Interactive Crime Map Integrating Geospatial Data to Improve Public Safety in the City of Lima

Insecurity is a growing concern in Latin America and some parts of the world, and the city of Lima is no exception. Crimes such as assaults, robberies and other crimes are on the rise, generating great concern among the population. In this context, it is important to interact with the population by geo-referencing danger zones with crime maps. This paper aims to develop a real-time application that allows the tracking and monitoring of danger zones, using machine learning techniques and visualization of critical zones. The methodology uses a dataset of crime reports provided by the Peruvian Police from 2018 to 2023, using tools such as Power BI and libraries such as Pandas, Plotly, and Mapbox from Python to extract latitude and longitude. The process includes data analysis and prediction, application development, interactive map creation, data visualization, and application testing. The study concludes that crime mapping is a valuable tool for combating insecurity. Integrating crime data with georeferencing technologies allows detailed visualization of crime patterns, enabling better resource planning, prioritization, and data-driven decision-making to address criminal activity effectively.