Spatial Autocorrelation and Vote Distribution in Ciudad Juárez: A Comparative Analysis (2018–2024)
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Abstract
Examining elections requires understanding not only the political system but also its manifestation in geographic space. In this context, the objective of this study is to analyze how vote concentration and its geographic interdependence shaped the outcomes of the 2018 and 2024 presidential elections in Ciudad Juárez, Mexico. Electoral precinct-level data collected from Mexico’s National Electoral Institute were utilized. Local spatial autocorrelation techniques were applied to identify statistically significant clusters. The data analysis reveals marked geographic polarization: Andrés Manuel López Obrador secured, on average, 53% of votes per electoral precinct in 2018, while Claudia Sheinbaum obtained 72% in 2024, both garnering stronger support in the northwestern and southeastern zones of the city. In contrast, opposition candidates concentrated their support in the northeastern zone. Empirical evidence supports the relationship between socioeconomic conditions and voting patterns. The neighborhood effect demonstrates the clustering of precincts with similar socioeconomic characteristics, confirming the role of “place” as an explanatory dimension for homogeneous voting patterns. This work contributes to the fields of electoral geography and urban studies, providing an analytical framework for future research exploring the interaction between space and politics.
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