Can we Trust Crime Predictors and Crime Categories? Expansions on the Potential Problem of Generalization

  • Nathan T. ConnealyEmail author


City-driven open data initiatives have made spatially referenced crime and risk factor data more readily available online, allowing for significance tests to determine the relationship between environment and crime. This paper uses a variety of open source data to assess risk factors for specific violent crime types (assault, homicide, rape, robbery) in three different cities. The results contribute to our understanding of 1) variation in intra-city risk factors for each violent crime type, 2) the degree of spatial overlap for high-risk places for each violent crime type within a city, and 3) the generalizability of risk factors across crime types and cities. Risk Terrain Modeling (RTM) was used to determine the risk factors associated with each crime type at the micro-level and conjunctive analyses of case configurations (CACC) determined the unique behavior settings at the highest risk for each specific violent crime in each city. The findings indicate that intra-city risk factors vary greatly for each violent crime, highrisk places for different violent crimes tend to not overlap spatially within a city, and risk factors are not generalizable across crime types or across cities. Researchers and law enforcement need to examine local, crime-specific contexts when assessing crime problems and generating solutions.


Risk terrain modeling Crime generators and attractors Conjunctive analysis Violent crime Situational crime prevention 



  1. Andresen, M., & Linning, S. (2012). The (in)inappropriateness of aggregating across crime types. Applied Geography, 35(2012), 275–282.CrossRefGoogle Scholar
  2. Andresen, M., Curman, A., & Linning, A. (2017). The trajectories of crime at places: Understanding the patterns of disaggregated crime types. Journal of Quantitative Criminology, 33(3), 427–449.CrossRefGoogle Scholar
  3. Barnum, J., Caplan, J., Kennedy, L., & Piza, E. (2017). The crime kaleidoscope; a cross-jurisdictional analysis of place features and crime in three urban environments. Applied Geography, 79, 203–211.CrossRefGoogle Scholar
  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.CrossRefGoogle Scholar
  5. Braga, A. A., Hureau, D., & Papachristos, A. V. (2012). An ex post facto evaluation framework for place-based interventions. Evaluation Review, 35(6), 592–626.CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. Brantingham, P., & Brantingham, P. (1995). Criminality of place. European Journal of Criminal Policy and Research, 3, 5–26.CrossRefGoogle Scholar
  8. Brantingham, P., & Brantingham, P. J. (2008). Crime pattern theory. In R. Wortley & L. Mazerolle (Eds.), Environmental criminology and crime analysis. New York: Routledge.Google Scholar
  9. Burgason, K., Drawve, G., Brown, T., & Eassey, J. (2017). Close only counts in alcohol and violence: Controlling violence near late-night alcohol establishments using a routine activities approach. Journal of Criminal Justice, 50, 62–68.CrossRefGoogle Scholar
  10. Caplan, J., & Kennedy, L. (2011). Risk terrain modeling compendium: For crime analysis. Newark: Rutgers Center on Public Security.Google Scholar
  11. Caplan, J. M., & Kennedy, L. W. (2016). Risk terrain modeling. In Crime predictions and risk reduction. Oakland: University of California Press.Google Scholar
  12. Caplan, J. M., & Kennedy, L. W. (2018). Risk terrain modeling diagnostics (RTMDx) version 1.5. Newark: Rutgers Center on Public Security.Google Scholar
  13. Caplan, J., Kennedy, L., Barnum, J., & Piza, E. (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.CrossRefGoogle Scholar
  14. Clarke, R. V. (1980). Situational crime prevention theory and practice. British Journal of Criminology, 20(2), 136–147.CrossRefGoogle Scholar
  15. Clarke, R. V. (1997). Situational crime prevention: Successful case studies. Albany: Harrow and Heston.Google Scholar
  16. Clarke, R. (2013). Situational crime prevention. In R. Wortley & L. Mazzerole (Eds.), Environmental criminology and crime analysis. Willan: Milton.Google Scholar
  17. Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44, 588–605.CrossRefGoogle Scholar
  18. Connealy, N., & Piza, E. (2019). Risk factor and high-risk place variations across different robbery targets in Denver, Colorado. Journal of Criminal Justice, 60, 47–56.CrossRefGoogle Scholar
  19. Drawve, G., & Barnum, J. (2018). Place-based risk factors for aggravated assault across police divisions in Little Rock, Arkansas. Journal of Criminal Justice, 41(2), 173–192.Google Scholar
  20. Felson, M., & Clarke, R. (1998). Opportunity makes the thief: Practical theory for crime prevention. Police Research Series, 5–18 (paper 98. London).Google Scholar
