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Predictive Soil Provenancing ( PSP ): An Innovative Forensic Soil Provenance Analysis Tool
Author(s) -
Caritat Patrice,
Simpson Timothy,
Woods Brenda
Publication year - 2019
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.14060
Subject(s) - raster graphics , forensic science , sample (material) , soil test , provenance , computer science , environmental science , soil classification , soil science , data mining , soil water , geology , artificial intelligence , geography , archaeology , chemistry , chromatography , petrology
Soil is a common evidence type used in forensic and intelligence operations. Where soil composition databases are lacking or inadequate, we propose to use publicly available soil attribute rasters to reduce forensic search areas. Soil attribute rasters, which have recently become widely available at high spatial resolutions, typically three arc‐seconds (~90 m), are predictive models of the distribution of soil properties (with confidence limits) derived from data mining the inter‐relationships between these properties and several environmental covariates. Each soil attribute raster is searched for pixels that satisfy the compositional conditions of the evidentiary soil sample (target value ± confidence limits). We show through an example that the search area for an evidentiary soil sample can be reduced to <10% of the original investigation area. This Predictive Soil Provenancing ( PSP ) approach is a transparent, reproducible, and objective method of efficiently and effectively reducing the likely provenance area of forensic soil samples.