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GIS Workshops: Local Presidential Results

Fall 2012 Workshop: Mapping Presidential Results in Your Neighborhood

   

Objectives

Identify the election patterns in Texas neighborhoods and then predict the % likelihood that any given community will vote Republican in the upcoming Presidential elections.  We will then view our results within Google Earth.

 

We will use the following variables to attempt to predict votes:

  • Income
  • Population Density
  • Presidential Election Results
  • Gubernatorial Election Results

Workshop Resources

Data Sources Used

Workshop Plan

Before Analysis: Explore Data

  • Navigate to the C:/ElectionWorkshop directory in Windows and launch (double-click) ElectionGIS2012.mxd.  Within ArcMap, we will explore each layer's Attribute Table and Symbology.


Step #1: Geocode Home Address

    • This step will plot a point at any address in Texas so we can analyze local demographics and voting patterns.
      • Click (1) Geocode Home Address on the toolbar
      • Select the Locations tab
      • Type any address in Texas
      • Click Find


    • Right-click the top result, and select Add Point



Step #2: Buffer Address

    • This step will draw a buffer (radius) around the address we just plotted.
      • Click (2) Buffer Address


      • Click Next
         

      • Ensure the Specified Distance and Units are set to 1 Mile.
      • Click Next


    • Last minute edit: Seems as if the default location is throwing an error message.
      • Hit the browse button, change the type to Shapefile
      • Save
    • Click Finish


Step #3: Spatial Join

    • This step will calculate summary statistics within our buffer.
      • Click (3) Spatial Join on the toolbar.

    • Ensure that the top drop-down box contains the name of the buffer we made in the previous step.
    • Click OK
    • When completed, right-click the newly created SpatialJoinBuffer layer in the layer list and select Open Attribute Table to view the results.


Step #4: Republican Likelihood 2012

      • This step requires the Spatial Analyst Extension to be enabled.
        • Customize/Extnesions at the top
        • Ensure Spatial Analyst is checked

    • This step will predict the % likelihood that any zip code in Texas will vote Republican in the upcoming Presidential election.
    • This is the crucial step, so get your thinking caps on and fired up!
    • There are six (6) variables.  For each variable we need to reclassify and assign a weight.
      • Click (4) Republican Likelihood 2012 on the toolbar.
      • **Note**: Please remember to hit the Classify button for each variable so the form can calculate the proper min/max values for the classifications.


    • For each of the six (6) variables we are considering, we must specify both the Reclassified Values and the Weight.
    • The Reclass field drop-down menus are all set to Value and should not be changed.
    • When complete, click OK.
    • This may take a couple of minutes, depending on the speed of your computer.
    • When comeplete, two new files will be generated.
      • predict - Raster layer containing likelihood on our 1 to 10 scale.
      • predictzip - contains our prediction in % format for each zip code in Texas.


Step #5: Export to Google Earth

    • This step will overlay our zip code prediction layer onto Google Earth.
      • Before launching the tool, we need to set the symbology to Graduated Colors.
      • Click (5) Export to Google Earth on the toolbar.


      • For the Layer, specify the predictzip file we just generated.
      • For the Output File, select a destination and name.  I recommend the Desktop as it is easily accessible.
      • For the Layer Output Scale, enter 1.
      • Click OK
      • When complete, navigate (outside of ArcGIS) to the directory you saved the KMZ file, and double-click it.

Please Evaluate Before You Leave =)

Census Geography Examples

The following displays popular Census geographies, going from large areas (County) to the smallest areas (Census Blocks).  The images below use Tarrant County, Texas as the example.