This blog article shows the importance of using GIS data for political data that can show trends and reasoning for certain turnouts and results.
The past election has been under a lot of controversy and I feel that GIS analysts are licking their chops at the opportunity to prove Trump's voter fraud analysis true or false.
However, in this article the study was conducted on three local referenda from College Station, Texas to geo-reference voters and non-voters.
The following results were found:
" We found that the extent of neighborhood effects in local elections is heavily inﬂuenced by the voter turn-out. If voter turn-out is clustered at intermediate and large scale, voting results tend to be clustered and also exhibit a sharp polarization between high and low values. If voter turn-out tends to be uniform/regular at intermediate scales but randomly distributed at both small and large scales, there appears to be less clustering in the voting results and thus lack of the neighborhood effect. If the voter turn-out pattern is mixeduniform/regular at the small scale, random at the intermediate scale, but clustered at the large scale, the voting results show a stronger neighborhood effect."
The results are basically saying that neighborhood turn out heavily effects results and the decisions being made. The neighborhood effect is the effect of a neighborhood heavily voting and creating a wave effect from their votes. If neighborhood voting is random and not uniform at smaller and larger scales then the neighborhood effect does not apply because at a large and small scale the voting was not uniform, allowing it to be spread across other options more evenly.
I think using GIS for political data is very cool and definitely something I would be interested in reading. This article was an easy read because of the correlations they found between voter turn out and results from elections and decisions made.
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(2), 159-173.