  Human trafficking is a crime that affects the lives of millions of people
across the globe. Traffickers exploit the victims through forced labor,
involuntary sex, or organ harvesting. Migrant smuggling could also be seen as a
form of human trafficking when the migrant fails to pay the smuggler and is
forced into coerced activities. Several news agencies and anti-trafficking
organizations have reported trafficking survivor stories that include the names
of locations visited along the trafficking route. Identifying such routes can
provide knowledge that is essential to preventing such heinous crimes. In this
paper we propose a Narrative to Trajectory (N2T) information extraction system
that analyzes reported narratives, extracts relevant information through the
use of Natural Language Processing (NLP) techniques, and applies geospatial
augmentation in order to automatically plot trajectories of human trafficking
routes. We evaluate N2T on human trafficking text corpora and demonstrate that
our approach of utilizing data preprocessing and augmenting database techniques
with NLP libraries outperforms existing geolocation detection methods.
