Mexico City has more than nine million inhabitants living in marginalized regions characterized by high crime rates and significant air pollution. According to data provided by the Government of Mexico City, the areas with the highest reported crime rates since 2014 are located in the central, northern, and eastern parts of the city; these same areas also record historically high levels of fine particulate matter (PM10 and PM2.5). Current studies are focusing on the psychological dimension and the possible link between air pollution and criminal behavior, suggesting that ongoing exposure to elevated levels of PM10, CO, and ozone may lead to cognitive deterioration, increased anxiety, depression, and aggressive conduct. In this context, how can we integrate, analyze, and explore crime and air quality data to identify correlations between these phenomena? This paper proposes an advanced urban data analytics framework that outlines phases for integrating datasets and detecting correlations between rising air pollution and the crimes recorded at the borough level in Mexico City. Using data mining techniques, we integrated and analyzed trends in PM10, PM2.5, CO, ozone, and both violent and non-violent crimes from 2016 to 2020, drawing on open data sources from the city. We included records of crimes such as robberies with violence, theft in public transportation and urban spaces, domestic violence, and homicides. These data were examined in greater depth using Pearson correlation and linear regression. The results indeed suggest a positive and statistically significant relationship between boroughs with higher crime rates and elevated air pollution.