Offender–Offence Profiling: Improving Burglary Solvability and Detection

  • Bronwyn Killmier
  • Katrin Mueller-JohnsonEmail author
  • Richard Timothy Coupe


This chapter considers the feasibility of predicting the subsets of offenders likely to be responsible for different sorts of unsolved burglaries by examining the distinctive associations between types of domestic burglaries and types of offenders. It extends our understanding of offender profiling to a high-volume offence and, by drawing on high-quality police data from South Australia, helps place a profiling approach to the improvement of crime solvability and detection on stronger empirical foundations. This approach is reliant on the small proportion of solved burglary cases where the culprits have been identified and will enhance ‘known offender targeting’. The analysis uses a random sample of 349 offenders convicted for committing domestic burglary in Adelaide in 2012 for which there are key data measuring offender characteristics, offending histories, and offence and dwelling characteristics. Using latent class analysis, the findings identify five offence types, three offender types and six offending history types. It was possible to link offence types with offender types, and offender types with offending history clusters, but a link directly from offence type to offending history type was not significant. Furthermore, the effect size of the relationship between offence type and offender type is relatively weak, and it appears doubtful whether it can be used to identify with sufficient accuracy the offender subsets in Adelaide’s locales. Offender–offence profiling may, therefore, make only small improvements to the solvability of high-volume crimes like domestic burglary.


