Sexual violence persists as a significant global public matter, manifesting in gender-specific ways. It is a pervasive phenomenon, with high prevalence rates reported across the globe, affecting individuals in a multitude of ways—physically, psychologically, and socially. This study focuses on female victims of sexual violence and employs a combination of spatial autocorrelation (Global and Local Moran’s) and four distinct regression models (Geographically Weighted Regression) to analyze the prevalence of sexual violence against women in Mexico City. The regression models employed a range of demographic and socio-economic variables, in addition to police records, to identify any spatial clustering and to examine the factors influencing the geographical distribution of the cases. The selected model was compared with the Ordinary Least Squares and Generalized Method of Moments models. The findings revealed significant concentrations of sexual violence in specific urban areas, underscoring the role of poverty levels, victims’ age group, and average years of schooling. Furthermore, spatial variations in these factors were identified, emphasizing varying vulnerabilities across different local areas.