Crime in an Affluent City: Applications of Risk Terrain Modeling for Residential and Vehicle Burglary in Coral Gables, Florida, 2004–2016

  • Derek Vildosola
  • Julian Carter
  • Eric R. Louderback
  • Shouraseni Sen RoyEmail author


Risk Terrain Modeling (RTM) — an innovative geospatial approach to analyze the locations of high crime areas within cities— was used to analyze criminogenic spaces and identify the riskiest places contributing to vehicle and residential burglary in the city of Coral Gables, Florida from 2004 to 2016. Official crime incident data on residential and vehicle burglary were provided by the Coral Gables Police Department. We investigated the role of environmental predictors of crime by analyzing the effects of the designated riskiest places including alcohol vendors, car dealers, gas stations, bars, secondary/post-secondary schools, grocery stores, and restaurants. We identified risky places and their proximity to the occurrence of residential and vehicle burglary with a regression process using the RTM technique to determine the Relative Risk Values (i.e., weighted risk) that each risk factor had. We examined different temporal designations, including day and night, day of the week, and monthly intervals. Our results indicated that restaurants and grocery stores located in the downtown area and along the US 1 highway were higher risk locations for the occurrence of both types of crime throughout the city. The locations of risky places in the study area were spatially consistent with high crime areas, supporting our main hypothesis. The use of RTM with targeted community policing strategies could move policy from a reactionary approach to more proactive solutions to crime prevention. Concepts drawn from routine activity theory, as well as crime and place perspectives, both receive moderate empirical support for vehicle and residential burglary outcomes.


Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.


