research-article

Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data

Authors:

Andrey Bogomolov,

Jacopo Staiano,

Alex PentlandAuthors Info & Claims

ICMI '14: Proceedings of the 16th International Conference on Multimodal Interaction

Pages 427 - 434

https://doi.org/10.1145/2663204.2663254

Published: 12 November 2014 Publication History

Abstract

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.

References

[1]

G. Biau. Analysis of a random forests model. The Journal of Machine Learning Research, 98888(1):1063--1095, 2012.

Digital Library

[2]

S. L. Boggs. Urban crime patterns. American Sociological Review, 30(6):pp. 899--908, 1965.

[3]

A. Bogomolov, B. Lepri, and F. Pianesi. Happiness recognition from mobile phone data. In SocialCom 2013, pages 790--795, 2013.

Digital Library

[4]

J. Bollen, H. Mao, and X. Zeng. Twitter mood predicts the stock market. Journal of Computational Science, 2(1):1--8, 2011.

[5]

G. E. P. Box and D. R. Cox. An Analysis of Transformations. Journal of the Royal Statistical Society. Series B (Methodological), 26(2):211--252, 1964.

[6]

J. Braithwaite. Crime, Shame and Reintegration. Ambridge: Cambridge University Press, 1989.

[7]

P. L. Brantingham and P. J. Brantingham. A theoretical model of crime hot spot generation. Studies on Crime & Crime Prevention, 1999.

[8]

L. Breiman. Bagging predictors. Machine Learning, 24(2):123--140, 1996.

[9]

A. L. Buczak and C. M. Gifford. Fuzzy association rule mining for community crime pattern discovery. In ACM SIGKDD Workshop on Intelligence and Security Informatics, page 2. ACM, 2010.

Digital Library

[10]

J. Caplan and L. Kennedy. Risk terrain modeling manual: Theoretical framework and technical steps of spatial risk assessment for crime analysis. Rutgers Center on Public Security, 2010.

[11]

S. Chainey, L. Tompson, and S. Uhlig. The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal, 21:4--28, 2008.

[12]

J. Cullen and S. Levitt. Crime, urban flight, and the consequences for the cities. Review of Economics and Statistics, (81):159--169, 2009.

[13]

Y.-A. de Montjoye, J. Quoidbach, F. Robic, and A. Pentland. Predicting personality using novel mobile phone-based metrics. In Social Computing, Behavioral-Cultural Modeling and Prediction, pages 48--55. Springer, 2013.

Digital Library

[14]

W. Dong, B. Lepri, and A. Pentland. Modeling the co-evolution of behaviors and social relationships using mobile phone data. In MUM 2011, 2011.

Digital Library

[15]

N. Eagle, M. Macy, and R. Claxton. Network diversity and economic development. Science, 328(5981):1029--1031, 2010.

[16]

J. Eck, S. Chainey, J. Cameron, and R. Wilson. Mapping crime: understanding hotspots. National Institute of Justice: Washington DC, 2005.

[17]

I. Ehrlich. On the relation between education and crime. 1975.

[18]

L. Ellis, K. Beaver, and J. Wright. Handbook of crime correlates. Academic Press, 2009.

[19]

R. B. Freeman. The economics of crime. Handbook of labor economics, 3:3529--3571, 1999.

[20]

E. Frias-Martinez, G. Williamson, and V. Frias-Martinez. An agent-based model of epidemic spread using human mobility and social network information. In Social Computing (SocialCom), 2011 International Conference on, pages 57--64. IEEE, 2011.

[21]

M. Gonzalez, C. Hidalgo, and L. Barabasi. Understanding individual mobility patterns. Nature, 453(7196):779--782, 2008.

[22]

C. Krumme, A. Llorente, M. Cebrian, A. Pentland, and E. Moro. The predictability of consumer visitation patterns. Scientific Reports, (1645), 2013.

[23]

J. Laurila, D. Gatica-Perez, I. Aad, J. Blom, O. Bornet, T. Do, O. Dousse, J. Eberle, and M. Miettinen. From big smartphone data to worldwide research: The mobile data challenge. Pervasive and Mobile Computing, 9:752--771, 2013.

