Obesity is a growing epidemic that is not only posing a problem to the health and economy of Americans, but to a wide range of people around the world. Globally, there are over 1 billion overweight adults, and among them 300 million are obese; further, in America, two out of three adults are either obese or overweight, causing them to be more susceptible to chronic diseases such as type 2 diabetes, cardiovascular disease, hypertension and stroke, and others. While genes may determine body weight by controlling calorie intake, the continuing rise in obesity is likely to be attributed to the change in society and people's values towards the concept of convenience and nutrition. Modernization, urbanization, economic growth, and globalization are a few that have been listed at the top due to their drastic impact to the life-style of modern people. Modernization and urbanization transform a place from a less developed one to a wealthier and more powerful state with a greater demand for higher standard living; thus, inhabitants would be demanding new sources of stimulants, like tastier food, even if it means higher proportions of fat and sugar. In addition, economic growth allows transnational corporations (TNCs) such as McDonald's to branch out to other countries causing homogenization; in other words, globalization is causing and spreading obesity to the world.
Although obesity seems to be correlated with wealthy people, as they can afford to eat a larger range of food. However, a World Health Organization (WHO) study and a Cornell University study have respectively shown that obesity is prevalent among developing countries due to under-nutrition; a deficiency in micronutrients as a result of eating cheap (fast) foods, also the fact that low-income working class people resort to eating fast food is leading them to obesity as a result of their hectic schedule. These studies have led me to realize that I've actually seen more overweight people in poorer regions rather than in rich neighborhoods of Los Angeles, such as the downtown area and on public transportation. This intrigued me into wondering whether there is a relationship between income and obesity.
In order to commence in answering my curiosity, my group and I have come up with a hypothesis that speculates the current obesity phenomenon relating to the distribution of income and McDonald's in LA county. We believe that wealthy people in this county have a lower tendency of becoming obese due to their compliance of the Hollywood trend; while low-income people have a higher chance of becoming so since they can barely afford cheap food and they simply do not have the time to dine properly in restaurants other than fast food chains.
Method
We utilized four data frames in our project to display the correlation between income and obesity in two selected cities specifically within LA county; namely, Beverly Hills and downtown LA. This is because we believe that these two locations have the largest difference in income; thus a significantly different prevalence of obesity.
The first data frame illustrates the prevalence of obesity in LA county paying special attention to Beverly Hills and downtown LA. We downloaded a health district base map from the Los Angeles County GIS Data Portal since obesity is considered a chronic disease and would be best to be shown on a map used by the Department of Public Health to plan and manage health service delivery across the County. Further, in order to display the percentage of obesity in different regions of LA, we found statistical data from the County of LA Department of Health Services website. Since we weren't able to locate a obesity shapefile on the web, we edited the attribute table of the health district map and added a new field called "obesity percentage"; that way we could assign each percentage value to its designated location in the county. Conversely, as the health district map did not show cities, we had to download a polygon file from the UCLA GIS data site which sorted cities by their zip codes. This way we were able to locate and make Beverly Hills and Downtown LA visible on this data frame, enabling us to identify the relationship between obesity in a wealthier region (Beverly Hills) and a poorer one (downtown LA).
The second data frame portrays the locations (28 in total) of McDonald's within West LA bordered by the 405 and 10 highway and the boundaries of downtown LA. The reason why we chose locations within the periphery of 405 and 10 is that there are no McDonald's at all in the city of Beverly Hills while there are 21 just in downtown LA. All the locations were manually selected from a website called fastfoodmap.com and we have utilized every location provided there which falls into our determined boundaries. After doing so, we created an excel file and inserted the addresses of McDonald's in the following categories: addresses, city, state, and zip code. This is to allow us to use the process of geocoding which converts descriptive locations (addresses) into georeferenced locations (x,y location). The distribution of McDonald's on the map is complimented with the previously created boundaries of Beverly Hills and downtown LA to convey the notion that McDonald's target customers in the poorer region of downtown LA and not Beverly Hills.
The third data frame depicts the income distribution in the city of Los Angeles in relation to Beverly Hills and downtown LA. We extracted median household income data in each zip code from a website called "zip atlas". In order to display this data on our map, we added a new field to the attribute table on another copy of the health district base map, then matched each income value to each location just as we did for the obesity data frame. After that, we created sixteen ranges for each income value to fall into and then gave all the values a color ramp. As usual, the boundaries for Beverly Hills and downtown LA were included into this map to indicate that Beverly Hills is where people with higher income reside and downtown LA is where people with lower income situate.
