Coal plant smokestacks have long been the poster-children of climate change. But transportation is the largest source of planet-warming greenhouse gases in the United States today – and the bulk of those emissions come from driving in cities and suburbs. For this project, we analyzed data from Boston University’s Database of Road Transportation Emissions to show readers how driving-related emissions in their hometown have changed over time and how they compare to emissions from other metro areas across the country.
Dozens of local media outlets from around the country — from Pittsburgh to Jacksonville — picked up the piece and used the dataset to report their own local stories on driving-related emissions. (See list below.)
Additionally, the map from the piece was requested and used by a representative of the Minnesota State House in a committee meeting on transportation-related emissions.
Boston (interview with researcher)
What was the hardest part of this project?
Climate change is often a subject that feels abstract to readers. Greenhouse gas emissions are not something we encounter with our senses every day. We do not see them with the naked eye, or even smell them by nose, the way we experience other pollution. Yet, most of our actions result in these emissions.
The hardest part of this project was finding a way to tell the visual story of driving-related GHGs in a way that would connect with readers.
The high level of detail offered by the road-level emissions maps from Boston University was a big draw because it helped make the abstract idea of driving-related emissions feel less abstract. Through the maps, readers could see emissions coming from the very roads they drive on every day, making a direct linkage back to the source: Cars. (The research team mapped emissions down to the road-segment level.)
However, relying on the detailed, road-level map data for the top visual was limiting for storytelling purposes, as these maps only show total emissions and cannot be easily translated to per-capita measurements (not as a technical issue, but rather an issue of emissions allocation). The per-capita data, however, is much more useful for comparing city by city greenhouse gas releases, and for readers who wanted to understand their own impact.
We reconciled these competing needs by using the detailed map as the base for our lead graphic, in order to provide readers with visual context, but overlaying it with information that is not directly reflected in the map in the form of a tooltip.
This top graphic provided a personalized “hook” to get people to understand the story more personally. Then we were able to use the rest of our data analysis to compare trends over time and across cities lower down.
What can others learn from this project?
A single dataviz doesn’t have to do all the work, but can serve as a visual “nut graf” that summarizes data graphics further down in the piece. We used our top visual as a compelling hook into the rest of the piece, but were able to tell a more nuanced story further down the page once we captured a viewers attention and established them in a sense of place.
Others can also learn to be bold in their use of maps to help locate readers within the story. We built our own large slippy map geolocated to a users’ location for this piece in order to help readers understand the story through connection to their own city/town. Readers were able to intimately explore the dataset at the “new view” (or zoomed in scale) because it offered a more interesting story and a more personal connection to the data.