Discerning Human Behavior from Mobility Data: Mobility data encompasses many elements, including location history, latitude coordinates, longitude coordinates, anonymized mobile device IDs, and timestamps. Such data are generated, for instance, by automobile navigation applications and by the mobile advertising ecosystem. Typical sources of mobility data contain extensive inaccuracies that result from a variety of sources, ranging from shortcomings in location services on mobile devices to the intentional misrepresentation of spatial coordinates by bad ecosystem actors. In this talk, we describe a production data pipeline, Darwin, which analyzes the location quality of mobility data to measure how accurately a set of mobility data represents true movement patterns. Darwin uses a number of measures that are ultimately combined into two quality scores: hyper-locality and clusterability. These measurements include techniques from information theory, the mean number of spatial clusters, the compactness of the clusters, and the differences between the empirical distribution of digits in the spatial coordinates and reference distributions.