UVX-ray: Urban Village Safety Risk Diagnosis Leveraging Multi-Source Urban Data

Urban villages are a unique phenomenon in the downtown segments of major cities in developing countries. Most of them are heavily populated, intensely constructed, and lack infrastructure, bringing potential safety risks to their residents. Therefore, diagnosing which risk factors in urban villages contribute to the increased risk incidence is crucial for urban authorities to better renovate and manage these areas. However, traditional approaches, such as fire and traffic investigations, are labor-intensive and time-consuming, making it challenging to diagnose risks timely. To address this problem, we propose a data-driven framework that leverages heterogeneous urban data to diagnose urban village safety risks through risk-level prediction and risk factor analysis. First, we propose a crowdsensing-based approach to discover urban village potential risk hotspots and then collect contextual data from multiple sources to represent them comprehensively. Second, we propose a multi-modal representation paradigm of urban village potential risk hotspots in a multi-view manner that utilizes pre-trained models for feature extraction to effectively retain information about risk events. Finally, we design an explainable risk diagnosing model that not only predicts the risk level but also automatically highlights salient features (e.g., overcrowded restaurants for high fire risk level). Experiments using real-world data collected from 125 urban villages in Xiamen show that our approach predicts the fire risk level and the traffic risk level with 89.9% and 89.4% accuracy, respectively. Moreover, relevant risk factors in urban villages can be automatically identified for in-depth analysis by our approach.