Integrating forensic information in a crime intelligence database

Abstract

Since 2008, intelligence units of six states of the western part of Switzerland have been sharing a common database for the analysis of high volume crimes. On a daily basis, events reported to the police are analysed, filtered and classified to detect crime repetitions and interpret the crime environment. Several forensic outcomes are integrated in the system such as matches of traces with persons, and links between scenes detected by the comparison of forensic case data. Systematic procedures have been settled to integrate links assumed mainly through DNA profiles, shoemarks patterns and images. A statistical outlook on a retrospective dataset of series from 2009 to 2011 of the database informs for instance on the number of repetition detected or confirmed and increased by forensic case data. Time needed to obtain forensic intelligence in regard with the type of marks treated, is seen as a critical issue. Furthermore, the underlying integration process of forensic intelligence into the crime intelligence database raised several difficulties in regards of the acquisition of data and the models used in the forensic databases. Solutions found and adopted operational procedures are described and discussed. This process form the basis to many other researches aimed at developing forensic intelligence models.

Digital transformations and the viability of forensic science laboratories: Crisis-opportunity through decentralisation

“…Many research and operational projects already illustrate the concept. They are linked to areas as diverse as high volume crimes (Rossy et al 2013), illicit markets (illicit drugs, counterfeit materials) and false ID documents (Baechler et al 2015), as well as internet and computer-related crimes (Pineau et al 2016) .…”

The polymorphism of crime scene investigation: An exploratory analysis of the influence of crime and forensic intelligence on decisions made by crime scene examiners

“…Forensic case data plays a key role in feeding the sustained process operated by IUs and the CICOP. Integrated into a shared database, they help detect and monitor a significant number of crime series and improve knowledge on typical and pervasive forms of criminality [34].…”

The CriLiM Methodology: Crime Linkage with a Fuzzy MCDM Approach

“…The distribution of crimes is of 2 per offender on average (with a minimum of 1, a maximum of 52, and a standard deviation of 3.14). A detailed distribution of the series and the relations between the events of the entire data set is presented in [3]. The reason why we chose this subset is that we only considered the crimes that met some basic requirements to compute a similarity, i.e., an offender, a location, a date, and a modus operandi being not null.…”

“…It is also based on the assumption that the offender physically leaves a trace of a satisfactory quality in more than one crime scene. Nevertheless, forensic information is much more reliable and always more integrated into intelligence databases [3].…”