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Collective Efficacy, Neighborhood and Geographical Units of Analysis: Findings from a Case Study of Swedish Residential Neighborhoods

Abstract

The concept of collective efficacy, defined as the combination of mutual trust and willingness to act for the common good, has received widespread attention in the field of criminology. Collective efficacy is linked to, among other outcomes, violent crime, disorder, and fear of crime. The concept has been applied to geographical units ranging from below one hundred up to several thousand residents on average. In this paper key informant- and focus group interview transcripts from four Swedish neighborhoods are examined to explore whether different sizes of geographical units of analysis are equally important for collective efficacy. The four studied neighborhoods are divided into micro-neighborhoods (N=12) and micro-places (N=59) for analysis. The results show that neighborhoods appear to be too large to capture the social mechanism of collective efficacy which rather takes place at smaller units of geography. The findings are compared to survey responses on collective efficacy (N=597) which yield an indication in the same direction through comparison of ICC-values and AIC model fit employing unconditional two-level models in HLM 6.

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Map 1

Notes

  1. Note however that Sampson & Raudenbusch (1999; see also Sampson 2012) elsewhere use census tracts to study disorder due to acknowledgement of a need for “smallest level of aggregation possible in measuring observed disorder, owing to considerable variability block-to-block within larger ecological units” (ibid: 619).

  2. Note that Weisburd et al. (2012) measure collective efficacy through voting participation, which could be argued to tap into other dimensions of social capital than collective efficacy.

  3. In the city of Malmö city areas averaged about 2000 residents in the year 2008, or close to 3000 residents if only city areas with at least 500 residents are counted (Malmö stad 2009).

  4. Bellevuegården in addition to the multi-family housing area has an area with 102 small houses and two large retirement homes with a total of 419 apartments that were excluded due to the focus on multi-family housing. Kroksbäck in addition has a large small house area with 625 small houses that was excluded. Note that the data in Table 2 covers the whole neighborhoods, including single family housing and retirement homes which are excluded in the study. There is no census data available for just the studied parts of Kroksbäck and Bellevuegården. In this paper when referring to neighborhoods Belleuegården and Kroksbäck it will unless otherwise stated mean the multi-family, non-retirement home areas of the neighborhoods.

  5. Note however that the street blocks in St Jean (2007) had a considerably smaller average population, 57, than the micro-neighborhoods in this study. In Hipp (2010) a micro-neighborhood normally consists of 11 households.

  6. In one case a yard is made up of the space outside the entrances to one building only.

  7. Additional buildings may exist on some yards that did not fit the criteria of multi-family non-retirement home housing.

  8. Two from tenant-ownership associations, three from tenant associations, one from self-management, one from a youth center

  9. One from a pensioners organization and one from a women’s organization

  10. The 11 remaining consist of two representatives of tenant associations organized at roughly half a micro-neighborhood in size, two representatives of self management at a yard, one representative of a youth center, three representatives of pensioneers organizations with neighborhood or city part wide activities, one from a womens organization with city part wide activities and the two representatives of the municipality. The focus on instrumental organizations was in part deliberate in relation to research pointing toward such organizations being of higher importance (Swaroop & Morenoff 2006), but in part it was due to a lack of willingness among contacted representatives of expressive organizations, in the form of ethnic-cultural organizations, to participate in the research.

  11. In some cases respondents use the word “area” (“område”) which can be interpreted as neighborhood, property manager area or micro-neighborhood. When it is clear from the context in the interview which type of geographical unit is meant it has been coded, but in most cases these references have been left out. Examples of when it is clear what type of geographical unit is meant is when cohesion is discussed in relation to a tenant association, in which case the area of operations for the tenant association is the geographical unit.

  12. Surveys that were collected by mail in many cases (n = 48) lacked a specific address with respondents rather opting to state their micro-neighborhood or neighborhood. Effective response rate where address was obtained is 51.1 %, for analysis on the micro-neighborhood level where an additional 41 responses were obtained the effective response rate was 54.3 % and on the neighborhood level an additional two respondents result in effective responserate of 54.5 %.

  13. For a brief discussion see Sampson (2012: 163)

  14. A total of 600 values for collective efficacy was registered, but incomplete spatial data resulted in a total of 561 surveys including valid values for both collective efficacy and micro-place, 596 for the micro-neighborhood and 597 for the neighborhood. The neighborhood level values are reported in Appendix Table 7.

