Brian Ewart

Crimes analysis software: ‘pins in maps’, clustering and Bayes net prediction

The OVER Project was a collaboration between West Midlands Police, UK, the Centre for Adaptive Systems, and Psychology Division, from the University of Sunderland. The Project was developed primarily to assist the Police with the high volume crime, burglary from dwelling houses. A developed software system enables the trending of historical data, the testing of ‘short term’ hunches, and the development of ‘medium’ and long term’ strategies to burglary and crime reduction, based upon victim, offender, location and details of victimisations. The software utilises mapping and visualisation tools and is capable of a range of sophisticated predictions, tying together statistical techniques with theories from forensic psychology and criminology. The statistical methods employed (including multi-dimensional scaling, binary logistic regression) and ‘data-mining’ technologies (including neural networks) are used to investigate the impact of the types of evidence available and to determine the causality in this domain. The final predictions on the likelihood of burglary are calculated by combining all of the varying sources of evidence into a Bayesian belief network. This network is embedded in the developed software system, which also performs data cleansing and data transformation for presentation to the developed algorithms. It is important that derived statistics from the software and predictions are interpretable by the intended users of the decision support system, namely Police sector managers, and this paper includes some of the design decisions based upon the forensic psychology and criminology literature, including the graphical representation of geographic data and presentation of results of analyses.

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.

Matching Crimes Using Burglars' Modus Operandi: A Test of Three Models

’Hard’ forensic evidence (eg DNA) may be the best means of linking crimes, but it is often absent at burglary crime scenes. Modus operandi information is always present to some degree, but little is known of its significance in matching burglaries. This paper evaluates the ability of three algorithms to match a target crime to the actual offender within a database of 966 offences. The first (RCPA) uses only MO information, the second (RPAL) only temporal and geographic data and a third (COMBIN) is a combination of the two. A score of one indicates a perfect match between the target crime and the case selected by the algorithm. The lowest possible rank is 965 showing that 965 cases were selected before the target offence. The RPAL and COMBIN each achieve a perfect match for 24 per cent of the crimes and succeed in matching over half of the crimes at a score of 10 or less. For prolific offenders, using MO information alone is better than temporal and geographic data, although the best performance is achieved when in combination. Behavioural, spatial and temporal information is collected by many Police Services. The value and means of utilising such data in linking crimes is clearly demonstrated.

Matching and Predicting Crimes

Our central aim is the development of decision support systems based on appropriate technology for such purposes as profiling single and series of crimes or offenders, and matching and predicting crimes.

Applying the Concept of Revictimization: Using Burglars' Behaviour to Predict Houses at Risk of Future Victimization:

A good predictor of a domestic burglary is whether the property suffered a prior victimization. Using officially reported burglaries, most houses appear to be victimized once and most repeat victims suffer twice only. Defining high-risk properties by waiting for the second burglary has its operational limitations. A police database of burglaries over 45 months is examined to explore whether the modus operandi distinguishes houses burgled once only, from those suffering a revictimization. The use of force, searching behaviour, type of property, place of entry, place of exit, alarm activation and use of a bogus official method of entry are discriminating features. Comparing non-repeats with ‘quick’ Repeats (ie within 365 days), searching behaviour, type of property, entry method and a bogus official strategy are discriminating features. Survival analyses on the latter group reveals that ramming and removing glass are significantly associated with being revictimized sooner rather than later. Conversely, exit via a window indicates a longer period to revictimization. The findings demonstrate the value of crime scene information held by the police and, when guided by an appropriate criminological or operational framework, the benefits of more substantive analyses to prevention and detection initiatives.

Data mining and crime analysis

An essential component of criminal investigation involves the interrogation of large databases of information held by police and other criminal justice agencies. Data mining and decision support systems have an important role to play in assisting human inference in this forensic domain that creates one of the most challenging decision‐making environments. Technologies range widely and include social network analysis, geographical information systems, and data mining technologies for clustering crimes, finding links between crime and profiling offenders, identifying criminal networks, matching crimes, generating suspects, and predicting criminal activity. This paper does not intend to cover the gamut of techniques available to the investigator of crime as this has been presented elsewhere (Oatley GC, Ewart BW, Zeleznikow J. Decision support systems for police: lessons from the application of data mining techniques to ‘soft’ forensic evidence. Artif Intell Law 2006, 14:35–100). Rather, the objective is to highlight issues of implementation and interpretation of the techniques available to the crime analyst. To this end, the authors draw from their experiences of working with real‐world crime databases (Oatley GC, Belem B, Fernandes K, Hoggarth E, Holland B, Lewis C, Meier P, Morgan K, Santhanam J, et al. The gang gun‐crime problem—solutions from social network theory, epidemiology, cellular automata, Bayesian networks and spatial statistics. Accepted: book chapter for IEEE publication Computational Forensics; 2008; Oatley GC, McGarry K, Ewart BW. Offender network metrics. WSEAS Trans Inf Sci Appl 2006, 3:2440–2448; Oatley GC, Ewart BW. Crimes analysis software: pins in maps, clustering and Bayes net prediction. Expert Syst Appl 2003, 25:569–588), involving gun and gang crime, fraud, terrorism, burglary, and retail crime.

From links to meaning: a burglary data case study

Our central aim is the development of decision support systems for purposes such as profiling single and series of crimes or offenders, and matching and predicting crimes. This paper presents research in this area for the high-volume crime of Burglary Dwelling House, examining the operational use of networks and the metric of brokerage from the social network analysis literature. Our work builds upon several years of experimentation using forensic psychology guided exploratory techniques from artificial intelligence, statistics and spatial statistics.

Criminal networks and spatial density

The authors work in this area [2,6,7], in collaboration with West Midlands Police (WMP), is with the high volume crime of Burglary from Dwelling Houses (BDH). The presented work involves the brokerage metric from social network analysis combined with a geographical component (not present in other approaches) to add to the interpretation of the network and its key players. Our work builds upon several years of experimentation using forensic psychology guided exploratory techniques from artificial intelligence, statistics and spatial statistics.