# Create raster Datasets with Proportional Value Allocation

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06-19-2014 08:20 AM
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New Contributor II
Hello,

I am trying to create raster data sets from census block groups of a state that result in proportional allocation based on the density of the block group it is taking the value from.

Essentially, I am trying to create a raster like the one in the second picture on this website:

http://sedac.ciesin.columbia.edu/data/collection/gpw-v3/methods/grid

I have tried searching all over the place including in all of the related toolboxes within ArcGIS.

Has anyone ever created raster files like this before? Is there an easy way to proportionally allocate quantitative data in a raster?

Than

Eric
Tags (3)
Highlighted
New Contributor II
Hello,

I am trying to create raster data sets from census block groups of a state that result in proportional allocation based on the density of the block group it is taking the value from.

Essentially, I am trying to create a raster like the one in the second picture on this website:

http://sedac.ciesin.columbia.edu/data/collection/gpw-v3/methods/grid

I have tried searching all over the place including in all of the related toolboxes within ArcGIS.

Has anyone ever created raster files like this before? Is there an easy way to proportionally allocate quantitative data in a raster?

Than

Eric

Hi Eric- you may have figured this out already, but one technique is to rasterize the source polygons, then create a count of cells within the block group, then use that to divide the block group population.  Something like this (ArcGIS 10.2.2):

`        #  Create the cell count raster        pyLURas = Lookup(pyNonZeroPop,"Count")        # Divide population raster by cell count raster; 'distributes' values into each cell in output        outDistRas = Float(pyNonZeroPop) / Float(pyLURas)        outDistRas.save(outputDistRaster)`

Then, you can check the values by running zonal stats (Sum) on the outputDistRaster - sum should be equal to block group population (or nearly, given rounding, etc).

Hope this helps,
Lisa