Mapping Child Care Access to Drive Community Solutions

Child care is a critical resource that supports working families, drives economic prosperity, and ensures children’s early development. However, in regions like the Great Lakes Bay Region (GLBR), stakeholders identified significant disparities in access to licensed child care providers, particularly in rural and underserved areas. To address these challenges, community leaders turned to data mapping and visualization to gain a better understanding of the child care landscape and create targeted, equitable solutions.

This case study explores how the GLBR Child Care Landscape map, powered by data visualization, helped reshape child care policies and strategies for families and providers.

The Challenge: Understanding and Addressing Child Care Inequities

The GLBR faced challenges in child care access similar to those seen across the nation:

1. Uneven Distribution: Families in rural areas and economically disadvantaged communities were struggling to find local child care providers.
2. Missed Needs in Programming: There was a lack of nontraditional operating hours, such as evening or weekend care, for shift workers needing flexibility.
3. Lack of Data-Driven Decision-Making: Policy makers, nonprofit organizations, and advocates lacked actionable data on where child care resources were most needed. Without sufficient insights, funds and programs often failed to support the communities benefiting most.

To tackle these problems, GLBR’s leaders needed a comprehensive visualization of child care providers in the region that categorized facilities by type, availability, and targeted services.

The Solution: Visualizing Child Care Access Through Data Mapping

Using a data-driven approach, the GLBR developed the Child Care Landscape map to compile key data points on:

• License Type: Dividing providers into Licensed Centers, Licensed Family Homes, and Licensed Group Homes.
• Operational Hours: Filtering for providers offering weekend, overnight, or evening services essential for nontraditional work schedules.
• Targeted Demographics: Highlighting offerings specific to certain age groups, including preschool-aged children and school-age-only services.

This interactive map helped visualize the distribution of child care providers across the region, made actionable by its ability to filter conditions for specific needs. By pinpointing where services were lacking, the visualization became a key tool for policymakers, service providers, and organizations devoted to improving family welfare.

Insights Gained: What the Map Revealed

The mapping project revealed critical gaps and disparities in child care availability:

1. Rural Areas Were Severely Underserved: Licensed providers were concentrated in urban hubs, leaving rural families with limited or no access to local child care.
2. Lack of Flexible Hours: The majority of providers operated only during normal business hours, leaving a gap for families requiring overnight or evening care.
3. Limited Capacity for Licensed Family Homes: Family-based child care, which offers a more personal, home-like environment, was unavailable in many areas, forcing families with younger children or cultural preferences for smaller settings to rely on less formal options.

With this data now available, the GLBR community could begin addressing these challenges in a targeted way.

The Impact: Data Driving Solutions

The GLBR Child Care Landscape map served as a catalyst for informed decision-making and impactful actions:

• Policy Realignment: Local and regional policymakers used the map to prioritize investment in rural child care deserts. By offering targeted financial subsidies or licensing support, they incentivized providers to set up care centers in under-resourced areas.
• Collaboration Among Employers and Providers: Manufacturing companies in industrial centers partnered with nonprofit child care providers to establish overnight and weekend care services, addressing the needs of shift workers.
• Empowered Families: The map was made publicly accessible, allowing parents to easily locate care providers tailored to their specific requirements — whether that was care in the evenings or facilities that worked with school-aged children.
• Increased Capacity: Licensing incentives encouraged more family-based care homes to reopen or expand in areas where personal, home-style care was in demand.

Key Results and Takeaways for GLBR

The data-driven approach transformed how child care policymakers, nonprofits, and providers tackled the issue of access. Key results included:

1. Expanded Services in Rural Areas: Over 10 rural child care centers were newly established within the first two years of adopting the map, serving hundreds of previously underserved families.
2. Increased Stakeholder Collaboration: Partnerships between employers and child care providers led to the launch of flexible care initiatives in industrial hubs.
3. Better Use of Funds: By targeting rural and underserved communities, funding programs achieved measurable successes, resulting in high-impact outcomes.

This case study demonstrates the importance of actionable data in empowering communities to create equitable solutions while fostering collaboration between public, private, and nonprofit stakeholders.

Conclusion: A Blueprint for the Future

The GLBR Child Care Landscape map shows how data visualization can turn abstract statistics into real-world solutions. By clearly displaying gaps in child care access, the map allowed stakeholders to make informed decisions, allocate resources effectively, and achieve meaningful, measurable outcomes. Communities across the nation can look to the GLBR as a model for how data-driven decision-making can successfully address child care inequities and support families.