  15. Although Hox (2002) discusses techniques for dealing with ICC-values in relation to a very small dataset with just five level 2 units, Maas & Hox (2005) show that level 2 variance components need more level 2 units to become stable.

  16. “IP4: Then we thought of something, IP1 and I which we think is fun to strengthen the neighborhood and maybe some cohesion and a bit of integration. It’s this about a singing choir, but we haven’t gotten there yet, but it is supposed to be like that.” (Focus group 110209)

  17. “M: What’s the best about living in Kroksbäck? IP: It is close.. There are a lot.. I dont feel like a stranger there, I feel like I live in Iraq. Many people that say hello to me, that know me, that know my family.” (Key informant 110209)

  18. Examples: “IP3: Yes, I have been thinking a bit over that question. I think, we try, there are a few of us. The tenant association does quite a lot, one of many. We try, but we are just too few. There is a lack of will and interest.” (Focus group 110126)

    “M: Do you think there is a sense of cohesion or belonging in the area? IP: Yes, we have that. We do. Unfortunately it is, I have to admit, a bit of a difficulty in reaching the young, those who are active in your age and work. It is difficult.” (Key informant 110928)

  19. Although no sensus data or similar is available for micro-neighborhoods estimations based on map data in a 200*200 m grid of income levels was used to get an approximation of SES in the micro-neigborhoods. The highest percentage of low income households was recorded in the public housing areas with most grids in the 40–60 % low income bracket (data not shown).

  20. Inclusion of a fixed lvl 2 effect of public housing reduces the ICC for the micro-neighborhood as level 2 to 1.8 %, most of the micro-neighborhood variance is thus explained by housing type which includes large variations in SES.

  21. Fixed-effect variance in non-hierarchical models with dummy-variables for each geographical unit of analysis yield r square of .162 for micro-places, .090 for micro-neighborhoods and .026 for neighborhoods separately.

  22. Survey respondents from the three yards on the northern half of the micro-neighborhood (n = 26) are registered for an average collective efficacy that is more than a micro-neighborhood standard deviation lower than the three yards in the southern half of the micro-neighborhood (n = 18 respondents).

  23. What Weisburd and colleagues (2009b: 20) call averaging: “[…] in measuring higher order geographic units miss local area effects”

References

  • Andresen, M. A., & Malleson, N. (2011). Testing the stability of crime patterns: implications for theory and policy. Journal of Research in Crime and Delinquency, 48, 58–82.

    Article  Google Scholar 

  • Bellair, P. E. (1997). Social interaction and community crime: Examining the importance of neighbor networks. Criminology, 35, 677–703.

    Article  Google Scholar 

  • Block, R. L. & Block, C. R. (1995). Space, place and crime: hot spot areas and hot places of liquor-related crime. In Eck, J. & Weisburd, D. (Eds). Crime and place, 145–183.

  • Braga, AA. (2007). Effects of hot spots policing on crime. A Campbell collaboration systematic review, Available online at http://campbellcollaboration.org/lib/download/118/. Accessed 13 May 2013.

  • Brantingham, P., & Brantingham, P. (1995). Criminality of place. European Journal on Criminal Policy and Research, 3(3), 5–26.

    Article  Google Scholar 

  • Brantingham, P. L., Brantingham, P. J., Vajihollahi, M., & Wuschke, K. (2009). Crime analysis at multiple scales of aggregation: A topological approach. In D. Weisburd, W. Bernasco, & G. J. N. Bruinsma (Eds.), Putting crime in its place. New York: Springer.

    Google Scholar 

  • Browning, C. R. (2002). The span of collective efficacy: extending social disorganization theory to partner violence. Journal of Marriage and Family, 64(4), 833–850.

    Article  Google Scholar 

  • Bruinsma, G. J., Pauwels, L. J., Weerman, F. M., & Bernasco, W. (2013). Social Disorganization, Social Capital, Collective Efficacy and the Spatial Distribution of Crime and Offenders An empirical test of six neighbourhood models for a Dutch city. British Journal of Criminology (Online advance access, published May 20, 2013).

  • Bursik, R.J., & Grasmick, H.G. (1993). Neighborhoods and crime: the dimensions of effective community control. Lexington, New York.

  • Chainey, S. & Desyllas, J. (2008). Modelling pedestrian movement to measure on-street crime risk. In: Liu, L. & Eck, J. (eds). Artificial crime analysis systems: using computer simulations and geographic information systems, 71–91.

