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I'm trying to figure out an easy way to take county level jobs point data (exported from the Census bureau's OnTheMap) and merge it with census block groups in order to give each block group a total amount of jobs.

The idea would be to visualize total jobs in a similar way as you can visualize total population on the block group level.

So far I have my block groups from TIGER block group shapefiles and I have my jobs point shapefile, but combining them is proving difficult as OnTheMap is pretty rough. I could, I believe, select each census block group in OnTheMap and export the data, add it up myself into a spreadsheet and combine them all manually to join by way of block group number, but that would take so long.

There has to be a better way, right?

I figure I should be able to run an anaysis of which points are falling in which blockgroup polygon and intersect them via that, but it doesn't work.

  • Could you post a picture of an example of what you are talking about? I am not familiar with the terms you are using, but is it points and polygons? Where the points are the jobs and the polygons are the reference you want to group the jobs into? – Keagan Allan Mar 27 '18 at 7:50
  • Yep basically! Here's a picture to help illustrate it: imgur.com/a/bKRu0 In it you can see that the dots represent a number of jobs and the polygon below is a census block group. What I need is to take all those dots and merge them with their associated block groups below so that each block group has one number represented for all jobs. – GeoPDX Mar 27 '18 at 15:37
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You could run an intersect between the points and polygon.

Then run the dissolved tool using the polygon census id as the dissolve field and sum the jobs as the stats.

Then perform a spatial join using the dissolved id and polygon is as the link.

That should give you jobs per polygon.

  • That worked perfectly. Thanks so much for your help! I knew there had to be a way to do it. – GeoPDX Mar 27 '18 at 16:46
  • Cool don't forget to press the little green tick if it worked. Glad it helped – Keagan Allan Mar 27 '18 at 16:47

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