Fostering equitable and complete neighborhoods

In the fall of 2016, the City of Madison launched a two-year effort to conduct a people-powered planning process to update Madison’s comprehensive plan. In the Neighborhoods and Housing issue area, residents expressed a dire need for “affordable housing in corridors with access to transit, schools, parks, libraries, neighborhood centers, and other amenities needed for daily living,” asking for more neighborhoods to meet that standard of completeness. While the Madison metropolitan area has grown steadily for the last six years, a 2013 report found that racial disparities between Madison neighborhoods created significant challenges for communities of color. The City of Madison responded to this finding by incorporating a distinct focus on racial equity into its strategic planning in 2017 including a goal that all Madison residents have equitable opportunities to live in and be part of strong, complete neighborhoods. Madison’s open data and innovation staff reached out to Sunlight for help using Tactical Data Engagement to address the demonstrated need for more complete neighborhoods in Madison.

Madison’s goals for open data

After working with Sunlight and the Johns Hopkins Center for Government Excellence in 2016 to refine its open data governance, Madison’s open data team decided to take their work to the next level by trying to directly connect with and address residents’ information needs. Madison’s open data champions wanted to use the on-going comprehensive planning process, Imagine Madison, as a launching pad for a new investigation into what kind of data residents would want to use to address local issues. Madison’s desire to work closely with residents around open data presented a perfect opportunity to pilot the Tactical Data Engagement approach starting in September 2017.

Madison’s story

To start, Sunlight helped Madison analyze public feedback from Imagine Madison. We found that residents were distinctly interested in more complete neighborhoods, and that information gaps likely contribute to some challenges in neighborhood well-being. To see how residents’ concerns overlapped with internal political will and strategic goals, Sunlight also analyzed Madison’s internal priorities which were expressed in an internal “Roadmap to Outcomes”. The roadmap showed that city employees were equally concerned and interested in helping Madison to build Complete Neighborhoods, and they had data sources that might aid in that effort. This direction was further bolstered by a community survey that found residents were interested in more open data related to complete neighborhoods. Since residents had asked Madison to prioritize complete neighborhoods – and internal conversations had highlighted opportunities to improve data practice around the City’s efforts to address neighborhood well-being – choosing complete neighborhoods as a focus area aligned both internal and external stakeholders.

Building on this focus area we next developed a list of community members who worked on neighborhood-related issues as potential interviewees. In building a list of interviewees, Reboot, Sunlight, and city staff worked together to assemble stakeholders representing various levels of data expertise or strategic decision-making power, from large funders in the area, to civically engaged individuals. Interviewees included community organizers, working data-driven professionals, business owners, civil servants, and individual problem-solvers. The goal in interviewing these stakeholders was be to listen to their stories and understand how they received, used, or shared neighborhood-related information in Madison. An important step of the process was including city staff in trainings giving them the tools to participate in Reboot’s design research sprint and take away some new skills in design research. This piece was essential for ensuring that city staff could replicate elements of the TDE process again in their future work.

Reboot and Sunlight interviewed Madison’s neighborhood data stakeholders with support from city staff to gain a better understanding of the open data environment. At the end of the interview stage, the research team and city staff synthesized insights to build out specific opportunities for residents to gain better access to or use public data to address neighborhood issues. The result of this research was a set of six open data user personas, all of whom are working on neighborhood issues, including illustrations of their data journeys and the pain points they face in trying to get data from the city and its communities. These journeys and pain points were refined into specific opportunities where Madison might convene or collaborate with specific neighborhood data users to improve city neighborhoods. The user personas report and the recommendations will be public as part of the conclusion of the refining stage.

Following the TDE process:

1. Finding a focus area

To find a focus area that residents felt was important, we used existing public channels for public communication. In Madison’s case, that meant plugging into the ongoing Imagine Madison comprehensive planning process. This approach was a great way for Madison to start listening for signals that showed who we could talk to about data needs in the community. Other cities interested in testing TDE approaches may look to their own urban planning processes as an open door to reach out to community members.

Imagine Madison produced a wide range of research about issues important to residents. To narrow down Madison’s TDE project to a specific open data-related issue, we sought out shared priorities from the research that overlapped with the city’s internal strategic priorities. Hearing about important issues, both inside and outside of the city, helped us ensure that Madison’s TDE work connected to genuine local issues.

2. Refining use cases

To determine how residents might want to use data to address neighborhood issues, Sunlight partnered with the social impact firm Reboot and their team of design-research experts to conduct in-depth research on local data needs and construct user personas. This process involved using Reboot’s design-thinking methodology to conduct in-person interviews with members of the community working on neighborhood issues, and build out user personas to describe how they use data in that work.

User Research Process Over the course of a two week research sprint, the Sunlight and Reboot team interviewed 36 stakeholders in Madison to understand their stories and information needs. Reboot’s design-research approach helped structure this process:

• Research design: Sunlight first worked with Reboot to develop research objectives, lines of inquiry and question guides, as well as target respondent characteristics.
• Key informant interviews: Before conducting interviews focused on understanding information needs relevant to complete neighborhoods, Sunlight and Reboot interviewed “key informants” who could provide context and direction, and help us refine our research objectives and lines of inquiry. Key informants included city staff and community leaders with expertise on open data and complete neighborhoods issues.
• User interviews: The bulk of the Sunlight and Reboot team’s time over two weeks on-site was spent conducting in-person interviews. “Snowballing”— asking those we interviewed who else we should be talking to — was a key aspect of our approach to ensure we reached relevant neighborhood actors. Whenever possible, we met interviewees where they were — in their offices, at their local coffee shop, or in at least one case, even on their living room sofa — meeting people in their own context helps with mutual understanding.
• Documentation and synthesis: Each interview was recorded, wherever possible, and transcribed. The Sunlight and Reboot team documented our observations and insights each day. We discussed emergent patterns and organized interview notes into various categories that helped analyze and synthesize this qualitative data into tangible findings.
• Constructing personas and journeys: These patterns and findings helped us identify six common open data user types and to map their processes for acquiring, analyzing, and applying data and information.

