Deep Multi-view Spatio-Temporal Network for Urban Crime Prediction
Pages 50 - 61
Abstract
Crimes sabotage various societal aspects, such as social stability, public safety, economic development, and individuals’ quality of life. To accurately predict crime occurrences can not only bring the peace of mind to individuals but also help distribute and manage police resources effectively by authorities. We aim to take into account plenty of environmental factors, such as data collected from Internet of Things (IoT) devices and social networks to predict crimes at city or a finer level. To this end, we propose a deep-learning-based spatio-temporal multi-view model, which explores the relationship between tweets, weather (a type of sensory data) and crime rate, for effective crime prediction. Our extensive experiments on a four-month crime dataset (covering 77 communities, 22 crime types, and 120 days) of Chicago city show that our model can achieve improvement over 19 out of 22 crime types (up to 6.7% in homicide). We also collect the corresponding weather information for different regions of Chicago city to support the crime prediction. Our experiments demonstrate that weather information can improve the performance of the proposed method.
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Published In
Jan 2021
230 pages
ISBN:978-3-030-69376-3
DOI:10.1007/978-3-030-69377-0
- Editors:
- Miao Qiao,
- Gottfried Vossen,
- Sen Wang,
- Lei Li
© Springer Nature Switzerland AG 2021.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 29 January 2021
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