Categories
Planning

How to Visualize Urban Accessibility and Walkability

Tools like WalkScore visualize how “walkable” a neighborhood is in terms of access to different amenities like parks, schools, or restaurants. It’s easy to create accessibility visualizations like these ad hoc with Python and its pandana library. Pandana (pandas for network analysis – developed by Fletcher Foti during his dissertation research here at UC Berkeley) performs fast accessibility queries over a network. I’ll demonstrate how to use it to visualize urban walkability. My code is in these IPython notebooks in this urban data science course GitHub repo.

First I give pandana a bounding box around Berkeley/Oakland in the East Bay of the San Francisco Bay Area. Then I load the street network and amenities from OpenStreetMap. In this example I’ll look at accessibility to restaurants, bars, and schools. But, you can create any basket of amenities that you are interested in – basically visualizing a personalized “AnythingScore” instead of a generic WalkScore for everyone. Finally I calculate and plot the distance from each node in the network to the nearest amenity:

Berkeley Oakland California street network walking accessibility and walkability

Categories
Planning

Urban Design and Complexity

Corbusier Paris planI am presenting at the 2015 Conference on Complex Systems tomorrow in Tempe, Arizona. My paper is on methods for assessing the complexity of urban design. If you’re attending the conference, come on by!

Here’s the paper.

Here’s the abstract:

Categories
Planning

LEED-ND and Neighborhood Livability

I recently co-authored a journal article titled LEED-ND and Livability Revisitedwhich won the Kaye Bock award. LEED-ND is a system for evaluating neighborhood design that was developed by CNU, USGBC, and NRDC. 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.