Spatiotemporal Crime Hotspots Analysis and Crime Occurrence Prediction

Advancement of technology in every aspect of our daily life has shaped an expanded analytical approach to crime. Crime is a foremost problem where the top priority has been concerned by the individual, the community and government. Increasing possibilities to track crime events give public organizations and police departments the opportunity to collect and store detailed data, including spatial and temporal information. Thus, exploratory analysis and data mining become an important part of the current methodology for the detection and forecasting of crime development. Spatiotemporal crime hotspots analysis is an approach to analyze and identify different crime patterns, relations, and trends in crime with identification of highly concentrated crime areas. In this paper spatiotemporal crime hotspots analysis using the dataset of the city of Chicago was done. First, we explored the spatiotemporal characteristics of crime in the city, secondary we explored the time series trend of top five crime types, Thirdly, the seasonal autoregressive integrated moving average model (SARIMA) based crime prediction model is presented and its result is compared to the one of the recently developed models based on deep learning algorithms for forecasting time series data, Long Short-Term Memory (LSTM). The results show that LSTM outperforms SARIMA model.