Latent Dirichlet Allocation Based Crime Code Clustering and Crime Prediction

EunSun Kim, Yoon-Sik Cho

http://doi.org/10.5626/JOK.2020.47.1.45

Predicting crime using crime data has become one of the most actively researched disciplines in major cites. Based on the research, law enforcement officials are shifting their efforts from crime investigation to crime prevention through predictive policing. Predictive policing highly relies on mathematics and statistics and identifies the underlying patterns of crimes. Based on these patterns, law enforcement officials can identify potential criminal activities. For accurate prediction, crime data must be well organized and managed. We first introduce one of the popular crime data set actively used by researchers. The data set categorizes each incident through a crime code. Examining the frequency of these codes allows regional agencies to predict the type of potential crimes, leading to effective predictive policing. In this research, we introduce a machine learning-based approach that can identify the similarity between the codes. Based on these similarities, we compute the frequencies of clusters and predict the code of potential crimes. Our experimental results show how our algorithm outperforms the statistical method.