Heathack 2022 Reflections

I showed up to #HeatHack2022 without a team hoping to connect with others excited about integrating data and policy. I’m proud to say our team’s idea took 2nd place! First, a shout out to @datadrivenlab @lifeandscience @ncscifest @moreheadplanet and the other great sponsors for an awesome event. Below are a few of the things I learned.

Looks can be deceiving. We started with what seemed like a simple question: If a town or city in the Triangle area wanted to plant 200 trees a year to reduce the urban heat island effect, where should they be planted? (learn about urban heat islands in the triangle). After our initial brainstorm it became clear we were going to need to integrate zoning information, heat data, permeability data, tree cover data among others. We also learned that some municipalities don't have a program for watering the trees.

Innovation does note mean novel. Our team decided to look to see if other cities had already figure this out. We talked about how innovation doesn’t mean the idea needs to be new (unlike in science where only “novel” ideas are often pursued). Applying an idea that works somewhere else is innovative too! We found a cool tree decision tool built for Boston (https://zoeywerbin.shinyapps.io/Boston_trees/) that served as a starting point for our work.

Play to your strengths. This is when I realized how many cobwebs had developed on my coding skills in the past three years… In another life or with a lot more time, I might have been able to modify this product to use data from the triangle but not in the next 4 hours. Given the limitations of time, our team worked in parallel with one member with a data science background integrating the data into a visual of where to plant the trees based on all the types of data we had. The other teammate and I began designing a prototype for our decision support tool using Figma.

 Our app would help cities/towns/residents explore where planting trees can have the greatest impact on urban heat. They could weight the location data based on their priorities as well as filter the right type of tree for that area.

Based on the users’ choices for temp, tree cover etc., the tool would generate a heatmap of top locations for placing trees as well as highlight which kinds of trees to plant.

Data driven policy is HARD. Our team was made up of a PhD grad and two current masters students. We worked on this question for about 9 hours and we still weren’t quite able to provide a data driven answer for where to place the first 200 trees. This drove home the realities of how hard it is to do data driven policy. Especially if we don’t provide city/local governments with the time, talent, and resources to process, interpret, and act on the data. Assuming there is even data to process. Many places lack enough local high resolution environmental data to inform these kinds of decisions.

It’s important to consider other options. A large open question that we considered but didn’t answer was, are there other similar priced solutions that would have a larger impact on urban heat islands?

We need more civic scientists. I see this as a place that the scientific community has so much opportunity to be a part of co-creating solutions to these types of policy relevant question by contributing their time and talent. The answer to the question of where to plant trees requires experts in climate change, ecology, urban planning, policy, public health and more.

Thank you. I can’t thank the organizers; my team mates Kira Moodliar and Andrew Zalesak; and the judges Max Cawley, Myleigh Neill, and Shenekia Weeks, enough for this opportunity!