From landscape design to economic development, equitable public space and infrastructure-reuse projects must interact with many different domains to fulfill their missions. But this web of partnerships and stakeholders also necessitates growing data collection and analysis responsibilities to measure performance and meet equity goals.
For the public spaces in the Five-City Equitable Development Workforce Pilot—a collaborative three-year effort to develop a replicable, scalable workforce training model for infrastructure reuse sites—these challenges are particularly acute. Three of the five sites work with external organizations to deliver their workforce training, and all five collaborate with a range of entities, including employers and wraparound service providers such as food pantries, to move participants along the pipeline to high-quality jobs.
In early June, members of the Five-City Pilot attended Policy Lab San Francisco–Workforce Development and Infrastructure Reuse, a convening coordinated by the High Line Network, a membership organization of equity-focused infrastructure-reuse projects, and Building Bridges Across the River, the DC-based organization that manages 11th Street Bridge Park and coordinates the pilot, to discuss shared challenges such as data collection. As part of the event, the authors led a workshop titled Narratives of Data: Data Collection for Effective Storytelling. Through this workshop, we helped participants, including the pilot sites, understand the importance of data sharing with partner organizations and empowered them to use data to tell their stories.
Understanding the power of data for storytelling
During our presentation, we explained a three-level data framework that can help sites and their partners understand their current level of data sophistication and how they can use the framework to promote their work:
- Level 1 consists of aggregate program direct-service or referral data (e.g., how many people were enrolled in or sent to internal or external services or training classes) and insights gained from staff interviews. These data are often required for grant reporting and are less likely to require data from external partners.
- Level 2 adds demographic and posttraining outcome data about individual Although these data require careful handling (e.g., deidentification) and often require data-sharing agreements with external partners, they are crucial for understanding equity. Data linked at the individual level can be analyzed by sites to assess if they’re consistently failing to help particular groups (e.g., residents who live closest to the public space, women of color, people with disabilities) at the same rate as other groups.
- Finally, level 3 incorporates data about a sites’ partnership structure and its community network, such as partnership network maps or community surveys. Though often reliant on external sources (e.g., partner websites or memoranda of understanding), these data are comparatively easier to obtain and add context for a more robust narrative about what opportunities the public space site brings to its surrounding neighborhoods.
As sites move up the levels, they can leverage the additional data sources to communicate an increasingly sophisticated narrative about their impact. Assessing a program’s role in narrowing earnings’ disparities, for example, requires information about posttraining outcomes (level 2) and existing disparities in the community (level 3).
As members of the High Line Network, all five pilot sites seek to ensure local residents share in the benefits created by new civic spaces, including economic development and housing affordability that may be beyond the expertise of park staff. Because parks can generate benefits (and potential harms) beyond their respective borders, park staff must similarly connect with those outside the park to amplify (or mitigate) that impact.
But this reliance on partner networks has also limited the Five-City Pilot sites’ ability to improve their data collection, which has largely remained at level 1 through the pilot’s second year. Although staff capacity has been an ongoing challenge, the sites’ biggest roadblock to reaching the second and third levels of data collection has been slow or nonexistent data-sharing from external partners. As an interviewee from one of the sites explained, “Having a lot of community partners and collaborative efforts is great, but [it means] you have to go through a lot of people to get data on things that [have happened].”
Improving data sharing practices within partner networks
The logistical challenges created by dense partner networks creates a tension that, left unaddressed, can stymie park practitioners’ efforts to understand and communicate their impact. As the number of partners grows, so too does the number of necessary data providers. To resolve this tension and begin using more robust data, sites can start attaching data-sharing agreements to contracts with partners. A well-crafted data-sharing agreement clearly identifies the following:
- the types of data to be shared, including clear definitions
- staff points of contact, including a process for handling staff turnover
- the frequency specified data will be shared
- steps to ensure data security, especially when working with sensitive data
In the context of workforce development, data security is particularly important because at level 2, external training and wraparound service providers are likely to have personally identifiable information (PII) such as a trainee’s address or date of birth. These data must be handled with sensitivity. Federal and state regulations establish specific rules for federal grantees working with PII, including specifications for secure data storage and transfer.
Data-sharing agreements are not a performance-measurement panacea, however. Equitable public space projects must still collect internal data, synthesize data across partners, and use the findings to communicate their impact. But effective data-sharing agreements can reduce friction between projects and their partners, making it easier for sites to develop and showcase the narratives created by their data.