Health Impacts of Fast-Food Restaurants & Parks

Obesity is a universal issue, but no where is it more recognized than in the United States, carrying an abundance of stereotypes concerning its "burger-loving" populace. When asked regarding the root cause of obesity in the United States, the Centers for Disease Control and Prevention (CDC) fingered unhealthy eating patterns and lack of activity as the main problem points. This study seeks to analyze the CDC's claim by examining California, the most populated state, comparing the health rankings of each of its counties with the number of fast-food restaurants and parks. These sources of unhealthy foods and activity will serve to corroborate or bring into question this direct link established by the CDC. Does the number of fast-food restaurants and state parks, and therefore their ease of access to the surrounding population, impact the obesity rate and the general health ranking (as gauged by the University of Wisconsin Population Health Institute) of the county they reside in?

All data has been modified from its original source. The exact configurations are explained in the later section: GIS Methodology. In summary, only data relevant to the study has been kept, and all remaining data has had their symbology and pop-up menus modified.

Given its enormous population, it is no surprise that California has an abundance of fast-food restaurants readily available to its consumers. The map below provides a visual on each fast-food restaurant as analyzed by Jennifer Bell, senior product manager of Esri. In total, there are 6,078 entries of fast-food restaurants, accounting for franchisees such as McDonald's, Wendy's, and In-N-Out. While there is most certainly other franchisees that serve what many people will consider fast-food, the giants of the industry will be more than enough to analyze any connection between the number of fast-food restaurants in each county to their obesity rates and health rankings.

While California has its fair share of greasy goods, it also boasts an impressive amount of parks: 8,845 in total counting everything from local parks to national ones. While some parks are smaller than others, all parks, regardless of size, were included in this study. Even small local parks can contain recreational facilities and opportunities for exercise or simply to leave the house. To better interpret this study, all parks should be regarded as possibilities for someone to shy away from an inactive lifestyle. This will be useful when examining the health rankings provided by the University of Wisconsin Population Health Institute.

The University of Wisconsin Population Health Institute ranked a multitude of different variables within each county, such as the proportion of babies born underweight or the frequency of motor vehicle accidents. For this study, we will be looking at two variables: the percentage of adults that report no leisure-time physical activity and the percentage of the population with limited access to healthy foods. The map to the right provides a quick overview of the counties and their rankings. Blue counties have the highest leisure-time physical activity level and access to healthy foods, whilst red counties have the lowest. Lighter counties have higher activity but lower access to healthy foods. Darker counties have lower activity but higher access to healthy foods. A legend is provided to better aid in visualization and understanding.

The CDC, alongside their claim on the main causes of obesity, provided additional statistics on health-related concerns in California counties. For example, they measured the proportion of the population who were smokers or heavy drinkers. Two variables will be relevant to this study: the crude prevalence of physical inactivity and the crude prevalence of obesity. Blue counties indicate a low prevalence of physical inactivity and obesity. On the other hand, red counties represent high inactivity and obesity. Note the similarities between this map and the map visualizing the University of Wisconsin Population Health Institute's rankings.

Recall the similarities between the previous two maps displaying the health rankings by the University of Wisconsin Population Health Institute and the obesity/activity data by the CDC. The swiping map below provides a visual aid for comparing the two. This pre-analysis observation suggests that the obesity and inactivity prevalence has some relationship to percentage of adults that report no leisure time physical activity and access to healthy foods. It is self-explanatory how there may be a similarity between the inactivity prevalence and no leisure time physical activity, but the similarity itself brings credibility to the two datasets as they corroborate one another.

We will begin by simply comparing the county obesity prevalence with the number of fast-food restaurants within its boundaries. Blue and orange counties, alongside their slightly lighter and darker shades, line up the study's expectation. A higher count of fast-food restaurants allows for easier access to the population, promoting unhealthy consumption and eventually, obesity.

However, white and dark brown counties go against the expectation, having a high obesity prevalence despite a lack of restaurants, or vice versa. This discrepancy suggests that the number of fast-food restaurants may not be the root cause of obesity. Even if there are an abundance of restaurants, people can simply choose not to go. While counties like Los Angeles and Ventura still have a large amount of obese individuals due to their high populations, they are overshadowed by the proportion of the population that is of normal or underweight. Additionally, counties similar to Modoc with no or few recorded fast-food restaurants still somehow have high obesity prevalence compared to other counties. This may be a problem regarding inactivity rather than the abundance of fast-food restaurants.

The number of fast-food restaurants also does not seem to impede the population's ability to access healthy foods, with numerous counties either having many restaurants AND high access to healthy foods or neither (denoted by white or dark brown shading). Thus far, the evidence is inconclusive regarding the impact fast-food restaurants have on the obesity rate, with some counties following the expectations while others breaking it. However, obesity at its core stems from consuming too many calories whilst burning too few. These discrepancies we've discovered so far may be explained by the count of parks in each county, suggesting that exercise may be counteracting the effects of the restaurants.

