Food market score
An adequate, nutritious diet is a necessity at all stages of life. Pregnant women, babies, children, adolescents, adults, and older adults depend on adequate nutrition for optimum development and maintenance of health and functioning. Inadequate diets can impair a child’s intellectual performance and have been linked to frequent school absence and poorer educational achievement. Nutrition also plays a significant role in causing or preventing a number of illnesses, such as cardiovascular disease, some cancers, obesity, type 2 diabetes, and anemia. These weight-associated illnesses are no longer restricted to adults as the prevalence of obesity has more than doubled in children in the last 40 years. Obese children have an increased risk of heart disease and of becoming obese adults.
Lower income families are less likely to have a nutritious diet than those with higher incomes. Food environments—defined by the types of foods available in a neighborhood, including stores, restaurants, schools, and worksites—also influences peoples’ food choices and their likelihood of being overweight or obese. There is a strong association between consumption of calorie-dense foods with low nutritional value and being overweight or obese when one or more calorie-dense meals are consumed per week. High-fat and high-sugar foods are available at most elementary and middle schools. Since the 1970s, the number of fast food restaurants has more than doubled in the United States, and the proportion of daily calorie intake from foods eaten away from home has increased.
Measures of food availability in the environment include distance to food retailers, cost of foods, and the number of food outlets in a given area. Due to the lack of standardization of food environment metrics and differences among populations studied, it is difficult to generalize the evidence on the relationship between food environments and health. Nevertheless, various cross-sectional and longitudinal studies show a positive association between the number of fast-food restaurants and/or convenience stores in a given area with body mass index (BMI), obesity and overweight rates; and a negative association with fruit and vegetable intake. The extent of this relationship can vary with race/ethnicity. In California, adults living in cities or counties with 16.7% healthy food retailers or less had a 20% higher prevalence of obesity and a 23% higher prevalence of diabetes than adults living in areas with 25% healthy food retailers or more; this relationship held true regardless of household income, race/ethnicity, age, gender, or the physical activity levels of respondents.
The original indicator investigated was the retail food environment index (RFEI), developed by the California Center for Public Health Advocacy. This indicator has been altered by the Center for Disease Control and Prevention (CDC) to the modified RFEI (mRFEI). The equations for each are below.
For this reason, Bay Area Regional Health Inequities Initiative recommends the adoption of the food market score, which is a relative measure of the number and variety of retail food resources within one mile, weighted by food offerings and distance.
This methodology was originally developed for San Francisco, modeling similar techniques used for the walkability measure in the Metropolitan Transportation Commission Snapshot analysis and Walkscore. It is a relative measure, so inherently some areas will score higher or lower depending on the variables listed in the table above. Weights for distance are based on typical walkable distances in an urban environment. Adjustments can be made based on the context of where this measure is adapted.
1) Open up the layer list and select the farmers’ markets, general grocery, convenience group, department stores, single category and other, and fruit and vegetable markets layers.
2) Zoom to your region of interest.
3) Click on GIS tools and select a target layer and click summarize data. Repeat for all six layers. In some cases, you may have more than 1,000 businesses in your current view; however, the program cannot download that many. One solution is to click “Selection” and then select the stores you are interested in downloading by drawing boxes around the items, which will create a light blue outline around them. Then, when using “Summarize Data,” select “Current Selections.”
4) After clicking “Summarize Data,” click “Download Data.” Make sure that your pop-up blocker is off, as the download window will appear as a pop-up. Proceed to save the resulting CSV file for geocoding later.
The next step is to create a “Supermarkets” category from the “General Grocery” file. In ArcGIS, add a new field called “Type.” Use the field calculator to assign “Supermarket” to all stores that are already classified as small or large chain stores. To determine whether other non-chain stores should be considered supermarkets, use the additional information about store size, revenue, and number of employees, as well as common knowledge of the retail stores in your community, to decide which stores should be classified as supermarkets. In San Francisco, stores in the general grocery category that had 5,000 square feet or more, made $1 million or more in annual sales, were part of a local chain, or had six to 20 employees and grossed between $500k to $1 million in sales were classified as supermarkets, but in less dense areas these criteria may not be as useful. For the remaining stores, label them as “Small Grocery” in the “Type” field using the field calculator. San Francisco has used Yelp searches and examination with Google Street View to verify that stores should be classified as “grocery” and not “convenience.” Then merge the files together as one shapefile using the merge tool in ArcGIS.
