Date of Degree

5-2018

Document Type

Dissertation

Degree Name

Ph.D.

Program

Earth & Environmental Sciences

Advisor(s)

Yehuda Klein

Committee Members

Jennifer Cherrier

Andrew Maroko

Theresa Goedeke

Reza Khanbilvardi

Brian Vant-Hull

Sarah Becker

Susan Lyon

Subject Categories

Environmental Sciences

Abstract

The goal of this study was to identify factors leading to heat stress hospitalizations visits in New York City through the use of climatological and social science data, thus enabling greater targeting of individuals and groups with heightened vulnerability to extreme heat. Recent research has established that climate change will increase overall temperatures in New York City in the future. Heat waves are predicted to increase in frequency and severity, adversely impacting public health and increasing heat vulnerability that could lead to heat stroke or other comorbidities. This dissertation takes into account existing data to generate a new model that seeks to answer the following fundamental research question: How do social vulnerability and environmental risk factors independently impact heat stress hospitalization visits in New York City? In order to address this question I created and tested the efficacy of a new regression model called the Heat Multiplicative Model (HMM) technique using NYC as a case study. The primary contribution of this model is the combined use of temperature data derived from two sources: space-based remotely-sensed moderate-resolution land data from Landsat satellite imagery and ambient temperature data from ground sensors, both of which are multiplied by social and environmental factors to develop new weighted factors that may be useful for public health research. HAM with the three variables (including Landsat) emerged as the better model because the components of the regression exhibited the correct variable interactions and were statistically significant. In conclusion, it appears there might be some slight value to utilizing two temperature variables within the regression to improve the R-square.

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