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Understanding how humans use and consume space by comparing stratifiedgroups, either through observation or controlled study, is key to designingbetter spaces, cities, and policies. GPS data traces provide detailed movementpatterns of individuals but can be difficult to interpret due to the scale andscope of the data collected. For actionable insights, GPS traces are usuallyreduced to one or more features which express the spatial phenomenon ofinterest. However, it is not always clear which spatial features should beemployed, and substantial effort can be invested into designing features whichmay or may not provide insight. In this paper we present an alternativeapproach: a standardized feature set with actionable interpretations that canbe efficiently run against many datasets. We show that these features candistinguish between disparate human mobility patterns, although no singlefeature can distinguish them alone.

Differentiating Population Spatial Behavior using Representative Features of Geospatial Mobility (ReFGeM)