基于Web of Science的痕迹类物证统计学量化研究文献可视化分析  

A Visual Analysis of Literature on Statistical Quantifi cation Studies of Trace Evidence Using Web of Science

作　　者：袁颖 YUAN Ying(Guizhou Police College,Guiyang 550005,China)

机构地区：[1]贵州警察学院,贵阳550005

出　　处：《刑事技术》2025年第2期197-205,共9页Forensic Science and Technology

基　　金：贵州省科技计划项目(黔科合基础-zk[2024]一般606)。

摘　　要：本文以Web of Science核心数据库为检索数据集,以VOSviewer1.6.20和CiteSpace6.2R6可视化分析软件为研究工具,检索了痕迹类物证统计学量化研究相关文献509篇,涉及57个国家、976个机构、267份期刊,从发文量、发文载体、关键词共现、关键词聚类四个方面对本领域文献各个节点进行了梳理,以详尽直观地了解痕迹类物证统计量化研究脉络与前沿热点。研究结果表明,痕迹类物证的统计量化方法研究文献量在过去几十年间呈现波动式增长;欧洲国家对于这一主题有较多的合作关系,形成了一个较为紧密的区域合作网络,荷兰法庭科学研究所为发文量最高的机构;指纹是痕迹类物证统计量化研究的重要对象,统计量化方法主要聚焦于似然比、贝叶斯网络等基于贝叶斯定理的方法;研究热点呈现从主观量化方法聚类(主观似然比)到客观量化方法聚类(基于特征的似然比方法、基于分数的似然比方法)转变的趋势。痕迹类物证统计量化研究目前存在高维数据解释困难、模型的误差率挑战、模型参数估计挑战等问题。本文建议从量化高维数据证据质量评价指标、开发动态调整容错率模型、采用多重验证和评估策略、拓展痕迹类证据新范式专家共识等方面进行改进。This study uses the Web of Science Core Collection as its search dataset,employing the visualization tools VOSviewer 1.6.20 and CiteSpace 6.2R6 to analyze 509 publications related to the statistical quantifi cation of trace evidence,spanning 57 countries,976 institutions,and 267 journals.The study examines key literature nodes from four perspectives:publication volume,publication outlets,keyword co-occurrence,and keyword clustering,providing researchers with a comprehensive and intuitive understanding of the research trends and emerging hotspots in the fi eld.The fi ndings reveal that over the past decades,the volume of research on statistical methods for trace evidence has shown fl uctuating growth.European countries have shown significant collaboration on this topic,forming a closely-knit regional cooperation network,with the Netherlands Forensics Institute being the most prolifi c institution.Fingerprints are a crucial subject of statistical quantifi cation of trace evidence,with statistical and quantitative methods primarily focusing on a series of methods based on Bayes’theorem,such as likelihood ratios and Bayesian networks.A trend in research hotspots is observed,transitioning from clusters of subjective quantifi cation methods(such as subjective likelihood ratios)to objective ones(such as feature-based and score-based likelihood ratios).Current challenges in the statistical quantifi cation of trace evidence include diffi culties in interpreting high-dimensional data,model error rates,and model parameter estimation.The study suggests improvements such as establishing quality assessment metrics for high-dimensional evidence,developing models with dynamically adjustable error tolerance,employing multiple validation and evaluation strategies,and fostering expert consensus on new paradigms in trace evidence.

关 键 词：痕迹类物证 贝叶斯框架 似然比 CITESPACE VOSviewer 可视化分析 

分 类 号：DF794.1[政治法律—诉讼法学]