Understanding disappearances in Mexico City: A data-driven analysis

Abstract

Mexico is facing an escalating crisis of violence, marked by a sharp increase in disappearances. However, key information remains unknown, such as the typical profile of victims, geographic hotspots, and the relationship between disappearances, economic conditions, and public security. We used a government database of 3,450 disappearances in Mexico City, together with scraped data, to analyze the phenomenon of missing persons. We found that disappearances are not homogeneously distributed along Mexico City, the central district has the highest disappearance rate per capita, which can be attributed to city mobility for job locations. Men account for 62.5% of missing persons, but women aged 15-19 are the most vulnerable group. There is a strong correlation (r=0.95) between reports of drug dealing and disappearances, both of which may be related to the presence of organized crime. Furthermore, when disappearances are normalized to account for mobility related to job locations, a strong negative correlation (r=–0.7) emerges between disappearances and housing prices. This suggests a pattern of socio-economic segregation in disappearances, with higher rates in areas with lower housing prices. Integrating data on disappearances, housing prices, reports of drug dealing, and perception of insecurity for each municipality, we implemented K-means algorithm. Without spatial information, K-means divided Mexico City in west and east. In the east side, people are more vulnerable to disappearances than those in the west side.