Peng Chen

Accurate crime spatiotemporal prediction is crucial for crime prevention. However, crime occurrences are influenced by diverse and interacting social factors, resulting in dynamically evolving distributions with non-stationarity and spatial heterogeneity. Most existing methods focus on data preprocessing or architectural enhancements and remain offline models, which limits their generalization capability. To address these challenges, we propose a novel spatial semantic-guided online learning framework. Specifically, we first compute the spatial semantic similarity between urban regions using points of interest. Based on this, we then introduce a contrastive learning objective guided by this similarity during training. This design aims to enhance the model’s ability to capture both the similarities and discrepancies among regions. During the prediction process, an iterative online learning strategy is employed to adapt to dynamically changing crime patterns. By continuously fine-tuning the model with streaming data, the proposed framework improves robustness and generalization under non-stationary crime spatiotemporal distributions. Finally, extensive experiments on real-world crime datasets indicate the effectiveness and stability of our proposed approach.

Objective crime risk and perceived safety constitute distinct yet interrelated dimensions of urban security, whose spatial discrepancies may lead to misaligned policy interventions. This study develops a street-level analytical framework to examine the (mis)match between perceived safety and crime risk in Chaoyang District, Beijing. An enhanced Street-view imagery (SVI) segmentation model with object detection was applied to extract streetscape elements and estimate perceived safety scores, which were then standardized and compared with street-level crime data, yielding two types of matches and two types of mismatches. Three conditions were analyzed using multinomial logit regression: (1) objective unsafety with low perceived safety, (2) objective safety with low perceived safety, and (3) objective unsafety with high perceived safety. Findings demonstrate how visual and social environmental factors jointly shape discrepancies between perceived and actual safety and identify potential determinants to mitigate such (mis)matches.

Current crime spatiotemporal prediction models are limited by the insufficient ability of POI data to represent the continuity and mixed-use nature of urban spatial functions. To address this, our study applies an urban region representation method based on building footprints and validates its effectiveness in improving the accuracy of crime spatiotemporal prediction. Specially, we first use the Region Dual Contrastive Learning algorithm to generate region representations as a region graph by integrating building footprints and POI data. Then, the region graph combined with crime data is input into crime prediction models to predict four crime types, including Burglary, Robbery, Felony Assault, and Grand Larceny. Finally, ablation experiments are conducted to quantify the contribution of building footprints to prediction improvement. The experimental results on New York City crime data indicate that (1) the region representations significantly improve deep learning model performance, with the most improved LSTM achieving average increases of 5.66% in Macro-F1 and 18.57% in Micro-F1, particularly benefiting baseline models with lower accuracy, and (2) the region representations yield more significant improvements for low-frequency crime categories and mitigates temporal memory decay in long-term predictions. These findings confirm that incorporating urban region representation based on building footprints effectively enhances crime spatiotemporal prediction performance, providing a more precise and efficient tool for urban security management to optimize police resource allocation and crime prevention strategies.

This study addresses the realistic constrained multi-objective optimization problem of police patrols by constructing a mathematical model tailored to the actual operational context of police patrols in China. To solve this problem, a modified PPS-MOEA/D algorithm is proposed and its performance is systematically evaluated against several state-of-the-art Constrained Multi-Objective Evolutionary Algorithms (CMOEAs). The results demonstrate the superiority of the proposed approach in terms of the solution quality and computational efficiency. Furthermore, the optimal solution set is discussed and visualized on a map, providing decision makers with practical and actionable insights that align with real-world patrol requirements. This research not only advances the theoretical framework for police patrol optimization, but also offers a practical tool for enhancing the effectiveness and efficiency of law enforcement operations in urban environments.

Recent advancements in crime prediction have increasingly focused on street networks, which offer finer granularity and a closer reflection of real-world urban dynamics. However, existing studies on street-level graph representation learning often overlook the variability in node features when aggregating information from neighboring nodes. This limitation reduces the model’s capacity to fully capture the diverse street attributes and their influence on crime patterns. To address this issue, we introduce an end-to-end deep spatio-temporal learning model that employs a graph attention mechanism (GAT) to analyze the spatio-temporal features of 110 call incidents. Experimental results show that our proposed model outperforms existing methods across multiple prediction metrics. Additionally, ablation studies confirm that the GAT’s capacity to capture spatial dependencies within the street network significantly enhances the model’s overall predictive performance.

