Literature DB >> 32111292

Establishing phone-pair co-usage by comparing mobility patterns.

Wauter Bosma1, Sander Dalm2, Erwin van Eijk2, Rachid El Harchaoui2, Edwin Rijgersberg2, Hannah Tereza Tops2, Alle Veenstra2, Rolf Ypma2.   

Abstract

In forensic investigations it is often of value to establish whether two phones were used by the same person during a given time period. We present a method that uses time and location of cell tower registrations of mobile phones to assess the strength of evidence that any pair of phones were used by the same person. The method is transparent as it uses logistic regression to discriminate between the hypotheses of same and different user, and a standard kernel density estimation to quantify the weight of evidence in terms of a likelihood ratio. We further add to previous theoretical work by training and validating our method on real world data, paving the way for application in practice. The method shows good performance under different modeling choices and robustness under lower quantity or quality of data. We discuss practical usage in court.
Copyright © 2019 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Cell phone; Evidence evaluation; Geographic locations; Likelihood ratio; Machine learning

Year:  2019        PMID: 32111292     DOI: 10.1016/j.scijus.2019.10.005

Source DB:  PubMed          Journal:  Sci Justice        ISSN: 1355-0306            Impact factor:   2.124


  1 in total

1.  A strawman with machine learning for a brain: A response to Biedermann (2022) the strange persistence of (source) "identification" claims in forensic literature.

Authors:  Geoffrey Stewart Morrison; Daniel Ramos; Rolf Jf Ypma; Nabanita Basu; Kim de Bie; Ewald Enzinger; Zeno Geradts; Didier Meuwly; David van der Vloed; Peter Vergeer; Philip Weber
Journal:  Forensic Sci Int Synerg       Date:  2022-05-06
  1 in total

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