  Analyzing the geographic movement of humans, animals, and other phenomena is
a growing field of research. This research has benefited urban planning,
logistics, animal migration understanding, and much more. Typically, the
movement is captured as precise geographic coordinates and time stamps with
Global Positioning Systems (GPS). Although some research uses computational
techniques to take advantage of implicit movement in descriptions of route
directions, hiking paths, and historical exploration routes, innovation would
accelerate with a large and diverse corpus. We created a corpus of sentences
labeled as describing geographic movement or not and including the type of
entity moving. Creating this corpus proved difficult without any comparable
corpora to start with, high human labeling costs, and since movement can at
times be interpreted differently. To overcome these challenges, we developed an
iterative process employing hand labeling, crowd voting for confirmation, and
machine learning to predict more labels. By merging advances in word embeddings
with traditional machine learning models and model ensembling, prediction
accuracy is at an acceptable level to produce a large silver-standard corpus
despite the small gold-standard corpus training set. Our corpus will likely
benefit computational processing of geography in text and spatial cognition, in
addition to detection of movement.
