Risk Terrain Modeling and Socio-Economic Stratification: Identifying Risky Places for Violent Crime Victimization in Bogotá, Colombia

Abstract

This research focused on the effect of the built environment on Bogotá’s violent crime by using the Risk Terrain Modeling (RTM) technique. The current study used 17 ecological variables, including micro-level data on the spatial distribution of socio-economic strata, and the location of an array of businesses and other features of the landscape. As suggested by the results of this study, the spatial distribution of violent crime in Bogotá is highly correlated with the allocation of socio-economic strata throughout its geography. A statistically valid RTM analysis identified the micro-level risk factors associated with three types of violent crime incidents, namely homicide, assault, and theft incidents. These results suggest that future violent crime incidents are more likely to occur at a reduced number of high-risk micro-places. Moreover, while homicide and assault incidents were more likely to cluster within the poorest areas of the city, theft incidents presented a higher risk of victimization near the city center, where economic activity and suitable targets concentrate. This study offers a unique account regarding the effect of socio-economic segregation on violent crime victimization across Bogotá’s geography and within different socio-economic strata classifications.

This is a preview of subscription content, log in to check access.

Fig. 1

Notes

  1. 1.

    http://www.dane.gov.co/files/geoestadistica/estratificacion/Tipo1.pdf

  2. 2.

    Original Spanish passage: “Estratificación social en Bogotá: de la política pública a la dinámica de la segregación social (Uribe-Mallarino 2008, pg. 150)

  3. 3.

    As of January 2018.

  4. 4.

    The results to this study were originally presented in June 2015 at a colloquium organized by the World Bank, to a select number of local stakeholders, including transportation managers, city hall officials and local NGOs in the city of Bogotá.

  5. 5.

    FIP calculations based on data from the Colombian National Police. All crime data was geo-referenced using the WGS84 reference coordinate system.

  6. 6.

    http://www.sdp.gov.co/portal/page/portal/PortalSDP/InformacionTomaDecisiones/Estadisticas/Bogot%E1%20Ciudad%20de%20Estad%EDsticas/2011/DICE115-CartillaEncuesMultipropos-2011.pdf.

  7. 7.

    Currency exchange rate between Colombian Pesos (COP) and U.S. Dollars (USD) as of August 2017.

  8. 8.

    Note that 1% of the city’s population lived in non-stratum designated units.

  9. 9.

    (14 factors * 2 operationalizations * 3 blocks * 2 “half increments”) + (3 factors * 1 operationalization * 3 blocks * 2 “half increments”) = 186 variables.

  10. 10.

    The cell size was 75 m, similar to RTM, and the search radius was set to 450 m. Thus, assuming the maximum spatial influence used with RTM.

References

  1. Agnew, R. (1999). A general strain theory of community differences in crime rates. Journal of Research in Crime and Delinquency, 36(2), 123–155.

    Article  Google Scholar 

  2. Beckett, K., & Godoy, A. (2010). A tale of two cities: A comparative analysis of quality of life initiatives in New York and Bogotá. Urban Studies, 47(2), 277–301.

    Article  Google Scholar 

  3. Beltrán, J. P. (2005). Bogotá región: crecimiento urbano en la consolidación del territorio metropolitano. JPB, Historia Ambiental de Bogota en el siglo XX. Bogotá.

  4. Bernasco, W., & Block, R. (2011). Robberies in Chicago: A block-level analysis of the influence of crime generators, crime attractors, and offender anchor points. Journal of Research in Crime and Delinquency, 48(1), 33–57.

    Article  Google Scholar 

  5. Bichler, G., Schmerler, K., & Enriquez, J. (2013). Curbing nuisance motels: An evaluation of police as place regulators. Policing: An International Journal of Police Strategies & Management, 36(2), 437–462.

    Article  Google Scholar 

  6. Blau, J. R., & Blau, P. M. (1982). The cost of inequality: Metropolitan structure and violent crime. American Sociological Review, 114–129.

  7. Brantingham, P. J., & Brantingham, P. L. (2008). Crime pattern theory. In R. Wortley & L. Mazerolle (Eds.), Environmental criminology and crime analysis (pp. 78–93). Cullompton: Willan.

