Meet Patrick Atwater. Patrick works for a data nonprofit called ARGO which is regarded as one of the best in the public data business. He and his team are currently working on a collaborative data project compiling water use information from across California’s water utilities.
How’d you get into data?
I have always had an interest in math and policy. In high school, I finished second place in a LA County Math Field Day, and in college I majored in both Math and Philosophy, Politics and Economics. After college, I did the Coro Fellowship in Public Affairs which was highly qualitative, and then worked for a public finance consulting company which was highly quantitative. Through my experience in working in and around government, I’ve witnessed some obvious ways to improve efficiency and efficacy in areas like street cleaning, education and water use.
What are your go-to analysis and visualization tools?
We use Apache Airflow at ARGO. Airflow is a Python-based workflow orchestration tool that allows us to automate data integration and processing. We use R for statistical analysis and Tableau’s free nonprofit version for ad hoc visualizations. Ultimately, we are tool agnostic but with a bias towards free and open-source software.
What issue in Los Angeles do you think has the most potential for a data-driven solution?
Achieving water efficiency, not just in Los Angeles but across the state. We use data to benchmark which reduction programs are the most effective, from changing landscapes, to installing water capture infrastructure at residential properties. The trick for us is integrating a large variety of data types from a multitude of suppliers. We had to invent a new business model. Essentially, we are run like a data utility for other utilities. Water utility companies pay us a monthly subscription for use of our data infrastructure.
This is all part of California’s move towards adapting to climate change, a difficult but not impossible process if we’re smart. Part of being smart means setting smart rates to help us achieve sustainability goals. Rates are set by the cost of service to a user type (i.e. the cost of sending water to a residential property, an a industrial property, etc.). We need to use pricing mechanisms that discourage waste, like marginal rate increases with use increases. Currently, 80% of the state’s water utilities already do this.
What’s your favorite “data-story”?
The story of data-driven water efficiency is still being written. We’ve seen operational deltas like $20 million in savings for one of our clients. We’ve seen how our members are beginning to proudly identify as part of the CA Data Collaborative (CaDC). Ultimately, the water story is part of the bigger story about CA’s future. Our water system was designed for a population half the size. We have to optimize every drop, and ensure water reliability no matter what the future holds.
To this end, we are embracing experimental and pretty avante garde approaches. We are currently working on creating a blockchain powered water savings token that people can spend and barter with for sustainability themed swag like discounts at local stores and special access with celebrities.
Predicted monthly savings for each household in the data set. The dark green line corresponds to median savings. Seasonal variation leads to swings in average savings from -1.5 to -2.7 gallons per square foot.
The blue line shows the observed usage for one household. The red line shows the expected usage based on the consumption of households with historically similar usage patterns. The vertical dotted line represents the time the turf removal was performed. A 29 percent reduction from expected water use is visible after the removal.
What advice do you have for someone looking to start using LA Counts datasets to tell their own stories?
You don’t need extensive data skills. A crisp, useful question is more important, and from there you can put together a team. Don’t underestimate the value of in-person meetings and collaboration around a cause.