Categories
Planning

Delivering Healthy and Sustainable Cities

I have a new article out now in The Journal of City Climate Policy and Economy, coauthored with a team that includes several of the folks from our recent series in The Lancet Global Health. The JCCPE article, “A Pathway to Prioritizing and Delivering Healthy and Sustainable Cities,” synthesizes findings and recommended policy actions arising from that recent TLGH series.

From the abstract:

Creating healthy and sustainable cities should be a global priority. Some cities prioritize 15-minute cities as a planning approach with co-benefits for health, climate change mitigation, equity, and economic recovery from COVID-19. Yet, as our recent The Lancet Global Health series on “Urban Design, Transport, and Health” showed, many cities have a long way to go to achieve this vision. This policy guideline summarizes the main findings of the series, which assessed health and sustainability indicators for 25 cities in 19 countries. We then outline steps governments can take to strengthen policy frameworks and deliver more healthy, equitable, and sustainable built environments. The Lancet Global Health series provided clear evidence that cities need to transform urban governance to enable integrated planning for health and sustainability and commit to policy implementation. Evidence-informed indicators should be used to benchmark and monitor progress. Cities need policy frameworks that are comprehensive and consistent with evidence, with measurable policy targets to support implementation and accountability. The series provided evidence-informed thresholds for some key urban design and transport features, which can be embedded as policy targets. Policies and interventions must prioritize identifying and reducing inequities in access to health-supportive environments. Governments should also invest in open data and promote citizen-science programmes, to support indicator development and research for public benefit. We provide tools to replicate our indicators and an invitation to join our 1,000 Cities Challenge via the Global Observatory of Healthy and Sustainable Cities.

For more, check out the JCCPE article itself. And you may also be interested in our recent The Lancet Global Health series of articles that developed similar themes in great depth.

Categories
Urban

Big Data in Urban Morphology

My new article “Spatial Information and the Legibility of Urban Form: Big Data in Urban Morphology” has been published in the International Journal of Information Management (download free PDF). It builds on recent work by Crooks et al, presenting workflows to integrate data-driven and narrative approaches to urban morphology in today’s era of ubiquitous urban big data. It situates this theoretically in the visual culture of planning to present a visualization-mediated interpretative process of data-driven urban morphology, focusing on transportation infrastructure via OSMnx.

OSMnx: Figure-ground diagrams of one square mile of each street network, from OpenStreetMap, made in Python with matplotlib, geopandas, and NetworkX

Categories
Academia

New Article: Craigslist Housing Markets in JPER

Our article “New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings” is finally appearing in print in the Journal of Planning Education and Research‘s forthcoming winter issue. We collected, validated, and analyzed 11 million Craigslist rental listings to discover fine-grained patterns across metropolitan housing markets in the United States.

Map of 1.5 million Craigslist rental listings in the contiguous U.S., divided into quintiles by each listing's rent per square foot. Published in JPER: the Journal of Planning Education and Research.

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
Academia

Urban Informatics and Visualization at UC Berkeley

The fall semester begins next week at UC Berkeley. For the third year in a row, Paul Waddell and I will be teaching CP255: Urban Informatics and Visualization, and this is my first year as co-lead instructor.

This masters-level course trains students to analyze urban data, develop indicators, conduct spatial analyses, create data visualizations, and build Paris open datainteractive web maps. To do this, we use the Python programming language, open source analysis and visualization tools, and public data.

This course is designed to provide future city planners with a toolkit of technical skills for quantitative problem solving. We don’t require any prior programming experience – we teach this from the ground up – but we do expect prior knowledge of basic statistics and GIS.

Update, September 2017: I am no longer a Berkeley GSI, but Paul’s class is ongoing. Check out his fantastic teaching materials in his GitHub repo. From my experiences here, I have developed a course series on urban data science with Python and Jupyter, available in this GitHub repo.