Why Small Is Better: Advancing the Study of the Role of Behavioral Contexts in Crime Causation

  • Dietrich Oberwittler
  • Per-Olof H. Wikström


In this chapter we argue, both from a theoretical (Situational Action Theory) and methodological(homogeneity of environmental conditions) point of view, that smallenvironmental units are preferable to large in the study of environmentaleffects on crime.

Most empirical research in the field of communities and crimeutilizes fairly large spatial units of several thousand residents,such as U.S. census tracts or even clusters of census tracts, thus evoking doubts about internal homogeneity. If geographical areas are heterogeneous in their environmental conditions, associations between structural conditions, social organization, and outcomes such as crime may be clouded or rendered insignificant. On the other hand, due to common financial restrictions, choosing more units often (but not necessarily) imply fewer subjects per units which may cause a ‘small number problem’, that is, that the prediction of events as rare as crime will lose precision (compared to the use of larger units with more subjects). The question then is how small can you go before this potential problem outweighs the benefits of more homogeneous areas? This chapter assesses the added value of using very small area units in a community survey on environmental influences on crime. This survey was carried out in 2005 as part of the Peterborough Adolescent and Young Adult Development Study (PADS+) and covers the UK city of Peterborough and some rural surroundings. For the purpose of this study, we used the smallest administrative unit which subdivides the city, isolating 550 areas with about 300 residents each. We sampled an average of 13 respondents per unit for a total sample of 6,600 respondents. Multilevel analyses and Sampson’s (1999a) ecometric approach are applied to compare the aggregate-level reliability of survey scales on this very small geographical level to the larger spatial level conventionally used for geographical analysis. The results show a considerable increase in between-neighborhood variance, reflecting a higher degree of homogeneity and statistical power for detecting particularly moderate to weak area-level effects. We use the collective efficacy scale and its subscales to illustrate these results.


