  Finding the factors contributing to criminal activities and their
consequences is essential to improve quantitative crime research. To respond to
this concern, we examine an extensive set of features from different
perspectives and explanations. Our study aims to build data-driven models for
predicting future crime occurrences. In this paper, we propose the use of
streetlight infrastructure and Foursquare data along with demographic
characteristics for improving future crime incident prediction. We evaluate the
classification performance based on various feature combinations as well as
with the baseline model. Our proposed model was tested on each smallest
geographic region in Halifax, Canada. Our findings demonstrate the
effectiveness of integrating diverse sources of data to gain satisfactory
classification performance.
