Examining the Optimal Placement of Cooling Centers to Serve Populations at High Risk of Extreme Heat Exposure in 81 US Cities
Bibliographic record
Abstract
OBJECTIVE: Although extreme heat can impact the health of anyone, certain groups are disproportionately affected. In urban settings, cooling centers are intended to reduce heat exposure by providing air-conditioned spaces to the public. We examined the characteristics of populations living near cooling centers and how well they serve areas with high social vulnerability. METHODS: We identified 1402 cooling centers in 81 US cities from publicly available sources and analyzed markers of urban heat and social vulnerability in relation to their locations. Within each city, we developed cooling center access areas, defined as the geographic area within a 0.5-mile walk from a center, and compared sociodemographic characteristics of populations living within versus outside the access areas. We analyzed results by city and geographic region to evaluate climate-relevant regional differences. RESULTS: Access to cooling centers differed among cities, ranging from 0.01% (Atlanta, Georgia) to 63.2% (Washington, DC) of the population living within an access area. On average, cooling centers were in areas that had higher levels of social vulnerability, as measured by the number of people living in urban heat islands, annual household income below poverty, racial and ethnic minority status, low educational attainment, and high unemployment rate. However, access areas were less inclusive of adult populations aged ≥65 years than among populations aged <65 years. CONCLUSION: Given the large percentage of individuals without access to cooling centers and the anticipated increase in frequency and severity of extreme heat events, the current distribution of centers in the urban areas that we examined may be insufficient to protect individuals from the adverse health effects of extreme heat, particularly in the absence of additional measures to reduce risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".