Abstract
Prior work in architectural and urban studies suggests that there is a strong correlation between people dynamics and crime activities in an urban environment. These studies have been conducted primarily using qualitative evaluation methods, and as such are limited in terms of the geographic area they cover, the number of respondents they reach out to, and the temporal frequency with which they can be repeated. As cities are rapidly growing and evolving complex entities, complementary approaches that afford social scientists the ability to evaluate urban crime theories at scale are required. In this paper, we propose a new method whereby we mine telecommunication data and open crime data to quantitatively observe these theories. More precisely, we analyse footfall counts as recorded by telecommunication data, and extract metrics that act as proxies of urban crime theories. Using correlation analysis between such proxies and crime activity derived from open crime data records, we can reveal to what extent different theories of urban crime hold, and where. We apply this approach to the metropolitan area of London, UK and find significant correlations between crime and metrics derived from theories by Jacobs (e.g., population diversity) and by Felson and Clarke (e.g., ratio of young people). We conclude the paper with a discussion of the implications of this work on social science research practices.
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References
Bogomolov, A., Lepri, B., Staiano, J., Oliver, N., Pianesi, F., Pentland, A.: Once upon a crime: Towards crime prediction from demographics and mobile data. In: ICMI (2014)
Boyd, D., Crawford, K.: Critical questions for big data. Information, Communication and Society 15(5), 662–679 (2012)
Chainey, S., Reid, S., Stuart, N.: When is a hotspot a hotspot? a procedure for creating statistically robust hotspot maps of crime. Innovations in GIS 9 Socio-economic Applications of Geographic Information Science (2002)
Chainey, S.P., Tompson, L., Uhlig, S.: The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal 21(1-2), 4–28 (2008)
Chaplin, R., Flatley, J., Smith, K.: Home office statistical bulletin: Crime in england and wales 2010/11. Home Office Statistical Bulletin (2011)
Christens, B., Speer, P.W.: Predicting violent crime using urban and suburban densities. Behavior and Social Issues (14), 113–127 (2005)
Eagle, N., Macy, M.: Network diversity and economic development. Science (1029) (2010)
Eck, J., Chainey, S., Cameron, J., Leitner, M., Wilson, R.: Mapping crime: Understanding hot spots. Special Report NIJ (2005)
Felson, M., Clarke, R.: Opportunity Makes the Thief: Practical theory of crime prevention. Home Office (1998)
Felson, M., Poulsen, E.: Simple indicators of crime by time of day. International Journal of Forecasting (19), 595–601 (2003)
Hubert, L.J., Golledge, R.G.: Measuring association between spatially defined variables: Tjostheim’s index and some extensions. Geographical Analysis (14), 273–278 (1982)
Jacobs, J.: The Death and Life of Great American Cities. Random House Inc. (1961)
Jansson, K.: British Crime Survey: Measuring crime for 25 years (2006)
Legendre, P.: Spatial autocorrelation: Trouble or new paradigm? Ecology 74(6), 1659–1673 (1993)
Newman, P.: Defensible Space: Crime Prevention Through Urban Design. Macmillian Pub. Co. (1972)
Paynich, R.: Identifying high crime areas. International Association of Crime Analysts (2) (2013)
Prasetyo, P.K., Gao, M., Lim, E.P., Scollon, C.N.: Social sensing for urban crisis management: The case of singapore haze. In: Proc of SocInfo 2013, pp. 478–491 (2013)
Sahbaz, O., Hiller, B.: The story of the crime: functional, temporal and spatial tendencies in street robbery. In: Proc of 6th International Space Syntax Symposium, Istanbul, pp. 4–14 (2007)
Shannon, C.E.: A mathematical theory of communication. The Bell System Technical Journal 27, 379–423, 623–656 (1948)
Clarke, C.S., Mashhadi, A., Capra, L.: Poverty on the cheap: estimating poverty maps using aggregated mobile communication. In: Proc of CHI 2014, pp. 511–520 (2014)
Snyder, M.: The impact of recent immigration on the london economy. Technical report, London School of Economics and Political Science (2007)
Song, W., Daqian, L.: Exploring spatial patterns of property crime risks in changchun, china. International Journal of Applied Geospatial Research 4(3), 80–100 (2013)
Tan, S.-Y., Haining, R.: An urban study of crime and health using an exploratory spatial data analysis approach. In: Gervasi, O., Taniar, D., Murgante, B., Laganà, A., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2009, Part I. LNCS, vol. 5592, pp. 269–284. Springer, Heidelberg (2009)
Tjostheim, D.: A measure of association for spatial variables. Biometrika (65,1), 109–114 (1978)
Tobler, W.R.: A computer movie simulating urban growth in the detroit region. Economic Geography 46, 234–240 (1970)
Wakamiya, S., Lee, R., Sumiya, K.: Social-urban neighborhood search based on crowd footprints network. In: Jatowt, A., Lim, E.-P., Ding, Y., Miura, A., Tezuka, T., Dias, G., Tanaka, K., Flanagin, A., Dai, B.T. (eds.) SocInfo 2013. LNCS, vol. 8238, pp. 429–442. Springer, Heidelberg (2013)
Wang, D., Ding, W., Lo, H., Stepinski, T., Salazar, J., Morabito, M.: Crime hotspot mapping using the crime related factors - a spatial data mining approach. Applied Intelligence 39(4), 772–781 (2006)
Wang, X., Gerber, M.S., Brown, D.E.: Automatic crime prediction using events extracted from twitter posts. In: Yang, S.J., Greenberg, A.M., Endsley, M. (eds.) SBP 2012. LNCS, vol. 7227, pp. 231–238. Springer, Heidelberg (2012)
U. H. WHO. Hidden cities: unmasking and overcoming health inequities in urban settings. WHO, Library Cataloguing-in-Publication Data (2010)
Wolfe, M.K., Mennis, J.: Does vegetation encourage or suppress urban crime? Evidence from Philadelphia, PA. Landscape and Urban Planning 108, 112–122 (2012)
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Traunmueller, M., Quattrone, G., Capra, L. (2014). Mining Mobile Phone Data to Investigate Urban Crime Theories at Scale. In: Aiello, L.M., McFarland, D. (eds) Social Informatics. SocInfo 2014. Lecture Notes in Computer Science, vol 8851. Springer, Cham. https://doi.org/10.1007/978-3-319-13734-6_29
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DOI: https://doi.org/10.1007/978-3-319-13734-6_29
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