A growing body of literature has identified mental and physical health outcomes associated with residential exposure to greenspace, and the strength of associations have been shown to vary between cities, by levels of neighborhood deprivation, and by race. This may be in part because of the greenspace exposure measures applied in research: commonly the normalized difference vegetation index (NDVI), a remote-sensing measure of vegetation density and health. The type and maintenance status of greenspace varies within and between cities, but NDVI alone cannot distinguish between well-maintained gardens, tree canopy coverage, fields, or overgrown vegetation in abandoned lots. Because these distinctions are relevant to how residents experience and interpret the greenspace around them, public health research should adopt measures that quantify different types of greenspace to better understand residents’ actual exposure.

We present a classification of neighborhood-level greenspace composition in Flint, Michigan. We assigned exposure areas to block groups as one-kilometer circular buffers around their population-weighted centroids, then measured the park area, tree canopy coverage, area covered by maintained and unmaintained vacant lots, and mean NDVI within these exposure areas. We used agglomerative hierarchical clustering to identify 3 neighborhood classifications based on these measures.  Most neighborhoods (Cluster 1, n=99) had approximately average values across all measures; 14 neighborhoods (Cluster 2) were characterized by high coverage by both maintained and unmaintained vacant lots and above-average NDVI; and 2 neighborhoods (Cluster 3) had high park area coverage, high tree canopy cover, and high NDVI.  Cluster 2 was associated with a higher concentration of Black residents, and Cluster 3 was associated with lower population density.  Better characterizing neighborhood-level greenspace exposures can improve research and lead to better policy recommendations.