Potential Causes of Fast Food Clustering in Ohio
Document Type
Panel Presentation
Keywords
Fast-Food, Ohio, Clustering
Biography
My name is Nathanial Crum, I am a Senior graduating this semester with a bachelors in Geography and a concentration in GIS. I was born in Athens, Ohio into a military family so I have lived all over the United States. I am interested in computer technologies and geographic systems and how versatile they can be. I also enjoy the outdoors whether I am relaxing, hiking, or camping. After graduation I am hoping to stay and work relatively local in a hopes to give back to the community in some way.
Major
Geography
Advisor for this project
Jonathan Kozar
Abstract
Abstract
This main purpose of this study was to see if different independent variables such as median household income, Black/African American population density, and higher total population density affect the amount and clustering of fast-food restaurants in the state of Ohio. This study was done to see if these variables caused certain areas around Ohio to have more availability to fast-food/unhealthy food options, so if further studies continue, people could possibly think of ways to combat potentially high volumes of fast-food restaurants in certain areas. To conduct this study, research was done on the internet and ArcGIS portals to find suitable data, such as Census data, USDA food data, and location data of major fast-food restaurants. Once all the data was collected, a software called ArcGIS Pro was used to create multiple maps representing each independent variable and visually compared them to a map representing major fast-food restaurants in Ohio. Multiple “Local Bivariate Relationship Analysis” tests were run to see if the variables being compared were statistically significant to one another. After running the analysis of the different variables, it was concluded the variables being studied were not statistically significant except total population density. Therefore, a higher fast-food density is not caused by a higher Black/African American population or lower median household income. Though a significant relationship between fast-food restaurants and total population density was found. This showed that a higher population typically led to more fast-food clustering.
Potential Causes of Fast Food Clustering in Ohio
Abstract
This main purpose of this study was to see if different independent variables such as median household income, Black/African American population density, and higher total population density affect the amount and clustering of fast-food restaurants in the state of Ohio. This study was done to see if these variables caused certain areas around Ohio to have more availability to fast-food/unhealthy food options, so if further studies continue, people could possibly think of ways to combat potentially high volumes of fast-food restaurants in certain areas. To conduct this study, research was done on the internet and ArcGIS portals to find suitable data, such as Census data, USDA food data, and location data of major fast-food restaurants. Once all the data was collected, a software called ArcGIS Pro was used to create multiple maps representing each independent variable and visually compared them to a map representing major fast-food restaurants in Ohio. Multiple “Local Bivariate Relationship Analysis” tests were run to see if the variables being compared were statistically significant to one another. After running the analysis of the different variables, it was concluded the variables being studied were not statistically significant except total population density. Therefore, a higher fast-food density is not caused by a higher Black/African American population or lower median household income. Though a significant relationship between fast-food restaurants and total population density was found. This showed that a higher population typically led to more fast-food clustering.