  8. Brantingham, P. L., & Brantingham, P. J. (1993). Nodes, paths and edges: Considerations on the complexity of crime and the physical environment. Journal of Environmental Psychology, 13(1), 3–28.

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Caplan, J. M. (2011). Mapping the spatial influence of crime correlates: a comparison of operationalization schemes and implications for crime analysis and criminal justice practice. Cityscape, 13(3), 57–83.

    Google Scholar 

  11. Caplan, J. M. & Kennedy, L. W. (2016). Risk Terrain Modeling: Crime Prediction and Risk Reduction. Berkeley, CA: Univ. of California Press.

    Google Scholar 

  12. Caplan, J. M., Kennedy, L. W., & Piza, E. L. (2013). Joint utility of event-dependent and environmental crime analysis techniques for violent crime forecasting. Crime and Delinquency, 59(2), 243–270.

    Article  Google Scholar 

  13. Caplan, J. M., Kennedy, L. W., Barnum, J. D., & Piza, E. L. (2017). Crime in context: Utilizing risk terrain modeling and conjunctive analysis of case configurations to explore the dynamics of criminogenic behavior settings. Journal of Contemporary Criminal Justice, 33(2), 133–151.

    Article  Google Scholar 

  14. Chainey, S., Tompson, L., & Uhlig, S. (2008). The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal, 21(1–2), 4–28.

    Article  Google Scholar 

  15. Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 588–608.

  16. Cornish, D. B., & Clarke, R. V. (Eds.). (1986). The reasoning criminal: Rational choice perspectives on offending. New York: Springer.

  17. Drawve, G. (2016). A metric comparison of predictive hot spot techniques and RTM. Justice Quarterly, 33(3), 369–397.

    Article  Google Scholar 

  18. Drawve, G., & Barnum, J. D. (2017). Place-based risk factors for aggravated assault across police divisions in Little Rock, Arkansas. Journal of Crime and Justice, 1-20.

  19. Drawve, G., Belongie, M., & Steinman, H. (2017). The role of crime analyst and researcher partnerships: a training exercise in Green Bay, Wisconsin. Policing: A Journal of Policy and Practice, pax092. https://doi.org/10.1093/police/pax092

  20. Drawve, G., Moak, S. C., & Berthelot, E. R. (2016a). Predictability of gun crimes: A comparison of hot spot and risk terrain modelling techniques. Policing and Society, 26(3), 312–331.

    Article  Google Scholar 

  21. Drawve, G., Thomas, S. A., & Walker, J. T. (2016b). Bringing the physical environment back into neighborhood research: The utility of RTM for developing an aggregate neighborhood risk of crime measure. Journal of Criminal Justice, 44, 21–29.

    Article  Google Scholar 

  22. Dugato, M. (2013). Assessing the validity of risk terrain modeling in a European city: Preventing robberies in the city of Milan. Crime Mapping, 5(1), 63–89.

    Google Scholar 

  23. Dugato, M., Calderoni, F., & Berlusconi, G. (2017). Forecasting organized crime homicides: risk terrain modeling of Camorra violence in Naples, Italy. Journal of Interpersonal Violence. https://doi.org/10.1177/0886260517712275

    Article  Google Scholar 

  24. Fajnzylber, P., Lederman, D., & Loayza, N. (2002). Inequality and violent crime. The Journal of Law and Economics, 45(1), 1–39.

    Article  Google Scholar 

  25. Gaviria, A., & Vélez, C. E. (2001). Who bears the burden of crime in Colombia? Washington, DC: World Bank.

    Google Scholar 

  26. Gaviria, A., Medina, C., Morales, L., & Núñez, J. (2010). The cost of avoiding crime: The case of Bogotá. In The economics of crime: Lessons for and from Latin America (pp. 101–132). Chicago: University of Chicago Press.

  27. Hipp, J. R. (2007). Income inequality, race, and place: Does the distribution of race and class within neighborhoods affect crime rates? Criminology, 45(3), 665–697.

