It is typically assumed that the percentage of voter turnout
in an area is at least minimally affected by local or neighborhood influence, whether
that means local media coverage of a certain issue or varying opinions on what
areas of the city or town need to be repaired, using local tax dollars, etcetera.
This study, by Danile Z. Sui and Peter
J. Hugill, attempts to spatially illustrate and analyze the trends between
voter turnout in an neighborhood and the different issues in each election, called the "neighborhood effect."
Sui and
Hugill chose College Station, TX, the home of Texas A&M, for their data
set. Because the city has very complex processes to initiate official political moves, such as creating city petitions and demanding recalls, but opperates
on a relatively small scale, College Station has
a very particular political environment perfect for a study examining local
elections in a spatial manner. They chose to explore the results of three
referenda on three different years, the first of which addressed the application
millions of dollars in tax bonds being set aside to improve the city’s
infrastructure (1995), the second a petition to halt the construction of a City Convention
Center (1997), and the third an appeal to reopen one of the oldest streets in the city,
that was closed by the City Council due to incessant traffic, all compared to the
voting turn out in each voting precincts. These precincts were used as if they were
neighborhoods, as they coincided with many of the main residential areas and are useful in depicting election results, although the results would not be as effective, or accurate, in larger cities.
In analyzing the data, the
researchers matched addresses on voters and non-voters, then applied the voting
results for each referenda to a map of the city, in order to compare the spatial
distribution in voter turnout and the actual results of each. Sui and Hugill
used a fairly complicated method to analyze this data called Ripley’s
K-Function to determine “whether
a given point pattern departs from randomness toward clustering or regularity”
(Sui and Hugill 2002, 163).
This is important in determining whether certain neighborhoods had any correlation
with the voter turnout, if there was clustering, or whether it was primarily indiscriminate.
The maps below illustrate the
results from this study. In the maps on the left (i, iii, and v), each point
represents a registered voter. It is clear that the first issue, the tax bond
for repairing city infrastructure was the least controversial, with a low voter
turnout and, as seen in figure ii, and passed with a majority in nearly every precinct
(with the solid gray sections of the circles representing for the referenda and
the white with dots against it in each precinct, seen in map ii). The distribution of these
results further illustrates the fact that the areas that would receive these improvements
had the most voter turnout, thus the most people who cared about such a
relatively mundane issue turned out to vote. In map iii, there is a much higher
voter turnout, as well as closer results in many of the precincts, seen in map
iv. Evidently, the idea of College Station having a city funded convention center
was quite controversial, as it was seen as a pure business expenditure paid for
with citizen tax dollars to some, but an effective stimulate for economic
growth in the area to others. The measure passed, but not with a large
majority.
However, the 1991 issue was the most controversial, illustrated in v and vi. There is a clear concentration of voters in map v, apparently extending south from A&M’s campus. In closing Munson Avenue, citizens needing to go north, presumably mostly students and employees if the college, obviously felt quite strongly that the road needed to be open, probably because it offered more options, thus less traffic, to get where they needed to go.
Sui and Hugill
determined that when there is a particularly high voter turnout in the precinct,
there tends to be a very clear preference in the area for a particular result,
which may be extremely different than the results in other precincts, clearly seen in the first and examples. However, the difference between these two incidents is
the actual voter turnout. There is certainly clustering of voters in the first example, but not to the same scale as the
third, where there is a distinct clustering in very large numbers, illustrating
the passion the certain neighborhoods felt for repealing the closure. Conversely, when
voter turnout is more scattered, there is not obvious
polarization of results, as seen in the even spread of voters and nearly split results overall in the second example. These results are
complicated and certainly do not show the full range of components affecting
voter turnout, but the use of GIS in this particular geographic area to
analyzed political results helps illustrate “the neighborhood effect,” where
communities can have a legitimate impact
on voter turnout, and thus influencing the outcomes of particular issues acutely affecting members
of these communities.
Sui, D.Z., & Hugill, P.J. (2002). A GIS-based spatial
analysis on neighborhood effects and voter turn-out: a case study in College
Station, Texas. Political Geography, 21. Retrieved
from https://lms.southwestern.edu/file.php/4373/Literature/Sui-2002-GIS_Voter_Turnout.pdf
