In-Class Workshop Introduction & Questions
New York City has recognized that not having access to good grocery stores leads to unhealthy and unequal communities. Those neighborhoods with poor access to food, which are often lower income or otherwise underserved, have higher rates of obesity, diabetes, and heart disease than those neighborhoods that are not "food deserts." In 2009, the city launched FRESH, a tax incentive and development program to encourage grocery store retailers and commercial developers to create better and more accessible grocery stores. Developers receive tax abatement for up to 22 years and looser zoning and building restrictions if they are approved for the program. It's a classic neoliberal solution: turning to private enterprise to solve public problems. But does it work? Does this infusion of public funds into private enterprise lead to healthier communities?
Session Objectives
Question 1: Has the economic incentive to develop made a demonstrated impact on the neighborhoods immediately surrounding grocery stores that have taken advantage of FRESH provisions? What is the projected impact moving forward into the future?
Question 2: Have areas of the city (health service areas, zip codes, neighborhoods) that have more grocery stores than less grocery stores produced better health outcomes? Are there other factors that are better indicators of neighborhood health?
Sample Data Files
1. NYC Commercial Overlay Districts | data documentation | more documentation
2. USDA areas with low food access (PolicyMap download) | Definition: Low Income and Low Access status, as of 2015. Includes low-income tracts with at least 500 people or 33 percent of the population living more than .5 miles (in urban areas) or more than 10 miles (in rural areas) from the nearest supermarket, supercenter, or large grocery store. A low-income tract has a poverty rate of greater than 20 percent, has a median family income (MFI) of less than or equal to 80 percent of the state-wide MFI, or is in a metropolitan area and its MFI is less than or equal to 80 percent of the metropolitan Tracts for where no data were available are labeled "Insufficient Data" on the map.
3. Locations of FRESH Supermarkets as of 2015-2016 | This file shows point locations of supermarkets that have taken advantage of the FRESH tax incentive program.
4. NYC Hospitals | A map of locations of hospitals in New York City.
5. NYC Health Areas | A boundary file for the designated health service areas in New York City.
6. CDC Synthesis of BRFSS for NYC (featured dataset) | This file represents changes in estimated obesity rates in NYC between 2014-2015 according to the CDC's Behavioral Risk Factor Surveillance Survey (BRFSS) 500 estimate series (see survey methodology). The indicators on this set are: 1) estimated obesity rate by census tract in 2014 (y2014_rate), 2) estimated obesity rate by census tract by census tract in 2015 (y_2015_rate), and 3) change between 2014-15 in percentage points (year_change).
Recommended Tools
ArcGIS Online (you must sign up at https://guides.nyu.edu/appointment)
Geocode by Awesome Table (Google Sheets extension)
Pre- Workshop Instructions
Use any of the available datasets and or data sources suggested to create layers within ArcGIS Online that would theoretically help someone answer either of the two questions posed above. Before we meet in class, see if you can accomplish the following steps:
1. Create an account with ArcGIS Online by accepting the e mail invite that was sent to you. Don't lose your password.
2. Become familiar with the maps and datasets dashboard within ArcGIS Online.
3. Connect the CDC obesity file to ArcGIS Online by first downloading it (using Chrome is recommended), then going to My Map > Add > Add Layer from file.
4. Display a map that categorizes NYC according to obesity rate by census tract in 2014. To do this, click on the layer icon (which usually says "A "and is a square), then click the solid color bar, and then select "by value." Scroll down the column list and select the variable y2014_rate.
5. Take some notes about what you see. Where are areas that have particularly high incidence rates? We will continue with this workshop in class.
