Abstract
Crime activity in many cities worldwide causes significant damages to the lives of victims and their surrounding communities. It is a public disorder problem, and big cities experience large amounts of crime events. Spatio-temporal prediction of crimes activity can help the cities to have a better allocation of police resources and surveillance. Deep learning techniques are considered efficient tools to predict future events analyzing the behavior of past ones; however, they are not usually applied to crime event prediction using a spatio-temporal approach. In this paper, a Convolutional Neural Network (CNN) together with a Long-Short Term Memory (LSTM) network (thus CLSTM-NN) are proposed to predict the presence of crime events over the city of Baltimore (USA). In particular, matrices of past crime events are used as input to a CLSTM-NN to predict the presence of at least one event in future days. The model is implemented on two types of events: “street robbery” and “larceny”. The proposed procedure is able to take into account spatial and temporal correlations present in the past data to improve future prediction. The prediction performance of the proposed neural network is assessed under a number of controlled plausible scenarios, using some standard metrics (Accuracy, AUC-ROC, and AUC-PR).
Highlights
C RIME related problems are at the base of important and crucial issues for many societies living in large cities worldwide. [1] showed that crime and neighborhood disorder may negatively impact the health of urban residents by increasing the resident risk of experiencing violence and impacting their mental health as a form of depression for being in constant contact with assaults, blows and shots
Maps of size 8 × 8 of larceny and street robbery crime events were used as input to the CLSTM-NN neural network, for predicting the presence of crime events one day ahead using d past days, with d = 1, . . . , 7
The CLSTM-NN provided better results in the second scenario by reaching and accuracy of 0.86 and a AUC-PR of 0.93 for the larceny crime using sequences of matrices of events occurred in d = 7 days
Summary
C RIME related problems are at the base of important and crucial issues for many societies living in large cities worldwide. [1] showed that crime and neighborhood disorder may negatively impact the health of urban residents by increasing the resident risk of experiencing violence and impacting their mental health as a form of depression for being in constant contact with assaults, blows and shots. Crime rate reduction is at the core of many local policies driven by active plans supported by police action and local authorities. In this line, the use of mathematical, statistical and/or computational models able to predict crime events beforehand would help the police to generate preventive plans for areas at high risk, and to speed up the process of solving crimes, with the consequent reduction of crime rate. Among the proposed solutions to predict crime events, the use of machine learning techniques have gained great importance due to its recent success in solving real world problems.
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