  Understanding how humans use and consume space by comparing stratified
groups, either through observation or controlled study, is key to designing
better spaces, cities, and policies. GPS data traces provide detailed movement
patterns of individuals but can be difficult to interpret due to the scale and
scope of the data collected. For actionable insights, GPS traces are usually
reduced to one or more features which express the spatial phenomenon of
interest. However, it is not always clear which spatial features should be
employed, and substantial effort can be invested into designing features which
may or may not provide insight. In this paper we present an alternative
approach: a standardized feature set with actionable interpretations that can
be efficiently run against many datasets. We show that these features can
distinguish between disparate human mobility patterns, although no single
feature can distinguish them alone.
