Urban Street Network Analysis in a Computational Notebook
15 Pages Posted: 12 Feb 2020
Date Written: January 17, 2020
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.
Keywords: accessibility, civil engineering, computer science, geocomputation, GIS, jupyter, network science, notebook, open street map, osmnx, pedagogy, python, road network, routing, spatial analysis, street network, transportation, urban analytics, urban informatics, urban geography, urban planning
JEL Classification: R14, R40
Suggested Citation: Suggested Citation