The last data frame is a consolidation of all three maps into one map and this will show the obvious correlation between obesity and income in a spatial sense; also, through illustrating the relationship between McDonald's locations and income distribution, it would explain the fact that McDonald's had targeted low-income customers and this had led them to be more susceptible to obesity.
Results
As we looked at the first map, Prevalence of Obesity in Los Angeles, we were able to notice the trend as to where obesity was most and least dominant. Areas where obesity was most dominant were East LA, San Antonio, Southeast and Compton were where obesity occurs most among population; while areas of least dominance were Alhambra and Glendale. Nonetheless, when we performed a comparative analysis between the selected regions, Beverly Hills and downtown LA, it was obvious that downtown LA had the higher percentage of obesity, 21% to above 25%, where Beverly Hills fell into the 16 to 20% range. Looking at the McDonald's Locations in Selected Regions map, we noticed that there was an obtrusive comparison between Beverly Hills and downtown LA. As shown, there were 21 branches of McDonald's in the downtown region, while there was absolutely no branch in Beverly Hills, except for the 7 that were located in the out skirts of the city. For the third map, Median Household Income in Los Angeles, the focus of this map was to show the average amount of income each zip code holds. The color ramp suggested that the darker the blue was, the higher the income was for that region; Beverly Hills was one of the darkest ones with over $75000. In contrast, downtown LA had some of the lowest income values with $5000 to $24999.
Conclusion/ Discussion
In conclusion, the four maps have justified my hypothesis that wealthy people in LA county have a lower tendency of becoming obese; while low-income people have a higher chance of becoming so. The maps respectively indicated that in Beverly Hills, average income was above $75000, there was no McDonald's within the region and had a low prevalence of obesity; in the mean time, downtown LA had a low average income, simultaneously a location of 21 branches of McDonald's, also home to people contributing to the highest percentage of obesity in the county. There is definitely a correlation between income and obesity.We assume that wealthier people in Beverly Hills are less obese because Los Angeles is the home of Hollywood stars and unquestionably there are standards or status quos regarding fashion, fitness and health where these high-end people would want to comply; consequently, going to the gym and eating healthy foods with less fat and sugar have become a habit which acts as a obesity repellant for them. In contrast, low-income people are much less likely to comply to these trends due to their limited monetary resources. In addition, low-income people are limited to eating cheaper foods with less nutritious values because they simply cannot afford better quality food; also, due to the fact that many of them work in labor intensive industries such as being cleaners or construction workers; their working schedule is highly structured and inflexible and this causes them to be deprived of time to go get a decent meal and so they would go for fast food often; this is also the reason why McDonald's and other fast food chains target working class people and this explains the significant abundance of McDonald's in downtown LA in contrast to Beverly Hills. It is depressing to realize that Los Angeles is in a situation where obesity is synchronized with low-income population due to fast food chains being one of the affordable options for them; however, the gradually popularized community farms would alleviate this problem as these community-supported farms sell reasonably priced and nutritious food to the general population.
Works Cited
"WHO | Obesity and Overweight." Web. 14 Sept. 2011. <http://www.who.int/dietphysicalactivity /publications/facts/obesity/en/>.
"Obesity In America | The Epidemic." Weight Loss Tips | Visceral Fat Loss Tips. Web. 14 Sept. 2011. <http://www.drkalsweightlosstips.com/obesity-in-america.html>.
Park, Alice. "Working Parents and Family Diets: Too Busy to Eat Right - TIME." Breaking News, Analysis, Politics, Blogs, News Photos, Video, Tech Reviews - TIME.com. 10 Sept. 2009. Web. 14 Sept. 2011. <http://www.time.com/time/health/article/0,8599,1921349,00.html>.
"Median Household Income in Los Angeles, CA by Zip Code." Zip Code, Area Code, City & State Profiles | ZipAtlas. Web. 14 Sept. 2011. <http://zipatlas.com/us/ca/los-angeles/zip-code-comparison/median-household-income.htm>.
McDonalds locations
http://www.fastfoodmaps.com/
http://www.mcdonalds.com/
Prevalence of Obesity
County of LA, Department of Health Services, Public Health
http://publichealth.lacounty.gov/ha/reports/habriefs/lahealth073003_obes.pdf
UCLA GIS data
http://gis.ats.ucla.edu/
Los Angeles County Enterprise GIS
http://egis3.lacounty.gov/eGIS/
Health Districts shapefile
http://egis3.lacounty.gov/dataportal/index.php/2010/09/28/health-district-hd/
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