Urban Street Network Analysis in a Computational Notebook

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. 1

Characterising the structural pattern of urban road networks in Ghana using geometric and topological measures

“…The resulting output of the indicators was visualised using Matplotlib and is discussed in the results section. For a step‐by‐step procedure on the use of OSMnx to analyse road network data, the reader is referred to Boeing (2020).…”

Integrating Human Behavior and Safety Measure into Evacuation Route Planning in a Volcanic Crisis

“…We collected the streets and the waterways data in the study areas from Open Street Map. Using OSMNx software, we modeled the streets and the waterways as a multi-directed graph G(V, E), where V denotes a set of vertices representing the crossroads, and E denotes a set of edges represents the streets (Boeing,[24,25] ). The acquired networks consist of 2,507 nodes with 6,554 edges.…”

A collaborative GIS programming course using GitHub Classroom

“…Lecturers could utilize tools such as nbgrader for automatic grading (Hamrick et al., 2017). Computational notebooks are valuable for documenting readily available libraries (Boeing, 2020b) or exploratory analysis. Yet, using an integrated development environment (e.g., Spyder, PyCharm) may ease the process of developing a QGIS plugin.…”