2018 Conference

Individual Paper Presentation

This research uses established research in climatology and the social sciences to understand the factors that lead to heat stress hospitalization visits in New York City. Heat waves are predicted to increase in frequency and severity, which would adversely impact public health- increasing heat vulnerability that could lead to heat stroke or other comorbidities. This research takes into account existing research to generate a new model that seeks to answer the following fundamental research question:

 How do social vulnerability and environmental risk factors impact heat stress hospitalization visits in New York City?

 To address this question the goals of this research are to create and test 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 use of temperature derived from Landsat imagery and ambient temperature from ground sensors, and then multiplying these variables against the social and environmental factors, to develop a methodology that could be useful for public health research. The primary research deliverable for this presentation will be the following:

1. Construction of the HMM compared to the control regression model to understand the environmental and social factors that contribute to heat stress hospitalization rates
2. Analysis of the influencing factors that increase vulnerability in a neighborhood and suggest a neighborhood that would benefit from the implementation of green infrastructure, which leads to lowering ambient temperatures

 It is a novel approach because typically one temperature dataset is utilized due to temporal data resolution issues. From spatial planning perspective, the HMM model should improve reliability in predicting the spatial and temporal variation in heat stress hospitalizations in New York City.