Building Multi-modal Crime Profiles with Growing Self Organising Maps

Profiling is important in law enforcement, especially in understanding the behaviours of criminals as well as the characteristics and similarities in crimes. It could provide insights to law enforcement officers when solving similar crimes and more importantly for pre-crime action, which is to act before crimes happen. Usually a single case captures data from the crime scene, offenders, etc. and therefore could be termed as multi-modality in data sources and subsequently has resulted a complex data fusion problem. Traditional criminal profiling requires experienced and skilful crime analysts or psychologists to laboriously associate and fuse multi-modal crime data. With the ubiquitous usage of digital data in crime and forensic records, law enforcement has also encountered the issue of big data. In addition, law enforcement professionals are always competing against time in solving crimes and facing constant pressures. Therefore, it is necessary to have a computational approach that could assist in reducing the time and efforts spent for the laborious fusion process in profiling multi-modal crime data. Besides obtaining the demographics, physical characteristics and the behaviours of criminals, a crime profile should also comprise of crime statistics and trends. In fact, crime and criminal profiles are highly interrelated and both are required in order to provide a holistic analysis. In this chapter, our approach proposes the fusion of multiple sources of crime data to populate a holistic crime profile through the use of Growing Self Organising Maps (GSOM).

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