‘Data Stories’ Track COVID Impact, Inform Policy in Boston

Researchers collected survey and online data to tell the story of how the pandemic affected Boston’s diverse communities and how urban policymakers can use that information to navigate the path forward.

a Boston playground with signs on the fence indicating it is closed due to COVID-19
<a href="https://www.shutterstock.com/image-photo/dorchester-boston-massachusetts-april-21-2020-1711085944" target="_blank">Shutterstock/Michael Moloney</a>
MetroLab Network has partnered with Government Technology to bring its readers a segment called the MetroLab Innovation of the Month Series, which highlights impactful tech, data and innovation projects underway between cities and universities. If you’d like to learn more or contact the project leads, please contact MetroLab at info@metrolabnetwork.org for more information. 

In this month’s installment of the Innovation of the Month series, we focus on a combination of efforts happening in Boston to combine existing data about quality of life at the neighborhood level with surveys about residents’ behavior during the pandemic. This data was then distilled into digestible stories that help the researchers better connect with their community on these topics. MetroLab’s Ben Levine spoke with Dan O’Brien, Alina Ristea and David Brade from Northeastern University about their project.

Ben Levine: Your team is working on multiple efforts, rolled up under the “Data-Support System for a City During a Pandemic” project. What are the different parts of this project, and who has been involved in them?

Dan O’Brien: The data-support system for Boston comprises two main efforts. First, the COVID in Boston Database contains numerous administrative and Internet-gathered data sources we curated that capture the events and conditions of greater Boston before, during and after the onset of the pandemic. Second, the “Living in Boston During COVID-19” survey, which we conducted with UMass Boston’s Center for Survey Research and the Boston Public Health Commission (BPHC), collected responses from 1,600 Bostonians about their behaviors, experiences and attitudes throughout the pandemic. The combination of these two resources has spoken to a wide variety of questions and concerns, especially highlighting a range of racial and socioeconomic inequities — from infection rates, to the ability to social distance, to housing and evictions, to local economic activity.

In addition to our collaborators on the survey, we have worked closely with a variety of partners to design and collect these resources. We are now working closely with numerous public agencies, including BPHC, the Mayor’s Office of New Urban Mechanics, the Department of Neighborhood Development, the Metropolitan Area Planning Council, Boston Police Department, the Office of Neighborhood Services and the Massachusetts Bay Transportation Authority, as we turn our attention to insights that can inform the recovery.

Levine: What kinds of data are in the COVID in Boston Database? How do you expect they could best be leveraged?

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Alina Ristea:
The COVID in Boston Database includes a mixture of both administrative records and data scraped from online sources, and we have created variable-by-variable documentation for potential users. The database was originally constructed over the summer and is being updated this month. The administrative records include property assessments, building permits, code and property violations, food inspections, and Boston’s CityScore performance tracking. During the updates, we will add 911 and 311 data, as well as weekly and daily COVID infection cases for Massachusetts. The online data includes Craigslist posts, Yelp! reviews, Airbnb listings, and Places of Interest (POI) from various sources (e.g., Foursquare). These data can be leveraged for multiple topics regarding urban systems due to their massive metadata, geographic specificity and longitudinal nature. They can also be merged to facilitate more sophisticated analyses. The database supports a wide range of questions, including commerce and economic activity, like tracking restaurant viability through Yelp! and food inspections; dynamics of housing and disadvantage through Craigslist and code and property violations; urban planning and gentrification through building permits; crime and disorder through code and property violations, and 911 and 311 reports; and, of course, public health through analyses of the timeline of infections across communities.

Levine: Can you tell us about the “Living in Boston During COVID-19” survey? How does this effort interlock with the database?

O’Brien: Surveys are a unique window into the experiences and perspectives of individuals, something that is often undervalued in the age of big data. Through the survey responses we see how the residents of Boston’s various communities have been impacted by the pandemic; their concerns about the threats the virus and the economic recession pose to themselves, their families and their neighborhoods; their attitudes toward masks, social distancing and transmission risk; and the challenges they have faced in keeping their families safe, healthy and fed. The depth of these responses and what they mean for communities, however, is greatly enhanced if we can coordinate them with other data sets. Crucially, extensive data from the Boston Area Research Initiative (BARI) on neighborhoods allow us to observe the context within which these respondents are operating locally on various dimensions, from crime to economic activity to housing.

