Urban Heat Resilience in NYC: A Spatial Analysis of Cooling Center Accessibility and Health Outcomes

• In Event: Environmental Determinants of Health: Evidence from Air, Water, and Heat

Abstract

Building on the prior evaluation of the “Cool it! NYC” initiative, this study employs spatial network analysis and mixed effects Poisson regression to quantify how public cooling center accessibility mitigates heat related hospitalizations in New York City’s highest risk neighborhoods. Drawing on 2018 and 2023 Heat Vulnerability Index data from the NYC Department of Health, geocoded locations and operating schedules for 542 cooling centers, and ZIP level heat illness admission records, we will generate walking and public transit catchment areas using GTFS informed network modeling in ArcGIS to calculate average travel times and center densities per 100,000 residents. A mixed effects Poisson model will then estimate the effect of these accessibility metrics, controlling for age distribution, median income, building age, and tree canopy cover, on annual heat illness admissions, with robustness checks via negative binomial and quantile regressions across alternative catchment thresholds. We hypothesize that in HVI 4~5 ZIP codes each one kilometer reduction in average travel time corresponds to a 10~15 percent decline in hospitalizations and that increasing center density by one per 100,000 residents yields a 5~8 percent reduction in admissions. By identifying “cooling deserts” and prioritizing areas such as East Flatbush and Far Rockaway, this work will produce an open source Python toolkit for accessibility analysis and deliver policy briefs to NYC Parks and emergency management stakeholders, thereby advancing data‐driven urban heat resilience strategies.

Author

• Hanyu Hanyu Wang, New York University
  Presenting Author