论文标题
探索犯罪预测的时空时期和跨型相关性
Exploring Spatio-Temporal and Cross-Type Correlations for Crime Prediction
论文作者
论文摘要
犯罪预测在增强城市的公共安全和可持续发展方面起着影响力。随着数据收集和集成技术的最新进展，已记录了大量具有与犯罪相关信息丰富的城市数据和良好的时空日志。这样的有用信息可以提高我们对城市犯罪的时间进化和空间因素的理解，并可以增强准确的犯罪预测。在本文中，我们进行了犯罪预测，利用城市犯罪的跨型和时空相关性。特别是，我们从时间和空间观点验证了不同类型的犯罪之间存在相关性，并提出一个连贯的框架，以数学上对这些相关性进行数学建模以进行犯罪预测。现实数据的广泛实验结果验证了所提出的框架的有效性。进行了进一步的实验，以了解犯罪预测中不同相关性的重要性。
Crime prediction plays an impactful role in enhancing public security and sustainable development of urban. With recent advances in data collection and integration technologies, a large amount of urban data with rich crime-related information and fine-grained spatio-temporal logs has been recorded. Such helpful information can boost our understandings about the temporal evolution and spatial factors of urban crimes and can enhance accurate crime prediction. In this paper, we perform crime prediction exploiting the cross-type and spatio-temporal correlations of urban crimes. In particular, we verify the existence of correlations among different types of crime from temporal and spatial perspectives, and propose a coherent framework to mathematically model these correlations for crime prediction. The extensive experimental results on real-world data validate the effectiveness of the proposed framework. Further experiments have been conducted to understand the importance of different correlations in crime prediction.