  21. Gerell, M. (2018). Bus stops and violence, are risky places really risky? European Journal on Criminal Policy and Research, 24(4), 351–371.CrossRefGoogle Scholar
  22. Graham, K., Bernards, S., Osgood, W., & Wells, S. (2006). Bad nights or bad bars? Multi-level analysis of environmental predictors of aggression in late-night large-capacity bars and clubs. Addiction, 101(11), 1569–1580.CrossRefGoogle Scholar
  23. Groff, E., & McCord, E. S. (2011). The role of neighborhood parks as crime generators. Security Journal, 25(1), 1–24.CrossRefGoogle Scholar
  24. Haberman, C. (2017). Overlapping hot spots? Criminology & Public Policy, 16(2), 633–660.CrossRefGoogle Scholar
  25. Hart, T. C., & Miethe, T. D. (2015). Configural behavior settings of crime event locations: Toward an alternative conceptualization of criminogenic microenvironments. Journal of Research in Crime and Delinquency, 52, 373–402.CrossRefGoogle Scholar
  26. Heffner, J. (2013). Statistics of the RTMDx utility. In J. Caplan, L. W. Kennedy, & E. Piza (Eds.), Risk terrain modeling diagnostics utility user manual: Version 1.0. Newark: Rutgers Center on Public Security.Google Scholar
  27. Kennedy, L. W. (1983). The urban kaleidoscope: Canadian perspectives. New York: McGraw-Hill Ryerson Limited.Google Scholar
  28. Kennedy, L., Caplan, J., Piza, E., & Buccine-Schraeder, H. (2016). Vulnerability and exposure to crime: Applying risk terrain modeling to the study of assaults in Chicago. Applied Spatial Analysis and Policy, 9(4), 529–548.Google Scholar
  29. Kypri, K. (2015). Evidence of harm from late night alcohol sales continues to strengthen. Addiction, 110(6), 965–966.CrossRefGoogle Scholar
  30. Lersch, K. (2017). Risky places: An analysis of carjackings in Detroit. Journal of Criminal Justice, 52, 34–40.CrossRefGoogle Scholar
  31. Lum, C., Koper, C. S., & Telep, C. W. (2011). The evidence-based policing matrix. Journal of Experimental Criminology, 7(1), 3–26.CrossRefGoogle Scholar
  32. Lum, C., Koper, C. S., Wu, X., Johnson, W., & Stoltz, M. (2018). The proactive policing lab. Final report to the Laura and John Arnold Foundation. Fairfax: George Mason University.Google Scholar
  33. Miethe, T. D., Hart, T. C., & Regoeczi, W. C. (2008). The conjunctive analysis of case configurations: An exploratory method for discrete multivariate analyses of crime data. Journal of Quantitative Criminology, 24, 227–241.CrossRefGoogle Scholar
  34. Piza, E., & Gilchrist, A. (2018). Measuring the effect heterogeneity of police enforcement actions across spatial contexts. Journal of Criminal Justice, 54, 76–87.CrossRefGoogle Scholar
  35. Sampson, R. J. (2012). Great American city: Chicago and the enduring neighborhood effect. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  36. Scribner, R., Cohen, D., Kaplan, S., & Allen, S. (1999). Alcohol availability and homicide in New Orleans: Conceptual considerations for small area analysis of the effect of alcohol outlet density. Journal Studies on Alcohol, 60(3), 310–316.CrossRefGoogle Scholar
  37. Shaw, C. R., & McKay, H. D. (1942). Juvenile delinquency in urban areas. Chicago: University of Chicago Press.Google Scholar
  38. Szkola, J., Piza, E., & Drawve, G. (2019). Risk terrain modeling: Seasonality and predictive validity. Justice Quarterly, 1–22.
  39. Valasik, M. (2018). Gang violence predictability: Using risk terrain modeling to study gang homicides and gang assaults in East Los Angeles. Journal of Criminal Justice, 58, 10–21.CrossRefGoogle Scholar
  40. Valasik, M., Brault, E., & Martinez, S. (2019). Forecasting homicide in the red stick: Risk terrain modeling and the spatial influence of urban blight on lethal violent in Baton Rouge. Social Science Research: Louisiana.Google Scholar
  41. Weisburd, D. (2015). The law of crime concentration and the criminology of place. Criminology, 53(2), 133–157.CrossRefGoogle Scholar
  42. Weisburd, D., Groff, E., & Yang, S. (2012). The criminology of place: Street segments and our understanding of the crime problem. Oxford: Oxford University Press.CrossRefGoogle Scholar
  43. Weisburd, D., Telep, C., Braga, A., Cave, B., Bowers, K., Eck, J., & Hinkle, J. (2016). Place matters. Cambridge University Press.Google Scholar
  44. Wright, R. T., & Decker, S. H. (1997). Armed robbers in action: Stickups and street culture. Lebanon: Northeastern. University Press.Google Scholar
  45. Wright, R., Logie, R., & Decker, S. (1995). Criminal expertise and offender decision making: An experimental study of the target selection process in residential burglary. Journal of Research in Crime and Delinquency, 32(1), 39–53.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.John Jay College of Criminal JusticeThe City University of New York Graduate CenterNew YorkUSA

Personalised recommendations