The Efficacy of Ideographic Models for Geographical Offender Profiling

详细信息    查看全文

• 作者：David Canter (1)
  Laura Hammond (1)
  Donna Youngs (1)
  Piotr Juszczak (1)
  
• 关键词：Geographical profiling ; Ideographic models ; Burglary ; Dragnet ; Criminal spatial behaviour
• 刊名：Journal of Quantitative Criminology
• 出版年：2013
• 出版时间：September 2013
• 年：2013
• 卷：29
• 期：3
• 页码：423-446
• 全文大小：566KB
• 参考文献：1. Bennell C, Snook B, Taylor PJ, Corey S, Keyton J (2007a) It’s no riddle, choose the middle: the effect of number of crimes and topographical detail on police officer predictions of serial burglars-home locations. Crim Justice Behav 34(1):119-32 CrossRef
  2. Bennell C, Taylor P, Snook B (2007b) Clinical versus actuarial geographic profiling strategies: a review of the research. Police Pract Res 8(4):335-45 CrossRef
  3. Bennell C, Emeno K, Snook B, Taylor P, Snook B (2009) The precision, accuracy and efficiency of geographic profiling predictions: a simple heuristic versus mathematical algorithms. Crime Map J Res Practice 1(2):65-4
  4. Block R, Bernasco W (2009) Finding a serial burglar’s home using distance decay and origin destination patterns: a test of empirical Bayes journey-to-crime estimation in the Hague. J Invest Psychol Off Prof 6(3):187-11
  5. Canter D (2007) Mapping murder: the secrets of geographical profiling. Virgin Books, London
  6. Canter D (2009) Developments in geographical offender profiling: commentary on bayesian journey-to-crime modelling. J Invest Psychol Off Prof 6:161-66
  7. Canter D, Hammond L (2006) A comparison of the efficacy of different decay functions in geographical profiling for a sample of US serial killers. J Invest Psychol Off Prof 3:91-03
  8. Canter D, Hammond L (2007) Prioritising burglars: comparing the effectiveness of geographical profiling methods. Police Pract Res 8(4):371-84 CrossRef
  9. Canter D, Hodge S (2000) Criminal’s mental maps. In: Turnball LS, Hallisey-Hendrix E, Dent BD (eds) Atlas of crime. Oryx Press, Phoenix, pp 187-91
  10. Canter D, Larkin P (1993) The environmental range of serial rapists. In: Canter D, Alison L (eds) Criminal detection and the psychology of crime. Ashgate, Aldershot
  11. Canter D, Shalev K (2008) Putting crime in its place: psychological process in crime site location. In: Canter D, Youngs D (eds) Principles of geographical offender profiling. Ashgate, Aldershot
  12. Canter D, Youngs D (eds) (2008a) Principles of geographical offender profiling. Ashgate, Aldershot
  13. Canter D, Youngs D (eds) (2008b) Applications of geographical offender profiling. Ashgate, Aldershot
  14. Canter D, Youngs D (2009) Investigative psychology: offender profiling and the analysis of criminal action. Wiley, Chichester
  15. Canter D, Coffey T, Huntley M, Missen C (2000) Predicting serial killers-home base using a decision support system. J Quant Criminol 16(4):457-78 CrossRef
  16. Costanzo CM, Halperin WC, Gale N (1986) Criminal mobility and the directional component in journeys to crime. In: Figlio R, Hakim S, Rengert G (eds) Metropolitan crime patterns. Willow Tree Press, Monsey
  17. Duin R (1976) On the choice of the smoothing parameters for parzen estimators of probability density functions. IEEE Trans Comput C-25(11):1175-179 CrossRef
  18. Emeno K, Bennell C (2011) The effectiveness of calibrated versus default distance decay functions for geographic profiling: a preliminary examination of crime type. Psychol Crime Law. doi:10.1080/1068316X.2011.621426
  19. Goodwill A, Alison L (2005) Sequential angulation, spatial dispersion and consistency of distance attack patterns from home in serial murder, rape and Burglary. Psychol Crime Law 11(2):161-76 CrossRef
  20. Hammond L (2009) Spatial patterns in serial crime: modelling offence distribution and home-crime relationships for prolific individual offenders. Unpublished Doctoral Thesis: University of Liverpool
  21. Hammond L, Youngs D (2011) Decay functions and criminal spatial processes: geographical offender profiling of volume crime. J Invest Psychol Off Prof 8(1):90-02
  22. Harries K, Le Beau J (2007) Issues in the geographic profiling of crime: review and commentary. Police Pract Res 8(4):321-33 CrossRef
  23. Leitner M, Kent J (2009) Bayesian journey to crime modelling of single and multiple crime type series in Baltimore County, MD. J Invest Psychol Off Prof 6:213-36
  24. Leitner M, Kent J, Oldfield I, Swoope E (2007) Geoforensic analysis revisited—the application of newton’s geographic profiling method to serial burglaries in London, UK. Police Pract Res 8(4):359-70 CrossRef
  25. Levine N (2002) Crimestat II: spatial modeling. Report for the US Department of Justice, August 13th, 2002
  26. Levine N (2005) CrimeStat III. Crime Mapping News. 7(2), Spring. 8-0
  27. Levine N (2009) Introduction to the special issue on bayesian journey-to-crime modelling. J Invest Psychol Off Prof 6(3):167-85
  28. Levine N, Block R (2011) Bayesian journey to crime estimation: an improvement in geographic profiling methodology. Prof Geogr 63(2):213-29 CrossRef
  29. Levine N, Lee P (2009) Bayesian journey-to-crime modelling of juvenile and adult offenders by gender in Manchester. J Invest Psychol Off Prof 6:237-51
  30. Lundrigan S, Canter D (2001) Spatial patterns of serial murder: an analysis of disposal site location choice. Behav Sci Law 19:595-10 CrossRef
  31. Lundrigan S, Czarnomski S (2006) Spatial characteristics of serial sexual assault in New Zealand. Aus NZ J Criminol 32(2):218-31 CrossRef
  32. Nunez-Garcia J, Kutalik Z, Cho K-H, Wolkenhauer O (2003) Level sets and minimum volume sets of probability density functions. J Approx Reason 34(1):25-7 CrossRef
  33. Parzen E (1962) On the estimation of a probability density function and mode. Annal Mathem Stat 33:1065-076 CrossRef
  34. Paulsen DJ (2004) Geographic profiling: Hype or hope?—preliminary results into the accuracy of geographic profiling software. Paper presented at the UK Crime Mapping Conference, London, UK
  35. Paulsen D (2005) Connecting the dots: assessing the accuracy of geographic profiling software. Polic Int J Police Strat Manag 29(2):306-34 CrossRef
  36. Paulsen D (2006) Human vs. machine: a comparison of the accuracy of geographic profiling methods. J Invest Psychol Off Prof 3(2):77-9
  37. Rich T, Shively M (2004) A methodology for evaluating geographic profiling software: final report. Abt Associates Inc., Cambridge
  38. Rich T, Shively M, Adedokun L (2004) NIJ roundtable for developing an evaluation methodology for geographic profiling software. Abt Associates, Cambridge
  39. Rossmo DK (2000) Geographic profiling. CRC Press, LLC, Boca Raton
  40. Smith W, Bond JW, Townsley M (2009) Determining how journeys-to-crime vary measuring inter- and intra-offender crime trip distributions. In: Weisburd D, Bernasco W, Gerben J, Bruinsma N (eds) Putting crime in its place. Filiquarian Publishing, London
  41. Snook B (2004) Individual differences in the distances travelled by serial burglars. J Invest Psychol Off Prof 1:53-6
  42. Snook B, Canter DV, Bennell C (2002) Predicting the home location of serial offenders: a preliminary comparison of the accuracy of human judges with a geographic profiling system. Behav Sci Law 20:109-18 CrossRef
  43. Snook B, Taylor PJ, Bennell C (2004) Geographic profiling: the fast, frugal, and accurate way. Appl Cogn Psychol 18:105-21 CrossRef
  44. Snook B, Zito M, Bennell C, Taylor PJ (2005) On the complexity and accuracy of geographic profiling strategies. J Quant Criminol 21(1):1-6 CrossRef
  45. Taylor PJ, Bennell C, Snook B (2009) The bounds of cognitive heuristic performance on the geographic profiling task. Appl Cogn Psychol 23:410-30 CrossRef
  46. Tonkin M, Woodhams J, Bond JW, Loe T (2010) A theoretical and practical test of geographical profiling with serial vehicle theft in a UK Context. Behav Sci Law 28:442-60
  47. Van Koppen PJ, De Keiser JW (1997) Desisting distance decay: on the aggregation of individual crime trips. Criminology 35(2):505-13 CrossRef
  48. Van Koppen MV, Elffers H, Ruiter S (2011) When to refrain from using likelihood surface methods for geographical offender profiling: an ex ante test of assumptions. J Invest Psychol Off Prof 8(3):242-56
  49. Warren J, Reboussin R, Hazelwood RR, Cummings A, Gibbs N, Trumbetta S (1998) Crime scene and distance correlates of serial rape. J Quant Criminol 14(1):35-9 CrossRef
  50. Wolberg, J. (2005) Data analysis using the method of least squares: extracting the most information from experiments. Springer: ISBN 3540256741
  51. Yeung DY, Chow C (2002). Parzen-window network intrusion detectors. Proceedings of the sixteenth international conference on pattern recognition 4: 385-88
  
