New answers tagged

0

I found that using zonal statistics worked best.


1

Sadly the Nibble tool requires an integer input. However, you could create an integer grid by multiplying you data by a factor equivalent to your acceptable scale. So, if you can accept, say five decimal places, create an integer grid from your raw input raster by multiplying by 100000. Perform the nibble and then divide by the same factor. Obviously ...


1

It can be done be done by using the intersect tool and select by location. First step: Use the intersect tool. When used on a single layer the output are only the parts that were overlapping. Second step: Use the Select by location function to select the features in your original layer and give them the uniqe number via the field calculator. (if needed ...


1

Ended up solving this using a combination of the split feature by attribute tool by USGS, union tool, excel and clip. Split up the shapefile from point 1 into single days using the split by attribute tool by USGS. Use union tool to combine the single days from step 1 to create a new combined geodatabase. This will now have a column for every date that the ...


2

This would be much easier to do using python/arcpy... Anyhow, here is a model builder approach (with a python snippet in field calculator [couldn't help it]) Build a model that looks something like this: The model uses the following tools. Con - set all raster cells to a single value Raster to Polygon - Converts raster extent to polygon Get Raster ...


0

The number of possible solutions are somewhat limited without the use of Python. However, I'd suggest using the Fishnet function with polygons as output. Make sure that the fishnet cells covers the corresponding cell in your grid (this can take a few trials while creating the fishnet). You now need to enumerate your polygons. Copypaste values from excel in ...


3

As I pointed out, you had identical observations. Additionally, you were not using the "resolution" argument in the raster function so, were only creating 100 pixel observations to predict to. I had to fix the tab returns in the file that you posted on dropbox, which was not appreciated. Here is code that I got to work. library(gstat) ...


1

One way to do this would be to add an attribute to your polygon layer, say "VALUE", and assign it numeric value of 1000 or more. Then convert the polygon layer to a raster. In arcpy using the spatial analyst module these rasters can be multiplied easily. elevRast = arcpy.Raster("path/elevrast") polyRast = arcpy.Raster("path/polygonraster") resultRast = ...


4

You are confusing terms and thus, confusing us. The expected input for kriging prediction in the gstat krige function is a systematic array of points and not polygons. It would also be nice if you provided a reproducible code example of what you have tried. You can use the extent of an sp object to create an array of points for the kriging prediction using ...



Top 50 recent answers are included