  1. Barnum, J. D., Caplan, J. M., Kennedy, L. W., & Piza, E. L. (2017). The crime kaleidoscope: A cross-jurisdictional analysis of place features and crime in three urban environments. Applied Geography, 79, 203–211.CrossRefGoogle Scholar
  2. Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G., & Portugali, Y. (2012). Smart cities of the future. The European Physical Journal Special Topics, 214, 481–518.CrossRefGoogle Scholar
  3. Braga, A. A., & Clarke, R. V. (2014). Explaining high-risk concentrations of crime in the city: Social disorganization, crime opportunities, and important next steps. Journal of Research in Crime and Delinquency, 51(4), 480–498.CrossRefGoogle Scholar
  4. Braga, A. A., Hureau, D. M., & Papachristos, A. V. (2011). The relevance of micro places to citywide robbery trends: A longitudinal analysis of robbery incidents at street corners and block faces in Boston. Journal of Research in Crime and Delinquency, 48(1), 7–32.CrossRefGoogle Scholar
  5. Bowers, K. (2014). Risky facilities: crime radiators or crime absorbers? A comparison of internal and external levels of theft. Journal of Quantitative Criminology, 30(3), 389–414.Google Scholar
  6. Bunting, R. J., Chang, O. Y., Cowen, C., Hankins, R., Langston, S., Warner, A., ... & Roy, S. S. (2018). Spatial patterns of larceny and aggravated assault in Miami–Dade County, 2007–2015. The Professional Geographer, 70(1), 34–46.Google Scholar
  7. Caplan, J. M., & Kennedy, L. W. (2016). Risk terrain modeling: Crime prediction and risk reduction. Berkeley: University of California Press.Google Scholar
  8. Caplan, J. M., Kennedy, L. W., & Miller, J. (2011a). Risk terrain modeling: Brokering criminological theory and GIS methods for crime forecasting. Justice Quarterly, 28(2), 360–381.CrossRefGoogle Scholar
  9. Caplan, J. M., Kennedy, L. W., & Miller, J. (2011b). Risk terrain modeling: Brokering criminological theory and GIS methods for crime forecasting. Justice Quarterly, 28(2), 360–381.CrossRefGoogle Scholar
  10. 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 & Delinquency, 59(2), 243–270.Google Scholar
  11. Caplan, J. M., Kennedy, L. W., Barnum, J. D., & Piza, E. L. (2015). Risk terrain modeling for spatial risk assessment. Cityscape, 17(1), 7.Google Scholar
  12. Carter, J., Louderback, E.R., Vildosola, D., & Sen Roy, S. (2019). Crime in an Affluent City: Spatial Patterns 546 of Property Crime in Coral Gables, FL. European Journal on Criminal Policy and Research.
  13. Ceccato, V. (2016). Public space and the situational conditions of crime and fear. International Criminal Justice Review, 26(2), 69–79.CrossRefGoogle Scholar
  14. Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44, 588–608.CrossRefGoogle Scholar
  15. Cowen, C., Louderback, E. R., & Sen Roy, S. (2018). The role of land use and walkability in predicting crime patterns: A case study of Miami-Dade County, 2007-2015. Security Journal.
  16. Dugato, M., Favarin, S., & Bosisio, A. (2018). Isolating target and neighbourhood vulnerabilities in crime forecasting. European Journal on Criminal Policy and Research, 24(4), 393–415.Google Scholar
  17. Felson, M. (2002). Crime and everyday life: Insights and implications for society. Thousand Oaks: Pine Forge Press.Google Scholar
  18. Frank, R., Dabbaghian, V., Reid, A., Singh, S., Cinnamon, J., & Brantingham, P. (2011). Power of criminal attractors: Modeling the pull of activity nodes. Journal of Artificial Societies and Social Simulation, 14(1), 6.CrossRefGoogle Scholar
  19. Giménez-Santana, A., Caplan, J. M., & Drawve, G. (2018). Risk Terrain Modeling and Socio-Economic Stratification: Identifying Risky Places for Violent Crime Victimization in Bogotá, Colombia. European Journal on Criminal Policy and Research, 24(4), 417–431.Google Scholar
  20. Greene, J. R. (2006). Encyclopedia of Police Science: 2-volume set. Routledge.Google Scholar
  21. Groff, E. R. (2008). Adding the temporal and spatial aspects of routine activities: A further test of routine activity theory. Security Journal, 21, 95–116.CrossRefGoogle Scholar
  22. Groff, E., & Lockwood, B. (2013). Criminogenic facilities and crime across street segments in Philadelphia. Journal of Research in Crime and Delinquency, 51(3), 277–314.CrossRefGoogle Scholar
  23. Haberman, C., & Ratcliffe, J. (2015). Testing for temporally differentiated relationships among potentially criminogenic places and census block street robbery counts. Criminology, 53(3), 457–483.CrossRefGoogle Scholar
  24. Hardyns, W., & Rummens, A. (2018). Predictive Policing as a New Tool for Law Enforcement? Recent Developments and Challenges. European Journal on Criminal Policy and Research, 24(3), 201–218.Google Scholar
  25. Harries, K. (1999). Mapping crime: Principles and practice. Beverly Hills: National Institute of Justice.Google Scholar
  26. Hesseling, R. B. P. (1992). Using data on offender mobility in ecological research. Journal of Quantitative Criminology, 8(1), 95–112.CrossRefGoogle Scholar
  27. Hipp, J. R. (2016). General theory of spatial crime patterns. Criminology, 54(4), 653–679.CrossRefGoogle Scholar
  28. Hollis-Peel, M., Reynald, D. M., van Bavel, M., Elffers, H., & Welsh, B. (2011). Guardianship for crime prevention: A critical review of the literature. Crime, Law and Social Change, 56, 53–70.CrossRefGoogle Scholar
  29. Irvin-Erickson, Y. (2014). Identifying risky places for crime: An analysis of the criminogenic spatiotemporal influences of landscape features on street robberies. PhD diss., Rutgers University-Graduate School-Newark.Google Scholar
  30. Kennedy, L. W., & Caplan, J. M. (2012). A theory of risky places. Newark: Rutgers Center on Public Security.Google Scholar