  • Cohen, L.E., & Felson, M. (1979). Social change and crime rate trends: a routine activity approach. American sociological review, Vol. 44, No. 4, Aug., 1979 588–608.

  • Cohen, D. A., Inagami, S., & Finch, B. (2008). The built environment and collective efficacy. Health & Place, 14(2), 198–208.

    Article  Google Scholar 

  • Felson, M., & Cohen, L. E. (1980). Human ecology and crime: a routine activity approach. Human Ecology, 8(4), 389–406.

    Article  Google Scholar 

  • Gatti, U., & Tremblay, R. E. (2007). Social capital and aggressive behavior. European Journal on Criminal Policy and Research, 13(3–4), 235–249.

    Article  Google Scholar 

  • Gerell, M. (2012). The small does matter: Neighborhoods and spatial distribution of disorder. In Bragadóttir, Ragnheiður (ed). NSFKs 54 forskerseminar. Scandinavian Research Council for Criminology.

  • Gerell, M. (2013). Skadegörelse, bränder, grannskap och socialt kapital. Malmö: Malmö University Publications in Urban Studies (MAPIUS).

    Google Scholar 

  • Gibson, C. L., Zhao, J., Lovrich, N. P., & Gaffney, M. J. (2002). Social integration, individual perceptions of collective efficacy, and fear of crime in three cities. Justice Quarterly, 19(3), 537–564.

    Article  Google Scholar 

  • Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.

    Article  Google Scholar 

  • Green, M., & Flowerdew, R. (1996). New evidence on the modifiable areal unit problem. Spatial analysis: modelling in a GIS environment, 41–54.

  • Hipp, J. R. (2010). Micro-structure in micro-neighborhoods: a new social distance measure, and its effect on individual and aggregated perceptions of crime and disorder. Social Networks, 32, 148–159.

    Article  Google Scholar 

  • Hipp, J. R., & Boessen, A. (2013). Egohoods as waves washing across the city: a new measure of “neighborhoods”. Criminology, 51(2), 287–327.

    Article  Google Scholar 

  • Hox, J. J. (2002). Multilevel analysis: techniques and applications. Mahwah: Erlbaum.

    Google Scholar 

  • Ivert, A. K., Chrysoulakis, A., Kronkvist, K., & Torstensson-Levander, M. (2013). Malmö områdesundersökning 2012: Lokala problem, brott och trygghet. Malmö: Rapport från institutionen för kriminologi.

    Google Scholar 

  • Johnson, B. D. (2010). Multilevel analysis in the study of crime and justice. In: A. R. Piquero & D. Weisburd (eds.), Handbook of quantitative criminology. New York: Springer.

    Google Scholar 

  • Kennedy, L. W., Caplan, J. M., & Piza, E. L. (2012). A primer on the spatial dynamics of crime emergence and persistence. Newark: Rutgers center on Public Security.

    Google Scholar 

  • Kornhauser, R. (1978). Social sources of delinquency: an appraisal of analytic models. Chicago: Chicago University Press.

    Google Scholar 

  • Loeber, R., & Wikström, P. O. (1993). Individual pathways to crime in different types of neighborhoods. In: D. P. Farrington, R. J. Sampson, & P. O. Wikström (eds.), Integrating individual and ecological aspects of crime, BRÅ Report 1993: 1. Stockholm: Swedish National Council for Crime Prevention.

    Google Scholar 

  • Loeber, R., & Wikström, P. O. (2000). Do disadvantaged neighborhoods cause well-adjusted children to become adolescent delinquents? A study of male juvenile serious offending, individual risk and protective factors, and neighborhood context. Criminology, 38(4), 1109–1142.

    Article  Google Scholar 

  • Logan, J. R. (2012). Making a place for space: spatial thinking in social science. Annual Review of Sociology, 38, 507–524.

    Article  Google Scholar 

  • Lupton, R. (2003). ‘Neighborhood effects’: can we measure them and does it matter? CASEpaper 73. London: London School of Economics.

    Google Scholar 

  • Maas, C. J., & Hox, J. J. (2005). Sufficient sample sizes for multilevel modeling. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 1(3), 86–92.

    Article  Google Scholar 

  • Malmö Stad (2009). Områdesfakta Malmö, accessed through malmo.se June 10 2011.