    Article  Google Scholar 

  28. Kennedy, L. W., & Van Brunschot, E. G. (2009). The risk in crime. Rowman & Littlefield.

  29. Kennedy, L. W., Caplan, J. M., Piza, E. L., & Buccine-Schraeder, H. (2015). Vulnerability and exposure to crime: Applying risk terrain modeling to the study of assault in Chicago. Applied Spatial Analysis and Policy, 1–20.

  30. Kennedy, L. W., & Caplan, J. M. (2012). A theory of risky places. Research brief. Newark: Rutgers Center on Public Security.

    Google Scholar 

  31. Krahn, H., Hartnagel, T. F., & Gartrell, J. W. (1986). Income inequality and homicide rates: Cross-National Data and criminological theories. Criminology, 24, 269.

    Article  Google Scholar 

  32. LeBeau, J. L. (2011). Sleeping with strangers: Hotels and motels as crime attractors and crime generators. Patterns, Prevention, and Geometry of Crime, 77–102.

  33. Levine, N., Wachs, M., & Shirazi, E. (1986). Crime at bus stops: A study of environmental factors. Journal of Architectural and Planning Research, 339–361.

  34. Llorente, M. V., Escobedo, R., Echandía, C., & Rubio, M. (2001). Violencia homicida en Bogotá: más que intolerancia. Bogota: CEDE Universidad de Los Andes.

    Google Scholar 

  35. Moser, C., Winton, A., & Moser, A. (2005). Violence, fear, and insecurity among the urban poor in Latin America. The Urban Poor in Latin America, 125.

  36. Park, R. E., Burgess, E. W., & McKenzie, R. D. (1925). The City. Chicago: University of Chicago Press.

  37. Piza, E., Feng, S., Kennedy, L., & Caplan, J. (2017). Place-based correlates of motor vehicle theft and recovery: Measuring spatial influence across neighborhood context. Urban Studies, 54(13), 2998–3021.

    Article  Google Scholar 

  38. Ratcliffe, D. (Ed.). (2012). A nature conservation review: Volume 1: The selection of biological sites of national importance to nature conservation in Britain (Vol. 1). Cambridge: Cambridge University Press.

    Google Scholar 

  39. Roncek, D. W., & Faggiani, D. (1985). High schools and crime: A replication. The Sociological Quarterly, 26(4), 491–505.

    Article  Google Scholar 

  40. Roncek, D. W., & Pravatiner, M. A. (1989). Additional evidence that taverns enhance nearby crime. Sociology and Social Research, 73(4), 185–188.

    Google Scholar 

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

    Google Scholar 

  42. Shaw, C. R., & McKay, H. D. (1942). Juvenile delinquency and urban areas. Chicago: University of Chicago Press.

  43. Soares, R. R., & Naritomi, J. (2010). Understanding high crime rates in Latin America: The role of social and policy factors. In The economics of crime: Lessons for and from Latin America (pp. 19–55). Chicago: University of Chicago Press.

  44. Thibert, J., & Osorio, G. A. (2014). Urban segregation and metropolitics in Latin America: The case of Bogotá, Colombia. International Journal of Urban and Regional Research, 38(4), 1319–1343.

    Article  Google Scholar 

  45. Uribe-Mallarino, C. (2008). Estratificación social en Bogotá: de la política pública a la dinámica de la segregación social. Universitas Humanística, 65(65).

  46. Wortley, R. K., & Mazerolle, L. (2008). Environmental criminology and crime analysis: situating the theory, analytic approach and application. In: R. K. Wortley & L. Mazerolle (Eds.), Environmental criminology and crime analysis (pp. 1–18). Willan: Cullompton, Devon.

    Google Scholar 

  47. Yunda, J. G. (2017). "Juntos pero no revueltos": the influence of the social stratification system on urban densification patterns in Bogotá, Colombia. Doctoral dissertation.

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Alejandro Giménez-Santana.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Giménez-Santana, A., Caplan, J.M. & Drawve, G. Risk Terrain Modeling and Socio-Economic Stratification: Identifying Risky Places for Violent Crime Victimization in Bogotá, Colombia. Eur J Crim Policy Res 24, 417–431 (2018). https://doi.org/10.1007/s10610-018-9374-5

Download citation

Keywords

  • Risk terrain modeling
  • Crime analysis
  • Urban segregation