  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 randomly-generated 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.
