Street networks underlie city circulation and human dynamics, but it has historically been challenging for urban researchers to acquire and consistently analyze high-quality street network data. OpenStreetMap shifts this paradigm with its trove of worldwide data. This talk presents OSMnx, a Python package to make the collection of data and the creation and analysis of street network models easy, consistent, and automatable for any study site in the world. It demonstrates 1) the tailored and automated downloading and construction of street networks from OpenStreetMap; 2) the algorithmic cleaning and simplification of network topology; 3) the acquisition of network elevation/grade data and building footprints; and 4) the ability to analyze street networks, calculate routes, project and visualize networks, and calculate geometric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure, topology, resilience, and sustainability of the network. Finally, it presents preliminary research that examines 27,000 street networks at various scales across the US, including every American city/town, urbanized area, and Zillow-defined neighborhood.