STEP 03. The next step is to assign quality weights to each store type. To do this, San Francisco did a small sample survey of supermarkets, small grocery stores, convenience/liquor stores, produce markets, meat markets, and chain pharmacies in different parts of the city, using a store survey that looked at the variety of healthy or whole foods available in each surveyed store. The survey contained sections for produce, dairy, whole grains, and protein. The produce section represented 51% of the total possible points (59 points possible), while the dairy, whole grains, and protein sections accounted for 10%, 19%, and 20% of the points respectively. To arrive at the final store type scores, the median number of points for each store type was divided by the median supermarket points (57). Final scores are listed in Table 5.
Table 5: Store Types and Weights
Other jurisdictions could adopt these scores or choose to conduct a survey of their local stores using San Francisco’s survey instrument. Create a new field for “Type Score” and populate it with the appropriate score for each store type.
STEP 04. The next step is to do a spatial join to all of the food stores within one mile of each intersection and to assign a distance score for each intersection–store join. The distance scores are as follows: if the store is less than 0.25 miles from an intersection it gets a 1.00, if it is between 0.25 and 0.49 miles it gets a 0.90, and if it is between 0.50 and 1.00 miles away from the intersection it gets a 0.75. The easiest way to make these joins and to attach the appropriate score is to create buffers around the intersections. Start by making a quarter-mile buffer around each intersection. Then make another quarter-mile buffer around the first quarter-mile buffer, excluding the buffer shape area (so it resembles a donut). Then make one last half-mile buffer around the half-mile donut buffer to create another donut buffer that covers the area 0.50 to 1.00 miles from each intersection. Using these three new buffer shapefiles use the spatial join tool to do a one-to-many join of the food markets to each of the buffers (specify that the points must be completely within the buffer, not intersecting)—making sure that an ID field that relates back to the original intersection is preserved. The result will be three new shapefiles that have the intersections listed many times with the different stores that are within each distance specific buffer. In each file, create a new field titled “Distance Score” and populate that column with the appropriate distance score (1.00, 0.90, or 0.75) depending on whether the file relates to the less than quarter-mile buffer, the second quarter-mile buffer, or the final half-mile buffer. Merge the three files into one. There will likely be thousands of records at this point.
STEP 05. Now that you have a master file that has a unique record for every intersection-to-store join, with the accompanying store type score and distance score, create a new field for “DT Score.” Before populating this field, select all of the records for intersections connected to a convenience/liquor store with a distance score of 0.9 or 0.75 and delete them. Convenience stores that are more than quarter-mile away are not considered because residents would not travel further than that to go to a convenience store. Next, use the field calculator to multiply the distance score by the store type score for each record to populate the DT Score field. To account for the overabundance of some store types skewing the results, a score cap is applied to each store type. To do this, select the records by store type and summarize by intersection, essentially creating eight summary tables by intersection. Adjust the sums in each table so that an intersection receives no more than the equivalent of three stores of any type within one-quarter of a mile; in other words, 3.00 points for supermarkets, 2.70 points for produce stores, and 2.16 points for other grocery stores. For meat and seafood markets, pharmacies, and convenience and liquor stores, the top number of points an intersection should receive from each store type is 0.70, 0.82, and 0.50 respectively—or the equivalent of two stores within that quarter mile. There is no score cap for farmers’ markets.
STEP 06. Merge the eight tables into one and summarize the capped products of store type score times distance score for each intersection. The resulting table should have the same number of records as the intersections shapefile, unless some intersections had no stores within one mile, in which case they may not be represented. Join this summary table by attributes using the intersection ID to the intersections shapefile. Now every intersection should have a score for the number and variety of retail food resources within one mile, weighted by food offerings and distance. Create a new field called “Final Score.” Populate this field by normalizing the DT Score Sum to a score of zero to 100 using the formula (x – min(x))/(max(x) – min(x)) * 100.
STEP 07. To visualize the intersection scores over a continuous surface, create a raster image using inverse distance weighting. Average scores can be generated for small geographic areas, like neighborhoods or Census tracts, by using the zonal statistics to table tool.