Census Tract Informal Social Control Moral Context Super Output Area Crime Causation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bailey, T. C., & Gatrell, A. C. (1995). Interactive spatial data analysis. London: Longman.Google Scholar
  2. Bellair, P. E. (1997). Social interaction and community crime: Examining the importance of neighbor networks. Criminology, 35, 677–703.CrossRefGoogle Scholar
  3. Hox, J. (2002). Multilevel analysis: Techniques and applications. Mawhaw: Erlbaum.Google Scholar
  4. Kubrin, C. E., & Weitzer, R. (2003). New directions in social disorganization theory. Journal of Research in Crime and Delinquency, 40(4), 374–402.CrossRefGoogle Scholar
  5. Land, K. C., McCall, P. L., & Cohen, L. E. (1990). Structural covariates of homicide rates: Are there any invariances across time and space. American Journal of Sociology, 95(4), 922–963.CrossRefGoogle Scholar
  6. Martin, D. (2000). Towards the geographies of the 2001 UK census of population. Transactions of the Institute of British Geographers, 25, 321–332.CrossRefGoogle Scholar
  7. McCord, E. S., Ratcliffe, J. H., Garcia, R. M., & Taylor, R. B. (2007). Nonresidential crime attractors and generators elevate perceived neighborhood crime and incivilities. Journal of Research in Crime and Delinquency, 44(3), 295–320.CrossRefGoogle Scholar
  8. McVie, S., & Norris, P. (2006). Neighbourhood effects on youth delinquency and drug use (Edinburgh study of youth transitions and crime, working paper 10). Edinburgh: Centre for Law and Society.Google Scholar
  9. Miethe, T. D., & Meier, R. F. (1994). Crime and its social context: Towards an integrated theory of offenders, victims, and situations. Albany: State University of New York Press.Google Scholar
  10. Murrey, D. M., Varnell, S. P., & Blitstein, J. L. (2004). Design and analysis of group-randomized trials: A review of recent methodological developments. American Journal of Public Health, 94(3), 423–432.CrossRefGoogle Scholar
  11. Oberwittler, D. (2003). Die Messung und Qualitätskontrolle kontextbezogener Befragungsdaten mithilfe der Mehrebenenanalyse - am Beispiel des Sozialkapitals von Stadtvierteln. ZA-Informationen, 53, 11–41.Google Scholar
  12. Office of the Deputy Prime Minister. (2004). The English Indices of Deprivation 2004: Summary. London.Google Scholar
  13. Openshaw, S., & Taylor, P. J. (1981). The modifiable areal unit problem. In: N. Wrigley & R. J. Bennet (Eds.), Quantitative geography: A British view (pp. 60–70). London: Routledge.Google Scholar
  14. Openshaw, S. (1984). Modifiable areal unit problem. Norwich: GeoBooks.Google Scholar
  15. Ouimet, M. (2000). Aggregation bias in ecological research: How social disorganization and criminal opportunities shape the spatial distribution of juvenile delinquency in Montreal. Canadian Journal of Criminology, 42, 135–156.Google Scholar
  16. Raudenbush, S. W. (1999). Statistical analysis and optimal design in cluster randomized trials. Psychological Methods, 2(2), 173–185.CrossRefGoogle Scholar
  17. Raudenbush, S., & Bryk, A. (2002). Hierarchical linear models: applications and data analysis methods (2.A.). Thousand Oaks: Sage.Google Scholar
  18. Raudenbush, S. W., & Sampson, R. J. (1999). Ecometrics: Toward a science of assessing ecological settings, with appliance to the systematic social observation of neighborhoods. Sociological Methodology, 29, 1–41.CrossRefGoogle Scholar
  19. Reynolds, H. D. (1998). The modifiable area unit problem: empirical analysis and statistical simulation. PhD thesis, University of Toronto.Google Scholar
  20. Sampson, R. J., Raudenbush, S. W., & Earls, F. J. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277, 918–924.CrossRefGoogle Scholar
  21. Sampson, R. J., & Raudenbush, S. W. (1999). Systematic social observation of public spaces: A new look at disorder in urban neighborhoods. American Journal of Sociology, 105(3), 603–651.CrossRefGoogle Scholar
  22. Sampson, R. J., Morenoff, J. D., & Gannon-Rowley, T. (2002). Assessing “neighborhood effects”: Social processes and new directions in research. Annual Review of Sociology, 28, 443–478.CrossRefGoogle Scholar
  23. Sampson, R. (2006). How does community context matter? Social mechanisms and the explanation of crime rates. In: P.-O. H. Wikström & R. Sampson (Eds.), Crime and its explanation: Contexts, mechanisms and development. Cambridge: Cambridge University Press.Google Scholar
  24. Shaw, C., & McKay, H. D. (1969 [1942]). Juvenile Delinquency and Urban Areas. Chicago: Chicago University Press.Google Scholar
  25. Silver, E., & Miller, L. L. (2004). Sources of informal social control in Chicago neighborhoods. Criminology, 42(3), 551–583.CrossRefGoogle Scholar
  26. Smith, W. R., Frazee, S. G., & Davison, E. (2000). Furthering the integration of routine activity and social disorganization theories: Small units of analysis and the study of street robbery as a diffusion process. Criminology, 38(2), 489–523.CrossRefGoogle Scholar
  27. Snijders, T., & Bosker, R. (1999). Multilevel Analysis: An introduction to basic and advanced multilevel analysis. London: Sage.Google Scholar
  28. Taylor, R. (2001). Breaking away from broken windows: baltimore neighborhoods and the nationwide fight against crime, grime, fear, and decline. Boulder, CO: Westview Press.Google Scholar
  29. Taylor, R. (2002). Fear of crime, social ties, and collective efficacy: Maybe masquerading measurement, maybe Deja Vu all over again. Justice Quarterly, 19(4), 773–792.CrossRefGoogle Scholar
  30. Wikström, P.-O. H. (2004). Crime as alternative: Towards a cross-level situational action theory of crime causation. In: J. McCord (Ed.), Beyond empiricism: Institutions and intentions in the study of crime (pp. 1–37). New Brunswick: Transaction.Google Scholar
  31. Wikström, P.-O. H. (2005). The social origins of pathways in crime. Towards a developmental ecological action theory of crime involvement and its changes. In: D. P. Farrington (Ed.), Testing integrated developmental/life course theories of offending (pp. 211–145). New Brunswick: Transaction.Google Scholar
  32. Wikström, P.-O. H. (2006). Individuals, settings and acts of crime. situational mechanisms and the explanation of crime. In: P.-O. H. Wikström & R. J. Sampson (Eds.), The explanation of crime: Context, mechanisms and development. Cambridge: Cambridge University Press.Google Scholar
  33. Wikström, P.-O. H. (2007a). The social ecology of crime: The role of the environment in crime causation. In: H.-J. Schneider (Ed.), Internationales Handbuch der Kriminologie (Vol. 1). Berlin: de Gruyter.Google Scholar
  34. Wikström, P.-O. H. (2007b). Deterrence and deterrence experiences: Preventing crime through the threat of punishment. In: S. Shoham, O. Beck, & M. Kett (Eds.), International handbook of penology and criminal justice (pp. 345–378). London: CRC Press.Google Scholar
  35. Wikström, P.-O. H. (2007c). In search of causes and explanations of crime. In: R. King & E. Wincup (Eds.), Doing research on crime and justice (2nd ed.). Oxford: Oxford University Press.Google Scholar
  36. Wikström, P.-O. H., & Dolmen, L. (2001). Urbanisation, neighbourhood social integration, informal social control, minor social disorder, and fear of crime. International Review of Victimology, 8, 121–140.Google Scholar
  37. Wikström, P.-O. H., & Ceccato, V. (2004). Crime and social life: A space-time budget study. Paper presented at the annual meeting of the American Society of Criminology. Nashville, TN. November 2004.Google Scholar
  38. Wikström, P.-O. H., & Butterworth, D. (2006). Adolescent crime: Individual differences and lifestyles. Collumpton: Willan Publishing.Google Scholar
  39. Wikström, P.-O. H., & Treiber, K. (2007). The role of self-control in crime causation: Beyond Gottfredson and Hirschi’s general theory of crime. European Journal of Criminology, 4(2), 237–264.CrossRefGoogle Scholar
  40. Wikström, P.-O. H., & Treiber, K. (2008). What drives persistent offending? The neglected and unexplored role of the social environment. In: J. Savage (Ed.), The development of persistent criminality. Oxford: Oxford University Press.Google Scholar
  41. Wooldredge, J. (2002). Examining the (Ir)Relevance of aggregation bias for multilevel studies of neighborhoods and crime with an example comparing census tracts to official neighborhoods in Cincinnati. Criminology, 40(3), 681–709.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Dietrich Oberwittler
    • 1
    • 2
  • Per-Olof H. Wikström
    • 3
  1. 1.Department of CriminologyMax Planck Institute for Foreign and International Criminal LawFreiburgGermany
  2. 2.University of FreiburgGermany
  3. 3.Institute of Criminology, University of CambridgeUnited Kingdom

Personalised recommendations