用于建模、研究和预防城市犯罪的数字影子
A Digital Shadow for Modeling, Studying and Preventing Urban Crime
AI总结：
本文提出融合犯罪学理论、专家知识与真实犯罪、地理及社会经济数据的数据驱动智能体仿真数字影子，并在马拉加以30余万报案校准，可复现城市犯罪模式以支持研究与预防。
摘要：
犯罪是城市安全面临的最大威胁之一。全球约80%的人口生活在犯罪水平较高的国家。城市中发生的大多数犯罪都出现在其城市环境内。本文介绍了一个用于建模和模拟城市犯罪的数字影子平台的开发与验证。该数字影子采用数据驱动的基于智能体的建模与仿真技术构建，这些技术适合捕捉个体之间以及个体与其环境之间的动态交互。我们的方法将知名犯罪学理论以及执法机构（LEA）、政策制定者和其他利益相关方的专业知识转化并整合到一个理论模型中；该理论模型又与真实犯罪数据、空间（制图）数据和社会经济数据相结合，形成一个刻画市民日常行为的城市模型。该数字影子还已针对马拉加市进行实例化，我们在该市可获得超过300,000份报案记录。该实例已利用这些报案记录以及该市的其他地理和社会经济信息完成校准。据我们所知，我们的数字影子是首个面向大型城市区域、使用大规模真实犯罪报案数据集进行校准并对城市环境进行精确表示的数字影子。模型校准后的性能指标（以预测性警务中广泛使用的指标衡量）表明，根据历史数据，我们模拟生成的犯罪与该市犯罪的总体模式相匹配。我们的数字影子平台可以成为一种有价值的工具，用于在日常尺度上建模和预测城市环境中的犯罪行为，从而成为政策制定者、犯罪学家、社会学家、LEA等研究和预防城市犯罪的有用工具。
英文摘要：
Crime is one of the greatest threats to urban security. Around 80 percent of the world's population lives in countries with high levels of criminality. Most of the crimes committed in the cities take place in their urban environments. This paper presents the development and validation of a digital shadow platform for modeling and simulating urban crime. This digital shadow has been constructed using data-driven agent-based modeling and simulation techniques, which are suitable for capturing dynamic interactions among individuals and with their environment. Our approach transforms and integrates well-known criminological theories and the expert knowledge of law enforcement agencies (LEA), policy makers, and other stakeholders under a theoretical model, which is in turn combined with real crime, spatial (cartographic) and socio-economic data into an urban model characterizing the daily behavior of citizens. The digital shadow has also been instantiated for the city of Malaga, for which we had over 300,000 complaints available. This instance has been calibrated with those complaints and other geographic and socio-economic information of the city. To the best of our knowledge, our digital shadow is the first for large urban areas that has been calibrated with a large dataset of real crime reports and with an accurate representation of the urban environment. The performance indicators of the model after being calibrated, in terms of the metrics widely used in predictive policing, suggest that our simulated crime generation matches the general pattern of crime in the city according to historical data. Our digital shadow platform could be an interesting tool for modeling and predicting criminal behavior in an urban environment on a daily basis and, thus, a useful tool for policy makers, criminologists, sociologists, LEAs, etc. to study and prevent urban crime.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
