Abstract
This paper addresses the intricate phenomenon of missing persons, a major problem in Mexico, extending beyond the simple occurrences of disappearances. By developing a historical and spatial data analysis, this research comprehensively examines missing person data from open, official, and social media sources. We apply a data mining framework based on digital media and openly accessible government databases to characterize and visually represent crimes such as enforced disappearances along the temporal and spatial dimensions. The data analysis methodology takes a comprehensive approach, segmenting data by age, sex, nationality, geographic location, and period. This segmentation unveils patterns in space and time, thus contributing to a better understanding of the factors influencing missing person phenomena and valuable insights into the dynamics of missing person incidents that have impacted many states and regions in Mexico over the past decade.
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Authors of this paper want to thank to: IPN, COFAA, SIP Projects 20241571 and 20240707-2284; CONAHCYT PROJECTS: 7051 and CF-2023-G-1170, UPIITA-IPN, and ESCOM-IPN for their support.
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Zagal, R., Claramunt, C., Hernandez, C., Mata, F. (2024). Exploring Spatio-temporal Dynamics: A Historical Analysis of Missing Persons Data in Mexico, Revealing Patterns and Trends. In: Lotfian, M., Starace, L.L.L. (eds) Web and Wireless Geographical Information Systems. W2GIS 2024. Lecture Notes in Computer Science, vol 14673. Springer, Cham. https://doi.org/10.1007/978-3-031-60796-7_3
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DOI: https://doi.org/10.1007/978-3-031-60796-7_3
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