Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in Chicago
As global urbanization accelerates, communities have emerged as key areas where social conflicts and public safety risks clash. Traditional crime prevention models experience difficulties handling dynamic crime hotspots due to data lags and poor spatiotemporal resolution. Therefore, this study propo...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/9/7/179 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849409054165696512 |
|---|---|
| author | Yuxiao Fan Xiaofeng Hu Jinming Hu |
| author_facet | Yuxiao Fan Xiaofeng Hu Jinming Hu |
| author_sort | Yuxiao Fan |
| collection | DOAJ |
| description | As global urbanization accelerates, communities have emerged as key areas where social conflicts and public safety risks clash. Traditional crime prevention models experience difficulties handling dynamic crime hotspots due to data lags and poor spatiotemporal resolution. Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. By employing a community topology and incorporating historical crime, weather, and holiday data, ST-GCN captures spatiotemporal crime trends, while Informer identifies temporal dependencies. Moreover, the model leverages a fully connected layer to map features to predicted latitudes. The experimental results from 320,000 crime records from 22 police districts in Chicago, IL, USA, from 2015 to 2020 show that our model outperforms traditional and deep learning models in predicting assaults, robberies, property damage, and thefts. Specifically, the mean average error (MAE) is 0.73 for assaults, 1.36 for theft, 1.03 for robbery, and 1.05 for criminal damage. In addition, anomalous event fluctuations are effectively captured. The results indicate that our model furthers data-driven public safety governance through spatiotemporal dependency integration and long-sequence modeling, facilitating dynamic crime hotspot prediction and resource allocation optimization. Future research should integrate multisource socioeconomic data to further enhance model adaptability and cross-regional generalization capabilities. |
| format | Article |
| id | doaj-art-a40a42bd5e2045029df81bb2e3fd41b6 |
| institution | Kabale University |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-a40a42bd5e2045029df81bb2e3fd41b62025-08-20T03:35:37ZengMDPI AGBig Data and Cognitive Computing2504-22892025-07-019717910.3390/bdcc9070179Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in ChicagoYuxiao Fan0Xiaofeng Hu1Jinming Hu2School of Information Technology and Cyber Security, People’s Public Security University of China, Beijing 100038, ChinaSchool of Information Technology and Cyber Security, People’s Public Security University of China, Beijing 100038, ChinaSchool of Information Technology and Cyber Security, People’s Public Security University of China, Beijing 100038, ChinaAs global urbanization accelerates, communities have emerged as key areas where social conflicts and public safety risks clash. Traditional crime prevention models experience difficulties handling dynamic crime hotspots due to data lags and poor spatiotemporal resolution. Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. By employing a community topology and incorporating historical crime, weather, and holiday data, ST-GCN captures spatiotemporal crime trends, while Informer identifies temporal dependencies. Moreover, the model leverages a fully connected layer to map features to predicted latitudes. The experimental results from 320,000 crime records from 22 police districts in Chicago, IL, USA, from 2015 to 2020 show that our model outperforms traditional and deep learning models in predicting assaults, robberies, property damage, and thefts. Specifically, the mean average error (MAE) is 0.73 for assaults, 1.36 for theft, 1.03 for robbery, and 1.05 for criminal damage. In addition, anomalous event fluctuations are effectively captured. The results indicate that our model furthers data-driven public safety governance through spatiotemporal dependency integration and long-sequence modeling, facilitating dynamic crime hotspot prediction and resource allocation optimization. Future research should integrate multisource socioeconomic data to further enhance model adaptability and cross-regional generalization capabilities.https://www.mdpi.com/2504-2289/9/7/179crime predictioninformerST-GCNspatial-temporalChicago |
| spellingShingle | Yuxiao Fan Xiaofeng Hu Jinming Hu Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in Chicago Big Data and Cognitive Computing crime prediction informer ST-GCN spatial-temporal Chicago |
| title | Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in Chicago |
| title_full | Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in Chicago |
| title_fullStr | Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in Chicago |
| title_full_unstemmed | Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in Chicago |
| title_short | Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in Chicago |
| title_sort | research on a crime spatiotemporal prediction method integrating informer and st gcn a case study of four crime types in chicago |
| topic | crime prediction informer ST-GCN spatial-temporal Chicago |
| url | https://www.mdpi.com/2504-2289/9/7/179 |
| work_keys_str_mv | AT yuxiaofan researchonacrimespatiotemporalpredictionmethodintegratinginformerandstgcnacasestudyoffourcrimetypesinchicago AT xiaofenghu researchonacrimespatiotemporalpredictionmethodintegratinginformerandstgcnacasestudyoffourcrimetypesinchicago AT jinminghu researchonacrimespatiotemporalpredictionmethodintegratinginformerandstgcnacasestudyoffourcrimetypesinchicago |