Decision support systems for police: Lessons from the application of data mining techniques to “soft” forensic evidence

The paper sets out the challenges facing the Police in respect of the detection and prevention of the volume crime of burglary. A discussion of data mining and decision support technologies that have the potential to address these issues is undertaken and illustrated with reference the authors’ work with three Police Services. The focus is upon the use of “soft” forensic evidence which refers to modus operandi and the temporal and geographical features of the crime, rather than “hard” evidence such as DNA or fingerprint evidence. Three objectives underpin this paper. First, given the continuing expansion of forensic computing and its role in the emergent discipline of Crime Science, it is timely to present a review of existing methodologies and research. Second, it is important to extract some practical lessons concerning the application of computer science within this forensic domain. Finally, from the lessons to date, a set of conclusions will be advanced, including the need for multidisciplinary input to guide further developments in the design of such systems. The objectives are achieved by first considering the task performed by the intended systems users. The discussion proceeds by identifying the portions of these tasks for which automation would be both beneficial and feasible. The knowledge discovery from databases process is then described, starting with an examination of the data that police collect and the reasons for storing it. The discussion progresses to the development of crime matching and predictive knowledge which are operationalised in decision support software. The paper concludes by arguing that computer science technologies which can support criminal investigations are wide ranging and include geographical information systems displays, clustering and link analysis algorithms and the more complex use of data mining technology for profiling crimes or offenders and matching and predicting crimes. We also argue that knowledge from disciplines such as forensic psychology, criminology and statistics are essential to the efficient design of operationally valid systems.

[1]  Andrew Hunter,et al.  Feature Selection Using Probabilistic Neural Networks , 2000, Neural Computing & Applications.

[2]  Shakil Ahmed.  Strategies for partitioning data in association rule mining , 2003, SGAI Conf..

[3]  Kate J. Bowers,et al.  A GIS-linked database for monitoring repeat domestic burglary , 2001 .

[4]  Jesus Mena,et al.  Investigative Data Mining for Security and Criminal Detection , 2002 .

[5]  Henry A. Kautz,et al.  Reasoning about plans , 1991, Morgan Kaufmann series in representation and reasoning.

[6]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[7]  Pierre Margot,et al.  Inference structures for crime analysis and intelligence: the example of burglary using forensic science data , 1999 .

[8]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[9]  Yorick Wilks,et al.  Intelligent Indexing of Crime Scene Photographs , 2003, IEEE Intell. Syst..

[10]  Giles Oatley,et al.  Matching Crimes Using Burglars' Modus Operandi: A Test of Three Models , 2005 .

[11]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[12]  Peter Elias,et al.  Social class and the standard occupational classification , 1995 .

[13]  W AhaDavid.  Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms , 1992 .

[14]  Agnar Aamodt,et al.  Explanation-Driven Case-Based Reasoning , 1993, EWCBR.

[15]  Dominic A. Clark,et al.  Representing uncertain knowledge - an artificial intelligence approach , 1993 .

[16]  Franco Taroni,et al.  Statistics and the Evaluation of Evidence for Forensic Scientists , 2004 .

[17]  S. Oskamp.  OVERCONFIDENCE IN CASE-STUDY JUDGMENTS. , 1965, Journal of consulting psychology.

[18]  Edward J. Green,et al.  Cluster analysis of burglary M/Os. , 1976 .

[19]  Rui Camacho,et al.  Inductive Logic Programming , 2004, Lecture Notes in Computer Science.

[20]  P Margot,et al.  Case based reasoning in criminal intelligence using forensic case data. , 2003, Science & justice : journal of the Forensic Science Society.

[21]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[22]  Usama M. Fayyad,et al.  Data mining and KDD: Promise and challenges , 1997, Future Gener. Comput. Syst..

[23]  G. Gigerenzer.  How to Make Cognitive Illusions Disappear: Beyond “Heuristics and Biases” , 1991 .

