Workshop: Analyze Bay Area Parking in Python
The Bay Area has a parking problem. Our nine-county region has 15 million parking spots — enough to wrap around the earth 2.3 times. While the time spent looking for a spot can make it seem like more parking is a good thing, it’s not. Because parking enables and encourages driving, it’s part of the reason that single-occupancy vehicles account for the lion’s share of greenhouse gas emissions in California. What’s more, parking is precious space that we could use for other things that improve public health like housing or parks.
To start understanding the Bay Area’s massive parking supply, TransForm advised our friends at SPUR who created the Bay Area Parking Census. The Parking Census is the first database quantifying parking spaces in the region — organized by on- and off-street parking, and residential vs. non-residential parking. And the data is free.
Now, planners, politicians, advocates, and data-curious people have a new tool to analyze Bay Area parking. Knowing just how much parking we have helps inform proactive parking management, as well as reduction policies and strategies. The Parking Census can answer “how much” and “where” questions like:
How much parking is within walking distance of a high-frequency bus stop?
Where is Santa Clara County’s highest concencentration of parking spaces?
What’s the ratio of on- vs. off-street parking in the Bay Area?
How much parking is located on-street where a proposed bus-only lane will be?
If you care about reducing driving and greenhouse gas emissions, but diving deep into the data isn’t your thing, check out this parking policy webinar hosted by SPUR with policy ideas on how to reduce, manage and convert the region’s parking supply.
For the data nerds, climate advocates, housing advocates, and transportation planners among us, we’ve put together a workshop that explains how to use Python, a free, open-source software, to analyze the data. This workshop makes the parking database accessible to anyone with limited Python experience. (If you’re a GIS user, there is a tutorial hosted by SPUR here.)
If you dream in spreadsheets, this workshop is for you. To complete the workshop you will need the following resources, all available for free:
Limited familiarity with Anaconda Navigator, Jupyter Notebooks, and the geopandas library
The Bay Area Parking Census. (You will need the "parking spaces per acre" dataset.)
The Python workshop is in three sections where you will learn how to:
Download and open the data in Python Jupyter Notebooks and clean the data to prepare for analysis.
Tabulate the data to quantify the different types of parking spaces in the Bay Area.
Calculate and make a map displaying the amount of parking in a specific geographic area. We use two locations in Santa Clara County as examples.
The workshop provides code to tabulate the amounts of parking and create data visuals such as the maps and bar chart below.
The bar chart below shows the number of parking spaces in the Bay Area by type: on-street vs. off-street.
We hope you’ll dive in and let us know what you learn.
The Census was produced by the Mineta Transportation Institute in partnership with SPUR and researchers Mikhail Chester, PhD; Alysha Helmrich, PhD; and Rui Li, in consultation with TransForm. Thanks to support from the Mineta Transportation Institute and the Robert Wood Johnson Foundation, the parking census data is freely available on MTI’s website and SPUR’s website, making it easy to analyze and join with other datasets.
This post was co-authored by Maya Love, who worked on this project as a CORO Fellow. Special thanks to Shiying Wang who supported the creation of the Python notebook.