With the rapid advancement of Internet technology and the increasing volume of police reports, relying solely on extensive human labor and traditional natural language processing methods for key element extraction has become impractical. Applying advanced technologies such as large language models to improve the effectiveness of police report extraction has become an inevitable trend in the field of police data analysis. This study addresses the characteristics of Chinese police reports and the need to extract key elements by employing large language models specific to the public security domain for entity extraction. Several lightweight (6/7b) open-source large language models were tested as base models. To enhance model performance, LoRA fine-tuning was employed, combined with data engineering approaches. A zero-shot data augmentation method based on ChatGPT and prompt engineering techniques tailored for police reports were proposed to further improve model performance. The key police report data from a certain city in 2019 were used as a sample for testing. Compared to the base models, prompt engineering improved the F1 score by approximately 3%, while fine-tuning led to an increase of 10–50% in the F1 score. After fine-tuning and comparing different base models, the Baichuan model demonstrated the best overall performance in extracting key elements from police reports. Using the data augmentation method to double the data size resulted in an additional 4% increase in the F1 score, achieving optimal model performance. Compared to the fine-tuned universal information extraction (UIE) large language model, the police report entity extraction model constructed in this study improved the F1 score for each element by approximately 5%, with a 42% improvement in the F1 score for the “organization” element. Finally, ChatGPT was employed to align the extracted entities, resulting in a high-quality entity extraction outcome.

Measuring human perception of environmental safety and quantifying the street view elements that affect human perception of environmental safety are of great significance for improving the urban environment and residents’ safety perception. However, domestic large-scale quantitative research on the safety perception of Chinese local cities needs to be deepened. Therefore, this paper chooses Chaoyang District in Beijing as the research area. Firstly, the network safety perception distribution of Chaoyang District is calculated and presented through the CNN model trained based on the perception dataset constructed by Chinese local cities. Then, the street view elements are extracted from the street view images using image semantic segmentation and target detection technology. Finally, the street view elements that affect the road safety perception are identified and analyzed based on LightGBM and SHAP interpretation framework. The results show the following: (1) the overall safety perception level of Chaoyang District in Beijing is high; (2) the number of motor vehicles and the proportion of the area of roads, skies, and sidewalks are the four factors that have the greatest impact on environmental safety perception; (3) there is an interaction between different street view elements on safety perception, and the proportion and number of street view elements have interaction on safety perception; (4) in the sections with the lowest, moderate, and highest levels of safety perception, the influence of street view elements on safety perception is inconsistent. Finally, this paper summarizes the results and points out the shortcomings of the research.

In contemporary research, the street network emerges as a prominent and recurring theme in crime prediction studies. Meanwhile, graph representation learning shows considerable success, which motivates us to apply the methodology to crime prediction research. In this article, a graph representation learning approach is utilized to derive topological structure embeddings within the street network. Subsequently, a heterogeneous information network that incorporates both the street network and urban facilities is constructed, and embeddings through link prediction tasks are obtained. Finally, the two types of high-order embeddings, along with other spatio-temporal features, are fed into a deep neural network for street-level crime prediction. The proposed framework is tested using data from Beijing, and the outcomes demonstrate that both types of embeddings have a positive impact on crime prediction, with the second embedding showing a more significant contribution. Comparative experiments indicate that the proposed deep neural network offers superior efficiency in crime prediction.

Street-view images can help us to better understand the city environment and its potential characteristics. With the development of computer vision and deep learning, the technology of semantic segmentation algorithms has become more mature. However, DeeplabV3+, which is commonly used in semantic segmentation, has shortcomings such as a large number of parameters, high requirements for computing resources, and easy loss of detailed information. Therefore, this paper proposes LM-DeeplabV3+, which aims to greatly reduce the parameters and computations of the model while ensuring segmentation accuracy. Firstly, the lightweight network MobileNetV2 is selected as the backbone network, and the ECA attention mechanism is introduced after MobileNetV2 extracts shallow features to improve the ability of feature representation; secondly, the ASPP module is improved, and on this basis, the EPSA attention mechanism is introduced to achieve cross-dimensional channel attention and important feature interaction; thirdly, a loss function named CL loss is designed to balance the training offset of multiple categories and better indicate the segmentation quality. This paper conducted experimental verification on the Cityspaces dataset, and the results showed that the mIoU reached 74.9%, which was an improvement of 3.56% compared to DeeplabV3+; and the mPA reached 83.01%, which was an improvement of 2.53% compared to DeeplabV3+.