Using Data to Understand Upward Mobility Conditions in Fresno, California
Display Date
File
File
Download Report
(697.5 KB)

A mural of a vintage "Fresno" postage stamp painted on a building in Fresno, California.

Despite being one of the nation’s most productive agricultural hubs, Fresno and the Central Valley of California are experiencing persistently high poverty rates (PDF), educational shortfalls, and a loss of skilled workers. And decades of housing inequality and environmental injustice have left many neighborhoods with inadequate housing and poor health outcomes.

This case study documents how the Central Valley Community Foundation (CVCF) and the Urban Institute created a publicly available data dashboard to measure progress toward addressing these challenges. It offers valuable insights for leaders interested in creating a central hub for local data and using those data to inform strategies for advancing upward mobility in their communities. 

THE GOAL

CVCF had three goals for creating the Fresno Mobility Metrics Data Dashboard. CVCF wanted to 

  • understand key questions about Fresno’s ability to support residents’ upward mobility, including how Fresno compares with peer communities;
  • combine Mobility Metrics data from the Upward Mobility Initiative with local data sources to better understand local conditions for mobility; and
  • create a publicly available central hub for regional data on upward mobility.

HOW WE DID IT

Engaging data stakeholders in the region. To ensure the dashboard met local needs, CVCF assembled a group of leaders from local government and community-based organizations. They gave feedback on early dashboard design elements, identified critical supplemental data sources, and determined areas for future work. 

Designing charts and web pages to meet CVCF’s needs. To answer CVCF’s key questions about conditions for upward mobility in the region, we made a series of charts comparing Fresno with peer communities, highlighting differences across racial and ethnic groups and geographies. Other charts tracked changes over time and spatial patterns across Fresno neighborhoods. The dashboard also directs users to other regional data and allows them to download the data powering the charts. 

Ensuring the dashboard is sustainable. Urban built the dashboard code with the aim that CVCF could easily make simple data updates. CVCF has taken lead in updating the dashboard’s code base, with Urban providing support when technical issues arise.  

LESSONS LEARNED

During this process, we have learned four lessons that may be relevant for others looking to use data and build dashboards to understand and improve upward mobility in their communities.

Adopt continuous, iterative, and targeted dissemination. CVCF conducted targeted outreach with the data dashboard to the academic community, meeting with and presenting to librarians, teachers, professors, researchers, and students. CVCF has found that the dashboard is most viewed during and immediately after these events, though it also has sustained use outside of events. These successes show the effectiveness of identifying key audiences and tailoring outreach strategies to their needs.

Determine how the dashboard will be maintained.  For dashboard projects involving multiple partners and requiring regular maintenance, we encourage collaborators to clearly articulate how the dashboard will be maintained and align dashboard updates to the release of underlying data. In line with this learning, we plan to begin a new round of collaboration focused on supporting CVCF update the dashboard and add features in the coming weeks and around the release of the 2025 Mobility Metrics. 

Leverage software development for training and capacity building. The development phase of the project can support training and capacity building for a partner maintaining a piece of software. As we have learned this lesson, we have increasingly involved CVCF not only to maintain the dashboard but also when adding new features to it.

Ensure the dashboard is mobile friendly. Thirty percent of American adults earning less than $30,000 own a smartphone but do not subscribe to a broadband internet service at home. Given this context and our intention to build software that can inform underserved communities, we prioritized making the dashboard easy to use on mobile phones and would encourage similar projects to do the same.