Fine-Grained Crime Prediction in an Urban Neighborhood
Abstract
Crime is a serious problem that has severe implications on city resources. In this work, we present a novel probabilistic model that predicts the occurrence of various crime types by learning from previous crime incidents and takes advantage of joint dependencies across crime types. We perform a preliminary evaluation using a real-world dataset of crime incidents reported in Memphis across several precincts which shows the promise of our approach.
Publication Title
2018 IEEE International Smart Cities Conference, ISC2 2018
Recommended Citation
Kent, C., & Venugopal, D. (2019). Fine-Grained Crime Prediction in an Urban Neighborhood. 2018 IEEE International Smart Cities Conference, ISC2 2018 https://doi.org/10.1109/ISC2.2018.8656734
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