  • McPherson, M., Silloway, G., & Frey, DL. (1984). Crime, fear and control in neighborhood commercial centres: an executive summary, NCJRS 94225.

  • McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 415–444.

  • Mellgren, C. (2011). What’s neighbourhood got to do with it?: the influence of neighbourhood context on crime and reactions to crime. Faculty of Health and Society, Malmö University.

  • Morenoff, J. D., Sampson, R. J., & Raudenbusch, S. W. (2001). Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology, 39(3), 517–560.

    Article  Google Scholar 

  • Oberwittler, D. & Wikström, P.O.H. (2009). Why small is better: advancing the study of the role of behavioral contexts in crime causation. In: Weisburd, D., Bernasco, W., Bruinsma, GJN. (Eds), (2009a). Putting crime in its place: units of analysis in geographic criminology. New York: Springer

  • Openshaw, S. (1996). Developing GIS-relevant zone-based spatial analysis methods. In: P. Longley & M. Batty (eds.), Spatial analysis: modeling in a GIS environment (pp. 55–73). New York: Wiley.

    Google Scholar 

  • Park, R.E., & Burgess, E.W. (1925/1967) The city: suggestions for the investigation of human behavior in the urban environment. Chicago: University of Chicago Press.

  • Pauwels, L., & Hardyns, W. (2009). Measuring community (dis) organizational processes through key informant analysis. European Journal of Criminology, 6(5), 401–417.

    Article  Google Scholar 

  • Raudenbush, S. W., & Sampson, R. J. (1999). Ecometrics: toward a science of assessing ecological settings, with application to the systematic social observation of neighborhoods. Sociological methodology, 29(1), 1–41.

  • Sampson, RJ. (2006a) How does community context matter? social mechanisms and the explanation of crime. In: Wikström, POH. & Sampson, RJ. (eds). The explanation of crime: context, mechanisms, and development. 31-60. Cambridge: Cambridge University Press.

  • Sampson, R. J. (2006b). Collective efficacy theory: lessons learned and directions for future inquiry. In: F. Cullen, J. Wright, & K. Ilevins (eds.), Taking stock: the status of criminological theory, Vol. 15 of advances in criminological theory. Piscataway: Transaction Publishers.

    Google Scholar 

  • Sampson, R. J. (2012). Great American city. Chicago and the enduring neighborhood effect. Chicago: University of Chicago Press.

    Book  Google Scholar 

  • Sampson, R. J. (2013). 2012 Presidential address to the American society of criminology. The place of context: a theory and strategy for criminology’s hard problems. Criminology, 51(1), 1–31.

    Article  Google Scholar 

  • Sampson, R. J., & Graif, C. (2009). Neighborhood social capital as differential social organization: resident and leadership dimensions. American Behavioral Scientist, 52(11), 1579–1605.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: a multilevel study of collective efficacy. Science, 277(5328), 918–924.

    Article  Google Scholar 

  • Sampson, R. J., & Wikström, P. O. (2008). The social order of violence in Chicago and Stockholm neighborhoods: a comparative inquiry. In S. N. Kalyvas, I. Shapiro, & T. Masoud (Eds.), Order, conflict, and violence (pp. 97–119). Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Sherman, Lawrence. (1995). Hot spots of crime and criminal careers of places. In: Eck, J. & Weisburd, D. Crime and place, 35–52. Monsey, NY: Criminal Justice Press.

  • St Jean, P. K. B. (2007). Pockets of crime: Broken windows, collective efficacy and the criminal point of view. Chicago: University of Chicago Press.

    Book  Google Scholar 

  • Steenbeek, W. (2011). Social and physical disorder. How community, business presence and entrepreneurs influence disorder in Dutch neighborhoods. Utrecht University: Wörman print services.

  • Sutherland, A., Brunton-Smith, I., & Jackson, J. (2013). Collective efficacy, deprivation and violence in london. British Journal of Criminology, 53, 1050–1074.

    Article  Google Scholar 

  • Swaroop, S., & Morenoff, J. D. (2006). Building community: The neighborhood context of social organization. Social Forces, 84(3), 1665–1695.

    Article  Google Scholar 

  • Taylor, R. B. (1997). Social order and disorder on street blocks and neighborhoods: ecology, microecology, and the systemic model of social disorganization. Journal of Research in Crime and Delinquency, 34, 113–155.

