Analysis Finds Three Times More Farmers’ Markets in Areas with the Lowest Obesity Rates

GIS Contributor

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An independent analysis conducted by mapping analytics firm PetersonGIS shows that locations with the highest obesity rates contain the fewest farmers’ markets.

To produce the analysis, March 2011 data from the US Department of Agriculture (USDA) on market locations throughout the United States were mapped and correlated with county-aggregated obesity statistics* from the Center for Disease Control and Prevention (CDC).

Some market addresses weren’t completely geocoded in the USDA dataset. Those addresses that had not been geocoded were fixed to the extent possible, re-geocoded, and merged in with the main dataset.

In all, 5,858 markets were mapped. These data were subsequently intersected with the county obesity data, which had been categorized into four obesity cateogries (see table, below), and summarized to obtain a farmers’ market count per obesity category.

Map of Obesity Rates as Compared to Farmers' Market Locations
Map of Obesity Rates as Compared to Farmers’ Market Locations by PetersonGIS.com

The analysis found that counties that fall into obesity category 1, meaning that 12% – 25% of the population aged 20 or older have a body-mass index of 30 or higher, contain 0.26% farmers’ markets by area. Counties that fall into obesity category 4, meaning that 35% – 45% of the population aged 20 or older have a body-mass index of 30 or higher, contain 0.08% farmers’ markets by area.

The data shows that there are three times as many farmers’ markets in the category 1 counties as there are in the category 4 counties.

Please note that correlation is not the same as causation.

PetersonGIS is a mapping analytics company. Let us know if we can help you with data analysis. Contact us at info@petersongis.com.

* This data represents estimates of obesity percentages of U.S. adults of ages 20 and older, where obesity is defined as BMI less than or equal to 30 kg / m2. The estimates are from the Center for Disease Control and Prevention (CDC). CDC used data from the Behavioral Risk Factor Surveillance System (BRFSS) for 2006, 2007, and 2008 and from the U.S. Census to estimate the number and prevalence of cases of diabetes and obesity among adults of ages 20 and older , for all 3,141 counties in the United States, as described in Estimated County-Level Prevalence of Diabetes and Obesity, United States, 2007, Morbidity and Mortality Weekly Report, November 20, 2009 / 58(45);1259-1263.

About the Author

Gretchen Peterson is the principal consultant for PetersonGIS as well as the author of GIS Cartography: A Guide to Effective Map Design.

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11 thoughts on “Analysis Finds Three Times More Farmers’ Markets in Areas with the Lowest Obesity Rates”

  1. It would be interesting to see the correlation between population and fast food restaurants. I notice many counties in the mid west may not have farmer’s markets… they also may not have fast food restaurants. The population of some counties can not sustain farmer’s markets… they are also likely to grow their own vegetables. I don’t think this map really shows a correlation… it’s not about farmers markets. The biggest hole in our food supply is in the inner city. Not the country. Fast food restuaurants are where the population is. So really there is no correlation between farmer’s markets and obesity. Because farmer’s market occur where the population is and you have high obesity where there is no population, which in turn doesn’t make sense. You map would work better if it focused on the ten fattest cities and the location of farmers markets in those cities.

  2. A study implies months or years of documented scientific research by experts on the topic being studied. This is more like an assignment you’d expect in an introductory undergraduate/graduate course on GIS. I applaud the effort and ambition but calling it a “study” is incredibly inappropriate.

  3. Agreed – this surprised me to see that PetersonGIS did the analysis and wrote the article. And David’s point about the analysis itself – density as opposed to area seems like it would yield more useful info. However, as a promotional piece – very good idea!

  4. The study reported on here is testable and verifiable. I’m happy to have the feedback to make this better. Remember that the relative simplicity or complexity of an analysis has little to do with the impact that results can have. In many cases, a simple analysis is much better than a more complex analysis, especially with regard to how it is received (easier to understand, criticize, make better). I’ve done a lot of extremely complex studies that only a few people will ever take the time to really understand. While they are useful in their own ways, their impact is decidedly different.

    For anyone who would like to verify the results, please download the farmers’ market data that I geo-coded here (metadata included in the zip file): https://www.box.com/s/rbijjeog9g00rvenot1r. I’ll make that available for a year.

    With regard to population density, it is interesting to see what happens, the number of farmers’ markets by population actually remains fairly constant (and fairly small) within all four categories: 0.001735%, 0.001975%, 0.001929%, 0.001875% for categories 1-4 respectively. However, the population density alone is very telling within the four categories (again from 1 through 4): 150/sq mi, 78/sq mi, 65/sq mi, 45/sq mi.

    The correlation between population density alone and obesity is rather strong (also see: http://www.nature.com/oby/journal/v15/n8/full/oby2007251a.html). Distances to farmers’ markets is likely to be much lower in higher populated places, which may be a factor. The opposing viewpoint is that distances to fast food and other likely anti-correlates are also closer.

    Hopefully this analysis will encourage people to explore their own ideas regarding correlates.

  5. At the request of the author, I have changed the term from study to analysis. In my opinion, the debate over semantics is not as important as using the results of this data analysis to further a debate about what factors influence or are indicators for the prevalence of obesity in this country.

  6. RE: Density

    Agreed, when I first say the links to this page, I was about to unleash a rant about “bad science” and “biased statistics”, However, I was glad to see that the population density is being discussed. I would really like to see the population density layer added to the above map though, as once the morons in the mass media get a hold of it, they will fail to even acknowledge that detail.

  7. I’m not sure what we are seeing here is really a correlation between farmer’s markets and obesity as much as it says something about income and obesity. Might be worth a look to see if you get similar results when looking at income.

    Obesity is a result of too many processed carbs, grains, and sugar. Those tend to be the cheapest things to buy. People with lower income tend to eat less meat and produce because of the expense. Also, farmer’s markets generally are started in places where people have the disposable income to pay a little extra for fresh produce and meat beyond what the grocery store offers.

    It would also be interesting to know what percent of the population actually shops at farmer’s markets every week/month. I would guess less than 5%.

  8. Farmers markets thrive in areas where people are more health-conscious: ie wealthier and more educated people, but it doesn’t CAUSE people in the area to be healthy. Putting 5 farmers markets in the middle of Hicksville, Alabama is not going to slim people down – they will more than likely continue to eat cheap, processed and fast food because of it’s low cost and their lack of education.

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