JOURNAL ARTICLE

Crime forecasting: A spatio-temporal analysis with deep learning models.

  • Published In: Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.), 2025, v. 25, n. 5. P. 4090 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Mao, Li; Du, Wei; Wen, Shuo; Li, Qi; Zhang, Tong; Zhong, Wei 3 of 3

Abstract

This article focuses on using deep learning models, specifically a hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) architecture, to predict daily crime counts across spatial grids in Philadelphia. The study formulates crime forecasting as a spatiotemporal sequence prediction problem and demonstrates that preprocessing crime data through equal-width binning into 10 intervals significantly improves model accuracy compared to using raw data. Comparative experiments show that while traditional models like RNN, GRU, and LSTM perform better on raw data, the CNN-LSTM model outperforms others when applied to binned data, balancing prediction accuracy and spatial granularity. The research highlights the importance of data preprocessing in enhancing predictive performance and supports the use of CNN-LSTM for practical crime forecasting applications.

Additional Information

  • Source:Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.). 2025/09, Vol. 25, Issue 5, p4090
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:1472-7978
  • DOI:10.1177/14727978251337993
  • Accession Number:186643460
  • Copyright Statement:Copyright of Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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