Environmental Justice Data Liberation

We’ve come across a few allied projects looking at environmental justice data specifically, and thought it would be nice to share!

Environmental Enforcement Watch

In May, Christina and I gave a talk at CSV,Conf,v6 about things we’ve learned liberating US energy system data. We focused a lot on the challenge of making data accessible to advocates. The following talk was analogous, but focused on environmental justice data. The speaker was Kelsey Breseman (@ifoundtheme) from the Environmental Data and Governance Initiative (EDGI) and their project Environmental Enforcement Watch (EEW). EEW is trying to hold polluters accountable using federally reported data, by making that data more accessible to and understandable by the people who are affected. They’re scraping the data from the web and creating a database that folks can query using Google CoLab notebooks. At the same time they’re trying to get EPA the full underlying database accessible to the public.

You can watch her excellent talk here:

I was struck by how many parallels there were between our work. We’re both trying to mitigate the poor curation of government data, and make it more accessible way to the public. EDGI also seems very open and GitHub centered and is trying to operate as a horizontal organization. They support themselves through foundation grants and volunteer labor. Nobody works on EDGI full time. They have a fiscal sponsorship agreement through Earth Science Information Partners (ESIP).

If you’re interested in public data and environmental justice they seem like a great organization! Maybe we can collaborate at some point.


Automated Data Wrangling

An illustration from the Frog and Toad children's books, where Frog and Toat are eating cookies. The caption has been altered to say "We must stop data cleaning!" cried Toad as he continued to clean the data.
Frog and Toad are Data Wranglers

We work with a lot of messy public data. In theory it’s already “structured” and published in machine readable forms like Microsoft Excel spreadsheets, poorly designed databases, and CSV files with no associated schema. In practice it ranges from almost unstructured to… almost structured. Someone working on one of our take-home questions for the data wrangler & analyst position recently noted of the FERC Form 1: “This database is not really a database – more like a bespoke digitization of a paper form that happened to be built using a database.” And I mean, yeah. Pretty much. The more messy datasets I look at, the more I’ve started to question Hadley Wickham’s famous Tolstoy quip about the uniqueness of messy data. There’s a taxonomy of different kinds of messes that go well beyond what you can easily fix with a few nifty dataframe manipulations. It seems like we should be able to develop higher level, more general tools for doing automated data wrangling. Given how much time highly skilled people pour into this kind of computational toil, it seems like it would be very worthwhile.

Like families, tidy datasets are all alike but every messy dataset is messy in its own way.

Hadley Wickham, paraphrasing Leo Tolstoy in Tidy Data

Catalyst partners release energy transition resources

In the past two weeks, Catalyst partners Energy Innovation and the Rocky Mountain Institute have released two major resources based on open data to help stakeholders better understand the energy transition in the US electricity sector. We’re excited to say that Catalyst team members prepared data from Catalyst’s Public Utility Data Liberation project and provided analytical support for both resources.

Energy Innovation’s Coal Cost Crossover 2.0 provided an update to their 2018 report, which projected that by 2025 three quarters of the nation’s coal power plants would be uneconomic. The 2.0 shows that the economics of coal power in the US have deteriorated more rapidly than expected. The report finds that 80% of existing coal plants are either uneconomic or slated to retire before 2025. Economic viability is assessed by comparing coal plant operating costs with estimates of building new renewable facilities nearby, using the levelized cost of wind and solar energy estimates from the National Renewable Energy Laboratory’s Renewable Energy Deployment System (ReEDS) model. Coal operating costs are derived from fuel and operations/maintenance data from FERC and EIA, or from estimates from the National Energy Modeling System where FERC and EIA data was unavailable. 

The Rocky Mountain Institute recently released the first version of their Utility Transition Hub, an interactive data portal that allows users to track, quantify, and understand how investments, operations, policies, and regulations shape outcomes in the electricity sector. Stakeholders can explore the energy transition in the power sector as a whole, group subsidiary utilities by their parent company, or make comparisons between utilities. Cleaned data from FERC and EIA underly Tableau visualizations which help users to evaluate historical performance on emissions reductions and investments in renewables, and to assess the alignment of resource planning and climate commitments with a 1.5 degree C trajectory.

Comparing attributes of Duke Energy Corporation's operating subsidiaries, segmented by plant type.
Comparing attributes of Duke Energy Corporation’s operating subsidiaries, segmented by plant type, on RMI’s Utility Transition Hub.

Publishing PUDL with Datasette

Users have been asking for live access to our data forever, either via a PUDL API or a web interface, but we didn’t feel like we had the resources to maintain that kind of service and ensure it was reliable. Then a few weeks ago we came across an awesome open source project called Datasette that takes SQLite databases, wraps them in a Docker container, and lets users explore the data with their web browser.

It’s perfect for publishing read-only, infrequently updated data. That’s exactly what we’re doing with PUDL, and we’re already storing the data in SQLite, so it only took an afternoon to get the development version of our databases published. This goes a long way toward satisfying some of our data access goals for less technical users, which we touched on a few weeks ago in this post.

Our Datasette instance can be found at and it contains both the raw FERC Form 1 DB, with all of the Form 1 data from 1994-2019, and our PUDL DB, which includes the EIA 860 and EIA 923 data from 2009-2019, and the subset of the (113!) FERC Form 1 tables that we’ve taken the time to clean up so far.

The system has already made it easier for us to collaborate and share the huge pile of data we’ve compiled over the last four years. We’re looking forward to using this system to get our data into the hands of more users.

Just a few examples of custom SQL queries or whole tables:

Please give it a spin, and let us know what you think! This is still experimental, and the interface will probably evolve. If you find problems, feel free to create an issue on GitHub, or drop us a line at Also, we’re still hoping to get the EIA 861 and FERC 714 integrated by the end of the year. See our Data We Wrangle page for additional datasets of interest. And if you’ve got other favorite tools for publishing live, open data, let us know in the comments.