VisCrimePredict: a system for crime trajectory prediction and visualisation from heterogeneous data sources
Open multidimensional data from social media and similar sources often carries insightful information on social issues. With the increase of high volume data and the proliferation of visual analytics platforms, it becomes easier for users to interact with and select meaningful information from large data sets. The prevention of crime is a crucial issue for law-enforcing agencies tasked with maintaining societal stability. The ability to visualise crime patterns and predict imminent incidents accurately opens new possibilities in crime prevention. In this paper, we present VisCrimePredict, a system that uses visual and predictive analytics to map out crimes that occurred in a region/neighbourhood. VisCrimePredict is underpinned by a novel algorithm that creates trajectories from heterogeneous data sources such as open data and social media with the aim to report incidents of crime. VisCrimePredict uses a Long Short Term Memory (LSTM) algorithm for trajectory prediction. A proof of concept implementation of VisCrimePredict and an experimental evaluation of crime trajectory prediction accuracy using LSTM neural network concludes the paper.