巴西可卡因走私的环境扫描:来自地理空间智能和自然语言处理方法的证据
Jairo Jesús Pinto Hidalgo1, Jorge Antonio Silva Centeno2
1Technical expert, United Nations Office on Drugs and Crime (UNODC) - Brazil; Researcher - Postgraduate Program in Geodetic Sciences, Federal University of Paraná, UFPR, Curitiba, Paraná, Brazil.
Science & justice : journal of the Forensic Science Society
|November 29, 2023
概括
这项研究使用开源数据和地理空间智能来绘制巴西可卡因走私路线的地图. 这些发现有助于了解毒品流动,并为预防策略提供信息.
科学领域:
- 地理信息科学 地理信息科学
- 犯罪学 犯罪学
- 公共卫生 公共卫生
背景情况:
- 贩运可卡因对国家安全和公共卫生构成重大风险.
- 了解人口贩运路线对于阻止和政策制定至关重要.
- 关于贩毒组织 (DTO) 传统数据的有限可用性阻碍了分析.
研究的目的:
- 开发一种方法来收集和处理有关可卡因扣押的开源数据.
- 通过地理空间分析,确定巴西的趋势和潜在的可卡因走私流.
- 展示开源情报对贩毒监控的有用性.
主要方法:
- 从巴西联邦政府新闻网站收集数据.
- 自然语言处理 (NLP) 和地理空间智能的应用.
- 从2022年开始创建一个数据集,包含939个可卡因扣押记录.
主要成果:
- 确定了巴西境内可卡因走私的趋势和潜在流动.
- 结果与有关毒品贩运模式的现有文献一致.
- 证明了开源信息对知识生成的有效性.
结论:
- 开源数据分析是毒品贩运研究中环境扫描的宝贵工具.
- 拟议的方法可以补充传统的监测工作.
- 调查结果支持制定加强的预防和执法政策.
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