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I have created a probability raster for the appearance of certain features, but want to turn this into a useful decision support tool. The thought is to have bounding boxes which enclose enough probability values to meet a threshold, such that it would require a much larger area to reach a 75% probability of find a feature when bounding low-probability pixels and a much smaller area to do so when bounding high-probability pixels. We have been experimenting with the Neighborhood tools in Spatial Analyst and wonder if there is a way to process these in reverse - instead of specifying a neighborhood area and generating statistics, to have the statistics dictate the size of the neighborhood. If anyone has any insight into this it would be much appreciated! Best, Doug
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08-30-2013
09:43 AM
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It turned out to be a server issue, despite ArcMap giving me map algebra and format errors (along with the dreaded 999999). It's a very strange situation (complicated by the fact that Spatial Analyst would not work but equivalent tools in 3D analyst would) but since I moved the .gdb to a local hard drive things have cleared up. Thank you for your advice - I will be sure to double check these settings in the future as well.
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06-06-2013
04:35 AM
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I am working with a LIDAR generated DEM for an island, and there is a lot of noise and artificial geometry in the raster. I can successfully clip the island with a vector mask, but that returns a very irregularly shaped raster and seems to be breaking all of my spatial analyst tools. Ideally, I would like to classify every value outside of the mask as 0 to maintain a workable raster, but I am unsure of how to do this. Every tool that I've used with the mask (extract, clip) just gives me the island-shaped raster. My question is two-fold - is there a better way to do this and are irregularly shaped rasters fundamentally unusable with surface modeling tools? [ATTACH=CONFIG]25006[/ATTACH]
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06-05-2013
06:39 AM
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