Ristea: We have also analyzed the survey responses alongside two sources of anonymized, cellphone-generated mobility data: Cuebiq and SafeGraph. Both have been used extensively by researchers worldwide during COVID to understand the impacts of mobility on transmission, changes in human behaviors and the effectiveness of social distancing policies. Of particular interest, SafeGraph captures patterns of visitation to POI, like grocery stores and parks. We are using the combination of these data and surveys to give greater depth to analyses of the factors that drive transmission and the equity implications of activities and movements during the pandemic.  

Levine: What did you find in the creation of the bite-size data stories? Were any of the findings particularly surprising?

O’Brien: As we have released the insights from our work, we wanted to be true to the dual purposes of scientific rigor and public impact. The former requires long-winded reports that are thorough in their methodology and precise in the description of results. These are not necessarily accessible to our most important audience: the policymakers and practitioners who are too consumed with serving their communities to piece through such a report. For this reason, we decided to slice the reports up into “data stories” that could provide one actionable insight at a time.

David Brade: The data stories have been able to drive conversations with communities typically less interested in the impact and revelations of data. Releasing these data stories via social media, especially Twitter, has captured the attention of the media, community leaders and elected officials. This has allowed the community to engage with us and each other and elevated the understanding of subthemes of the pandemic, such as mask-wearing, high-risk behaviors, asymptomatic spread and the political polarization of the pandemic. We’ve found the data stories to be a key driver in highlighting the value of the overall series of reports.

Levine: Can you go into detail on one of these data stories? What lessons were particularly compelling, and how do you think the information can be used moving forward?

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Brade:
The Balancing Food Access and Infection Risk data story examined which households were able to adopt strategies to secure food while limiting their risk of exposure. The analysis revealed very clear strategies for this across the socioeconomic spectrum. Residents from neighborhoods of color visited the grocery store far more often than others. When we posted this data story, a number of academics started to generate explanations, including not being able to afford larger, less frequent purchases or the difficulty of lugging groceries for those without a car. Somewhat surprisingly, it turned out that the tendency of residents in affluent neighborhoods to have their food and groceries delivered entirely explained these disparities. It was a stark and underappreciated illustration of how income disparity is a major driver in how one navigates exposure during the pandemic.

Levine: This model of measuring the differences in the effects of COVID-19 across different neighborhoods in the same city is fascinating. Can other cities adopt this model? What are the next steps for Boston regarding this project?

O’Brien: Absolutely. Some parts of this work are more ambitious than others, but they are all replicable. The piece that is most immediately accessible to cities around the country is the thoughtful use of administrative records to better understand the needs of communities. This has been at the heart of the trend toward data-driven policy and practice in recent years and is of the utmost importance now. Cities should be scrupulously tracking business licenses to know where closures are; housing courts records to know where evictions are; 311 and 911 reports to know where issues and tensions are rising; and building permits to know where capital is returning (and where it’s not). Given that these data systems typically already exist, it is not a far bridge to cross to use them to pinpoint the communities most in need and, more specifically, the precise needs they have now and will have in the coming months. Scraping Internet data to complement these resources requires an extra layer of skill and capacity but is certainly within reach for many communities.

Last, surveys are expensive and require forethought and patience. But many universities and private vendors are prepared to help. We often forget as a society that we are not out of the woods just yet, and even when we achieve herd immunity through vaccination, economists anticipate a lengthy recovery period lasting months if not years. Surveys will continue to help us see, understand and feel the experiences and perspectives of community members as we navigate these challenging and unprecedented times in a way that “big data” is unable to do.

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Lauren Harrison is the managing editor for Government Technology magazine. She has a degree in English from the University of California, Berkeley, and more than 10 years’ experience in book and magazine publishing.