According to The National Bureau of Economic Research, the number of fast food restaurants in the United States have more than doubled over the past thirty years. They are almost everywhere and with the convenience and lower cost to eat, people keep coming back for their calorie-dense food made with unhealthy ingredients. At the same time, the prevalence of obesity diseases has risen sharply in the U.S.. With the consumption of fast food and obesity problem both increase over time, this phenomenon inevitably leads to the belief that fast food is the leading cause of obesity in the U.S.. Yet should we say that fast food is to blame for the U.S.’s rising obesity rates? In this report, as a starting point to answer the relationship between fast food restaurant and obesity in the U.S., I would like to find out is there a correlation between fast food restaurant locations and state obesity rates.
In example 1, dot density map was used to present the burger places geographically with colors for different categories. I think it is a good example that I can apply to show fast food restaurants’ distribution across the United States.
As for the obesity problem, I would like to present it using choropleth map as shown in example 2, applying color intensity to denote the relative magnitude of the obesity rate within different states.
Methods & Process
From Kaggle, I grabbed the dataset (Fast Food Restaurants Across America) which listed out ten thousands of fast food restaurants’ locations in the United States. Then I exported the dataset (USA States) from Carto Data Library and added a column for state obesity rates. I retrieved the data of adult obesity rate by state from Adult Obesity in the United States in which obesity was classified as having a body mass index (BMI) of ≥ 30.
I imported the two datasets I collected into Carto to get ready for creating location-based data visualizations.
1. Creating Choropleth Map to Present Obesity Rate by State
First of all, I used the dataset of USA States with state obesity rates to generate the map. In order to create a choropleth map, I colored the state by the value of adult obesity rate and chose the color scheme of blue with different color intensities to show the relative magnitude. Besides, I labeled that states using their abbreviation but with the pop-up window to show the full name of the state and its obesity rate. Also, I designed the legend indicating the rate range of different colors.
2. Adding Another Layer to Show Fast Food Restaurants Density
Secondly, I added another layer using the dataset, Fast Food Restaurants Across America, to locate fast food restaurants in the United States. I used the dot map to show the density of fast food restaurants. I tried to use different colors to show the brands (figure 2), but then I found the colors were overlapping with the background choropleth map and the information became unclear.
Besides, I realized that it’s not important to know which fast food restaurant it is at this point, so I gave up the idea and chose to use orange, the complementary color of blue, to represent all fast food restaurants (figure 3).
Result & Reflection
From the final output (figure 3) above, we could see that the states with relatively high obesity rates indeed have easy access to fast food restaurants. However, we could not conclude that there is an increase in the obesity rate in areas with a higher density of fast food restaurants because the map also shows that the states with lower obesity rate like California and New York also have high fast food restaurants density.
I think I should normalize the data by population first because the number of fast food restaurants in an area might be proportional to its population density. To see the availability of fast food restaurant rather than the quantity of it would be more appropriate for analysis. For future directions of this experiment, I would like to know what other factors are affecting the obesity epidemic. Is it about personal lifestyle, access to fresh food, or other behavior and phenomenon? Also, who’s affected by the overweight problem in terms of income level, or demographic factors, such as age, race, and gender.
- Carto: A software as a Service cloud computing platform that provides GIS, web mapping, and spatial data science tools for creating location-based data visualizations.