Statistical Methods for the Forensic Analysis of Geolocated Event Data

Simple item page
dc.contributor.author Galbraith, Christopher
dc.contributor.author Smyth, Padhraic
dc.contributor.author Stern, Hal
dc.contributor.department Center for Statistics and Applications in Forensic Evidence
dc.date 2020-08-07T14:20:37.000
dc.date.accessioned 2021-02-25T00:40:47Z
dc.date.available 2021-02-25T00:40:47Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2020
dc.date.embargo 2020-08-07
dc.date.issued 2020-06-01
dc.description.abstract <p>A common question in forensic analysis is whether two observed data sets originated from the same source or from different sources. Statistical approaches to addressing this question have been widely adopted within the forensics community, particularly for DNA evidence. Here we investigate the application of statistical approaches to same-source forensic questions for spatial event data, such as determining the likelihood that two sets of observed GPS locations were generated by the same individual. We develop two approaches to quantify the strength of evidence in this setting. The first is a likelihood ratio approach based on modeling the spatial event data directly. The second approach is to instead measure the similarity of the two observed data sets via a score function and then assess the strength of the observed score resulting in the score-based likelihood ratio. A comparative evaluation using geolocated Twitter event data from two large metropolitan areas shows the potential efficacy of such techniques.</p>
dc.description.comments <p>This is a presented paper published as Galbraith, Christopher, Padhraic Smyth, and Hal S. Stern. "Statistical methods for the forensic analysis of geolocated event data." <em>Digital Investigation</em> (2020).</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/csafe_conf/67/
dc.identifier.articleid 1066
dc.identifier.contextkey 18818302
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath csafe_conf/67
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/93854
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/csafe_conf/67/DFRWS_2020.pdf|||Sat Jan 15 01:27:22 UTC 2022
dc.subject.disciplines Forensic Science and Technology
dc.subject.disciplines Statistics and Probability
dc.title Statistical Methods for the Forensic Analysis of Geolocated Event Data
dc.type article
dc.type.genre presentation
dspace.entity.type Publication
relation.isOrgUnitOfPublication d8a3c72b-850f-40f6-87c4-8812547080c7
File
Original bundle
Now showing 1 - 1 of 1
Name:
DFRWS_2020.pdf
Size:
3.86 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Conference Proceedings and Presentations