Digital Library

[24]

H. Mehlum, K. Moene, and R. Torvik. Crime induced poverty traps. Journal of Development Economics, (77):325--340, 2005.

[25]

G. Mohler, M. Short, P. Brantingham, F. Schoenberg, and G. Tita. Self-exciting point process modeling of crime. Journal of the American Statistical Association, (106):100--108, 2011.

[26]

E. B. Patterson. Poverty, income inequality, and community crime rates. Criminology, 29(4):755--776, 1991.

[27]

S. Raphael and R. Winter-Ebmer. Identifying the effect of unemployment on crime. Journal of Law and Economics, 44(1), 2001.

[28]

J. H. Ratcliffe. A temporal constraint theory to explain opportunity-based spatial offending patterns. Journal of Research in Crime and Delinquency, 43(3):261--291, 2006.

[29]

J. Shang, T. W. Zheng, Y., and E. Chang. Inferring gas consumption and pollution emission of vehicles throughout a city. In KDD 2014, 2014.

Digital Library

[30]

C. Shannon. A mathematical theory of communication. Bell System Technical Journal, 27:379--423, 623--656, July, October 1948.

[31]

M. B. Short, M. R. D'Orsogna, V. B. Pasour, G. E. Tita, P. J. Brantingham, A. L. Bertozzi, and L. B. Chayes. A statistical model of criminal behavior. Mathematical Models and Methods in Applied Sciences, 18(supp01):1249--1267, 2008.

[32]

S. R. Singh, H. A. Murthy, and T. A. Gonsalves. Feature selection for text classification based on gini coefficient of inequality. Journal of Machine Learning Research-Proceedings Track, 10:76--85, 2010.

[33]

V. K. Singh, L. Freeman, B. Lepri, and A. S. Pentland. Predicting spending behavior using socio-mobile features. In Social Computing (SocialCom), 2013 International Conference on, pages 174--179. IEEE, 2013.

Digital Library

[34]

C. Smith-Clarke, A. Mashhadi, and L. Capra. Poverty on the cheap: Estimating poverty maps using aggregated mobile communication networks. In 32nd ACM Conference on Human Factors in Computing Systems (CHI2014), 2014.

Digital Library

[35]

C. Song, Z. Qu, N. Blumm, and A. Barabasi. Limits of predictability in human mobility. Science, (327):1018--1021, 2010.

[36]

V. Soto, V. Frias-Martinez, J. Virseda, and E. Frias-Martinez. Prediction of socioeconomic levels using cell phone records. In UMAP 2011, pages 377--388, 2011.

Digital Library

[37]

J. Staiano, B. Lepri, N. Aharony, F. Pianesi, N. Sebe, and A. Pentland. Friends don't lie Inferring personality traits from social network structure. In ACM Ubicomp 2012, pages 321--330, 2012.

Digital Library

[38]

R. Tarling and K. Morris. Reporting crime to the police. British Journal of Criminology, (50):474--479, 2010.

[39]

J. L. Toole, N. Eagle, and J. B. Plotkin. Spatiotemporal correlations in criminal offense records. ACM Trans. Intell. Syst. Technol., 2(4):38:1--38:18, July 2011.

Digital Library

[40]

A. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe. Predicting elections with twitter: What 140 characters reveal about political sentiment. ICWSM, 10:178--185, 2010.

[41]

E. Tuv, A. Borisov, G. Runger, and K. Torkkola. Feature selection with ensembles, artificial variables, and redundancy elimination. The Journal of Machine Learning Research, 10:1341--1366, 2009.

Digital Library

[42]

T. Wang, C. Rudin, D. Wagner, and R. Sevieri. Learning to detect patterns of crime. In Machine Learning and Knowledge Discovery in Databases, pages 515--530. Springer, 2013.

Digital Library

[43]

X. Wang, M. S. Gerber, and D. E. Brown. Automatic crime prediction using events extracted from twitter posts. In Social Computing, Behavioral-Cultural Modeling and Prediction, pages 231--238. Springer, 2012.

Digital Library

[44]

S. K. Weinberg. Theories of criminality and problems of prediction. The Journal of Criminal Law, Criminology, and Police Science, 45(4):412--424, 1954.

