Supervised and Unsupervised Machine Learning Methodologies for Crime Pattern Analysis

Abstract

Crime is a grave problem that affects all countries in the world. The level of crime in a country has a big impact on its economic growth and quality of life of citizens. In this paper, we provide a survey of trends of supervised and unsupervised machine learning methods used for crime pattern analysis. We use a spatiotemporal dataset of crimes in San Francisco, CA to demonstrate some of these strategies for crime analysis. We use classification models, namely, Logistic Regression, Random Forest, Gradient Boosting and Naive Bayes to predict crime types such as Larceny, Theft, etc. and propose model optimization strategies. Further, we use a graph based unsupervised machine learning technique called core periphery structures to analyze how crime behavior evolves over time. These methods can be generalized to use for different counties and can be greatly helpful in planning police task forces for law enforcement and crime prevention.