de Caritat Patrice, Simpson Timothy, Woods Brenda
Australian Federal Police, GPO Box 401, Canberra, ACT, 2601, Australia.
National Centre for Forensic Studies, University of Canberra, Bruce, ACT, 2617, Australia.
J Forensic Sci. 2019 Sep;64(5):1359-1369. doi: 10.1111/1556-4029.14060. Epub 2019 Apr 16.
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.
土壤是法医和情报行动中常用的一种证据类型。在土壤成分数据库缺乏或不完善的情况下，我们建议使用公开可用的土壤属性栅格数据来缩小法医搜索区域。土壤属性栅格数据最近已广泛以高空间分辨率（通常为三弧秒，约90米）获取，它是通过挖掘土壤属性与几个环境协变量之间的相互关系数据得出的土壤属性（带有置信限）分布的预测模型。针对每个土壤属性栅格数据，搜索满足证据土壤样本成分条件（目标值±置信限）的像素。我们通过一个例子表明，证据土壤样本的搜索区域可缩小至原始调查区域的<10%。这种预测土壤溯源（PSP）方法是一种透明、可重复且客观的方法，能够高效且有效地缩小法医土壤样本的可能溯源区域。