    Article  Google Scholar 

  • Tita, G. E., & Radil, S. M. (2010). Making space for theory: the challenges of theorizing space and place for spatial analysis in criminology. Journal of Quantitative Criminology, 26, 467–479.

    Article  Google Scholar 

  • Uchida, C.D., Swatt, M.L., Solomon, S.E. & Varano, S. (2014). Neighborhoods and crime: collective efficacy and social cohesion in Miami-Dade county. National Institute of Justice Report.

  • Weisburd, D., Bernasco, W., & Bruinsma, G. J. N. (Eds.). (2009a). Putting crime in its place: units of analysis in geographic criminology. New York: Springer.

    Google Scholar 

  • Weisburd, D., Bruinsma, G. J. N., & Bernasco, W. (2009b). Units of analysis in geographic criminology: historical development, critical issues, and open questions. In: Weisburd D., Bernasco W., & Bruinsma, G. J. N., (eds.), (2009) . Putting crime in its place: units of analysis in Geographic criminology (pp. 3–31). New York: Springer.

  • Weisburd, D., Bushway, S., Lum, C., & Yang, S. M. (2004). Trajectories of crime at places: a longitudinal study of street segments in the city of Seattle. Criminology, 42(2), 283–324.

    Article  Google Scholar 

  • Weisburd, D., Groff, E. R., & Yang, S. M. (2012). The criminology of place. Street segments and our understanding of the crime problem. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Weisburd, D., Morris, N. A., & Groff, E. R. (2009c). 25, Springer. New: York. Hot spots of juvenile crime: a longitudinal study of arrest incidents at street segments in Seattle, Washington. Journal of Quantitative Criminology vol.

  • Weisburd, D., Telep, C. W., & Braga, A. A. (2010). The importance of place in policing. Empirical evidence and policy recommendations. Stockholm: Swedish National Council for Crime Prevention.

    Google Scholar 

  • Weisburd, D., Wyckoff, L. A., Ready, J., Eck, J. E., Hinkle, J. C., & Gajewski, F. (2006). Does crime just move around the corner? A controlled study of spatial displacement and diffusion of crime and control benefits. Journal of Criminology, 44(3), 549–592.

    Article  Google Scholar 

  • Wikström, P. O. H., Oberwittler, D., Treiber, K., & Hardie, B. (2012). Breaking rules: the social and situational dynamics of young people’s urban crime. Oxford: Oxford University Press.

    Google Scholar 

  • Wikström, P. O. H., & Sampson, R. J. (2003). Social mechanisms of community influences on crime and pathways in criminality. In: B. B. Lahey, T. E. Moffit, & A. Caspi (Eds.), Causes of Conduct disorder and juvenile Delinquency. New York: Guilford Press.

    Google Scholar 

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Acknowledgments

This paper is part of the project “Fire, vandalism, neighborhood, and social capital”, funded by Länsförsäkringars forskningsfond and led by professor Per-Olof Hallin of the Urban studies department at Malmö university. The author would like to thank professor Lieven Pauwels of Ghent University for invaluable comments and advice.

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Correspondence to Manne Gerell.

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Parts of these results have previously been submitted in a report (in Swedish) to Länsförsäkringars forskningsfond and printed in the Malmö University Publications In Urban Studies series (MAPIUS) no. 11 (Gerell 2013). Some results have been published in the NSFK (Scandinavian Research Council for Criminology) seminar report 2012 (Gerell 2012).

Appendix

Appendix

Appendix 1

Table 7 Trust and informal social control (two items each), collective efficacy mean of trust and informal social control. Low numbers imply high levels of trust, control or collective efficacy. Table includes all responses with valid values for both collective efficacy and neighborhood

Appendix 2

Table 8 Descriptive survey data and comparison with census data (2011) for three of the variables. Note that census data includes single family housing areas for two of the neighborhoods in addition to a retirement home in one of the neighborhoods. Chi square significance test of difference between observed and expected values reported with *** = p <0.001, other values p > 0.05

Appendix 3

Table 9 Interview question examples pertaining to main themes of the paper

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Gerell, M. Collective Efficacy, Neighborhood and Geographical Units of Analysis: Findings from a Case Study of Swedish Residential Neighborhoods. Eur J Crim Policy Res 21, 385–406 (2015). https://doi.org/10.1007/s10610-014-9257-3

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Keywords

  • Collective efficacy
  • Geography
  • Neighborhood
  • Micro-neighborhood
  • Micro-place