Jong Wai Tommee
Assignment 7
This is a policy analysis project on Boston area supermarkets. The goal of this project is to find
out the service area of local supermarkets in the Boston metropolitan region as defined by the Boston
Region MPO and Metropolitan Area Planning Council (MAPC)’s 101 communities to determine if areas
of insufficient supermarket service exist in the region. By finding out how many supermarkets are in the
region, the distances between individual supermarkets, the methods of getting to the supermarkets and
the population served by each individual supermarket, we can find out where areas where there is
insufficient supermarket coverage and whom (i.e. what population types) does this affect. Conversely,
this project can also find out areas where there are large clusters of supermarkets, which is a possible
sign of over-sufficient supermarket coverage.
Primarily, the most important layer needed for this project is the list of supermarkets in the
region. Due to the fact that my argument focuses on the ability of middle income people to purchase
groceries that are available only at chain stores, my definition of supermarket will be very narrow for the
interest of this project. I plan to limit my supermarket selection to eight local and regional chains that
primarily sell conventional groceries (i.e. not focused on organic foods such as Whole Foods). The eight
chains are: Stop and Shop (for this project’s purposes, no distinction between super and non super Stop
and Shops will be made), Shaws/Star Market, RocheBrothers/Sudbury Farms, Foodmaster, Pricerite,
Hannaford, Save-A-Lot, and Market Basket. Most of the stores have more than 100 employees with a
few exceptions.
There is not currently any layer of the 101 communities of the Boston region and so this layer
will either be created by clipping the entire Massachusetts state communities layer or by converting a
list stored in Excel into a layer. Roads and major roads will be added as background. No other layers will
be needed but a wide array of tools will be employed (see below).
Important additional layers include transportation and census demographics such as income,
age, etc. Although I primarily intend for this project to stay as a geographic project, it is also interesting
to look to see if areas with certain population or income levels is affected.
The GIS layers I anticipate I will be using are the following:
1.
Census 2000 POLY datalayer for Massachusetts (each map joined with downloaded census
data)
2. MassGIS roads ARC layer
3. MassGIS major roads ARC layer
4. Geocoded supermarkets layer
The procedures of completing this project involve the following steps:
1 The project starts out with data gathering. I used Reference USA to geocode a full list of supermarkets
in Eastern Massachusetts. Because this list includes convenience stores, wholesale clubs, and other
stores I do not intend to focus on, I made a separate list that includes only stores from the eight chains I
mentioned above and only in communities under the jurisdiction of the MAPC. I used the attributes of
geographic setting (NCAIS 445110), metropolitan region, and number of employees (100+) to obtain the
initial list and then went through each of the individual supermarket chains’ websites to check on the
accuracy.
2 To create an initial map of the 101 MAPC communities, I will probably first add the MassGIS polygon
layer with all the Massachusetts communities and then clip out the 101 communities.
3 The layer created from Step 1 will be added onto the map. I had trouble with finding the correct
geographic coordinate system in an earlier demonstration so I might still need some help on this. I think
I attempted to set it up to GCS North American 1983 but I don’t quite remember.
4 From American Factfinder, I plan to obtain demographic information on total population, household
income, age, and travel to work. Again, information will have to be clipped so that the focus is only on
communities within MAPC jurisdiction. Attribute tables will be created for each variable. Each of these
variables will be displayed in their own separate maps in a way similar to what I had done in Assignment
3. These will be the background layers.
5 The layers derived from the data found in step 3 will then be transposed onto the map created in step
2 by an ordinary joint (just like Assignment 3). Each variable will have its own map.
6 Finally, a network analysis will be conducted on supermarkets layer. This will show the result of the
distance between each supermarket in each community.
In the end, I should have one large map showing the initial details (the 101 communities and the
supermarkets layer, and three or four smaller maps detailing the overlay of the supermarkets layer and
network analyst results on top of three or four demographic layers.
The analysis of the maps is as follows:
1 Large map
-
Shows where supermarkets are distributed in the region and how close they are from each
other. One can observe where there are clusters and where there are possible food deserts. Are
supermarkets serving equitable levels of population in the area?
The smaller maps go deeper in analysis of food clusters/food deserts.
2 Income level map
-
Details where high income and low income residents concentrate. One will find out if
supermarkets agglomerate in certain areas on account of income.
3 Age level map
-
Similar to the income level map, this map will show whether residents based on age (especially
those over 65) have close access to supermarkets.
4 Walkers map
-
Certain places have a larger proportion of walkers. Many of these residents most likely do not
have access to a car. This map will find out if supermarkets have a tendency to locate in autocentric areas.
If time permits, I might include a map on race and ethnicity vs. supermarket location. This is actually
a very potent variable but I worry it would be too controversial and hard to analyze. Also it would not be
easy to extract from the Census Bureau, the information of which I find to be highly biased anyways.