1. Locational Intelligence Research
of Fleet Farm Store Expansion
for Week 4 of Saint Mary U’s
Advanced Modeling
__________________________________________
Kevin Byrne
September 29, 2009

2. Goal
To use locational analysis methods and tools for
shortlisting and choosing a site for a new Fleet
Farm big box store in central Minnesota that
maximizes revenue and new job growth but
minimizes business closures and unnecessary
job loss for the selected city.

3. My Methodology-Steps
1. I located and offloaded zipcode shapefiles for the US, then used selection by attributes menu with a
query in ArcMap to identify the six Fleet Farm stores in central Minnesota. The shapefiles were
polygons so I had to offload and install an extension titled Xpro Tools to convert each polygon to a
centroid point.
2. That permitted me to create, using ArcToolbox, five 15 mile ring-buffers out from each existing store’s
centroid to gage “hot rings” to judge potential locations.
3. I located and offloaded census tract shapefiles, then joined them to income demographic .dbf files to
portray mean HH income as a choropleth map. The same was done with township shapefiles to
portray population density.
4. Fleet Farm’s likely competition was shortlisted to Menards and Runnings using website
demographics derived from Fleet Farm’s web traffic.
5. Using the web and email queries I researched other “non-technical” but important selection criteria for
two locations I judged as finalists.
6. I applied ring buffers, income, population density, competition-avoidance, and contextual criteria to
make – and justify – a decision choosing a city as the best candidate for Fleet Farm’s newest store.

6. Table from Quantcast that judged web visitor
income for Fleet Farm’s site, rebalanced.
$k %–old %–diff %–new
0-30 13 +6 19
30-60 30 +2 32
60-90 32 -2 30
100+ 26 -6 20
total 101 (?) na 101 (?)

12. 56308
56387
55014
55445
55128
55044