NSF Org: |
IIS Div Of Information & Intelligent Systems |
Recipient: |
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Initial Amendment Date: | September 4, 2020 |
Latest Amendment Date: | July 14, 2021 |
Award Number: | 2039951 |
Award Instrument: | Standard Grant |
Program Manager: |
Wendy Nilsen
wnilsen@nsf.gov (703)292-2568 IIS Div Of Information & Intelligent Systems CSE Direct For Computer & Info Scie & Enginr |
Start Date: | January 1, 2021 |
End Date: | December 31, 2022 (Estimated) |
Total Intended Award Amount: | $249,998.00 |
Total Awarded Amount to Date: | $265,998.00 |
Funds Obligated to Date: |
FY 2021 = $16,000.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
3112 LEE BUILDING COLLEGE PARK MD US 20742-5100 (301)405-6269 |
Sponsor Congressional District: |
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Primary Place of Performance: |
MD US 20742-3370 |
Primary Place of Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | D-ISN-Illicit Supply Networks |
Primary Program Source: |
01002122DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
Wildlife trafficking occurs in both physical and virtual (both open and dark) crime ecosystems; its illicitness is typically masked by the legal wildlife trade. The security and economic impacts of unfettered wildlife trafficking are existential threats to the U.S. To begin to address these impacts, information gathering and analysis are needed; for example, machine learning tools to aid discovery and help trace the status and trends of illegal goods could significantly support law enforcement at U.S. Ports of entry. The promise of inferences to bolster the disruption of wildlife trafficking networks depends on a scientific community with capacity to distinguish 1) legal from illegal trade; 2) the financial, and 3) flows of other illicit goods within and across crime ecosystems. This research converges engineering, computer and data science, and social science in a deliberate fashion to improve understanding of illicit supply network operations and strengthen ability to detect, disrupt and dismantle them. This proposal integrates operational, computational, financial, social, cultural, and economic expertise to build new research capacity to: 1) identify analytically relevant data; 2) leverage united data and predictive methods to draw associations and make inferences about interventions to combat wildlife trafficking; 3) expand the research community by suggesting novel research problems and directions, engaging civil society, federal agencies, and private or non-profit entities; and 4) crystalize research questions for the future. Although the team will focus on wildlife, the applicable methodology and research questions are transferable to other problems such as human trafficking.
The project?s four-phase research approach: 1) distills relevant analytic parts of the problem to diverse experts; 2) initiates team and research capacity building activities to enable analytically relevant unification of data; 3) implements activities to collate, organize, and identify ways to analyze collected data through three strategic face-to-face meetings, world cafe?s, a literature review and informational interviews; and 4) catalyzes research questions through a wrap-up brainstorming session. Central to this proposal are two undergraduate interdisciplinary team-projects directly responding to needs identified by the anti-wildlife trafficking community and lying at the intersection of science and society. The convergence of expertise in environmental crimes, computer science, conservation biology, operations modeling and analytics contributes to advancing knowledge in at least three fundamental ways by: 1) understanding the landscape of physical and virtual criminal ecosystems; 2) assessing data, technical and scientific needs associated with linking the ecosystems, and 3) developing a strategy to deploy intelligent techniques (e.g., information retrieval, analytics, AI and engineering) to characterize and disrupt wildlife trafficking networks. Three strategy meetings will generate in-depth discussion among experts from various fields (e.g., social science, computer science, data science, engineering) and organizations (e.g., parastatals, foundations, civil society organizations, universities, private sector industries and government agencies), and open new research directions and questions, illustrating the relevance of science for disrupting wildlife trafficking networks to our research community. Future research agendas may enhance discovery of other illicit supply chain activities that help meet national security, law enforcement and economic development needs and policies.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This 18-month National Science Foundation-funded planning grant (Division of Information & Intelligent Systems Award IIS-2039951) was a new collaboration between University of Maryland, Worcester Polytechnic Institute and Focused Conservation Solutions.Our goal was to build research capacity for creating an integrative team of scientists, policymakers and domain experts to disrupt wildlife trafficking networks through convergence of physical and virtual ecosystems.Wildlife traffickers operate across physical and virtual crime ecosystems with growing proficiency and agility, posing serious risks to national, environmental, and human security.Our goal was to build research capacity for creating an integrative team of scientists, policymakers and domain experts to disrupt wildlife trafficking networks through convergence of physical and virtual ecosystems. The knowledge gaps we were interested in accessessing were: (1) The evidentiary burden of wildlife trafficking in physical and virtual ecosystems; (2) How data can establish causality and inference. Our planning mission addressed how a new team of operations engineers, computer scientists and social scientists could come together to resolve and validate data for disrupting wildlife trafficking to help/support responses of authorities and experts.
Planning activities included a World Caf? with 47 researchers and practitioners from 6 continents, workshops with 28 people from 2 continents, #Data4Wildlife Hackathon with 97 students and working professionals from 28 countries and 11 senior practitioners as mentors. Planning grant Workshops and World Caf? affirmed that disrupting WISNs requires sustainable evidence ?meaningful data that can meet legal thresholds throughout a cyber-physical crime ecosystem which links geographically or cyber-located source locations, transit methods and means, and retail sales; and parsing legal from illegal transactions and activities. Our research community confirmed that an avalanche of superfluous data related to the explosion of wildlife crime coupled with the prevalence of wildlife trafficking in cyber spaces has created a ?data soup? that challenges authorities in their attempt to parse out relevant material to drive investigations and prosecutions. Our #DataforWildlife Hackathon revealed data can be ready to use, of limited availably or not available/difficult to acquire. Our team, led by WPI undergraduates, created a natural language processing tool (NLP) currently in use by the 10 countries and 81 regional NGOs comprising the International Union for Conservation of Nature in West and Central Africa (IUCN-PACO) after being presented to 30 Secretaries of the Environment from Africa at the Lusaka Agreement Task Force meeting, 03/2022.
Last Modified: 04/01/2023
Modified by: Meredith L Gore
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