Advancing Geographic Equity Using Spatial Analysis
On this All In webinar, speakers shared their experience using spatial analysis in their respective fields and facilitated interactive conversation focused on effective practices to transform systems and improve the health and well-being of residents in their cities.
The Community Geographic Information Systems (CGIS), at the Center for Urban and Regional Affairs (CURA), examined city spending with an equity lens through an analysis of the City of St. Paul’s Capital Improvement Budget (CIB). The research finds that spending is unequal across the City, with some indications that it is also inequitable. In particular, the analysis shows east side planning districts experiencing pronounced disadvantages. The report also finds that the allocation process does not explicitly take into account geographic balance or racial equity.
The Center for Spatial Data Science and the School of Social Service Administration, both out of The University of Chicago, sought to develop a process and platform that uses publicly available data to present a system-wide picture of whether public funds are appropriately matched to identified community health needs. This study analyzes the distribution of public funds for health and health-related human services within the City of Chicago.
When you look at the numbers, do some neighborhoods really get more Capital Improvement Budget funds than others? The East Side Neighborhood Development Center (ESNDC) partnered with the Kris Nelson Community-Based Research Program at the University of Minnesota’s Center for Urban and Regional Affairs (CURA) to answer this question, along with examining which neighborhoods were getting the most CIB spending and if the CIB process was equitable.
CURA’s CGIS program is a partner in the National Neighborhood Indicators Partnership, a collaboration between the Urban Institute and local organizations in over 30 cities working to further the development and use of neighborhood-level information systems for community building and local decision-making.
This study uses a unique dataset of government contracts with nonprofit organizations in New York City between 1997 and 2001 to study the relationship between the allocation of social services funding across neighborhoods and neighborhood need. We distinguish between local organizations serving their immediate neighborhoods and distributive organizations serving multiple neighborhoods. Overall, contract dollars allocated to both organizational types are positively associated with socioeconomic disadvantage, although distributive organizations are less likely to be physically located in needy neighborhoods. However, contract dollars for services targeted to specific populations are sometimes negatively associated with the prevalence of these targeted populations, especially when those contracts go to distributive organizations.
Social science research, public and private sector decisions and allocations of federal resources often rely on data from the American Community Survey (ACS). However, this critical data source has high uncertainty in some of its most frequently used estimates. Using 2006–2010 ACS median household income estimates at the census tract scale as a test case, this study explored spatial and non-spatial patterns in ACS estimate quality. The study found spatial patterns of uncertainty in the northern U.S. to be different from the southern U.S. as well as different in suburbs from urban cores. The results indicate that data quality varies in different places, making cross-sectional analysis both within and across regions less reliable.