2019/04/16 by Patrice de Caritat, Timothy Simpson, Timothy W. Simpson +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Environmental Science · #Forensic and Genetic Research #Image Processing and 3D Reconstruction #Soil Geostatistics and Mapping
paper · doi:10.1111/1556-4029.14060
openalex publication_date 2019/04/16 · openalex created_date 2019/04/25 · openalex updated_date 2026/07/29
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.