Novel Patrol Route Optimization Method Based on Big Data Analysis

Big data analysis can enhance police activities and promote improvements in police policy. Police agencies in various countries recognize the need for big data-based policing and are engaged in various research and field support activities. However, big data analysis has rarely been applied in actual patrolling for crime prevention. Therefore, this study established a novel model that uses big data to efficiently respond to emergency reports of crimes. First, we extracted hotspots that were regions of crime incidence based on the correlation between community environment data and crime data. Second, we used a new optimization method based on a genetic algorithm to generate a patrol sequence that required the shortest travel time to the crime area. Finally, we verified the efficiency of the patrol routes using the real-time traffic data of a map API. The proposed big data–based model can be applied to existing patrolling systems and can enhance the efficiency of all police departments.