基于网络的可卡因贩运流动建模与位移效应研究

中文摘要 AI 辅助

全球可卡因市场正经历前所未有的激增。关于可卡因如何从生产国贩运至消费市场的信息十分有限，且往往仅基于观察到的贩运路线（例如缉获数据）。本文提出了一种基于网络的模型，用于刻画实际跨国可卡因流动，而不仅限于被缉获的流动。在该模型中，可卡因通过由陆路和海路连接构成的国际运输网络进行路由，每条链路被赋予一个针对集装箱运输的拦截风险指标。该拦截风险指标利用主成分分析（PCA）将执法检查、集装箱贸易和运输连通性相关的特征整合为拦截风险的三个关键驱动成分。随后，每条链路上的拦截风险指标由这些驱动成分构建，其中每个成分的重要性通过一个强度参数来表征。给定一组强度参数，假设可卡因从生产国到消费国的路由选择遵循最小化总拦截风险的路径，并受各国供需约束的限制。最后，通过将相应的流动结果与已知贩运活动进行对比，对强度参数进行校准。基于与校准参数相关的最终贩运流动，我们能够识别“盲点”（例如，在观测到少量或未观测到贩运活动的链路上存在非零流动），并为水床效应提供更多证据（例如，因执法部门加大拦截力度而导致贩运路线发生变化）。

英文摘要

The worldwide cocaine market is undergoing an extraordinary surge. Insights on how cocaine is trafficked from production countries to consumer markets are limited, and often based on observed trafficking routes (e.g., seizures) alone. In this paper, we introduce a network-based model of actual transnational cocaine flows, beyond flows seized. In this model, cocaine is routed through an international transportation network of land and sea connections, where each link is assigned an interception risk metric for containerized transport. This interception risk metric combines features related to law enforcement inspections, containerized trade, and transport connectivity into three key driving components of interception risk using Principal Component Analysis (PCA). The interception risk metric on each link is then constructed from these driving components, where the importance of each component is characterized by a strength parameter. Given a set of strength parameters, cocaine is assumed to be routed from production countries to consumption countries via routes that minimize the total risk of interception, subject to country-specific supply and demand constraints. In the end, the strength parameters are calibrated by comparing the corresponding flow outcomes with known trafficking activity. From these final trafficking flows associated with the calibrated parameters, we are able to identify `blind spots' (e.g., nonzero flows on links with small or none observed trafficking activity) and provide more evidence of the waterbed effect (e.g., changing routes as a result of increased interdiction efforts by law enforcement).

发表机构

• Delft University of Technology（代尔夫特理工大学）
• Erasmus University Rotterdam（鹿特丹伊拉斯姆斯大学）
• Netherlands Defence Academy（荷兰国防学院）
• Tilburg University（蒂尔堡大学）

机构由 AI 辅助整理，请以论文原文为准。