We will now compare the data obtained from the University of Wisconsin Population Health Institute and CDC with the data on all parks in California. Our previous analysis concerning fast-food restaurants revealed that the relationship between obesity and restaurants is not cut and dry. While some counties followed the expectation of experiencing more obesity with more restaurants, others had low obesity in spite of it. Let us see if this holds true with parks.

Using the map that compares the number of parks with the report on how many adults do not perform physical activities during leisure time, we can see that many counties line up with expectations. Blue counties house many parks, and only a small proportion of adults report no physical activities. On the other hand, orange counties have few parks, and a larger proportion of adults report no physical activities. Dark brown counties have many parks but little physical activities, meaning that while there are many opportunities to enter a park, many people choose not to do so. Note that these dark brown counties are also often the ones with high obesity rates.

Furthermore, when comparing the park data with the obesity prevalence, we see that almost all counties are blue, orange, or dark brown. Many counties follow the negative relationship between number of parks and the obesity prevalence, and those that don't can be interpreted as people simply not going to parks. This raises an interesting question. Some counties support the CDC's claim, while others seem to go against it. Thus far, we've established some sort of connection between obesity, inactivity, parks, and restaurants. However, how strong of an impact do these variables really have on one another?

Throughout this study, the ArcGIS's relationship style was heavily utilized to compare and contrast the data. The 3x3 color grid it uses to differentiate the counties contains four points on the extreme that are confidently aligned with expectations or swayed away from expectations. Blue and red counties demonstrate the positive relationship between fast-food restaurants and obesity prevalence, or the negative relationship between parks and obesity prevalence. White and dark brown counties did the opposite, and their interpretation is more complex than simply noting how obesity rises or falls if the count of fast-food restaurants rises or falls.

If obesity is low despite a large amount of restaurants, or high despite a large amount of parks, then it can be interpreted that the actual count of such facilities is not enough of a motivator for people to consume or partake in activities. While the abundance of a facilities promotes ease of access, it is meaningless if the consumer has no intention to access it in the first place.

If obesity is high despite there being few fast-food restaurants, or low despite there being few parks, then that is a clear indicator that the count of facilities is not the main cause of obesity within that county. Considering what we have learned thus far, it may be more appropriate to analyze specific motivators or characteristics of a county that would increase or decrease participation in fast-food restaurants or parks. Perhaps a county is naturally warmer all year round, dissuading people from visiting their local park if there is not enough shade. Perhaps a county generally houses more impoverish residents who turn to fast-food restaurants for something quick and cheap.

Ultimately, while some counties like San Bernardino has consistently fell within expectations throughout the study, other counties like Plumas have fluctuated between meeting expectations and breaking them. This leads me to conclude that simply the number of a facility present within a county is not enough information to establish a confident connection between them and the obesity prevalence/health rankings. However, it is a good step in the right direction. As stated previously, motivators for usage would have, most likely, been more effective. For example, poverty would be a key motivator that promotes cheap fast food while dissuading leisurely walks in the park as time is a precious resource if one is pressed for cash.

Fast-food restaurant data obtained from  Jennifer Bell on ArcGIS hub .

Park data obtained from the Living Atlas by Esri.

Obesity and inactivity prevalence data obtained from the  Centers for Disease Control and Prevention on ArcGIS hub .

Health ranking data obtained from the  University of Wisconsin Population Health Institute on ArcGIS .

All layers’ visibilities were modified to be visible from any zoom level.

All analysis was run with processing extent set to “display extent.”

Configuring “Fast Food Restaurants” from Jennifer Bell on ArcGIS Hub

1. Data Cleanup
   1. Ungroup “Fast_Food_Restaurants”.
   2. Delete all layers except for the first “Fast Food Restaurant” layer.
   3. Filter: State is CA.
   4. Rename “Fast Food Restaurant” to CaliforniaFastFood.
2. Configure Pop-ups
   1. Edit title to only display the name of the restaurant.
   2. Delete OBJECTID, ID, Letter, and Phone from fields list. Reorder list.
3. Configure Symbology
   1. Change small points to circles. Enlarge with preference.
   2. Add “Restaurant” to attributes. Unique restaurants are now color coded.
   3. Increase transparency to reduce intensity of colors.

Configuring “USA Parks” from Living Atlas

1. Add “United States State Boundaries” from Living Atlas.
   1. Filter: State Name is California.
2. Analysis: Overlay Layers
   1. Input: USA Parks
   2. Overlay Features: United States State Boundaries
   3. Layer Name: CaliforniaParks
3. Configure Pop-ups for CaliforniaParks
   1. Edit title to only display the name of the park.
   2. Delete all fields from the field list except for: NAME, FEATTYPE, 2023 Total Population, Area in Square Miles, 2023 People per square mile.
4. Configure Symbology
   1. Change CaliforniaParks symbol style to have a green fill color and outline color.

