权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

D-ISN: TRACK 2: Disrupting Wildlife Trafficking Networks through Convergence of Physical and Virtual Ecosystems

D-ISN：轨道 2：通过物理和虚拟生态系统的融合扰乱野生动物贩运网络

基本信息

批准号：
2039951
负责人：
Meredith Gore
金额：
$ 25万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2022-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039951&HistoricalAwards=false
关键词：
ISN TRACK Disrupting Wildlife Trafficking

项目摘要

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 cafés, 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.

野生动物贩运发生在现实和虚拟（公开和黑暗）犯罪生态系统中;其非法性通常被法律的野生动物贸易所掩盖。为了开始应对这些影响，需要收集和分析信息;例如，机器学习工具可以帮助发现并帮助追踪非法货物的状态和趋势，这可以大大支持美国入境口岸的执法。推断支持破坏野生动物贩运网络的承诺取决于科学界是否有能力区分1）法律的与非法贸易; 2）金融，以及3）犯罪生态系统内和跨犯罪生态系统的其他非法货物流动。这项研究融合了工程学、计算机和数据科学以及社会科学，旨在提高对非法供应网络运作的理解，并加强检测、破坏和拆除这些网络的能力。该提案整合了运营、计算、金融、社会、文化和经济方面的专业知识，以建立新的研究能力，从而：1）识别分析相关数据; 2）利用统一的数据和预测方法，对打击野生动物贩运的干预措施进行关联和推断; 3）通过提出新的研究问题和方向，让民间社会、联邦机构和私人或非营利实体参与进来，扩大研究界;（4）明确未来的研究问题。虽然该小组将侧重于野生动物，但适用的方法和研究问题可以转移到人口贩运等其他问题。该项目的四阶段研究方法：1）将问题的相关分析部分提炼给不同的专家; 2）启动团队和研究能力建设活动，以实现数据的分析相关统一; 3）通过三次战略性面对面会议、世界咖啡馆、文献综述和信息访谈，开展整理、组织和确定分析收集数据的方法的活动;以及4）通过总结性的头脑风暴会议来催化研究问题。该提案的核心是两个本科跨学科团队项目，直接响应反野生动物贩运社区确定的需求，并处于科学与社会的交叉点。环境犯罪、计算机科学、保护生物学、操作建模和分析方面的专业知识的融合有助于至少在三个基本方面推进知识：1）了解物理和虚拟犯罪生态系统的景观; 2）评估与连接生态系统相关的数据、技术和科学需求; 3）制定部署智能技术的战略（例如，信息检索，分析，人工智能和工程），以表征和破坏野生动物贩运网络。三次战略会议将在来自不同领域的专家之间进行深入讨论（例如，社会科学、计算机科学、数据科学、工程）和组织（例如，这些活动包括与私营部门（如准国营机构、基金会、民间社会组织、大学、私营部门行业和政府机构）的合作，并开辟了新的研究方向和问题，说明了科学在破坏野生动物贩运网络方面与我们研究界的相关性。未来的研究议程可能会加强对其他非法供应链活动的发现，这些活动有助于满足国家安全、执法和经济发展的需求和政策。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。