GIS and Local Politics: How GIS is Helpful in Analyzing Voting Patterns

In its early Years GIS software was used purely to create basic maps, but as time has gone on the possibilities for what it can do have grown exponentially. GIS has even found a major place in the world of political science. In their 2002 article A GIS-based spatial analysis on neighborhood effects and voter turn-out: a case study in College Station, Texas Danile Z. Sui and Peter J. Hugill uncover the base of what can be done in the field of Political Science using GIS. They hypothesize that neighborhood effects manifest themselves in different ways according to the spatial distribution of voter turn-out, and they use GIS to prove it.

The study area is Brian/College station a city with a population of 160,000 people in 2006, and the home to Texas A&M a university with 42,000 students. The complex system of city government in place causes many local issues. This in turn makes College station a perfect location for this experiment to occur in. This experiment looked at three major issues:

1)      In 1995: a $10 Million Bond for the acquisition of 3.5 acres of land for municipal service facilities, and to reconstruct, improve, and extend existing streets, to construct and improve sidewalks, traffic signals, and necessary drainage.

2)      In 1997: the construction of a city convention center.

3)      In 1999: an issue over an overused residential street named Munson Ave.

The GIS-based spatial analysis methodology was as follows: First voting lists were obtained from the city secretary’s office. These lists had the voter’s names, addresses, voting district, and whether or not they voted. It is important to note that the voting districts lined up with major residential subdivisions. Then using the addresses and ARC View’s address matching module the voters (and non-voters) were geocoded. On average 87% of voters were matched with an address the problems were due to either incomplete/incorrect addresses or multiple voters at the same address. Once the voters were geocoded they examined the spatial patterns of actual voter distribution. Followed by conducting a second-order voter distribution analysis using Ripley’s K-function (a common, yet complex formula used to analyze spatial distribution of data).

The First Bond Election was the least controversial and thus was least cared about by the voters. Voter turn-out was extremely low. It received little media coverage, had no local grass roots organization working for or against it, and passed by an overwhelming majority. Voters clearly perceive that it is appropriate to spend tax dollars. Those voters against the bond initiative seem to have been motivated primarily by fear of the potential negative environmental impacts of such municipal service facilities as incinerators and garbage disposal facilities. Contextual/neighborhood effects had a clear impact on voter turn-out, with those precincts slated for the infrastructural improvements having by far the biggest voter turn-out.

Unlike the tax bond issue of 1995, the 1997 referendum on the construction of a city convention center was controversial and received considerable media attention, and grass roots organizations sprang up to attack it. This referendum also confirmed voter clustering, only on a broader scale of between a 1 and 2 mile radius.

The third Issue of re-opening the street Munson Ave. was by far the most controversial issue; it had major media attention and Grass roots organizations on both sides of the issue. The ordinance to re-open Munson Avenue passed by a near 2–1 margin in almost all precincts it had an unusually large voter-turn out. Analysis of the geographic pattern of that turn-out shows it extending in a line south of the TAMU campus. This street provides major north-south relief to traffic. The geographic analysis showed that people in the voting precincts south of TAMU presumably saw the closing of Munson Avenue as restricting their travel behavior and reducing their route alternatives.

This paper shows that GIS-based spatial analysis is immensely helpful in exploring the impacts of voter turn-out on neighborhood effects. On a broader scale this proves that GIS can and should be applied in politics and that politics is deeply rooted in GIS technology and its applications. Enlarge the awareness of political geographers on the potentials of GIS in electoral geographical research. Evidently, the role of GIS has shifted from being simply a data storage/map making tool in the early 1990s to a analytical tool that is used to develop geographic knowledge.

https://lms.southwestern.edu/file.php/3722/Literature/Sui-2002-GIS_Voter_Turnout.pdf