• 作者单位：David Canter (1)
  Laura Hammond (1)
  Donna Youngs (1)
  Piotr Juszczak (1)
  
  1. International Research Centre for Investigative Psychology, University of Huddersfield, Ramsden Building, Queensgate Campus, Huddersfield, HD1 3DH, UK
  

文摘

Objectives Current ‘geographical offender profiling-methods that predict an offender’s base location from information about where he commits his crimes have been limited by being based on aggregate distributions across a number of offenders, restricting their responsiveness to variations between individuals as well as the possibility of axially distorted distributions. The efficacy of five ideographic models (derived only from individual crime series) was therefore tested. Methods A dataset of 63 burglary series from the UK was analysed using five different ideographic models to make predictions of the likely location of an offenders home/base: (1) a Gaussian-based density analysis (kernel density estimation); (2) a regression-based analysis; (3) an application of the ‘Circle Hypothesis- (4) a mixed Gaussian method; and (5) a Minimum Spanning Tree (MST) analysis. These tests were carried out by incorporating the models into a new version of the widely utilised Dragnet geographical profiling system DragNetP. The efficacy of the models was determined using both distance and area measures. Results Results were compared between the different models and with previously reported findings employing nomothetic algorithms, Bayesian approaches and human judges. Overall the ideographic models performed better than alternate strategies and human judges. Each model was optimal for some crime series, no one model producing the best results for all series. Conclusions Although restricted to one limited sample the current study does show that these offenders vary considerably in the spatial distribution of offence location choice. This points to important differences between offenders in the morphology of their crime location choice. Mathematical models therefore need to take this into account. Such models, which do not draw on any aggregate distributions, will improve geographically based investigative decision support systems.