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I have three separate feature datasets of 125 polygons that contain plant respiration, incident sunlight, and soil moisture data collected on three different dates. The polygons are perfectly coregistered/georeferenced; they are simply separate copies of the same dataset. The data should have been entered in one instance of the dataset, with one record per date, but this is what I have to work with.

I want to overlay these datasets and display the mean of the data from the three dates in one record? What tools can be used for this? I have thought of using Merge and then Spatial Join, but it looks to be very time-consuming.

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If your polygon dataset is identical except for the measurements like you stated, you can use one of your files as a main layer and Join all other files to the main one. Once you joined all your files, export the data to save it permenantly. After that you need to Add a field on your attribute table and simply calculate it as "A+B+C" / 3 to have the averages.

Necessary steps for join can be found here: http://help.arcgis.com/EN/arcgisdesktop/10.0/help/index.html#/Joining_attributes_in_one_table_to_another/005s0000002q000000/

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Spatial join offers a simple solution. You will need to set the merge rule for your respiration, sunlight, and moisture attributes.

Here is a screenshot of the merge rule menu. Select mean. If you need more info please let me know. enter image description here

  • Hello Anil & jbalk, thank you for your responses. I like the join idea, there will likely just be a lot of adding fields - I find Batch Add Field only adds your field to the first polygon in the list if it doesn't crash. I prefer a Spatial Join approach as it is more elegant, but I suppose I'll have to do it in many iterations, as I cannot use the tool on more than two datasets at once. Any experience with batching the SJ tool? – monkeyshines Aug 15 '16 at 12:24
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    Anil, after setting out a workflow, it became clear that your solution offers the shortest path to the result I want. Thanks! – monkeyshines Aug 15 '16 at 13:14
  • Nothing elegant in spatial join approach. Results might vary depending on data quality. Note that in any case attribute joins are much-much faster and more reliable – FelixIP Aug 15 '16 at 20:43
  • You can batch the SJ tool either in a python script or in model builder using an iterator. – jbalk Aug 15 '16 at 23:42

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