Analysing Spatio-Temporal Voting Patterns in Brazilian Elections Through a Simple Data Science Pipeline
DOI:
https://doi.org/10.5753/jidm.2021.1932Keywords:
data mining, machine learning, preferential voting, spatio-temporal patterns, voting behaviorAbstract
Since 1989, the first year of the democratic presidential election after a long period of a dictatorship regime, Brazil conducted eight presidential elections. Short and long-term shifts of power and two impeachment processes marked such a period. This instability is a research case in electoral studies, mainly regarding the understanding of citizens' voting behavior. Comprehending patterns in the population behavior can give us insight into phenomena and processes that affect democratic political decisions. In light of this, our paper analyses Brazilian electoral data at the municipal level from 1998 to 2018 using a simple data science pipeline, which consists of five steps: (i) data selection; (ii) data preprocessing; (iii) identification of spatial patterns, in which we seek to understand the role of space in the election results employing spatial auto-correlation techniques; (iv) identification of temporal patterns, where we investigate similar trends of votes over the years applying a hierarchical clustering method; and (v) evaluation of results. We study the presidential elections focusing on the right and left-wing parties most relevant for the period: the Brazilian Social Democracy Party~(PSDB) and the Workers' Party~(PT). We also analyse the congressman election data regarding parties ideologically to the right and left in the political spectrum. Through the obtained results, we found the existence of spatial dependence in every electoral year investigated. Moreover, despite the changes in the political-economic context over the years, neighboring cities seem to present similar voting behavior trends.
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