[24]  John Tait,et al.  A Case-Based Reasoning Tool For Vibration Analysis , 1999 .

[25]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[26]  Andrew Stranieri,et al.  Knowledge discovery for decision support in law , 2000, ICIS.

[27]  Michael E. Wall,et al.  Galib: a c++ library of genetic algorithm components , 1996 .

[28]  Ken Pease,et al.  How Efficiently Can We Target Prolific Offenders? , 2002 .

[29]  U. Burkard.  Methods for Data Analysis , 2004 .

[30]  Dan W. Patterson,et al.  Artificial Neural Networks: Theory and Applications , 1998 .

[31]  Hsinchun Chen,et al.  COPLINK Center: Information and Knowledge Management for Law Enforcement , 2004, DG.O.

[32]  H. Macfie,et al.  Key Texts in Multidimensional Scaling , 1984 .

[33]  Janet L. Jackson,et al.  Offender profiling: Theory, research and practice. , 1997 .

[34]  A. Rukhin.  Bayes and Empirical Bayes Methods for Data Analysis , 1997 .

[35]  Agnar Aamodt,et al.  Topics in Case-Based Reasoning , 1993, Lecture Notes in Computer Science.

[36]  Kaeko Yokota,et al.  Computer-Based Retrieval of Suspects Using Similarity of Modus Operandi , 2002 .

[37]  Henry G. Goldberg,et al.  Restructuring Transactional Data for Link Analysis in the FinCEN AI System , 1998 .

[38]  K. J. Lynch,et al.  Automatic construction of networks of concepts characterizing document databases , 1992, IEEE Trans. Syst. Man Cybern..

[39]  Douglas Walton,et al.  Legal argumentation and evidence , 2002 .

[40]  E. Morgan,et al.  The Principles of Judicial Proof , 1931 .

[41]  Andrew Stranieri,et al.  A hybrid rule – neural approach for the automation of legal reasoning in the discretionary domain of family law in Australia , 1999, Artificial Intelligence and Law.

[42]  Judea Pearl,et al.  Chapter 2 – BAYESIAN INFERENCE , 1988 .

[43]  K. Branting,et al.  Building Explanations from Rules and Structured Cases , 1991, Int. J. Man Mach. Stud..

[44]  Ken Pease,et al.  Repeat Victimisation: Taking Stock , 1998 .

[45]  Yorick Wilks,et al.  Processing Intelligent Indexing of Crime Scene Photographs , 2003 .

[46]  Michael J. McCullagh,et al.  Crime, repeat victimisation and GIS , 2001 .

[47]  Shane D. Johnson,et al.  The Stability of Space-Time Clusters of Burglary , 2004 .

[48]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[49]  Giles Oatley,et al.  Constructing a Bayesian belief network to determine the likelihood of burglary , 2002 .

[50]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[51]  I. D. Wilson,et al.  Data Clustering and Rule Abduction to Facilitate Crime Hot Spot Prediction , 2001, Fuzzy Days.

[52]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[53]  Allen Newell,et al.  Human Problem Solving. , 1973 .

[54]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[55]  Kalyan Moy Gupta,et al.  Empirical evaluation of retrieval in case-based reasoning systems using modified cosine matching function , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[56]  Jason Dykes,et al.  Seeking structure in records of spatio-temporal behaviour: visualization issues, efforts and applications , 2003, Comput. Stat. Data Anal..

[57]  Andrew Stranieri,et al.  A Strategy for Evaluating Web-Based Discretionary Decision Support Systems , 2002, ADBIS Research Communications.

[58]  David V. Canter,et al.  Predicting Serial Killers' Home Base Using a Decision Support System , 2000 .

[59]  Agnar Aamodt,et al.  Representing Temporal Knowledge for Case-Based Prediction , 2002, ECCBR.

[60]  Lakhmi C. Jain,et al.  Introduction to Bayesian Networks , 2008 .

