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I'm really struggling to calculate the Utilization Distribution Overlap Index (UDOI) and could really use some clarification on a previous answer.

There is a comprehensive guide to what i am trying to achieve here:

How to Calculate Utilization Distribution Overlap Index (UDOI) in ArcGIS Desktop?

I have followed the instructions through to the calculation of the integrand but I am really stuck trying to calculate the zonal sums. This is where i have got to :

"This is the integrand. To integrate it, notice that because the grid you are using has rectangles all the same size, dx dy never changes. That lets us factor it out, leaving us to just do the sum. That is,compute the zonal sum of the product grid using the polygon of intersection as the zone.This gets multiplied by dx (the x cellsize) and by dy (the y cellsize, usually equal to the x cellsize). For the example, I obtain a value of 0.183814."

  1. First can i clarify that the link in point 5 of Whuber's summary at the bottom of the answer is for the correct tool? The link takes you to the Focal Stats help when i would have thought that it should go to the Zonal Stats in the Zonal toolbox?
  2. In Whuber's example his first zonal sum comes to 0.183814. Mine is coming to over 9000 so I am assuming that I am doing something wrong. Could someone confirm that i am looking for a figure in the 0.something order of mag?
  3. Where it says "This gets multiplied by dx (the x cellsize) and by dy (the y cellsize, usually equal to the x cellsize". Is this explaining what the calc is doing or giving me an instruction?

I want to clarify that I am not criticizing Whuber's post as I have found it very useful.I just don't understand it enough to complete the calculation.

  • I have fixed the typo in the link and have clarified point (3). (I appreciate you calling attention to that ambiguity.) The values you get for the zonal sums will depend on the units you use. For instance, in my example the values would be many of orders of magnitude different if I had used densities per square meter rather than densities per square kilometer. The vertical axes in my illustrations should make it clear that 0.1838 is a reasonable value in this case: you should take a similar look at your datasets to decide whether 9000 is reasonable. – whuber Apr 14 '15 at 21:30

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