Offender–offence profiling Domestic burglary Solvability Offenders Offences 


  1. Antrobus, E., & Pilotto, A. (2016). Improving forensic responses to residential burglaries: Results of a randomized controlled field trial. Journal of Experimental Criminology, 12(3), 319–345.CrossRefGoogle Scholar
  2. Australian Bureau of Statistics. (2011). Census quick stats, all people—Usual residents, greater capital city statistical areas, Greater Adelaide, Code 4GADE (GCCSA).
  3. Australian Bureau of Statistics. (2013). Socio-Economic indexes for areas (SEIFA) using 2011 census data for greater capital city statistical areas, Greater Adelaide, Code 4GADE (GCCSA).
  4. Bennell, C., & Canter, D. V. (2002). Linking commercial burglaries by modus operandi: tests using regression and ROC analysis. Science & Justice, 42(3), 153–164.Google Scholar
  5. Bernasco, W. (2010). A sentimental journey to crime: Effects of residential history on crime location choice. Criminology, 48(2), 389–416.CrossRefGoogle Scholar
  6. Burrows, J., Hopkins, M., Hubbard, R., Robinson, A., Speed, M., & Tilley, N. (2005). Understanding the attrition process in volume crime investigations (Home Office Research Study 295). London: Home Office.Google Scholar
  7. Coupe, R. T., & Blake, L. (2006). Daylight and darkness strategies and the risks of offenders being seen at residential burglaries. Criminology, 44(2), 431–463.CrossRefGoogle Scholar
  8. Coupe, T., & Griffiths, M. (1996). Solving residential burglary (Police Research Group crime detection and prevention services, Paper 77). London: Home Office.Google Scholar
  9. Donnellan, G. (2012). Burglary solvability factors. Paper Presented at 4th International Evidence-Based Policing Conference, Cambridge, 4–6 July 2011.Google Scholar
  10. Farrington, D., & Lambert, S. (2006). Predicting offender profiles from offence and victim characteristics. In R. N. Kocsis (Ed.), Criminal profiling: International perspectives in theory practice and research. Totowa, NJ: Humana Press.Google Scholar
  11. Fox, B., & Farrington, D. (2012). Creating burglary profiles using latent class analysis: A new approach to offender profiling. Criminal Justice and Behavior, 39(12), 1582–1611.CrossRefGoogle Scholar
  12. Killmier, B. (2013). Offenders and their offences: Convicted burglars in Adelaide. (Unpublished MSt thesis). University of Cambridge.Google Scholar
  13. Kocsis, R. N., & Cooksey, R. (2002). Criminal psychological profiling of serial arson crimes. International Journal of Offender Therapy and Comparative Criminology, 46(6), 631–656.CrossRefGoogle Scholar
  14. Kocsis, R. N., Cooksey, R., & Irwin, H. (2002). Psychological profiling of offender characteristics from crime behaviours in serial rape offences. International Journal of Offender Therapy and Comparative Criminology, 46(6), 144–169.CrossRefGoogle Scholar
  15. Lammers, M. (2014). Are arrested and non-arrested serial offenders different? A test of spatial offending patterns using DNA found at crime scenes. Journal of Research in Crime and Delinquency, 51(2), 143–167.CrossRefGoogle Scholar
  16. Marshall, B., & Johnson, S. D.  (2005). Crime in rural areas: A review of the literature for the rural evidence research centre. Jill Dando Institute of Crime Science: University College London.Google Scholar
  17. Miethe, T., McCorkle, R., & Listwan, S. (2006). Crime profiles: The anatomy of dangerous persons, places, and situations (3rd ed.). Los Angeles, CA: Roxbury.Google Scholar
  18. Muthen, B., & Muthen, L. (2000). Mplus user’s guide. Los Angeles: Muthen and Muthen.Google Scholar
  19. Paine, C., & Ariel, B. (2013). Solvability analysis: Increasing the likelihood of detection in completed, attempted and in-progress burglaries. Paper Presented at the 6th International Evidence-Based Policing Conference, Cambridge, 8–10 July 2013.Google Scholar
  20. Promish, D., & Lester, D. (1999). Classifying serial killers. Forensic Science International, 105(3), 155–159.CrossRefGoogle Scholar
  21. Roman, J. K., Reid, S. E., Chalfin, A. J., & Knight, C. R. (2009). The DNA field experiment: A randomized trial of the cost-effectiveness of using DNA to solve property crimes. Journal of Experimental Criminology, 5, 345–369.CrossRefGoogle Scholar
  22. Snook, B. (2004). Individual differences in distance travelled by serial burglars. Journal of Investigative Psychology and Offender Profiling, 1(1), 53–66.CrossRefGoogle Scholar
  23. Taylor, P., & Bond, S. (2012). Crimes detected in England and Wales 2011/12 (Statistical Bulletin 08/12). London: Home Office.Google Scholar
  24. Taylor, P., & Chaplin, R. (2011). Crimes detected in England and Wales 2010/11 (Statistical Bulletin 10/11). London: Home Office.Google Scholar
  25. Tilley, N., Robinson, A., & Burrows, J. (2007). The investigation of high volume crime. In T. Newburn, T. Williamson, & A. Wright (Eds.), Handbook of criminal investigation (pp. 226–254). London: Willan Publishing.Google Scholar
  26. Tompson, L., & Coupe, R. T. (2017). Time and criminal opportunity. In G. J. N. Bruinsma & S. D. Johnson (Eds.), The Oxford handbook of environmental criminology. Oxford: Oxford University Press.Google Scholar
  27. van Koppen, P. J., & Jansen, R. W. J. (1998). The road to the robbery: Travel patterns in commercial robberies. The British Journal of Criminology, 38(2), 230–246.CrossRefGoogle Scholar
  28. Vaughn, M. G., DeLisi, M., Beaver, K., & Howard, M. (2008). Toward a quantitative typology of burglars: A latent profile analysis of career offenders. Journal of Forensic Science, 53(6), 1387–1392.Google Scholar
  29. software (2013). Accessible at
  30. Wright, O. (2013). Urban to rural: An exploratory analysis of burglary and vehicle crime with a rural context (unpublished MSt thesis). University of Cambridge.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bronwyn Killmier
    • 1
  • Katrin Mueller-Johnson
    • 2
    Email author
  • Richard Timothy Coupe
    • 2
  1. 1.Formerly of South Australia PoliceAdelaideAustralia
  2. 2.Institute of Criminology, University of CambridgeCambridgeUK

Personalised recommendations