Categories
Planning

Framework for Measuring Pedestrian Accessibility

I’m a co-author of a new article, “A Generalized Framework for Measuring Pedestrian Accessibility around the World Using Open Data,” which has just been published by Geographical Analysis. We developed an open source, containerized software framework for modeling pedestrian networks using open data to analyze disaggregate access to daily living needs. We worked with local partners in 25 cities around the world to demonstrate and validate this toolkit.

From the abstract:

Pedestrian accessibility is an important factor in urban transport and land use policy and critical for creating healthy, sustainable cities. Developing and evaluating indicators measuring inequalities in pedestrian accessibility can help planners and policymakers benchmark and monitor the progress of city planning interventions. However, measuring and assessing indicators of urban design and transport features at high resolution worldwide to enable city comparisons is challenging due to limited availability of official, high quality, and comparable spatial data, as well as spatial analysis tools offering customizable frameworks for indicator construction and analysis. To address these challenges, this study develops an open source software framework to construct pedestrian accessibility indicators for cities using open and consistent data. It presents a generalized method to consistently measure pedestrian accessibility at high resolution and spatially aggregated scale, to allow for both within- and between-city analyses. The open source and open data methods developed in this study can be extended to other cities worldwide to support local planning and policymaking. The software is made publicly available for reuse in an open repository.

For more, check out the article.

Categories
Data

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.

Categories
Data

Defining Urban Data Science

I’m a co-author on a new article out inĀ Environment and Planning B: Urban Analytics and City Science titled “A Roundtable Discussion: Defining Urban Data Science” (download free PDF). It arises from a panel discussion I participated in at the 2019 AAG Annual Meeting in DC. Vanessa Frias-Martinez, Song Gao, Ate Poorthuis, and Wenfei Xu joined me on the panel, which was organized and moderated by Wei Kang, Taylor Oshan, and Levi Wolf. From the abstract:

Categories
Urban

Urban Street Network Orientation

My new article, Urban Spatial Order: Street Network Orientation, Configuration, and Entropy, has just been published in one of my favorite journals: Applied Network Science (download free PDF). This study explores the spatial signatures of urban evolution and central planning. It examines street network orientation, connectivity, granularity, and entropy in 100 cities around the world using OpenStreetMap data and OSMnx for modeling and visualization:

City street network grid orientations, order, disorder, entropy, rose plot, polar histogram made with Python, OSMnx, OpenStreetMap, matplotlib.

So, who’s got a grid and who doesn’t? Each of the cities above is represented by a polar histogram (aka rose diagram) depicting how its streets orient. Each bar’s direction represents the compass bearings of the streets (in that histogram bin) and its length represents the relative frequency of streets with those bearings. The cities above are in alphabetical order. Here they are again, re-sorted from most-ordered/gridded city (Chicago) to most-disordered (Charlotte):

Categories
Planning

LEED-ND and Neighborhood Livability

I recently co-authored a journal article titled LEED-ND and Livability Revisited,Ā which won the Kaye Bock award.Ā LEED-ND is a system for evaluating neighborhood design that was developed by CNU, USGBC, and NRDC.Ā Many of its criteria, particularly site location and neighborhood pattern, accordingly reflect New Urbanist and Smart Growth principles and are inspired by traditional neighborhood design.