1. Jason Basquin GISC 6387 - GIS Workshop 7/31/07
Final Presentation
Jason Basquin
GISC GIS Workshop
7/31/07

2. Project Objective
Use ArcGIS to identify and measure racial gerrymandering and try to determine whether it has increased or decreased over the last decade.

3. Project Overview
Evaluated congressional redistricting patterns in Texas over the last decade.
This includes general elections for the 106th-110th Congresses ( ).

4. Definition
Gerrymander: To divide (a geographic area) into voting districts so as to give unfair advantage to one party in elections.
-The American Heritage® Dictionary of the English Language, Fourth Edition

5. Literature Review Summary
Two Types of Gerrymanders
Racial Gerrymander
Race is “durable” and can be accurately measured.
It is a well-documented proven fact that, as a group, minorities vote Democratic.
Race and ethnicity can withstand the impact of redistricting, whereas partisanship cannot.
Partisan Gerrymander
Partisanship does not create a “durable” voting bloc that you can rely on.
Partisanship cannot be measured from one election to another.
It is not accurate to regard a constituency’s partisan profile as a constant.
Voting behavior heavily influenced by the incumbent and local political issues.
M.E. Rush / Political Geography 19 (2000)

6. Literature Review Summary (continued)
The Identification of Gerrymanders
Strange shapes that wander across the political landscape to search for supporters or to lump opponents into packed districts.
The dilution of the minority vote by splitting the minority vote into multiple districts or by packing them all into 1 district.
Analysis of victory margins.
If the party responsible for the plan wins a significant number of narrow victories while the opposing party loses many seats narrowly and wins most of its seats overwhelmingly, suspicion arises that these results were manufactured.
O’laughlin, John. Department of Geography, University of Illinois at Champaign-Urbana. “The Identification and Evaluation of Racial Gerrymandering.”

7. Literature Review Summary (continued)
How do you measure a gerrymander?
Prediction model that calculates a composite variable known as the Democratic Party Strength Index (DPSI)
The only constant in Texas voting patterns is that black and Hispanic voting populations vote Democratic as a whole.
Other variables can be accurately measured only after the election takes place.
The reduced version of the prediction model can be modified to measure how much a district was racially gerrymandered after the redistricting is complete but before election results are known.
%DPSI = %(B2-B1) + %(H2-H1)
Mckee, Seth, Turgeon, Mathieu and Teigen, Jeremy M. University of Texas at Austin. “The Electoral Effects of Redistricting of Party Competition: Analysis of the 2002 Texas Congressional Elections.”

8. Literature Review Summary (continued)
Can you truly measure a gerrymander?
Daniel Levin proposes a mathematical formula that calculates a “Gerrymander Index.” He then follows that up by stating that his formula only provides a framework for discussion and that gerrymandering is not a mathematical issue but rather a political one.
Levin, Daniel Z. Michigan Journal of Political Science 9 (Winter 1988). “Measuring a Gerrymander.”

9. Data Census Tract polygons from the 1990 and 2000 census.
SF1 data for every census tract in Texas. Data downloaded by county.
Congressional district polygons for 106th-110th congresses.
Summary data for 110th congressional districts. *Reliable summary data was only available for this year. Had to derive district-wide data from census tract data for the other years.
General election results by congressional district.

10. Methodology
Step 1
GIS Data Set Generation
Step 2
Spatial Analysis

11. GIS Data Set Generation
Joined SF1 .dbf table to Census Tract .shp table
Problems Encountered
Nearly 4400 Census Tracts in Texas. Very large amount of data.
Fields in .shp were in integer format, SF1 data in text format.
Census Tract numbers repeat themselves. Created new field with tract number + county name. (ie. 1001Tarrant)
Some county names were spelled differently in .shp than they were in the census data.
Tracts each had 4 digits in .shp, didn’t have 4 digits in SF1 table. Manipulated .dbf table until fields matched up.
Etc. Etc. Etc….

12. GIS Data Set Generation
Prepare Congressional District Polygons
Problems Encountered
There are 32 districts in Texas, but the feature class had 95 polygons. Had to do some editing/polygon clean up until I had 32 polygons.

13. GIS Data Set Generation: Derivation of district data from census tract data.

14. GIS Data Set Generation: Derivation of district data from census tract data.

15. GIS Data Set Generation: Derivation of district data from census tract data.

16. Spatial Analysis Identifying a Racial Gerrymander
Strange shapes that wander around the political landscape.
Analysis of victory margins.
Measuring a Racial Gerrymander
DPSI = %(B2-B1) + %(H2-H1)

17. Identifying Strange Shapes: Methodology
Calculated X,Y coordinates for the centroid of each district.
Plotted the centroids as an X, Y event layer and exported to a shapefile.
Calculated the distance between centroids of the same district for consecutive elections.
Distance between centroids does not necessarily indicate that a gerrymander has taken place. Need to normalize the distance by population density (polygon area) to get a more meaningful measure.
Normalized distance by population density (average district area in this case) to come up with a “Shape Change Index” for each district.

18. Identifying Strange Shapes

19. Identifying Strange Shapes

20. Results

24. Average Shape Change Index

25. Average % Winning Vote Change

26. Average DPSI

27. Final Conclusion: Based on Objective
Gerrymandering has been neither increasing nor decreasing over the last decade. Although gerrymandering is more prevalent in some years than others, there is no evidence of a trend one way or the other.

28. Other Conclusions
High shape change alone does not always guarantee that a gerrymander has taken place. This may be especially true in a racially diverse state like Texas with a rapidly changing population profile.
Gerrymandering is cyclical and a detailed analysis over a longer time frame would probably show that gerrymandering is higher in the election after a new party gains control of the redistricting. Conversely, gerrymandering is likely at it’s lowest in the final year before the change in party control.
Even in a state as large as Texas, it only takes 1 successful gerrymander for a party to accomplish what they set out to do with their redistricting plan.
The DPSI formula is more useful to predict the results of a particular redistricting plan than it is to measure a gerrymander after the election has taken place.
Most of the time when a district has a party change, it is not the result of a gerrymander.
Even when there is no change in the district boundaries, there can still be a fairly significant change in voting percentages.