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I am currently working in Arc 10.1 with a grid of equally spaced polygons (created with the Grid Index Features tool). I've then broken this grid into 4 different layers, based on the number of points (form a different layer) that fall into each polygon of the grid.

This has created four patchworks, with a good deal of white space between polygons on the same layer.

What I would like to be able to do is take all the polygons in one layer and collapse them together to eliminate the white space.

Ideally, I would also like to be able to reverse the process after I've run some analyses.

I have included some images to help illustrate what I'm talking about. (Note I don't have enough reputation to link to the larger versions of the pictures individually, but you can view a gallery of them here:

1: I have a grid of evenly spaced polygons

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2: I have separated it out into four different polygon layers

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3: I am working with each layer separately

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4: I want to move the existing polygons on that layer alone to eliminate the empty space

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5: When the empty space is eliminated, all the polygons on the layer should make a single, solid mass

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6: Reversing the process would ideally return the polygons to their original orientation

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    In order to avoid having this closed as "unclear" you're probably going to have to add a lot more detail to the question. Editing the question to include a screen shot, with a sketch of what you want to happen would be appropriate. Guidelines for how you would gauge successful reversal would also help. – Vince May 12 '14 at 14:36
  • Step 5 doesn't make any sense. What attributes are these manufactured polygons going to have? And why aren't you doing this raster modeling with a raster datatype? – Vince May 12 '14 at 15:12
  • The individual polygons are being used as a reference frame for the point data from a different layer. I'm trying to run kernel density estimations on the data with a focus on keeping similar population densities grouped. I can break out the densities, but the white space is, unfortunately, still factored into the KDE, which is skewing my results. So I'm trying to consolidate them, run the analyses, break them back to their original form and then merge the results. I will admit, it may not be the ideal solution; finding out its utility is part of the reason I'm trying to do this. – user3628799 May 12 '14 at 15:25
  • Thanks for the edit. Could you go back to first principles and explain what you're trying to do - the kind of data, where exactly you're headed? I think we can be more helpful if you give us some more information! Steps 5 and 6 don't make any sense to me yet, no idea where you're going with this! – Simbamangu May 12 '14 at 15:54
  • Right now, I'm working with theoretical stuff based around kernel density estimations. The specific data isn't important at the stage I'm at; I'm just using a synthetic data set. I want to run the estimations on data with similar population density characteristics separately, so I create the different layers based on the density (StepS 2 & 3), but the empty space introduces new problems. Step 5 will let me run the KDE without empty space and then Step 6 will return the results to its original spatial orientation so the results of each layer's KDE can be rejoined to form the whole picture. – user3628799 May 12 '14 at 16:04
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One solution might be to pull your data into a feature dataset in a geodatabase and create a topoplogy for it with Must Not Have Gaps as a rule. You can validate the topology and create features for the gap areas, and then merge these to form one solid polygon. Keep a second copy of your original file as your “reversion” copy.

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