As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Both regulators and standards developing organizations have made extensive use of Monte Carlo collision risk analysis simulations using probabilistic models of aircraft flight. We have previously demonstrated a methodology for developing small unmanned aircraft system (sUAS) flight models that leverage open source geospatial information and map datasets to generate representative unmanned operations at low altitudes. This work expands upon previous research by evaluating the scalability and diversity of open source data to support currently needed risk assessments. We also provide considerations for pairing these trajectories with generative manned aircraft models to create encounters for Monte Carlo simulations.