Article | 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden | Multi-expert estimations of burglars’ risk exposure and level of pre-crime preparation based on crime scene data
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Title:
Multi-expert estimations of burglars’ risk exposure and level of pre-crime preparation based on crime scene data
Author:
Martin Boldt: Department of Computer Science and Engineering, Blekinge Institute of Technology, Sweden Veselka Boeva: Department of Computer Science and Engineering, Blekinge Institute of Technology, Sweden Anton Borg: Department of Computer Science and Engineering, Blekinge Institute of Technology, Sweden
Download:
Full text (pdf)
Year:
2017
Conference:
30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden
Issue:
137
Article no.:
004
Pages:
39-44
No. of pages:
6
Publication type:
Abstract and Fulltext
Published:
2017-05-12
ISBN:
978-91-7685-496-9
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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Law enforcement agencies strive to link crimes perpetrated by the same offenders into crime series in order to improve investigation efficiency. Such crime linkage can be done using both physical traces (e.g., DNA or fingerprints) or “soft evidence” in the form of offenders’ modus operandi (MO), i.e. their behaviors during crimes. However, physical traces are only present for a fraction of crimes, unlike behavioral evidence. This position paper presents a method for aggregating multiple criminal profilers’ ratings of offenders’ behavioral characteristics based on feature-rich crime scene descriptions. The method calculates consensus ratings from individual experts’ ratings, which then are used as a basis for classification algorithms. The classification algorithms can automatically generalize offenders’ behavioral characteristics from cues in the crime scene data. Models trained on the consensus rating are evaluated against models trained on individual profiler’s ratings. Thus, whether the consensus model shows improved performance over individual models.

Keywords: Multi-expert decision making, Classification, Crime linkage, Offender profiling

30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden

Author:
Martin Boldt, Veselka Boeva, Anton Borg
Title:
Multi-expert estimations of burglars’ risk exposure and level of pre-crime preparation based on crime scene data
References:

[1] Bennell, C. and Canter, D.V., Linking commercial burglaries by modus operandi: tests using regression and ROC analysis, Science & Justice, 42, 2002, pp. 153-164.


[2] Bennell, C. and Jones, N.J., Between a ROC and a hard place: A method for linking serial burglaries by modus operandi, Journal of Investigative Psychology and O?ender Profiling, 2, 2005, pp. 23–41.


[3] Boldt, M., Borg, A., Svensson, M., Hildeby, J., Predicting burglars’ risk exposure and level of pre-crime preparation using crime scene data, (to appear in) Journal of Intelligent Data Analysis, 2018, 22(1).


[4] Borg, A. and Boldt, M., Clustering Residential Burglaries Using Modus Operandi and Spatiotemporal Information, International Journal of Information Technology & Decision Making, 2016, 15(1), pp. 23-41.


[5] Bouhana, N., Johnson, S.D. and Porter, M., Consistency and specificity in burglars who commit prolific residential burglary, Legal and Criminological Psychology, 2014, 21(1), pp. 77-94.


[6] Canter, D., Psychology of o?ender profiling, Handbook of psychology in legal contexts, Chichester, UK, John Wiley and Sons, 2000.


[7] Cooke, R.A. and Szumal, J.L. The impact of group interaction styles on problem-solving effectiveness, Journal of Applied Behavioral Science, 1994, 30, pp. 415-437.


[8] Davies, A., The use of DNA profiling and behavioural science in the investigation of sexual o?ences, Medicine, Science and the Law, 1991, 31, pp. 95-101.


[9] Grubin, D., Kelly, P. and Brunsdon, C., Linking serious sexual assults through behaviour [Internet], 2001 [cited 2017-03-30], Available from: http://webarchive.nationalarchives.gov.uk/20110314171826


[10] Hazelwood, R.R. and Warren, J.I., Linkage analysis: Modus operandi, ritual, and signature in serial sexual crime, Aggression and Violent Behavior, 8, 2003, pp. 587-598.


[11] Markson, L., Woodhams, J., and Bond, J.W., Linking serial residential burglary: comparing the utility of modus operandi behaviours, geographical proximity, and temporal proximity, Journal of Investigative Psychology and Offender Profiling, 2010, 7(2), pp. 91-107.


[12] O’Hara, C.E. and O’Hara, G.L., Fundamentals of Criminal Investigation (7th ed.), Springfield, IL, Charles C Thomas Publisher Ltd., 1956.


[13] Pervin, L.A., Current Controversies and Issues in Personality, New York, JohnWiley and Sons, 2002.


[14] Reich, B.J. and Porter, M.D., Partially supervised spatiotemporal clustering for burglary crime series identification, Journal of Royal Statistical Society, Series A (Statistical Society), 178, 2015, pp. 465-480.


[15] Roman J., Reid, S., Reid, J., Chalfin, A., Adams, W. and Knight, C., The DNA Field Experiment: Cost-e?ectiveness Analysis of the Use of DNA in the Investigation of High-volume Crimes [Internet], 2008 [cited 2017-03-30], Available from: http://www.urban.org/UploadedPDF/411697dna field experiment.pdf


[16] Rossmo, K., Geographic Profiling, Boca Raton, CRC Press, 1999. [17] Santtila, P., Korpela, S. and H¨akk¨anen, H., Expertise and decision making in the linking of car crime series, Psychology, Crime & Law, 2004, 10(2), pp. 97-112.


[18] Sullivan, G.M. and Artino, A.R., Analyzing and Interpreting Data from Likert-Type Scales, Journal of Graduate Medical Education, 2013, pp. 541-542.


[19] Tonkin, M.,Woodhams, J., Bull, R. and Bond, J.W., Behavioural case linkage with solved and unsolved crimes, Forensic Science International, 212, 2012, pp. 146-153.


[20] Tonkin, M. and Woodhams, J., The feasibility of using crime scene behaviour to detect versatile serial o?enders: An empirical test of behavioral consistency, distinctiveness, and discrimination accuracy, Legal and Criminological Psychology, 2015, 22(1), pp. 99-115.


[21] Tsiporkova, E. and V. Boeva, V. Nonparametric recursive aggregation process, Kybernetika, The Journal of the Czech Society for Cybernetics and Inf. Sciences 2004, 40(1), pp. 51 - 70.


[22] Tsiporkova, E. and Boeva, V., Multi-step ranking of alternatives in a multi-criteria and multi-expert decision making environment, Information Sciences, 2006, 176(18), pp. 2673–2697.


[23] Tsiporkova, E., Tourw`e, T. and Boeva, V. A Collaborative Decision Support Platform for Product Release Definition. The Fifth International Conference on Internet and Web Applications and Services ICIW 2010, 2010, pp. 351-356.


[24] Woodhams, J., Hollin, C.R. and Bull, R.,The psychology of linking crimes: A review of the evidence, Legal and Criminological Psychology, 2010, 12(2), pp. 233-249.


[25] Wright, R.T. and Decker, S.H., Burglars on the job, Boston, MA, Northeastern University Press, 1994.


[26] Yokota, K. and Watanabe, S., Computer- Based Retrieval of Suspects Using Similarity of Modus Operandi, International Journal of Police Science & Management, 2002, 4(1), pp. 5-15.

30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden

Author:
Martin Boldt, Veselka Boeva, Anton Borg
Title:
Multi-expert estimations of burglars’ risk exposure and level of pre-crime preparation based on crime scene data
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Last updated: 2017-02-21