Select to view content in your preferred language

Dasymetric Tool: Dasy Output Excludes Some Blocks

4083
2
Jump to solution
02-27-2015 08:48 AM
JustinEddings
Occasional Contributor

I have been working with the Dasymetric Mapping 2014 tool for ArcGIS 10.2.2. I applied the following to the tool:

 

-Population: US Census Block 2010, for a region of CT

-Land Use: NLCD 2011, clipped to CT and Reclassified to 1-High, 2-Medium, 3-Low, 4-Uninhabited

-Key Field: OBJECTID

-Population: POP10

-Uninhabited: 4

-Coverage: 80% (Default)

 

A closer look at the "dasy_rast" saw some areas where entire blocks contained 0 NEWPOP/NEWDENSITY cells even with a positive population and inhabited classes. Anyone else run into this problem? Any thoughts as to why this might be happening are appreciated.

 

-Justin

0 Kudos
1 Solution

Accepted Solutions
JustinEddings
Occasional Contributor

After the weekend to think it over, I reset my procedure and used an FID rather than the BLOCKID10 field for the key field. The result fills in all the block areas with a population and residential class. I was able to convert the dasy_rast to a point feature class (NEWDENSITY, not NEWPOP) and use a spatial join to sum the regions of CT. At first, 0 value points made up about 75% of the feature class so I ran the conversion again using after selecting only the raster values >0.

View solution in original post

0 Kudos
2 Replies
JustinEddings
Occasional Contributor

After some more in depth review of the "dasy_rast" data I noticed that the BLOCKID10 does not match the US Census BLOCKID10. Maybe it was key area that caused this but only one way to find out.

-Justin

0 Kudos
JustinEddings
Occasional Contributor

After the weekend to think it over, I reset my procedure and used an FID rather than the BLOCKID10 field for the key field. The result fills in all the block areas with a population and residential class. I was able to convert the dasy_rast to a point feature class (NEWDENSITY, not NEWPOP) and use a spatial join to sum the regions of CT. At first, 0 value points made up about 75% of the feature class so I ran the conversion again using after selecting only the raster values >0.

0 Kudos