November 30, 2022 - Podcast

Episode 327 — Using data science to stop wildlife trafficking

Interpol estimates the illegal trade in wildlife to be worth up to $20 billion annually. Wild flora and fauna can be exploited by criminals along the entire supply chain, from poaching and transportation to processing and selling. Other illegal activities are often associated with wildlife crimes, including money laundering, corruption and document fraud.

Indiana University Data Science Professor Sunandan Chakraborty hopes to help disrupt the wildlife trafficking networks with a research project that was recently awarded over $200,000 from the National Science Foundation.

The project aims to take an interdisciplinary approach to the discovery, analysis and disruption of wildlife trafficking networks.

Researchers will seek to catalyze technological innovations by creating tools that empower experts to continuously discover and obtain actionable insights by exploring the wealth of data related to illicit networks.

Chakraborty says the project will advance our nation’s ability to counter wildlife trafficking activities through novel approaches for data discovery, analytics and modeling. Specifically, it will contribute new algorithms that provide capabilities to discover and automatically collect data related to wildlife trafficking from multiple platforms at an unprecedented scale. New algorithms will also enable the use these data to build computational models and study wildlife trafficking patterns and networks at the global level.

Chakraborty says that through the use of analytical techniques such as crime mapping, quantitative data analysis, and social network analysis, the project will address research questions related to the scale and the nature of illicit wildlife trade and the network structures of online wildlife trafficking.

Additionally, the project will also promote the progress of research in criminal activities that have an online footprint. Data collected over the course of the project will be made publicly available through a dataset search engine, making it possible for other researchers to enrich data-driven analyses through the dynamic discovery and linkage of previously unknown data, and allowing them to answer important questions.

Chakraborty says the research team's collaborations 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.