Conducting interviews through a two-week human-centered design research sprint worked for Madison, because our goal was to deeply explore the benefits of doing in-depth interviews with residents to develop user personas. This has been the Open Cities team’s most hands-on project to date. Getting a comprehensive view of how residents were using data in their work to build more complete neighborhoods helped us better understand how the City’s open data efforts might plug into their real needs and goals.

3. Designing a plan

With the Refine phase complete, we reviewed the preliminary user personas with city department heads. It was staff in the city’s Community Development Department (CDD) who saw an opportunity to take action. They were in the process preparing for a request for proposals for the 2018-2019 Safe and Thriving Communities grant, a project to reduce violence and the impact of violence in Madison’s Northside neighborhood. The department wanted nonprofits interested in the grant to use open data in their applications, and to connect their work to overarching trends and needs in the Northside. We decided to design a plan to help make that happen.

After talking through the grant application process with CDD, we created a co-design framework and prototyped a preliminary “data toolkit”—a resource to provide context and guidance connecting nonprofit applicants to relevant data sources and how to use them to make evidence-based arguments in their grant applications—to accompany the city’s request for proposals. We used the information we collected for the “Small CBO Director” persona to inform the product. From the user research we had complete in the Refine stage we also realized an opportunity to connect the “Small CBO Director” to the “Connector” persona to support nonprofit use of open data.

With that in mind, we held a co-design session with connectors who were both familiar with data and with Madison and Northside nonprofits and their needs. These include the University of Wisconsin’s Applied Population Lab, the Northside Planning Council, and Collaboration for Good, as well as the city’s CDD and Open Data team.

The result was a plan for improving the data toolkit prototype in ways targeted to support Northside nonprofits’ use of data in their safe and thriving communities grant proposals.

4. Implementing an intervention

The final intervention is a data toolkit that shows nonprofit staff who are applying for the Safe and Thriving Communities Initiative how to supplement their applications with data from public agencies. The toolkit includes explanation of how to make a data-informed case for their work, links to specific data sources, and contact information for people who are ready to answer questions about the data and how to use it correctly.

To make the toolkit event more accessible, we also created a short explainer video about the toolkit and the data it contains. The video will be shared on social media throughout the application period as well as at the in-person meetings that the city will hold about the opportunity.

In addition to supporting the nonprofits applying for the safe and thriving communities grant, we also hope the toolkit can be a resource to anyone in Madison looking to access relevant data about the Northside.

Next steps

Updated May 1, 2018

The immediate next steps for Sunlight and the City of Madison are to help Safe and Thriving Communities applicants use the toolkit. If you are reading this case study to inform your application for the initiative, we are happy to answer any questions about the data in the toolkit and how to use it.

We’ll also be measuring how well this resource improves applications. The current round of awards is the second round from this grant. The first round did NOT include a data toolkit, which creates a great opportunity for us to understand the impact of this support. We’ll be working with the city to understand whether this round of applications included more substantial use of data and evidence.

The city will assess the lessons learned from this data toolkit pilot to consider whether to adopt this practice for future grants with both CDD as well as other departments. The city might also consider creating toolkits for the other personas we outlined. For example, a toolkit for the “Community Activist” persona that supports neighborhood associations or community organizers.

Beyond data toolkits for evidence informed grant applications, user research identified a number of other use cases and opportunities to support community use of open data. Madison is well positioned to revisit those and design and implement additional interventions.

Summary of key findings

Sunlight’s analysis of public concerns and in-depth open data user research has uncovered the following findings relevant to the design and implementation of Madison’s open data program:

How residents acquire information

• Observations:
  • Skilled data users know how to find the data they need
  • Successful data users have the time and resources necessary to acquire data.
  • Academics, large nonprofits, and motivated community members are more likely to use data to inform action
  • Awareness about Madison’s open data portal is low, particularly among less skilled data users
• Strategies to engage:
  • Facilitate access to city and other data sources relevant to neighborhood development organizations.
  • Increase value and relevance of city data to neighborhood development organizations.
  • Increase user confidence in city data.

How residents analyze information

• Observations:
  • Successful data users either have or can access the technical expertise to analyze data.
  • Successful data users know who is responsible for collecting and maintaining data for the city, and are able to reach out to them for clarification.
• Strategies to Engage:
  • Establish points of contact for city data sources so that people know who to contact about data.
  • Enhance interpretability of city data.
  • Connect low-capacity CBOs to technical expertise.

How residents act on information

• Observations:
  • Successful data user have access to data at the granularity they need it.
  • Successful data users utilize multiple sources of data to improve their analysis and make their arguments more compelling.
  • Successful data users, in addition to their technical skills acquiring and analyzing data, are also strong communicators.
• Strategies to Engage:
  • Publish more indicator data, catered to CBO needs.
  • Highlight successful data to action use cases.