How to eliminate small features in a grid?

Question

(Update) My final goal is to take some logged point data (1 point about every 20 ft or so there are over 15,000 points) and interpolate that point data. Then I would like to convert the interpolated raster into distinct polygon zones based on the number of classes the data is in using the Natural Breaks classifications. So if I want it in 5 classes, there will be several polygons that make up these 5 classes, but within the tabular data I can see which of the 5 classes each polygon belongs to. In addition to this, I would like to eliminate the smaller polygons created by the interpolation, like say anything under 1 acre in size. Maybe there is a GP process I can run to do this? Let me know if I need to explain this better, and thanks for the help so far.

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I was thinking the "output cell size" setting would be the trick here but it defaults to a very small number (0.00003) and if I make it any bigger like even say "0.003" it results in the interpolated raster having virtually no data where both high and low values is a really high number and very low number.

If I use the default settings I get a decent kriged raster but I would like to adjust to exclude smaller areas, like say less than 1 acre in size. Basically I want the kriging interpolation to ignore areas that are smaller than an acre or even a 1/2 acre. Currently it is showing areas that are easily less then 0.001 acres in size of which I would like it to ignore those. Also if I zoom into it looks like the cell size grids are 8ft by 8ft. How do I get that larger to say 100 ft by 100 ft? Like I said I have tired putting larger number in the output cell size but get back basically nothing but extreme values. I must be doing something wrong. Any help would be appreciated.

Answer 1

The details about kriging, five classes, and natural breaks are irrelevant: the question ultimately asks about how to "eliminate" small polygons in an ordinally classified grid. One of the simplest ways uses the generalization operators, especially nibble. To apply these, you first have to identify the small-polygon patches. Do this by regiongrouping the grid. Convert that to the nibble mask by setting the small-count features to NoData via SetNull.

This process is fairly crude: any cell in a small polygon is assigned the value of its nearest large neighbor, regardless of how close that neighbor's value might be to the cell's original value. Expand provides more control. It allows you to specify which values a large polygon can "absorb." For instance, you might use it to allow large polygons to expand into any neighboring small polygons having an adjacent value. This would require cycling through several expand operations, one per value. The result might depend upon the sequence of expansions and could still leave a few small "orphan" features. Polishing the procedure with a final nibble would take care of those.

Another option is to return to the continuous grid on which the classification was based. Smooth it, such as by means of a focal mean or focal median. Start with a circular neighborhood approximately as large as the polygons to be eliminated and iteratively adjust its radius to achieve the desired effect. The smoothing will tend to reduce the fluctuations, which when classified will create fewer small polygons. Use con to paste the new classifications only into the small polygons (identified, as before, with regiongroup). Experimentation and iteration will be needed.