Rui Zhang, Kevin Stanley, Daniel Fuller, Scott Bell
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 that 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 that may or may not provide insight. In this article, 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.
Zhang R, Stanley KG, Fuller D, Bell S. Differentiating Population Spatial Behavior Using Representative Features of Geospatial Mobility (ReFGeM). ACM Trans. Spatial Algorithms Syst. 6, 1, Article 2 (February 2020), DOI:https://doi.org/10.1145/3362063