  Computational notebooks offer researchers, practitioners, students, and
educators the ability to interactively conduct analytics and disseminate
reproducible workflows that weave together code, visuals, and narratives. This
article explores the potential of computational notebooks in urban analytics
and planning, demonstrating their utility through a case study of OSMnx and its
tutorials repository. OSMnx is a Python package for working with OpenStreetMap
data and modeling, analyzing, and visualizing street networks anywhere in the
world. Its official demos and tutorials are distributed as open-source Jupyter
notebooks on GitHub. This article showcases this resource by documenting the
repository and demonstrating OSMnx interactively through a synoptic tutorial
adapted from the repository. It illustrates how to download urban data and
model street networks for various study sites, compute network indicators,
visualize street centrality, calculate routes, and work with other spatial data
such as building footprints and points of interest. Computational notebooks
help introduce methods to new users and help researchers reach broader
audiences interested in learning from, adapting, and remixing their work. Due
to their utility and versatility, the ongoing adoption of computational
notebooks in urban planning, analytics, and related geocomputation disciplines
should continue into the future.
