The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data
Open accessAbstract
In this paper, we present a novel approach to predict crime in a geographic space from multiple data sources, in particular mobile phone and demographic data. The main contribution of the proposed approach lies in using aggregated and anonymized human behavioral data derived from mobile network activity to tackle the crime prediction problem. While previous research efforts have used either background historical knowledge or offenders' profiling, our findings support 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. In our experimental results with real crime data from London we obtain an accuracy of almost 70% when predicting whether a specific area in the city will be a crime hotspot or not. Moreover, we provide a discussion of the implications of our findings for data-driven crime analysis.
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Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
Crime, Shame and Reintegration
Braithwaite, J. (1989) · doi:10.1017/cbo9780511804618 · Cited by 4,153
Limits of Predictability in Human Mobility
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234
Quantifying the Impact of Human Mobility on Malaria
Wesolowski, A., Eagle, N., Tatem, A. J., Smith, D. L., Noor, A. M., Snow, R. W., & Buckee, C. O. (2012) · Science · doi:10.1126/science.1223467 · Cited by 992
Happiness Recognition from Mobile Phone Data
Bogomolov, A., Lepri, B., & Pianesi, F. (2013) · 2013 International Conference on Social Computing · doi:10.1109/socialcom.2013.118 · Cited by 67
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Self-citationMeasuring objective and subjective well-being: dimensions and data sources
Voukelatou, V., Gabrielli, L., Miliou, I., Cresci, S., Sharma, R., Tesconi, M., & Pappalardo, L. (2021) · International Journal of Data Science and Analytics · doi:10.1007/s41060-020-00224-2 · Cited by 365
The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good
Lepri, B., Staiano, J., Sangokoya, D., Letouzé, E., & Oliver, N. (2017) · Studies in Big Data · doi:10.1007/978-3-319-54024-5_1 · Cited by 111
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Metadata
- Authors
- Bogomolov, A., Lepri, B., Staiano, J., Oliver, N., Pianesi, F., & Pentland, A.
- Venue
- Proceedings of the 16th International Conference on Multimodal Interaction
- Publisher
- ACM
- Published
- 2014-11-12
- Pages
- 427-434
- OpenAlex
- W2128338591
- Cited by
- 250
- References
- 6
- FWCI
- 27.06
- Type
- conference paper
- Language
- en
- Open access
- green
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- Bogomolov, A., Lepri, B., Staiano, J., Oliver, N., Pianesi, F., & Pentland, A. (2014). Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data. Proceedings of the 16th International Conference on Multimodal Interaction. https://doi.org/10.1145/2663204.2663254
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