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This post is adapted from this research paper that you can read/cite for more info. It analyzes and visualizes 100 cities around the world. By popular request, this is a quick follow-up to this post comparing the orientation of streets in 25 US cities using Python and OSMnx. Here are 25 more cities around the world: And for reference, here’s the original that looked only at American cities: Want t
This post is adapted from this research paper that you can read/cite for more info. It analyzes and visualizes 100 cities around the world. “We say the cows laid out Boston. Well, there are worse surveyors.” –Ralph Waldo Emerson. In 1960, one hundred years after Emerson’s quote, Kevin Lynch published The Image of the City, his treatise on the legibility of urban patterns. How coherent is a city’s
Check out the journal article about OSMnx. All figures in this article come from this journal article, which you can read/cite for more. The heart of Allan Jacobs’ classic book on street-level urban form and design, Great Streets, features dozens of hand-drawn figure-ground diagrams in the style of Nolli maps. Each depicts one square mile of a city’s street network. Drawing these cities at the sam
If you use OSMnx in your work, please cite the journal article. OSMnx is a Python package to retrieve, model, analyze, and visualize street networks from OpenStreetMap. Users can download and model walkable, drivable, or bikeable urban networks with a single line of Python code, and then easily analyze and visualize them. You can just as easily download and work with amenities/points of interest,
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