Spatiotemporal Prediction of Theft Risk with Deep Inception-Residual Networks Academic Article uri icon

abstract

  • Spatiotemporal prediction of crime is crucial for public safety and smart cities operation. As crime incidents are distributed sparsely across space and time, existing deep-learning methods constrained by coarse spatial scale offer only limited values in prediction of crime density. This paper proposes the use of deep inception-residual networks (DIRNet) to conduct fine-grained, theft-related crime prediction based on non-emergency service request data (311 events). Specifically, it outlines the employment of inception units comprising asymmetrical convolution layers to draw low-level spatiotemporal dependencies hidden in crime events and complaint records in the 311 dataset. Afterward, this paper details how residual units can be applied to capture high-level spatiotemporal features from low-level spatiotemporal dependencies for the final prediction. The effectiveness of the proposed DIRNet is evaluated based on theft-related crime data and 311 data in New York City from 2010 to 2015. The results confirm that the DIRNet obtains an average F1 of 71%, which is better than other prediction models.

published proceedings

  • SMART CITIES

author list (cited authors)

  • Ye, X., Duan, L., & Peng, Q.

citation count

  • 6

complete list of authors

  • Ye, Xinyue||Duan, Lian||Peng, Qiong

publication date

  • January 2021

publisher