Mapping TOC through text mining application on judiciary data set

Cartography of crime has a rather established tradition in criminological literature. As well known, the spatial dimension of transnational organized crime is historically addressed through statistical, quantitative and computerized tools. The paper proposes a new statistical tool to map and characterize TOC, a text mining methodology, by recalling those crime mapping exercises. The Authors apply text mining methodology to the judiciary data set derived from the case law dealing with organized crime and related unlawful phenomena. The paper aims to show how the proposed methodology provides a comprehensive set of quantitative and qualitative information about the observed phenomena. Considering its potentiality in characterizing TOC through judiciary data set, the method can contribute to an evidence-based design of indicators and policies to prevent TOC more efficiently. It could also be an essential tool for strengthening intelligence, police and judiciary enforcement strategies. Regarding indicators, through the linkage with official databases (e.g. ORBIS or ASIA), it is possible to identify the dynamics of TOC networks (actors involved; related offences; behavioral clusters; business or social context where they occur). Based on this information, we can make risk correlations and proxy indicators dealing with TOC (e.g. geo-risk indicators). Text mining has to be considered a neutral methodology regarding the nature of a given criminal phenomenon (drug crimes, human trafficking, smuggling, corruption organized crime) and its legal definition being able to catch the multi-spatialities of TOC beyond the different how definitions of TOC