  A well-crafted police patrol route design is vital in providing community
safety and security in the society. Previous works have largely focused on
predicting crime events with historical crime data. The usage of large-scale
mobility data collected from Location-Based Social Network, or check-ins, and
Point of Interests (POI) data for designing an effective police patrol is
largely understudied. Given that there are multiple police officers being on
duty in a real-life situation, this makes the problem more complex to solve. In
this paper, we formulate the dynamic crime patrol planning problem for multiple
police officers using check-ins, crime, incident response data, and POI
information. We propose a joint learning and non-random optimisation method for
the representation of possible solutions where multiple police officers patrol
the high crime risk areas simultaneously first rather than the low crime risk
areas. Later, meta-heuristic Genetic Algorithm (GA) and Cuckoo Search (CS) are
implemented to find the optimal routes. The performance of the proposed
solution is verified and compared with several state-of-art methods using
real-world datasets.
