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Shared mobility-on-demand services are evolving rapidly in cities around the world. As a prominent example, ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public availability of detailed temporal and spatial data on ridesourcing trips has stifled research in how new services interact with traditional mobility options and how they impact travel in cities. Improving data-sharing agreements is opening unprecedented opportunities for research in this area. This study’s goal is to study emerging patterns of mobility using the recently released City of Chicago public ridesourcing data. The data are supplemented with weather, transit, and taxi data to gain a broader understanding of ridesourcing’s role in the mobility ecosystem. Considering the analysis data is large and contains numerical and categorical variables, K-prototypes is utilized for its ability to accept mixed variable type data. An extension of the K-means algorithm, its output is a classification of the data into several clusters called prototypes. Six ridesourcing prototypes were identified, described, and discussed in this study. Identified user segments are defined by adverse weather conditions, competition with alternative modes, spatial patterns, and tendency for ridesplitting.