Detailed police data with spatial and temporal information are crucial for analyzing crime patterns and making police decisions. They support theory formation, evidence-based policy-making, and crime prevention. This project focuses on developing an algorithm for the automatic and user-friendly anonymization of police data (crime, emergency call, and GPS data with precise address and time information). Two challenges are the anonymization of personal data and the risk of identification through detailed location and time data. A collaboration between criminologists and computer scientists is needed to develop a user-friendly algorithm that automatically processes police data, protects privacy, and provides valid criminological insights. This includes analyses of personal data, aggregation levels, and statistical techniques, and the development of a scalable algorithm and user interface.