Keywords: Community indicators, principal components analysis, data democratization, community health needs assessment, data visualizationIntroductionMeasuring impact has become increasingly important in philanthropy as foundations seek to learn more about the impact of their investments by measuring the achievements of the programs and projects they support. In an era where demand seems only to increase and resources will always be limited, identifying needs and priorities must be the foundation of any philanthropic endeavor. As the nonprofit sector has grown in size and scale over the past 30 years, so has the pressure on nonprofits that deliver health and human services to operate efficiently. To reduce administrative costs and put more dollars into services, community indicators are increasingly becoming part of assessing need.This article attempts to provide an approach that enhances the usefulness of community-indicator projects. We build upon the work of previous community-indicator scholars while employing a methodological approach - principal components analysis (PCA) - used in the work Our Patchwork Nation: The Surprising Truth About the Real America (Chinni & Gimple, 2010) The result is a more informative approach to assessing community needs that is easily understandable, visually appealing, and more applicable to a broad audience. We believe the lessons learned from our approach to community-indicator projects can help other grantmakers increase the effectiveness of data-intensive, large-scale community-indicator work.First, we provide an overview of the rationale that drove our approach. This is followed by a brief overview of the literature related to recent critiques of community-indicator work; the critique of the current practice in community indicators is highlighted in the context of nascent data-democratization efforts. We then apply PCA to the multitude of community indicators developed as part of a community health needs assessment for Kent County, Michigan.False DichotomyPundits often point to the near ubiquitous Republican and Democratic map of America's counties as way of highlighting our nation's political leanings. Closer inspection, however, is warranted. Chinni and Gimple (2010) expose the problems associated with this false dichotomy.Raised in the Detroit suburb of Warren, Chinni writes of being particularly vexed by the blue labeling of Michigan's Wayne and Washtenaw counties - counties with striking differences. Wayne County is home to Detroit. In 2010 Wayne's median income was $40,590 and per capita income was $21,405; the overall poverty rate was 22.5 percent and the child poverty rate was 32.6 percent (U.S. Census Bureau, 2007 - 2010e). The proportion of the population with a bachelor's degree was just 20.4 percent (U.S. Census Bureau, 2007 - 2010f ). Washtenaw County, home to Ann Arbor and the University of Michigan, is about 40 miles west of Detroit. The median income in Washtenaw was $56,708 in 2010, per capita income was $30,594, and the overall poverty and child poverty rates were just 13.7 percent (U.S. Census Bureau, 2007 - 2010c). The proportion of Washtenaw's population with a bachelor's degree or greater was 50.6 percent, more than twice that of Wayne (U.S. Census Bureau, 2007 - 2010d).How could Wayne County and Detroit, synonymous with urban decay, and Washtenaw County and Ann Arbor, often listed among the top places in to live in the U.S., be thought of as similar because they are blue? This question prompted a move beyond the broad generalization of red and blue designations toward a new paradigm - a more nuanced approach that more accurately characterizes the diversity of the United States by classifying each of America's 3,141 counties into one of 12 community types.1Community Indicators: A Brief HistoryCommunity indicators are a system of measures designed, developed, and analyzed by community members to provide neighborhood-level information for community-building and policymaking (National Neighborhood Indicators Partnership, 2012; Smolko, Strange, & Venetoulis, 2006). …