@article{YUAN2025, 
author = {Ying YUAN},
title = {A Visual Analysis of Literature on Statistical Quantification Studies of Trace Evidence Using Web of Science},
year = {2025},
journal = {Forensic Science and Technology},
volume = {50},
number = {2},
pages = {197-205},
keywords = {trace evidence, Bayesian framework, likelihood ratio, CiteSpace, VOSviewer, visualized analysis},
url = {https://www.sciopen.com/article/10.16467/j.1008-3650.2024.0078},
doi = {10.16467/j.1008-3650.2024.0078},
abstract = {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 quantification 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 field. The findings reveal that over the past decades, the volume of research on statistical methods for trace evidence has shown fluctuating 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 prolific institution. Fingerprints are a crucial subject of statistical quantification 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 quantification methods (such as subjective likelihood ratios) to objective ones (such as feature-based and score-based likelihood ratios). Current challenges in the statistical quantification of trace evidence include difficulties 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.}
}