Hybrid approach to crime prediction using deep learning

Prevention is better that Cure. Preventing a crime from occurring is better than investigating what or how the crime had occurred. Just like vaccination is given to a child to prevent disease, in today´s world with such higher crime rate and brutal crime happenings, it have become necessary to have a vaccination systems that prevents from crimes happening. By vaccinating society against crime it refers to various methods such as educating peoples, creating awareness, increasing efficiency and proactive policing methods and other deterrent techniques. Inspired by two different existing approach to crime prediction, the first one present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. Crime incident prediction has depends mainly on the historical crime record and various geospatial and demographic information ^[1]. Even though it´s promising, they do not take into account the rich and rapidly expanding social & web media context that surrounds incidents of interest. Next approach is based on the semantic analysis and natural language processing of Twitter posts via latent Dirichlet allocation, Topic detection Sentiment analysis^[3[4]]. But both the techniques faces inherent limitations. Crime that happens these days are have following key characteristics such as crimes repeating in a periodic fashion, crimes occurring as a result of some other activity and occurrence of crimes pre indicated by some other information.