This study examines the spatial and distributive dynamics of urban crime in the city of Chicago between 2022 and 2024, focusing on three major crime types: Theft, Assault, and Criminal Damage. Using density estimation (KDE) techniques, annual hotspots and the spatial intensity of crime were identified. The inequality in the spatial distribution of crimes was then assessed using the Gini index and Lorenz curves, calculated at the level of defined communities within the city. Using KDE, spatial patterns were found that persist over time for each type of crime, with some high-concentration regions located primarily in the city center and surrounding areas. Although the total number of incidents increased over the study period, the spatial extent of high-density areas remained relatively unchanged, suggesting a recurring spatial selection by offenders. The Gini indices: 0.564 for Theft, 0.537 for Assault, and 0.525 for Criminal Damage, indicate a moderate to high level of spatial inequality in the distribution of crimes, with a small number of communities concentrating a large proportion of the crime. The Lorenz curve coincides with the Gini index, showing a significant deviation from the line of equality, especially in the case of Theft. These results are consistent with other studies highlighting the concentration of crime in urban areas, which allows for spatial prediction of criminal activity. The results obtained are valid for the design of police strategies and for the efficient allocation of resources, which is helpful for the formulation of public policies for urban security.