The City of Atlanta, Georgia, is a fast-growing urban area with substantial economic and racial inequalities, subject to the impacts of climate change and intensifying heat extremes. Here, we analyze the magnitude, distribution, and predictors of heat exposure across the City of Atlanta, within the boundaries of Fulton County. Additionally, we evaluate the extent to which identified heat exposure is addressed in Atlanta climate resilience governance. First, land surface temperature (LST) was mapped to identify the spatial patterns of heat exposure, and potential socioeconomic and biophysical predictors of heat exposure were assessed. Second, government and city planning documents and policies were analyzed to assess whether the identified heat exposure and risks are addressed in Atlanta climate resilience planning. The average LST of Atlanta’s 305 block groups ranges from 23.7 °C (low heat exposure) in vegetated areas to 31.5 °C (high heat exposure) in developed areas across 13 summer days used to evaluate the spatial patterns of heat exposure (June–August, 2013–2019). In contrast to nationwide patterns, census block groups with larger historically marginalized populations (predominantly Black, less education, lower income) outside of Atlanta’s urban core display weaker relationships with LST (slopes ≈ 0) and are among the cooler regions ofmore »
Assessment of socio-environmental problems and the search for solutions often require intersecting geospatial data on environmental factors and human population densities. In the United States, Census data is the most common source for information on population. However, timely acquisition of such data at sufficient spatial resolution can be problematic, especially in cases where the analysis area spans urban-rural gradients. With this data release, we provide a 30-m resolution population estimate for the contiguous United States. The workflow dasymetrically distributes Census block level population estimates across all non-transportation impervious surfaces within each Census block. The methodology is updatable using the most recent Census data and remote sensing-based observations of impervious surface area. The dataset, known as the U.G.L.I (updatable gridded lightweight impervious) population dataset, compares favorably against other population data sources, and provides a useful balance between resolution and complexity.
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