Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/235804 
Authors: 
Year of Publication: 
2019
Citation: 
[Journal:] REGION [ISSN:] 2409-5370 [Volume:] 6 [Issue:] 3 [Publisher:] European Regional Science Association (ERSA) [Place:] Louvain-la-Neuve [Year:] 2019 [Pages:] 39-51
Publisher: 
European Regional Science Association (ERSA), Louvain-la-Neuve
Abstract: 
Computational notebooks offer researchers, practitioners, students, and educators the ability to interactively run code 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 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 can empower guides for introducing methods to new users and can 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.
Subjects: 
Computational Notebook
Jupyter
OpenStreetMap
OSMnx
Python
Street Network
Urban Planning
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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