Hotspots-based patrol route optimization algorithm for smart policing

Smart policing based on the analysis of big data ensures the development and sustainability of police policy. However, it is difficult to find instances in which the results of data analysis have been applied to actual policy in the field of crime prevention. The South Korean police force recognizes the need for smart policing and is engaged in various research and field support activities. Some examples that are especially relevant for crime investigation include analyzing the connections between cases and predicting the location of the next crime in a series of crimes and the location of suspects. However, it is difficult to find examples of police policy that use big data. Therefore, this study aims to suggest a model that uses big data to respond to emergency calls efficiently. First, we extract hotspots that are predicted to be locations of criminal activity based on an analysis of the association between community environment data and crime data. Second, we create a route having the shortest travel time to the crime location by developing a route optimization algorithm. Lastly, we assess the performance of the patrol routes in reflecting real-time traffic information. If the data application model suggested in this study could be adjusted and applied to the current police patrol system, the model could be used by each police department effectively.