III. BAY AREA LOCAL HEALTH DEPARTMENT EXAMPLES
The HOPE Collaborative, a project of Tides Center, seeks to create community-driven and sustainable environment change for Oakland residents through the enhancement of local food systems, small business, and workforce development opportunities. HOPE is working with Alameda County Public Health Department via the Oakland Food Policy Council to increase access to land to grow food, including an edible parks program and opportunities to facilitate the sale/lease/use of private property to urban agriculture groups. HOPE is working with the City of Oakland to update mobile food vending zoning, expanding beyond the current limited areas and the current pod format.
HOPE is also working with Inner City Advisors and Urban Development to:
+ Conduct a landscape analysis of food and economic justice projects working in low-income and communities of color in the county.
+ Provide capacity building to social entrepreneurs seeking to build their projects towards sustainable business models for food and economic justice in low-income communities of color.
Improve the ability of local food businesses in Oakland to provide quality fresh and prepared foods.
+ Develop a comprehensive food retailer improvement initiative targeted at Oakland-based corner stores to provide Oakland residents access to high-quality fresh and prepared food options.
The county government is one of the largest employers in Santa Clara County, with a workforce of more than 15,000 in more than 30 departments and agencies. Many employees eat in one of six county-owned cafeterias and cafes, or purchase snacks and drinks from one of more than 200 vending machines. In addition, the county serves six million meals annually to the custodial population through the county hospital, jails, ranches, and other sites.
In 2011 and 2012, the Santa Clara County Public Health Department’s Center for Chronic Disease & Injury Prevention developed a comprehensive set of nutrition standards (with funding from CDC’s Communities Putting Prevention to Work obesity prevention initiative) based on national guidelines, including the 2010 Dietary Guidelines for Americans. These standards were developed with input from state and national experts and in collaboration with an interagency group that included senior-level representatives from nine county departments. This group, called the Nutrition Standards Committee, worked collaboratively for a year to develop the standards to ensure that food and beverages offered, purchased, or served at county facilities and those provided by county departments were of maximum nutritional value.
The standards were organized by food environment. These included meetings and events, vending machines, cafeterias and cafes, county-leased properties, and custodial populations. The standards were approved by the county board of supervisors in March 2012 and were published and disseminated soon after through an internal marketing campaign and employee trainings.
Assessments in the early stages of implementation revealed improvements in the mix of products offered in vending machines and in the availability of healthier food items in cafeterias, cafes, and custodial sites. The County Nutrition Standards were also used as a model for six cities in Santa Clara County, several other counties across California, and by several other states.
California Center for Public Health Advocacy, PolicyLink, UCLA Center for Health Policy Research. 2008. Designed for Disease: The Link Between Local Food Environments and Obesity and Diabetes. http://www.publichealthadvocacy.org/designedfordisease.html. Accessed November 2013.
Gibson DM. 2011. The Neighborhood Food Environment and Adult Weight Status: Estimates from Longitudinal Data. American Journal of Public Health. 101(1):71-78.
HOPE Collaborative. 2014. Our Work. http://www.hopecollaborative.net/our-work. Accessed June 2014.
Papas MA, Alberg AJ, Ewing R, Helzlsouer KJ, Gary TL, Kalssen AC. 2007. The Built Environment and Obesity. Epidemiologic Reviews 29(1):129-143.
Robert Wood Johnson Foundation Commission to Build a Healthier America. 2009. Improving the Health of All Americans through Better Nutrition. http://www.rwjf.org/en/research-publications/find-rwjf-research/2009/07/improving-the-health-of-all-americans-through-better-nutrition.html. Accessed November 2013.
Santa Clara County. Santa Clara County Nutrition Standards 2012. 2012. http://www.sccgov.org/sites/sccphd/en-us/Newsandevents/Documents/Nutrition%20Standards/Nutrition_Standards_NEW_july2012_v3.pdf. Accessed June 2014.
U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. 2011. Children’s Food Environment State Indicator Report, 2011. http://www.cdc.gov/obesity/downloads/childrensfoodenvironment.pdf. Accessed July 2014.
U.S. Department of Agriculture and U.S. Department of Health and Human Services. 2010. Dietary Guidelines for Americans. 7th ed, Washington, DC: U.S. Government Printing Office.
Zenk SN, Lachance LL, Schulz AJ, Mentz G, Srimathi K, Ridella W. 2009. Neighborhood Retail Food Environment and Fruit and Vegetable Intake in a Multiethnic Urban Population. American Journal of Health Promotion 23(4):255-264.