Green Space Data Challenge: Physical Environment Winners

Next up in our Q&A series with the winners of the Green Space Data Challenge, we’ll hear about Joe Edgerton, Veronica Lucchese, and Lili Badri’s analysis of green space in Baltimore that took first prize in the Physical Environment category. Please join us in wishing Joe, Veronica, and Lili congratulations!

The Physical Environment Category Winner: Mapping Green Space in Baltimore’s Communities

Joe Edgerton, Veronica Lucchese, and Lili Badri work together at the University of Maryland Center for Environmental Science Integration and Application Network (IAN), a group of scientists dedicated to solving problems — not just identifying them — through science communication, stakeholder engagement, and production of environmental report cards. Joe is an assistant science communicator for IAN with a B.S. in biology, Veronica is a graduate research assistant and first-year Ph.D. student studying environment and society, and Lili is a science communication intern with a Master’s degree in environmental science.

They pooled their skills — ranging from science communication to data analysis to environmental science — and their passion for environmental justice to conduct an analysis of green space in Baltimore that took first place in the Green Space Data Challenge for the Physical Environment category.

Q: Why did you take on this data challenge?

A: We’ve all worked together on watershed report cards, like the one for Chesapeake Bay, but we liked the idea of learning more about green space and working with those data sets. In addition, the work we do at IAN is centered on science communication, which we’re all passionate about, so we saw this challenge as a way to sharpen our data analysis and problem-solving skills.

Q: Tell us about your project. What problem did you want to solve and what did you do with the data?

A: Our project was an analysis of green space in Baltimore, a city we’re all familiar with and which we felt was a feasible area for investigation in terms of size. After doing some research, we also saw that Baltimore has experienced green space challenges over the years, so it seemed like a good fit for us. According to the Trust for Public Land’s analysis, although Baltimore is only about 8% parkland, walkable park access for all residents is 88%. Keep in mind, though, that’s an overall score. Certain neighborhoods have walkability challenges.

We wanted to identify a good threshold measure for urban greenspace per capita for Baltimore and see how different neighborhoods scored using that threshold. To start, we found a paper that talked about ideal green space per capita and identified a range. We thought that could be a great threshold to use for scoring.

We used the Environmental Protection Agency’s (EPA) EnviroAtlas as our main dataset. EnviroAtlas provides geospatial data and analysis on the relationships between people and nature.

We broke Baltimore into geographic sections — North, Northeast, South, and so on — and then we broke those sections into neighborhoods. So, we have greenspace-per-capita scores at the neighborhood level.

We created a map that scored the city’s sections with color coding — green for high green space per capita and red for low. The map shows that North, West, and Northwest Baltimore have the most green space per capita, while downtown East Baltimore has the least.

We also wanted to explore community members’ perceptions of publicly available green space in Baltimore. For that, we used data from City78 that include user reviews of public parks for the entire U.S. We filtered the data for Baltimore parks and generated a word cloud to provide a snippet of what people were saying and the main themes. Some of the themes were recreational, like “fishing,” “fun,” “dogs,” “kids,” and “play.” Other reviews were more descriptive, using words like “nice,” “great,” “good,” and “beautiful.” Overall, the perceptions were quite positive.

Q: How did you decide which datasets to use? Was this your first time using green space data?

A: Some of us have worked with green space data before but not all of us. In choosing datasets, we wanted to make sure that we could get enough data to do the study. We were happy that at least one of the datasets, EnviroAtlas, had very high-resolution data that we could use, especially to address our main question, on scoring green space per capita.

Q: Why did you choose to focus on this topic?

A: Science is the primary driver behind research, but we wanted to do more than that. We also wanted to provide a service to a community in need. This study offers new information that could be used to set goals for creating more green space in areas of Baltimore that need it the most and holding policymakers accountable for meeting those goals. Funders could use it too, to help decide where to invest their resources and drive policy change.

Q: How do you envision communities using what you created?

A: We all worked really hard on this, and it would be great to see people use it. There are several nonprofits in Baltimore that could use these data to support their green space agenda. For example, everyone thinks they know what green space is, but there are some important loopholes — like parking lot islands. Builders actually count those little patches of grass as green space so that they can skirt green space requirements. People need to know about things like that.

Q: What would you like to see happen with your project next?

If we were to extend the project, we would want to go deeper on our threshold measure, which is just a general threshold that we used for this study and that doesn’t reflect Baltimore’s specific needs. Ideally, we would talk with people in the communities and get the perspective of city planning officials to develop a threshold measure for driving change in Baltimore.

We would also love to see our research made available to the public — either in a publication or on a website where people could access and use the data.