[61]  Yelena Yesha,et al.  Data Mining: Next Generation Challenges and Future Directions , 2004 .

[62]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[63]  Gregory Piatetsky-Shapiro,et al.  The KDD process for extracting useful knowledge from volumes of data , 1996, CACM.

[64]  T. Louis,et al.  Bayes and Empirical Bayes Methods for Data Analysis. , 1997 .

[65]  John Zeleznikow,et al.  Building intelligent legal information systems , 1994 .

[66]  J. Edward Jackson,et al.  Key Texts in Multidimensional Scaling , 1982 .

[67]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[68]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[69]  Natalya F. Noy,et al.  Knowledge-Acquisition Interfaces for Domain Experts: An Empirical Evaluation of Protégé-2000 , 2000 .

[70]  Kenneth D. Bailey,et al.  Interpreting Smallest Space Analysis , 1974 .

[71]  David Leake,et al.  Case-base maintenance: the husbandry of experience , 2001 .

[72]  A. T. Schreiber,et al.  Prolog-based infrastructure for RDF: performance and scalability , 2003 .

[73]  Giles Oatley,et al.  Data mining and knowledge discovery in databases workshop , 2005 .

[74]  John MacIntyre,et al.  SMART software for decision makers KDD experience , 2002, Knowl. Based Syst..

[75]  J. Wigmore.  The principles of judicial proof as given by logic, psychology, and general experience, and illustrated in judicial trials , 1988 .

[76]  Hsinchun Chen,et al.  COPLINK: managing law enforcement data and knowledge , 2003, CACM.

[77]  Donna Peuquet,et al.  An Event-Based Spatiotemporal Data Model (ESTDM) for Temporal Analysis of Geographical Data , 1995, Int. J. Geogr. Inf. Sci..

[78]  K. Pease,et al.  WHAT IS DIFFERENT ABOUT HIGH CRIME AREAS , 1992 .

[79]  James F. Allen.  Temporal reasoning and planning , 1991 .

[80]  Richard Adderley,et al.  Data mining case study: modeling the behavior of offenders who commit serious sexual assaults , 2001, KDD '01.

[81]  D. Schum.  The Evidential Foundations of Probabilistic Reasoning , 1994 .

[82]  D. Canter.  Offender profiling and criminal differentiation , 2000 .

[83]  James V. Rauff.  Data Mining: A Tutorial-Based Primer , 2005 .

[84]  A. DykesJ.,et al.  Seeking structure in records of spatio-temporal behaviour , 2003 .

[85]  Jan Wielemaker,et al.  An Overview of the SWI-Prolog Programming Environment , 2003, WLPE.

[86]  Andrew Hunter,et al.  Application of neural networks and sensitivity analysis to improved prediction of trauma survival , 2000, Comput. Methods Programs Biomed..

[87]  Charu C. Aggarwal,et al.  Towards systematic design of distance functions for data mining applications , 2003, KDD '03.

[88]  David W. Aha,et al.  Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms , 1992, Int. J. Man Mach. Stud..

[89]  Agnar Aamodt,et al.  Knowledge acquisition and learning by experience—the role of case-specific knowledge , 1995 .

[90]  Yoram Reich,et al.  Measuring the value of knowledge , 1995, Int. J. Hum. Comput. Stud..

[91]  Brian Everitt,et al.  Cluster analysis , 1974 .

[92]  A. Tversky.  Features of Similarity , 1977 .

[93]  Ian Witten,et al.  Data Mining , 2000 .

[94]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[95]  Angi Voß,et al.  Reasoning with complex cases , 1997 .

[96]  Maurice Bruynooghe,et al.  Predictive data mining in intensive care , 2006 .

[97]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[98]  I. D. Wilson,et al.  Predicting the geo-temporal variations of crime and disorder , 2003 .

[99]  Dale Addison.  Intelligent Computing Techniques: A Review , 2004 .

