Forensic pollen geolocation techniques used to identify the origin of boll weevil re-infestation

Jones, G. D.

doi:10.1080/00173134.2012.667832

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Recent work has expanded the capabilities of pollen for forensic analysis (Jones, 2012;Goodman et al, 2015), but the technique remains used more often in archeology than for forensic geolocation.…”

Section: Introductionmentioning

confidence: 99%

Global forensic geolocation with deep neural networks

Grantham¹,

Reich²,

Laber³

et al. 2019

Preprint

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An important problem in forensic analyses is identifying the provenance of materials at a crime scene, such as biological material on a piece of clothing. This procedure, known as geolocation, is conventionally guided by expert knowledge of the biological evidence and therefore tends to be application-specific, labor-intensive, and subjective.Purely data-driven methods have yet to be fully realized due in part to the lack of a sufficiently rich data source. However, high-throughput sequencing technologies are able to identify tens of thousands of microbial taxa using DNA recovered from a single swab collected from nearly any object or surface. We present a new algorithm for geolocation that aggregates over an ensemble of deep neural network classifiers trained on randomlygenerated Voronoi partitions of a spatial domain. We apply the algorithm to fungi present in each of 1300 dust samples collected across the continental United States and then to a global dataset of dust samples from 28 countries. Our algorithm makes remarkably good point predictions with more than half of the geolocation errors under 100 kilometers for the continental analysis and nearly 90% classification accuracy of a sample's country of origin for the global analysis. We suggest that the effectiveness of this model sets the stage for a new, quantitative approach to forensic geolocation.

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Section: Introductionmentioning

confidence: 99%

Global forensic geolocation with deep neural networks

Grantham¹,

Reich²,

Laber³

et al. 2019

Preprint

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Global Forensic Geolocation with Deep Neural Networks

Grantham

Reich

Laber

et al. 2020

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary An important problem in modern forensic analyses is identifying the provenance of materials at a crime scene, such as biological material on a piece of clothing. This procedure, which is known as geolocation, is conventionally guided by expert knowledge of the biological evidence and therefore tends to be application specific, labour intensive and often subjective. Purely data-driven methods have yet to be fully realized in this domain, because in part of the lack of a sufficiently rich source of data. However, high throughput sequencing technologies can identify tens of thousands of fungi and bacteria taxa by using DNA recovered from a single swab collected from nearly any object or surface. This microbial community, or microbiome, may be highly informative of the provenance of the sample, but data on the spatial variation of microbiomes are sparse and high dimensional and have a complex dependence structure that render them difficult to model with standard statistical tools. Deep learning algorithms have generated a tremendous amount of interest within the machine learning community for their predictive performance in high dimensional problems. We present DeepSpace: a new algorithm for geolocation that aggregates over an ensemble of deep neural network classifiers trained on randomly generated Voronoi partitions of a spatial domain. The DeepSpace algorithm makes remarkably good point predictions; for example, when applied to the microbiomes of over 1300 dust samples collected across continental USA, more than half of geolocation predictions produced by this model fall less than 100 km from their true origin, which is a 60% reduction in error from competing geolocation methods. Moreover, we apply DeepSpace to a novel data set of global dust samples collected from nearly 30 countries, finding that dust-associated fungi alone predict a sample's country of origin with nearly 90% accuracy.

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Museums' Role: Pollen and Forensic Science

Warny

2013

Science

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Forensic pollen geolocation techniques used to identify the origin of boll weevil re-infestation

Cited by 3 publications

References 30 publications

Global forensic geolocation with deep neural networks

Global forensic geolocation with deep neural networks

Global Forensic Geolocation with Deep Neural Networks

Museums' Role: Pollen and Forensic Science

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