[45]

D. Weisburd. Place-based policing. Ideas in American Policing, 9:1--16, 2008.

[46]

D. Weisburd and L. Green. Defining the street-level drug market. 1994.

[47]

A. Wesolowski, N. Eagle, A. Tatem, D. Smith, R. Noor, and C. Buckee. Quantifying the impact of human mobility on malaria. Science, 338(6104):267--270, 2012.

[48]

Y. Zheng, L. Capra, O. Wolfson, and H. Yang. Urban computing: concepts, methodologies, and applications. ACM Transaction on Intelligent Systems and Technology, 2014.

Digital Library

[49]

Y. Zheng, T. Liu, Y. Wang, Y. Liu, Y. Zhu, and E. Chang. Diagnosing new york city's noises with ubiquitous data. In ACM Ubicomp 2014, 2014.

Digital Library

Cited By

Varshney GManankumar R MMaheshwari RChhabhaiya TKumar B(2024)Justice : A Predicting Criminal Acts According To IPC SectionInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT249021510:2(129-139)Online publication date: 12-Mar-2024
https://doi.org/10.32628/CSEIT2490215
Paramasivan KJaiswal SSubburaj RSudarsanam N(2024)Understanding the role of mobility in the recorded levels of violent crimes during COVID-19 pandemic: a case study of Tamil Nadu, IndiaCrime Science10.1186/s40163-024-00222-w13:1Online publication date: 14-Aug-2024
https://doi.org/10.1186/s40163-024-00222-w
Zhou BZhou HWang WChen LMa JZheng Z(2024)HDM-GNN: A Heterogeneous Dynamic Multi-view Graph Neural Network for Crime PredictionACM Transactions on Sensor Networks10.1145/3665141Online publication date: 14-May-2024
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Index Terms

Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data
1. Applied computing
  1. Law, social and behavioral sciences
2. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Conferences

ICMI '14: Proceedings of the 16th International Conference on Multimodal Interaction

November 2014

558 pages

ISBN:9781450328852

DOI:10.1145/2663204

General Chairs:
Albert Ali Salah
Boğaziçi University, Turkey
,
Jeffrey Cohn
University of Pittsburgh, USA
,
Björn Schuller
University of Passau, Germany and Imperial College London, UK
,
Program Chairs:
Oya Aran
Idiap Research Institute, Switzerland
,
Louis-Philippe Morency
University of Southern California, USA
,
Philip R. Cohen
Adapx, USA

Copyright © 2014 ACM.

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Published: 12 November 2014

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ICMI '14: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

November 12 - 16, 2014

Istanbul, Turkey

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ICMI '14 Paper Acceptance Rate 51 of 127 submissions, 40%;

Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Cited By

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https://doi.org/10.32628/CSEIT2490215
Paramasivan KJaiswal SSubburaj RSudarsanam N(2024)Understanding the role of mobility in the recorded levels of violent crimes during COVID-19 pandemic: a case study of Tamil Nadu, IndiaCrime Science10.1186/s40163-024-00222-w13:1Online publication date: 14-Aug-2024
https://doi.org/10.1186/s40163-024-00222-w
Zhou BZhou HWang WChen LMa JZheng Z(2024)HDM-GNN: A Heterogeneous Dynamic Multi-view Graph Neural Network for Crime PredictionACM Transactions on Sensor Networks10.1145/3665141Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3665141
Thoiba Singh NMehra MVerma ISingh NGandhi DAhmad Alladin M(2024)Advancing Crime Analysis and Prediction: A Comprehensive Exploration of Machine Learning Applications in Criminal Justice2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)10.1109/IDCIoT59759.2024.10467221(1339-1343)Online publication date: 4-Jan-2024
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Lettieri NGuarino AMalandrino DZaccagnino R(2024)Visual Knowledge Discovery and Criminal Justice. Insights from a Computational Crime Analysis ResearchArtificial Intelligence and Visualization: Advancing Visual Knowledge Discovery10.1007/978-3-031-46549-9_13(375-395)Online publication date: 25-Apr-2024
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