Jie Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Jie Zhang has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Hao Yin
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210046, China
1 shared publication - Hong Zhang
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing 210023, China
1 shared publication - Zengli Wang
Jiangsu Key Laboratory of Soil and Water Processes in Watershed, College of Geography and Remote Sensing, Hohai University, Nanjing 211100, China
1 shared publication
Latest Publications
- Open Access
- 7 May 2021
- Published by MDPI AG in ISPRS International Journal of Geo-Information
- Vol. 10 (5) , 314
An Adaptive Spatial Resolution Method Based on the ST-ResNet Model for Hourly Property Crime Prediction
Effective predictive policing can guide police patrols and deter crime. Hourly crime prediction is expected to save police time. The selection of spatial resolution is important due to its strong relationship with the accuracy of crime prediction. In this paper, we propose an adaptive spatial resolution method to select the best spatial resolution for hourly crime prediction. The ST-ResNet model is applied to predict crime risk, with historical crime data and weather data as predictive variables. A prediction accuracy index (PAI) is used to evaluate the accuracy of the results. Data on property crimes committed in Suzhou, a big city in China, were selected as the research data. The experiment results indicate that a 2.4 km spatial resolution leads to the best performance for crime prediction. The adaptive spatial resolution method can be used to guide police deployment.
- 7 May 2021
- Published by MDPI AG in ISPRS International Journal of Geo-Information
- Vol. 10 (5) , 314
Open AccessAn Adaptive Spatial Resolution Method Based on the ST-ResNet Model for Hourly Property Crime Prediction
Effective predictive policing can guide police patrols and deter crime. Hourly crime prediction is expected to save police time. The selection of spatial resolution is important due to its strong relationship with the accuracy of crime prediction. In this paper, we propose an adaptive spatial resolution method to select the best spatial resolution for hourly crime prediction. The ST-ResNet model is applied to predict crime risk, with historical crime data and weather data as predictive variables. A prediction accuracy index (PAI) is used to evaluate the accuracy of the results. Data on property crimes committed in Suzhou, a big city in China, were selected as the research data. The experiment results indicate that a 2.4 km spatial resolution leads to the best performance for crime prediction. The adaptive spatial resolution method can be used to guide police deployment.