Crime Analysis And Hotspot Prediction

  • Richa Sharma Vivekanand Education Society’s Institute of Technology, Hashu Advani Memorial Complex, Collector's Colony, Chembur, Mumbai, Maharashtra 400074
  • Yash Kaned Vivekanand Education Society’s Institute of Technology, Hashu Advani Memorial Complex, Collector's Colony, Chembur, Mumbai, Maharashtra 400074
  • Sunny Singh Vivekanand Education Society’s Institute of Technology, Hashu Advani Memorial Complex, Collector's Colony, Chembur, Mumbai, Maharashtra 400074
  • Amit Lund Vivekanand Education Society’s Institute of Technology, Hashu Advani Memorial Complex, Collector's Colony, Chembur, Mumbai, Maharashtra 400074
  • Bhisham Goplani Vivekanand Education Society’s Institute of Technology, Hashu Advani Memorial Complex, Collector's Colony, Chembur, Mumbai, Maharashtra 400074
Keywords: Crime, RNN, STNN, Binary Classifier.

Abstract

Crime is a major social and economic problem in almost every country, which threatens the safety of its citizens and also disrupts the economy of that nation. Understanding patterns in criminal activity will allow us to predict the crimes that may occur in the future and also predict their “hot spots” (the areas where they are most prominent to occur) and enables the authorities to more effectively and efficiently allocate manforce and resources to prevent or respond to incidents. Day by day crime is increasing, as there is an increase in unemployment, population density and other such factors. Crime has always been a problem for civilians as well as the authorities. The authorities are collecting and storing detailed data tracking crime occurrences. This data contains spatial and temporal data, which can be used to precisely predict the regional crime rates, detect and predict Crime Hotspots. Deep Learning and Neural Networks has been widely proven effective for detecting temporal patterns in a time series . We aspire to use the power of Deep Learning to help the authorities battle crime to provide a safer society for the civilians to live in.

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Published
2019-12-31