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Detecting Illegal Timber Trade

A collaboration between the Discovery Analytics Center, Virginia Tech, TRAFFIC & World Wildlife Fund
"Studies indicate that the illegal trade in wildlife and timber may help finance terrorism and organized crime across the world."

"As a result of weak forest governance, illegal timber accounts over 70% of some countries’ timber exports, such as Peru, Bolivia and the DRC."

"...particularly those [countries] in Africa and South America, between 50 and 90% of timber is harvested and traded illegally."

Analysis Case Studies

About

Illegal timber trade has adverse ecological as well as economic effects. Trafficking in wildlife, plant, and plant products (such as paper, timber) is outlawed under the US Lacey Act (originally introduced in 1990 and amended as recently as 2008). In addition to outlawing the trading of certain categories of products, the law also requires importers to declare the origin country, name, and quantity of products imported in other categories. The Lacey Act is considered a highly effective act, best evidenced by the $13M fine imposed in 2016 by the US Department of Justice on the Virginia company Lumber Liquidators Inc. for importing hardwood manufactured in China from timber illegally logged in Eastern Europe. Agencies tasked with enforcement of laws like the Lacey Act require automated tools to better identify "red flags" for potentially illegal timber shipments, utilizing timber trade data and other publicly available data. Such tools do not exist currently and thus there is a critical need for a better decision support system for assessing trade data for potential anomalies.

Our Solution

We present a machine learning solution to flag instances of potentially suspicious records from trade data. Our system merges disparate data sources such as IUCN Redlist, CITES, and trade data from Panjiva Inc. We use a deep learning based embedding approach to conduct unsupervised anomaly detection. Our user interface allows the analyst to visualize trade data using Sankey diagrams and inspect identified anomalies. To support user understanding, it allows the display of the anomalous records in the context of nearby records.

Team


Debanjan Datta
Discovery Analytics Center
Virginia Tech
Nathan Self
Discovery Analytics Center
Virginia Tech
Naren Ramakrishnan
Discovery Analytics Center
Virginia Tech




Amelia Meadows
World WildLife Fund
John Simeone
World WildLife Fund
Amy Smith
World WildLife Fund
Linda Walker
World WildLife Fund




Willow Outhwaite
TRAFFIC
Hin Keon Chen
TRAFFIC