circles are the distances, and the black numbers are the values assigned to each buffer:

enter image description here

The orange numbers show the value of the overlapping buffers, and something like the red area is where two groups threat levels overlap to create the highest overall threat.

1 Answer 1


You achieve this analysis in a multi-step process by extracting centroids and summarising these then join back to your data. This should be wrapped up in a model as then you could repeat/tweak your analysis. The essential steps are:

  1. Create your buffers, I used the multi-ring buffer tool and create a separate featureclass for each buffered point.
  2. To each dataset add a new field called threat
  3. Encode your 1, 2 and 3 values into your data, a simple select and field calculate will do.
  4. Merge all your feature classes into a single featureclass of overlapping buffers.
  5. Run the union tool of your merged dataset, where buffers overlap you'll get multiple smaller polygons, each with their threat value.
  6. Run feature to point tool, ensure point is INSIDE. These will have the threat values.
  7. Run add XY coordinates on point dataset
  8. Create a string field and concatenate the X and Y into a unique ID
  9. Run summary statistics tool on point dataset. Grouping by your string ID field and summing threat.
  10. Join the summary table to your point datasets by string ID field
  11. Run delete Identical tool deleting on your string ID field
  12. Run spatial join to attach the summed threat values back to the UNIONED dataset.
  13. Run delete Identical tool on your spatially joined data deleting on your string ID field

Steps 1 to 3 could be easily wrapped up into a model using an iterator to step through each of your points to create the respective multi-ringed buffer datasets.

output Rings labelled by their threat, and coloured by the overlapping summed values.

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