  31. Kennedy, L. W., Caplan, J. M., & Piza, E. (2011). Risk clusters, hotspots, and spatial intelligence: Risk terrain modeling as an algorithm for police resource allocation strategies. Journal of Quantitative Criminology, 27(3), 339–362.CrossRefGoogle Scholar
  32. Kennedy, L., Caplan, J., & Piza, E. (2015). Conjunctive Analysis Report: 2012 Residential Burglary in Arlington, TX. Accessed 5 April 2018.
  33. Kennedy, L. W., Caplan, J. M., Piza, E. L., & Buccine-Schraeder, H. (2016). Vulnerability and exposure to crime: Applying risk terrain modeling to the study of assault in Chicago. Applied Spatial Analysis and Policy, 9(4), 529–548.CrossRefGoogle Scholar
  34. Kennedy, L. W., Caplan, J. M., & Piza, E. L. (2018a). Risk-based policing: Evidence-based crime prevention with big data and spatial analytics. Oakland: University of California Press.CrossRefGoogle Scholar
  35. Kennedy, L. W., Caplan, J. M., & Piza, E. L. (2018b). Risk-based policing: Evidence-based crime prevention with big data and spatial analytics. Berkeley: University of California Press.CrossRefGoogle Scholar
  36. Kimpton, A., Corcoran, J., & Wickes, R. (2017). Greenspace and crime: An analysis of greenspace types, neighboring composition, and the temporal dimensions of crime. Journal of Research in Crime and Delinquency, 54, 303–337.CrossRefGoogle Scholar
  37. Koper, C. (2014). Assessing the practice of hot spots policing: Survey results from a national convenience sample of local police agencies. Journal of Contemporary Criminal Justice, 30(2), 1043986214525079.CrossRefGoogle Scholar
  38. Louderback, E. R., & Sen Roy, S. (2017). Integrating Social Disorganization and Routine Activity Theories and Testing Neighborhood Crime Watch Program Effectiveness: Case Study of Miami-Dade County, 2007–2015. British Journal of Criminology.
  39. Mastrofski, S. D., Weisburd, D., & Braga, A. A. (2010). Rethinking policing: The policy implications of hotspots of crime. In N. A. Frost, J. D. Freilich, & T. R. Clear (Eds.), Contemporary issues in criminal justice policy: Policy proposals from the American Society of Criminology Conference (pp. 251–264). Belmont: Wadsworth.Google Scholar
  40. Miami-Dade County (2016). Miami Dade Police Department 85A - Part 1 Crimes 5 Yrs Comparison. Accessed 31 May 2018.
  41. Ohyama, T., & Amemiya, M. (2018). Applying crime prediction techniques to Japan: A comparison between risk terrain modeling and other methods. European Journal on Criminal Policy and Research, 24(4), 469–487.Google Scholar
  42. Piza, E. L., Kennedy, L. W. & Caplan, J. M. (2018). Facilitators and impediments to designing, implementing, and evaluating risk-based policing strategies using risk terrain modeling: Insights from a multi-city evaluation in the United States. European Journal on Criminal Policy and Research, 24(4), 489–513.Google Scholar
  43. Ratcliffe, J. (2012). The spatial extent of criminogenic places: A Changepoint regression of violence around bars. Geographical Analysis, 44(4), 302–320.CrossRefGoogle Scholar
  44. Rutgers Center on Public Security (2019) RTMDx Software. Accessed 25 May 2019.
  45. Sampson, R. J. (2012). Great American City: Chicago and the enduring neighborhood effect. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  46. Sherman, L. W. (1998). Ideas in American policing: Evidence based policing. Policing. Accessed 9 April 2018.
  47. Steenbeek, W., & Hipp, J. (2011). A longitudinal test of social disorganization theory: Feedback effects among cohesion, social control, and disorder. Criminology, 49(3), 833–871.CrossRefGoogle Scholar
  48. Taylor, R. B. (1988). Human territorial functioning. New York: University of Cambridge Press.CrossRefGoogle Scholar
  49. Taylor, R. B., & Harrell, A. V. (1996). Physical environment and crime. Washington: National Institute of Justice.Google Scholar
  50. U.S. Census Bureau. (2016). U.S. Census Bureau Quick Facts: Coral Gables. Accessed 9 April 2018.
  51. University of Miami (2018) Fact Finder 2016–2017. Accessed 16 May 2018.
  52. Wang, D., Ding, W., Lo, H., Stepinski, T., Salazar, J., & Morabito, M. (2012). Crime hotspot mapping using the crime related factors—A spatial data mining approach. Applied Intelligence, 39, 772–781.CrossRefGoogle Scholar
  53. Weisburd, D., Bushway, S., Lum, C., & Sue-Ming, Y. (2004). Trajectories of crime at places: A longitudinal study of street segments in the city of Seattle. Criminology, 42(2), 283–321.CrossRefGoogle Scholar
  54. Weisburd, D., Groff, E. R., & Yang, S.-M. (2014). The importance of both opportunity and social disorganization theory in a future research agenda to advance criminological theory and crime prevention at places. Journal of Research in Crime and Delinquency, 51(4), 499–508.CrossRefGoogle Scholar
  55. Weisburd, D., Braga, A. A., Groff, E. R., & Wooditch, A. (2017). Can hot spots policing reduce crime in urban areas? An agent-based simulation. Criminology, 55(1), 137–173.CrossRefGoogle Scholar
  56. Wray, C. (2017). Preliminary Semiannual Uniform Crime Report, January–June, 2017.Google Scholar
  57. Zhang, H., & Song, W. (2014). Addressing issues of spatial spillover effects and non-stationarity in analysis of residential burglary crime. GeoJournal, 79, 89–102.CrossRefGoogle Scholar

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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Geography and Regional StudiesUniversity of MiamiCoral GablesUSA
  2. 2.Department of SociologyUniversity of MiamiCoral GablesUSA
  3. 3.Center for Computational SciencesUniversity of MiamiCoral GablesUSA

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