Moves on the Street: Classifying Crime Hotspots Using Aggregated Anonymized Data on People Dynamics.
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.
['Bogomolov|Andrey|A|', 'Lepri|Bruno|B|', 'Staiano|Jacopo|J|', 'Letouzé|Emmanuel|E|', 'Oliver|Nuria|N|', 'Pianesi|Fabio|F|', 'Pentland|Alex|A|']
crime big data analytics data mining predictive analytics