The Correlation Between Socioeconomic and Environmental Factors on Life Expectancy
Advisor(s)
Patrick Kearney; Illinois Mathematics and Science Academy
Discipline
Behavioral and Social Sciences
Start Date
21-4-2021 9:10 AM
End Date
21-4-2021 9:25 AM
Abstract
Awareness about the disparity between living in urban versus rural areas is essential when considering the parameters that influence one's longevity. There are many factors that can impact an individuals' health, such as proximity to services, access to nature, occupation opportunities, and more. It is important to understand how living in areas classified as urban, suburban, and rural can offer different variations of the elements listed above. In this research, we dive deeper into analyzing and comparing how conditions such as the population density, education, civilian labor force, poverty, mortality, public transportation, air quality, and unemployment rates influence the average life expectancies of all counties in Illinois, New York, California, Texas, and Florida. With this data, we will generate numerous econometric regression models to test for correlation between these variables and the life expectancies of urban, suburban, and rural counties in these five states. Using the quantitative results reflected in these models allows us to conclude how intensely these conditional factors affect the health and life expectancy of the citizens who live there.
The Correlation Between Socioeconomic and Environmental Factors on Life Expectancy
Awareness about the disparity between living in urban versus rural areas is essential when considering the parameters that influence one's longevity. There are many factors that can impact an individuals' health, such as proximity to services, access to nature, occupation opportunities, and more. It is important to understand how living in areas classified as urban, suburban, and rural can offer different variations of the elements listed above. In this research, we dive deeper into analyzing and comparing how conditions such as the population density, education, civilian labor force, poverty, mortality, public transportation, air quality, and unemployment rates influence the average life expectancies of all counties in Illinois, New York, California, Texas, and Florida. With this data, we will generate numerous econometric regression models to test for correlation between these variables and the life expectancies of urban, suburban, and rural counties in these five states. Using the quantitative results reflected in these models allows us to conclude how intensely these conditional factors affect the health and life expectancy of the citizens who live there.