EAGER: ISN: A Data Analytic Approach to Understanding Human Trafficking Networks

人口贩运对包括美国在内的许多国家的国家和国际安全构成严重威胁。人口贩运是为获取金钱或利益而对人类进行商业交换和剥削的犯罪行为，是一个普遍和恶意的人权问题，也是跨国犯罪的驱动因素。由于人口贩运发生在公众看不到的非法供应网络中，因此目前对人口贩运网络的结构和特征的了解有限。运筹学模型有可能通过提高有限的反贩运资源的效率，大大提高我们破坏人口贩运网络的能力，但由于对人口贩运网络的结构和联系知之甚少，它们的影响在很大程度上仍处于休眠状态，尚未得到探索。这一探索性研究的早期概念拨款（EAGER）将通过研究边境管制和过境中心拦截数据，从贩运者和受害者的角度确定劳动力和性贩运网络的关键方面，解决了解和减轻人口贩运的操作方法。数据分析和操作方法将由一个由业务研究、人口贩运和执法专家组成的跨学科小组开发和评估。通过更好地理解与传统供应链网络的相似性和主要差异，对劳动力和性交易网络构成的新见解将增强相关分析模型。该奖项将支持制定跨境人口贩运网络的高保真时空分析模型的研究。所开发的方法将结合数据科学和社会科学方法，并将纳入不同的地理和社会网络拓扑结构，考虑区域、文化和人口异质性。已经收集了六年的跨界拦截数据，包括350多个数据字段，其中包括详细的受害者和贩运者人口信息、始发地和目的地、招募方法和过境路线。分析结果将由描述招聘策略的定性人种学技术以及区域和文化的细微差别提供信息。从这一分析中获得的见解将作为平台，为从事人口贩运领域工作的业务研究人员构建了解情况的模型，并有望帮助向监督边境管制和执法机构以及反贩运组织资源分配的决策者提供信息。该奖项反映了美国国家科学基金会的法定使命，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。

Human trafficking poses a grave threat to national and international security of many countries, including the United States. Human trafficking is the criminal commercial exchange and exploitation of humans for monetary gain or benefit, and is a prevalent and malicious human rights issue as well as a driver of transnational crime. Because human trafficking occurs within illicit supply networks that are hidden from the public eye, insights into the structure and characteristics of human trafficking networks are currently limited. Operations research models have the potential to dramatically improve our ability to disrupt human trafficking networks by increasing the efficiency of limited anti-trafficking resources, but their impact remains largely dormant and unexplored, as the structure and linkages of human trafficking networks is little understood. This EArly-concept Grant for Exploratory Research (EAGER) will address operational methods to understand and mitigate human trafficking by studying border control and transit center intercept data to identify key aspects of labor and sex trafficking networks from the perspective of traffickers and victims. The data analysis and operational methods will be developed and evaluated by a transdisciplinary team of operations research, human trafficking, and law enforcement experts. New insights into the composition of both labor and sex trafficking networks will enhance related analytical models by better understanding the similarities and key differences with traditional supply chain networks. This award will support research to formulate high fidelity spatiotemporal analytical models of cross-border human trafficking networks. The methods developed will combine data science and social science approaches and will incorporate different geographic and social network topologies, accounting for regional, cultural and population heterogeneity. Six years of cross-border interception data has been collected, consisting of over 350 data fields with detailed victim and trafficker demographic information, origin and destination points, recruitment methods and transit routes. Analytical results will be informed by qualitative ethnographic techniques that describe recruitment tactics, and regional and cultural nuances. Insights from this analysis will serve as platforms on which to construct context-informed models for operations researchers working in the domain of human trafficking, and are expected to help inform policy makers that oversee resource allocation in border control and law enforcement agencies, as well as anti-trafficking organizations.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.