OSMnx v1.1.0 has been released. It includes several new features, bug fixes, and performance enhancements. I encourage you to upgrade and take advantage of everything new. I’ll summarize some of the key improvements in this post.
In 1974, Robert Caro published The Power Broker, a critical biography of Robert Moses’s dictatorial tenure as the “master builder” of mid-century New York. Moses profoundly transformed New York’s urban fabric and transportation system, producing the Brooklyn Battery Tunnel, the Verrazano Narrows Bridge, the Westside Highway, the Cross-Bronx Expressway, the Lincoln Center, the UN headquarters, Shea Stadium, Jones Beach State Park and many other projects. However, The Power Broker did lasting damage to his public image and today he remains one of the most controversial figures in city planning history.
Today, The Power Broker may be the most well-known biography of any urban planner ever. Less-known: on August 26, 1974, Moses issued a turgid 23-page statement denouncing Caro’s work as “full of mistakes, unsupported charges, nasty baseless personalities, and random haymakers.” Moses’s original typewritten statement survives today as a grainy photocopy in the New York City Parks Department archive. To better preserve and disseminate it, I extracted and transcribed its text using optical character recognition and edited the result to correct errors. My transcription of Moses’s statement, alongside Caro’s response to it, is available here.
My article, “Street Network Models and Indicators for Every Urban Area in the World” has been published by Geographical Analysis. This project was a massive undertaking and I’m excited to share it. As you might guess from the title, I modeled and analyzed the street network of each urban area in the world then deposited all the source code and models and indicators in open repositories for public reuse. The article also includes a high-level analysis of urban street network form across the world.
Cities worldwide exhibit a variety of street patterns and configurations that shape human mobility, equity, health, and livelihoods. Using boundaries derived from the Global Human Settlement Layer, I modeled and analyzed the street networks of every urban area in the world using OSMnx and OpenStreetMap raw data. In total, I modeled over 160 million street network nodes and over 320 million edges across 8,914 urban areas in 178 countries. I attached node elevations and street grades to every node/edge in the final models. All the final models were topologically simplified such that nodes represent intersections and dead-ends, and edges represent the street segments linking them.
Happy new year! After five years of development and over 2,000 code commits from dozens of contributors, OSMnx v1.0 has officially been released. This has been a long labor of love and I’m thrilled to see it reach this milestone.
Much has changed in recent months with new features added and a few things deprecated. Most of this development occurred in a major overhaul over the summer, which I covered at the time in three previous posts. Among these dozens of enhancements were major speed and efficiency improvements throughout the package, better visualization, a new geometries module for retrieving any geospatial objects from OSM, topological intersection consolidation, and much more. I encourage you to read those posts to familiarize yourself with what’s new.
My chapter “Exploring Urban Form Through OpenStreetMap Data: A Visual Introduction” has just been published in the new book Urban Experience and Design: Contemporary Perspectives on Improving the Public Realm edited by Justin Hollander and Ann Sussman.
From the abstract:
This chapter introduces OpenStreetMap—a crowdsourced, worldwide mapping project and geospatial data repository—to illustrate its usefulness in quickly and easily analyzing and visualizing planning and design outcomes in the built environment. It demonstrates the OSMnx toolkit for automatically downloading, modeling and visualizing spatial data from OpenStreetMap. We explore patterns and configurations in street networks and buildings around the world computationally through visualization methods—including figure-ground diagrams and polar histograms—that help compress urban complexity into comprehensible artifacts that reflect the human experience of the built environment. Ubiquitous urban data and computation can open up new urban form analyses from both quantitative and qualitative perspectives.
For more, check out the chapter.
My article “Off the Grid… and Back Again? The Recent Evolution of American Street Network Planning and Design” has been published by the Journal of the American Planning Association and won the 2020 Stough-Johansson Springer Award for best paper. It identifies recent nationwide trends in American street network design, measuring how urban planners abandoned the grid and embraced sprawl over the 20th century, but since 2000 these trends have rebounded, shifting back toward historical design patterns. In this post I discuss these findings and visualizations across the US today as well as over time, then discuss my analysis methods.
My new article “The Right Tools for the Job: The Case for Spatial Science Tool-Building” has been published in Transactions in GIS (free PDF). I originally presented this paper as the 8th annual Transactions in GIS plenary address at the AAG annual meeting last year. I argue that tool-building is an essential but poorly incentivized component of academic geography and social science more broadly. To conduct better science, we need to build better tools. Better tools and data models, spearheaded by academics, can help infuse theory into our field’s quantitative work where it is too often lacking. But if we want better tools, we have to build them. It is not ESRI’s job to satisfy all the theoretical needs of the spatial sciences.
This is a follow-up to last month’s post discussing the many new features, improvements, and optimizations made to OSMnx this summer. As this major improvement project now draws to a close, I will summarize what’s new(er) here. Long story short: there are a bunch of new features and everything in the package has been streamlined and optimized to be easier to use, faster, and more memory efficient.
First off, if you haven’t already, read the previous post about new features including topological intersection consolidation, automatic max speed imputation and travel time calculation, generalized points-of-interest queries, querying OSM by date, and API streamlining. This post covers new changes since then, including improved visualization and plotting, improved graph simplification, the new geocoder module, and other miscellaneous improvements.
There have been some major changes to OSMnx in the past couple months. I’ll review them briefly here, demonstrate some usage examples, then reflect on a couple upcoming improvements on the horizon. First, what’s new:
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.