SLC.20.010 – A transdisciplinary data-driven approach to create a resilient illicit drug trafficking and production fighting system

Route: Smart, liveable cities

Cluster question: 058 What are the patterns and causes of crime and how can we influence them?

Illicit drug trafficking and production (DTP) are the most profitable criminal activity of organised European crime groups (2019 EU Drugs Market Report). A persistent problem with organised crime is the mix of under- and upperworld activities (undermining), referred to as hidden (instead of high) impact crime. This makes it hard for law enforcement agencies to 1) detect DTP, 2) make sense of and prioritize the large number of signals of DTP, and 3) determine whether their approach to fight DTP is effective. Moreover, the DTP network is a complex adaptive system: if you tackle one link of the system it will easily adapt and morph into a new one. So, to understand and prevent DTP, means to unite forces and match their behaviour by making a resilient crime fighting system that respects the rule of law. To achieve this, we propose a transdisciplinary (combining different scientific disciplines, including social and data sciences, and involving societal stakeholders), data-driven (e.g., by using sensors, sniffer drones, machine learning) approach in which we focus on the complete crime fighting chain: 1) Responding: Determine efficient and effective ways to detect suspicious activity; 2) Monitoring: Decipher patterns in the DTP network; 3) Anticipating: Establish ways to forecast DTP activities to prepare the crime fighting system and people within it; and 4) Learning: Evaluate current practices to combat DTP in terms of their proposed effects, taking into account possible tensions that exist between crime fighting and the rule of law. Make our (theoretical) findings workable for practice and policy via lessons learned and technological solutions. To be able to address the complete chain, we will create a consortium with all the relevant stakeholders that are responsible for the response (e.g., police, FIOD), monitoring (e.g., CBS, Kadaster), anticipation (e.g., CCV, LIEC-RIEC), and learning (e.g., police academy).

Keywords

Big Data, drugs, resilience, undermining crime

Other organisations

Dutch Police Academy, Safety region Twente, Saxion Hogeschool, Vrije Universiteit Amsterdam (VU)

Submitter

Organisation Universiteit Twente (UT)
Name Prof. dr. E. (Ellen) Giebels
E-mail e.giebels@utwente.nl
Website https://people.utwente.nl/e.giebels