The CriLiM Methodology: Crime Linkage with a Fuzzy MCDM Approach

Abstract

Grouping events having similarities has always been interesting for analysts. Actually, when a label is put on top of a set of events to denote they share common properties, the automation and the capability to conduct reasoning with this set drastically increase. This is particularly true when considering criminal events for crime analysts; conjunction, interpretation and explanation can be key success factors to apprehend criminals. In this paper, we present the CriLiM methodology for investigating both serious and high-volume crime. Our artifact consists in implementing a tailored computerized crime linkage system, based on a fuzzy MCDM approach in order to combine spatio-temporal, behavioral, and forensic information. As a proof of concept, series in burglaries are examined from real data and compared to expert results.

Change points detection in crime-related time series: An on-line fuzzy approach based on a shape space representation

“…In the domain of crime analysis, such flexible and intuitive approaches for change points detection are particularly sought, especially for crime trends monitoring. Previous studies from the authors ( [1], [2], [3], and [4]) emphasize on the usefulness of crime trends monitoring activities and advocate the use of appropriate methods for considering the specificities and the constraints of the crime analysis domain, that is basically dealing with uncertainties. The automated process of change points detection is considered as a major step in the production of intelligence, supporting the activity of crime analysis (also sometimes referred to crime intelligence).…”

“…. , y 10 ), the change points x detected are x 3 and x 7 ; then x = (3, 7), W(s) = {s (1) , s (2) , s (3) }, with s (1) = (y 0 , y 1 , y 2 , y 3 ), s (2) = (y 4 , y 5 , y 6 , y 7 ), s (3) = (y 8 , y 9 , y 10 ). It has to be emphasized that these contiguous windows do not need to have the same size.…”

“…8). A more detailed description of this data set can be found in [3]. The segmentation settings are identical for both time series of the data set: values are normalized, two disjunctive thresholds are set (th DPU = 0.05 and th SSS = 2; only one threshold need to be exceeded to set a new segment), and K is set to 5.…”

Linking Foraging Domestic Burglary: An Analysis of Crimes Committed Within Police-Identified Optimal Forager Patches

“…Despite its importance, until recently only limited academic research had been conducted that examined the effectiveness of crime linkage through non-tangible information such as behavioural characteristics, target selection and offence location (Bennell and Jones 2005;Bennell and Canter 2002;Santtilla andKorpela et al 2004 andTonkin et al 2008). It is only within the last decade that the field of crime linkage has begun to forge its own corner within criminological study and this has been enabled and supported by extensive further research (Albertetti et al 2013;Bennell et al 2010a, b;Borg et al 2014;Bouhana et al 2016;Fox and Farrington 2014;Woodhams et al 2019;Reich and Porter 2015;Salo et al 2013;Tonkin 2012;Tonkin and Woodhams 2017;Turvey and Freeman 2016;Wang et al 2015 andZoete et al 2015) and creation of a dedicated crime linkage international network (C-LINK, 2021). What a number of these previous studies have suggested is that contrary to what one might expect it is actually inter-crime distance, as opposed to the modus operandi of an offender (Bennell and Jones 2005: 23) which is the most accurate linkage feature, particularly when researching serious acquisitive crime such as burglaries and stealing from motor vehicles (Bennell and Jones 2005;Bennell and Canter 2002;Davies et al 2012;Santtilla and Korpela et al 2004;Tonkin et al 2008;Tonkin et al 2008;.…”