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How to analyze spatial data stored in a non-traditional format.

Hello DSC folks,

I have some survey data that I'm trying to use for mapping, but I can't figure out a good way to actually map responses without losing a lot of data I would like to preserve.

We have about 700 student participants and we have the location of where they're currently studying, and then we ask them what regions of the country they would be willing to move to (9 subregions which are 3-9 states each). Each region has it's own column, and the responses are dummy-coded 1 if they checked that answer, 0 if not (they could check more than one). I also have broken those out into 50 columns so each state has its own, with the same responses (so if they checked Middle Atlantic, there would be a 1 in NY, PA, and NJ).

My question is somewhat twofold:

  1. How do I best visualize these data on a map or series of maps? I have some experience with ArcGIS, R spatial, and Tableau but am willing to learn others. 
  2. How do I best model the relationship between current location (or location of institution) and places where they are willing to move after graduation? Is spatial or aspatial analysis better, and how much would I benefit from learning a bit of ML here? 

Any advice would be much appreciated!

Tags: GIS, R, Tableau, data, learning, machine, map, mapping, maps, spatial

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