Measuring Ambient Population from Location-Based Social Networks to Describe Urban Crime

  • Cristina KadarEmail author
  • Raquel Rosés Brüngger
  • Irena Pletikosa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10539)


Recently, a lot of attention has been given to crime prediction, both by the general public and by the research community. Most of the latest work has concentrated on showing the potential of novel data sources like social media, mobile phone data, points of interest, or transportation data for the crime prediction task and researchers have focused mostly on techniques from supervised machine learning to show their predictive potential. Yet, the question remains if indeed this data can be used to better describe urban crime. In this paper, we investigate the potential of data harvested from location-based social networks (specifically Foursquare) to describe urban crime. Towards this end, we apply techniques from spatial econometrics. We show that this data, seen as a measurement for the ambient population of a neighborhood, is able to further describe crime levels in comparison to models built solely on census data, seen as measurement for the resident population of a neighborhood. In an analysis of crime on census tract level in New York City, the total number of incidents can be described by our models with up to \(R^2 = 56\%\), while the best model for the different crime subtypes is achieved for larcenies with roughly \(67\%\) of the variance explained.


Urban computing Social computing Computational social science Crime analysis Spatial econometrics Location-based social networks 


  1. 1.
    Anselin, L.: Spatial Econometrics Methods and Models. Studies in Operational Regional Science, vol. 4. Springer, Dordrecht (1988)CrossRefzbMATHGoogle Scholar
  2. 2.
    Anselin, L.: Local indicators of spatial association LISA. Geograph. Anal. 27(2), 93–115 (1995)CrossRefGoogle Scholar
  3. 3.
    Anselin, L., Cohen, J., Cook, D., Gorr, W., Tita, G.: Spatial analyses of crime. Crim. Justice 4(2), 213–262 (2000)Google Scholar
  4. 4.
    Anselin, L., Williams, S.: Digital neighborhoods. J. Urban.: Int. Res. Placemaking Urban Sustain. 9175, 24 (2015)Google Scholar
  5. 5.
    Arribas-Bel, D., Kourtit, K., Nijkamp, P.: The sociocultural sources of urban buzz. Environ. Plan. C: Gov. Policy 34(1), 188–204 (2016)CrossRefGoogle Scholar
  6. 6.
    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 (2014)Google Scholar
  7. 7.
    Brantingham, P.J., Brantingham, P.L.: Nodes, paths, and edges: consideration on the complexity of crime and the physical environment. J. Environ. Psychol. 13, 328 (1993)CrossRefGoogle Scholar
  8. 8.
    Brantingham, P.L., Brantingham, P.J.: Criminality of place: crime generators and crime attractors. Eur. J. Crim. Policy Res. 3, 526 (1995)CrossRefGoogle Scholar
  9. 9.
    Box, G.E.P., Cox, D.R.: An analysis of transformations. J. R. Stat. Soc. Ser. B (Methodol.) 26(2), 211–252 (1964)zbMATHGoogle Scholar
  10. 10.
    Cliff, A.D., Ord, J.K.: Spatial Processes: Models & Applications. Taylor & Francis (1981)Google Scholar
  11. 11.
    Cohen, L.E., Felson, M.: Social change and crime rate trends: a routine activity approach. Am. Sociol. Rev. 44(4), 588 (1979)CrossRefGoogle Scholar
  12. 12.
    Cohen, L.E., Kluegel, J.R., Land, K.C.: Social inequality and predatory criminal victimization: an exposition and test of a formal social inequality and predatory criminal victimization: an exposition and test of a formal theory. Am. Sociol. Rev. 5, 505–524 (1981)CrossRefGoogle Scholar
  13. 13.
    De Nadai, M., Staiano, J., Larcher, R., Sebe, N., Quercia, D., Lepri, B.: The death and life of great Italian Cities. In: WWW 2016 (2016)Google Scholar
  14. 14.
    Eagle, N., Macy, M., Claxton, R.: Network diversity and economic development. Science 328(5981), 1029–1031 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Gerber, M.S.: Predicting crime using Twitter and kernel density estimation. Decis. Support Syst. 61(1), 115–125 (2014)CrossRefGoogle Scholar
  16. 16.
    Goodchild, M.F.: Citizens as sensors: the world of volunteered geography. GeoJournal 69, 211–211 (2007)CrossRefGoogle Scholar
  17. 17.
    Graif, C., Sampson, R.J.: Spatial heterogeneity in the effects of immigration and diversity on neighborhood homicide rates. Homicide Stud. 13(3), 242–260 (2009)CrossRefGoogle Scholar
  18. 18.
    Jacobs, J.: The Death and Life of Great American Cities. Vintage Books, New York City (1961)Google Scholar
  19. 19.
    Karamshuk, D., Noulas, A., Scellato, S., Nicosia, V., Mascolo, C.: Geo-spotting: mining online location-based services for optimal retail store placement. In: KDD 2013 (2013)Google Scholar
  20. 20.
    Kutner, M.H., Nachtsheim, C.J., Neter, J.: Applied Linear Regression Models, 4th edn. McGraw-Hill Irwin, New York (2004)Google Scholar
  21. 21.
    Lee, B.A., Iceland, J., Sharp, G.: Charting Change in American Communities Over Three Decades Key Findings, Gregory Racial and Ethnic Diversity Goes Local (2012)Google Scholar
  22. 22.
    Pappalardo, L., Vanhoof, M., Gabrielli, L., Smoreda, Z., Pedreschi, D., Giannotti, F.: An analytical framework to nowcast well-being using mobile phone data. Int. J. Data Sci. Anal. 2(12), 75–92 (2016)CrossRefGoogle Scholar
  23. 23.
    Peet, R.K.: The measurement of species diversity. Ann. Rev. Ecol. Syst. 5(1), 285–307 (1974)CrossRefGoogle Scholar
  24. 24.
    Pratt, T.C., Cullen, F.T.: Assessing macro-level predictors and theories of crime: a meta-analysis. Crime Justice 32, 373–450 (2005)CrossRefGoogle Scholar
  25. 25.
    Rey, S., Anselin, L.: PySAL: a Python library of spatial analytical methods. In: Fischer, M., Getis, A. (eds.) Handbook of Applied Spatial Analysis, vol. 37, pp. 175–193. Springer, Heidelberg (2009)Google Scholar
  26. 26.
    Sampson, R., Rauenbush, S., Earls, F.: Neighborhoods and violent crime: a multilevel study of collective efficacy. Science 277, 918–924 (1997)CrossRefGoogle Scholar
  27. 27.
    Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Sheldon, A.L.: Equitability indices: dependence on the species count. Ecology 50(3), 466–467 (1969)CrossRefGoogle Scholar
  29. 29.
    Shmueli, G.: To explain or to predict? Stat. Sci. 25(3), 289–310 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Traunmueller, M., Quattrone, G., Capra, L.: Mining mobile phone data to investigate urban crime theories at scale. In: Aiello, L.M., McFarland, D. (eds.) SocInfo 2014. LNCS, vol. 8851, pp. 396–411. Springer, Cham (2014). doi: 10.1007/978-3-319-13734-6_29 Google Scholar
  31. 31.
    Wang, H., Kifer, D., Graif, C., Li, Z.: Crime rate inference with big data. In: KDD 2016 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Cristina Kadar
    • 1
    Email author
  • Raquel Rosés Brüngger
    • 1
  • Irena Pletikosa
    • 1
  1. 1.Information Management Chair, D-MTECETH ZurichZürichSwitzerland

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