D-ISN/​Collaborative Research: An Interdisciplinary Approach to the Discovery, Analysis, and Disruption of Wildlife Trafficking Networks

Indiana University, Bloomington IN

Investigators

Abstract

This Disrupting Operations of Illicit Supply Networks (D-ISN) project aims to address the illegal trade in wild animals. Wildlife trafficking is one of the most common illicit activities globally and poses a substantial human cost along with detrimental social and economic impacts, including increased crime, violence, and environmental destruction. The COVID-19 pandemic, likely the result of a virus that spread to humans from a wildlife market, demonstrates that wildlife trafficking can have serious public health and biosafety implications. This project seeks to catalyze technological innovations by creating tools that empower domain experts to continuously discover and obtain actionable insights by exploring the wealth of data related to illicit networks that spread over multiple sources. The project will advance our Nation's ability to counter wildlife trafficking activities through novel approaches for data discovery, analytics, and modeling. The project will also promote the progress of research in criminal activities that have an online footprint. Data collected in the course of the project will be made publicly available through a dataset search engine, making it possible for researchers to enrich data-driven analyses through the dynamic discovery and linkage of previously unknown data, and allowing them to answer important questions. The project team's collaboration with non-governmental organizations and discussions with law enforcement agencies will facilitate an interactive process that can fine-tune disruption techniques and suggest pragmatic real-world implementation strategies and policy recommendations. The project uses an interdisciplinary approach – combining methods and tools from computer science and engineering as well as wildlife criminology to advance the state of the art and build fundamental knowledge in methods for the discovery and exploration of data related to illicit activities with an online footprint, as well as enhance wildlife trafficking research. Specifically, this project contributes new algorithms that provide capabilities to: 1) discover and automatically collect data related to wildlife trafficking from multiple platforms at an unprecedented scale; and 2) use these data to build computational models and study wildlife trafficking patterns and networks at the global level. Through the use of analytical techniques such as crime mapping, quantitative data analysis, and social network analysis, this project will address research questions related to the scale and the nature of illicit wildlife trade, network structures of online wildlife trafficking, and empirically-driven disruption models that can be used to best tackle them. The algorithms are adaptable to different domains and data, support the discovery of both unstructured data and structured datasets, and will serve as the basis for usable tools that empower domain experts to continuously discover and monitor relevant data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.