A predictive policing application to support patrol planning in smart cities

Recently, patrol planning and other predictive policing strategies were improved for Smart Cities applications. In such public safety context, some police departments have been recording crime events in their databases to compose better strategies to understand and predict crime incidence. This work presents the ROTA-Analytics, a web-based application which aims to provide crime incidence forecasting as outputs. This crime incident forecasting helps patrol supervisors to elaborate the list of predefined locations (points) and staying time at which each police vehicle must patrol. ROTA-Analytics supports multiple machine and statistical learning methods selection to create an environment of crime prediction in different areas of the city. All the phases from time series creation to the automatic machine selection are discussed and exemplified with real data from Natal City in Brazil. Finally, we evaluated our architecture by using two regression strategies for different spatial granularity levels.