Sali Kharazi



When I returned to Los Angeles from my semester studying abroad in London… I was in the best shape of my life!
With that, I soon realized that eating healthy in America is more of a challenge than driving quickly on the 405 during rush hour.

In picking data sets to analyze for this project, I wanted to look at the statistics for obesity worldwide before I narrowed my scope to the United States specifically.
Originally, I wanted to find out how America ranked amongst other countries in its obesity stats and then find out why the rates are so high compared to the rest of the world.
My hunch was that America’s food laws are a lot less strict than the rest of the world and therefore, the food we have here is not healthy for people to be consuming. I wanted to get down and dirty with the FDA’s rules & regulations and also compare portion sizes.

Unfortunately, the data sets I was looking for were not plentiful enough for me to draw any concrete conclusions for my hunches regarding food laws. Instead, I focused on the part of my hypothesis that I was able to prove; the fact that the United States ranks among the top three obese countries in the world. And with that, I focused on the top 15 obese states and tried to find a trend that explained why the rates of obesity are so high in those areas.

To better explain my hypothesis and findings, I have provided four graphics.


Visual 1: Word graph

Using the World Health Organization’s document, ‘Global Strategy on Diet, Physical Activity and Health: Obesity and overweight’ facts and FAQ’s, I created a “Wordle” to highlight the words most commonly used when talking about the causes, effects and facts of being obese/overweight.[Data source:]

I thought it was necessary to highlight what comes up the most in discussing the topic of global obesity — but I thought it was even more important to be able to show these findings in the simplest way possible. Using a word graph allows the reader to see the most commonly used words associated with this topic in a glance.
Using a dark background and bright words makes the data more visually appealing, and overall, more memorable.


Visual 2: Animated line graph

Global obesity is a cluster of numbers and figures.
The above still image shows 15 countries and their obesity/overweight population over the course of almost 3 decades.
At a glance, it’s a bunch of tangled lines and numbers.
But what I tried to do was simply the image and highlight what I wanted the reader to see.
Because I was focusing on the United States, I needed to separate its data from the rest.
I also wanted to show the reader the countries that had the closest figures to the U.S. and also, show the gap between the top 5 obese/overweight countries and the 6th country on that list that differs vastly in its obesity rate.

In order to simplify this image, I created an animation that separates each country of focus, then shows the whole graph.
To see this animation, please click on the picture.

Please note: Australia is in the top 5, but it is not highlighted in my animation. The reason for that is because in this graphic, Australia’s obese/overweight population rates stop being collected in 1999 and therefore, I could not be sure of where they would truly stand in 2007 with the rest of the top 5.


Visual 3: Bar graph with visual map

In focusing on the U.S., I used a data set to determine the top 15 obese states in the country. In order to see if there was a trend between the rates and the location of the states, I marked the states on a map.[Data source:]

It can clearly be seen that the most obese states in the country are all in the same general area of the country.
I thought it was a really interesting find and realized that it would not be this apparent if the states weren’t clearly marked on a map for people to see.
Even if someone is really good at geography, showing the map makes a stronger visual impact than just listing the states by name and noting their rates.


Visual 4: Bar graph with visual map

[Data source:]

Because it was such a shock to see that the top 15 obese states were all in the same general area, I decided to go one step further and try to figure out what could possibly be the cause of this.
The hypothesis I decided to test was whether or not the income of the state had any correlation with the fact that it had a high rate of obesity.
Above, the 15 states with the lowest income rates are listed. In order to get the best idea of whether or not there was a correlation, I also mapped this data.

What can clearly be seen is that 10 out of the 15 lowest income states are part of the obesity cluster shown in Visual 3.
Using these maps and bar graphs together, takes simple data and puts the trend right in front of a person.

It’s like a connect-the-dots picture, with the dots having already been connected for whoever is trying to solve the puzzle!

Full data sets below:
Download file "statedataset.xlsx"


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