centrality – Geoff Boeing

The Structure of Street Networks

I recently coauthored an article titled “A Review of the Structure of Street Networks” with Marc Barthelemy in Transport Findings. On a personal note, Marc has long been a personal hero of mine and was the 2nd most cited author in my dissertation, after Mike Batty (who I also recently had the pleasure of collaborating with).

From the abstract:

We review measures of street network structure proposed in the recent literature, establish their relevance to practice, and identify open challenges facing researchers. These measures’ empirical values vary substantially across world regions and development eras, indicating street networks’ geometric and topological heterogeneity.

For more, check out the article.

New Article on Computational Notebooks

I have a new article out in Region: Journal of the European Regional Science Association, “Urban Street Network Analysis in a Computational Notebook.” It reflects on the use of Jupyter notebooks in applied data science research, pedagogy, and practice, and it uses the OSMnx examples repository as an example.

From the abstract:

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.

For more, check out the article.