Moves on the Street: Classifying Crime Hotspots Using Aggregated Anonymized Data on People Dynamics
Moves on the Street: Classifying Crime Hotspots Using Aggregated Anonymized Data on People Dynamics
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DOI：
10.1089/big.2014.0054
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发表时间：
2015-09-01
期刊：
影响因子：
4.6
通讯作者：
Pentland, Alex
中科院分区：
文献类型：
--
作者：
Bogomolov, Andrey;Lepri, Bruno;Pentland, Alex
实时数据流提供的丰富信息为改变生活的技术进步铺平了道路，从促进知识交流到自我理解和自我监控，在许多方面提高了人们的生活质量。此外，对匿名和聚合的大规模人类行为数据的分析为理解人类行为的全球模式提供了新的可能性，并帮助决策者解决具有社会重要性的问题。在本文中，我们强调了大数据应用带来的潜在社会效益，重点是公民安全和预防犯罪。首先，我们介绍了新兴的社会公益大数据研究领域。接下来，我们详细介绍了一个案例研究，该案例研究解决了犯罪热点分类问题，即根据过去的数据对城市中哪些区域更容易发生犯罪进行分类。在提出的方法中，我们使用来自匿名和聚合移动网络数据的人口统计信息以及人类流动性特征。从移动网络基础设施收集的人类行为数据，结合基本的人口统计信息，可以用来预测犯罪，这一假设得到了我们研究结果的支持。我们的模型是建立在伦敦真实犯罪数据的基础上并对其进行评估的，在对城市中某个特定区域在下个月是否会成为犯罪热点进行分类时，准确率接近70%。
The wealth of information provided by real-time streams of data has paved the way for life-changing technological advancements, improving the quality of life of people in many ways, from facilitating knowledge exchange to self-understanding and self-monitoring. Moreover, the analysis of anonymized and aggregated large-scale human behavioral data offers new possibilities to understand global patterns of human behavior and helps decision makers tackle problems of societal importance. In this article, we highlight the potential societal benefits derived from big data applications with a focus on citizen safety and crime prevention. First, we introduce the emergent new research area of big data for social good. Next, we detail a case study tackling the problem of crime hotspot classification, that is, the classification of which areas in a city are more likely to witness crimes based on past data. In the proposed approach we use demographic information along with human mobility characteristics as derived from anonymized and aggregated mobile network data. The hypothesis that aggregated human behavioral data captured from the mobile network infrastructure, in combination with basic demographic information, can be used to predict crime is supported by our findings. Our models, built on and evaluated against real crime data from London, obtain accuracy of almost 70% when classifying whether a specific area in the city will be a crime hotspot or not in the following month.