[100]  David A. Schum,et al.  A Probabilistic Analysis of the Sacco and Vanzetti Evidence: Kadane/A Probabilistic , 1997 .

[101]  Richard Adderley,et al.  Data Mining at the West Midlands Police: A Study of Bogus Official Burglaries , 2000 .

[102]  Hsinchun Chen,et al.  Using Coplink to Analyze Criminal-Justice Data , 2002, Computer.

[103]  Jerry H. Ratcliffe,et al.  Aoristic Signatures and the Spatio-Temporal Analysis of High Volume Crime Patterns , 2002 .

[104]  Frédéric Mesnard,et al.  Proceedings of the 13th International Workshop on Logic Programming Environments , 2003 .

[105]  Agnar Aamodt,et al.  Knowledge-Intensive Case-Based Reasoning and Sustained Learning , 1990, ECAI.

[106]  Hazem M. Raafat,et al.  Relational Spatial Topologies for Historical Geographical Information , 1994, Int. J. Geogr. Inf. Sci..

[107]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[108]  Bohn Stafleu van Loghum,et al.  Online … , 2002, LOG IN.

[109]  John R. Bourne,et al.  Similarity-based reasoning about diagnosis of analog circuits , 1988, IEA/AIE '88.

[110]  Bret M. Territo,et al.  Crime analysis through computer mapping: By C.B. Block M. Dabdoub, and S. Fregly, Police Executive Research Forum, Washington D.C., 1995, paperback, xiv + 287 pp., US$29.95 (+ $3.75 s and h), ISBN 1-878734-34-2 , 1996 .

[111]  Alex Hirschfield,et al.  Mapping and Analysing Crime Data : Lessons from Research and Practice , 2001 .

[112]  A. Cawsey.  Book Reviews: Participating in Explanatory Dialogues: Interpreting and Responding to Questions in Context , 1995, CL.

[113]  William B. Thompson,et al.  Reconstructive Expert System Explanation , 1992, Artif. Intell..

[114]  Bob J. Wielinga,et al.  Prolog-based Infrastructure for RDF: Scalability and Performance , 2003, PSSS.

[115]  Donna J. Peuquet,et al.  An approach for time-based analysis of spatiotemporal data , 1994 .

[116]  Timothy Chklovski,et al.  Learner: a system for acquiring commonsense knowledge by analogy , 2003, K-CAP '03.

[117]  Giles Oatley,et al.  Applying the Concept of Revictimization: Using Burglars' Behaviour to Predict Houses at Risk of Future Victimization , 2003 .

[118]  J. Edward Jackson,et al.  The User's Guide to Multidimensional Scaling , 1985 .

[119]  A. Tversky,et al.  On the psychology of prediction , 1973 .

[120]  Gang Wang,et al.  Crime data mining: a general framework and some examples , 2004, Computer.

[121]  Vladimir Batagelj,et al.  Pajek - Analysis and Visualization of Large Networks , 2001, Graph Drawing Software.

[122]  D. Schum,et al.  A Probabilistic Analysis of the Sacco and Vanzetti Evidence , 1996 .

[123]  Kougen Zheng,et al.  GIS: A Weapon to Combat the Crime , 2001, ISAS-SCI.

[124]  Gang Wang,et al.  Automatically detecting deceptive criminal identities , 2004, CACM.

[125]  Finn Verner Jensen,et al.  Introduction to Bayesian Networks , 2008, Innovations in Bayesian Networks.

[126]  Ken Pease,et al.  THE TIME COURSE OF REPEAT BURGLARY VICTIMIZATION , 1991 .

[127]  Giles Oatley,et al.  Crimes analysis software: 'pins in maps', clustering and Bayes net prediction , 2003, Expert Syst. Appl..

[128]  Stefan Wess,et al.  Topics in Case-Based Reasoning , 1994 .

[129]  David J. Spiegelhalter,et al.  Probabilistic Networks and Expert Systems , 1999, Information Science and Statistics.