Configuring “PLACES: Obesity” from Centers for Disease Control and Prevention

1. Data Cleanup
   1. Delete all layers except for “Counties.”
   2. Filter: State name is California.
   3. Rename “Counties” to “PLACES.”
2. Configure Pop-ups
   1. Edit title to only display the name of the county.
   2. Edit text to include and clarify information relevant to the study question.
   3. Add the fields list to the pop-ups content.
   4. Add only four fields: County name, Total Population estimates, Obesity crude prevalence (%), and Physical inactivity crude prevalence (%).
3. Configure Symbology
   1. Add the two fields regarding prevalence as the attributes.
   2. Choose the relationship style.
   3. Change symbol style such that red indicates a county that has a high prevalence in obesity and inactivity, while blue, the opposite.
   4. Edit labels to clarify the above.

Configuring “County Health Rankings 2024” from Wisconsin Population Health Institute

1. Data Cleanup
   1. Ungroup “County Health Rankings 2024.”
   2. Delete all layers except for “County.”
   3. Filter: State Abbreviation is CA.
   4. Rename “County” to “HealthRankings.”
2. Configure Pop-ups
   1. Edit title to only display the name of the county and state.
   2. Edit text to include and clarify information relevant to the study question.
   3. Add the fields list to the pop-ups content.
   4. Add only five fields: Name, State Abbreviation, County Population, Percentage of adults that report no leisure-time physical activity, and Percentage of the population with limited access to healthy foods.
3. Configure Symbology
   1. Add the two fields regarding percentage as the attributes.
   2. Choose the relationship style.
   3. Change symbol style such that blue indicates a county that is both active and has the highest access to healthy foods, while red, the opposite.
   4. Edit labels to clarify the above.

Creating "PLACESWithFastFood"

1. Analysis: Summarize Within
   1. Input: CaliforniaFastFood
   2. Overlay Features: PLACES
   3. Result Layer Name: PLACESWithFastFood
2. Overlay Cleanup
   1. The overlay will bring back unnecessary data in the new layer's pop-up. Edit pop-ups back to how they were in PLACES, then add new text and include the restaurant count to the field list under the auto-generated name “count of points.” Specify in the text that the count of points refer to the restaurants.
3. Configure Symbology
   1. Add the "Obesity crude prevalence (%)" and "Count of Points" as attributes.
   2. Choose the relationship style.
   3. Change symbol style such that red indicates a county that is both obese and has a large amount of fast-food restaurants comparatively, while blue, the opposite.
   4. Edit labels to clarify the above.

Creating "RankingsWithFastFood"

1. Analysis: Summarize Within
   1. Input: CaliforniaFastFood
   2. Overlay Features: HealthRankings
   3. Result Layer Name: RankingsWithFastFood
2. Overlay Cleanup
   1. The overlay will bring back unnecessary data in the new layer's pop-up. Edit pop-ups back to how they were in HealthRankings, then add new text and include the restaurant count to the field list under the auto-generated name “count of points.” Specify in the text that the count of points refer to the restaurants.
3. Configure Symbology
   1. Add the "Count of Points" and "Percentage of people with limited access to health foods" as attributes.
   2. Choose the relationship style.
   3. Change symbol style such that red indicates a county that has a high count of fast-food restaurants and the lowest access to healthy foods, while blue, the opposite.
   4. Edit labels to clarify the above.

Creating "RankingsWithParks"

1. Analysis: Summarize Within
   1. Input: CaliforniaParks
   2. Overlay Features: HealthRankings
   3. Result Layer Name: RankingsWithParks
2. Overlay Cleanup
   1. The overlay will bring back unnecessary data in the new layer's pop-up. Edit pop-ups back to how they were in HealthRankings, then add new text and include the park count to the field list under the auto-generated name “count of polygons.” Specify in the text that the count of polygons refer to the parks.
3. Configure Symbology
   1. Add the "Count of Polygons " and "Percentage of adults that report no leisure-time physical activity" as attributes.
   2. Choose the relationship style.
   3. Change symbol style such that blue indicates a county that has many parks with few adults reporting no leisure-time activity, while red, the opposite.
   4. Edit labels to clarify the above.

Creating "PLACESWithParks"

1. Analysis: Summarize Within
   1. Input: CaliforniaParks
   2. Overlay Features: PLACES
   3. Result Layer Name: PLACESWithParks
2. Overlay Cleanup
   1. The overlay will bring back unnecessary data in the new layer's pop-up. Edit pop-ups back to how they were in PLACES, then add new text and include the park count to the field list under the auto-generated name “count of polygons.” Specify in the text that the count of polygons refer to the parks.
3. Configure Symbology
   1. Add the "Count of Polygons " and "Obesity crude prevalence (%)" as attributes.
   2. Choose the relationship style.
   3. Change symbol style such that blue indicates a county that has many parks and low obesity prevalence, while red, the opposite.
   4. Edit labels to clarify the above.