Introduction
Being a resident of the City of Eau Claire for nearly three years, one issue that has commonly arose among myself and many close friends is the lack of an adequate outfitting/archery store. This issue has perplexed me for years given that this region of the state is consistently recognized as one of the better hunting areas in the country. Buffalo County for example (southwest of Eau Claire County), is considered one of the best Whitetailed Deer hunting counties in the entire country. The hunting industry in the United States is a multi-billion-dollar industry and given that the City of Eau Claire is easily the largest urban population center in northwestern Wisconsin, there is an incumbent need for there to be an archery/outfitting store in Eau Claire. In Eau Claire, there are currently two large outdoor retailers and they are Scheels and Gander Mountain. While these stores do carry outdoor gear, they are large cooperations that lack the ability to give the expertise and individual attention that is needed for customers to truly succeed in the sport of archery and bowhunting. Archers who also shoot on a competitive basis need special equipment and the flexibility that pro-shops provide in their ability to be more flexible in their inventory.
Literature Review
When looking for a potential location for new retail business, there are multiple factors that need to be considered. These factors include demographics, traffics routes, consumer demand (GIS for Retail Business). Demographics for residential areas is key for a business to be successful. Therefore, potential neighborhoods should beranked/classified by the likelihood of demographics being customers (Weber). In ArcMap, an effective tool to be used to measure the distance from sites of interests is the Euclidean Distance tool (Abramovich). This can be used to see how far potential customers are from a potential store location as well as distances from highways and other high traffic areas. Once parameters have been set, the weighted overlays provide a means in which multiple different analysis factors can be considered and shown in a single model, greatly increasing the effectiveness of the spatial analysis (Suárez-Vega).
Data
For this project data from the Eau Claire City
geodatabase from the Q-drive was used. This geodatabase provided the
basic information for the Eau Claire such as roads. Along with this geodatabase I used data from the United States Census Bureau
from the American Factfinder at the census block level. This data included
various population statistics at the for the city including population and median income levels as the along with block group shapefile. Highways, and highway exists feature classes were obtained via the
ESRI the geodatabase also found on the Q-drive.
Methods
I reclassified the population density into three categories. This was determined by
classifying the data into three groups jenks natural breaks so that three population
density categories could be created ranking areas from highest and lowest population
densities. The median income raster was classified into a similar manor being classified
into three groups of the lowest average median income, median average income and then
highest median income levels. The Euclidean Distance raster was reclassified in 3 categories
at the kilometer level. I then created two weighted overlay maps with the three rasters using
map algebra.
To begin my analysis, I began by preprocessing my
data so that all my layers would have the same projection. After reprojecting
all my files into the NAD_1983_HARN_WISCRS_EauClaire_Meter projected coordinate
system. I then clipped the highways, highway exists, and roads feature classes
by the Eau Claire County Census Block Group shapefile. I then manipulated the
different block group files from the Census Bureau into the correct formats, so
they could be joined to the census block shapefile. Once these tasks were
completed, I could then start to begin my analysis.
I
began my analysis by creating two rasters from the census block shapefile. They
were population density and median income. After creating the two rasters, I
then, used the Euclidean Distance function on the highway exists feature class to
find areas within the city than were easy accessible from the major highways as
these areas receive high amounts of traffic. After the Euclidean distance function
was run (Figure 1), I created a raster of the Euclidean Distance function I into a raster.
Once the three rasters were created, I then began to reclassify the three raster images.  |
| Figure 1 Euclidean Distance function used on highway exists |
I reclassified the population density into three categories. This was determined by
classifying the data into three groups jenks natural breaks so that three population
density categories could be created ranking areas from highest and lowest population
densities. The median income raster was classified into a similar manor being classified
into three groups of the lowest average median income, median average income and then
highest median income levels. The Euclidean Distance raster was reclassified in 3 categories
at the kilometer level. I then created two weighted overlay maps with the three rasters using
map algebra.
Results
The
first weighted overlay map I created I only used the population density and
median income rasters (Figure 2) as I wanted to see how the demographics can affect
potential site location. Not being satisfied with the results, I then made second
weighted overlay map where I incorporated the Euclidean distance raster.
Looking at the final results, there are multiple areas that standout between
the maps. Areas located in southern regions of city appear to optimal placement
for a new business. However, when the accessibility was incorporated, locations
on the northern and western sections of the city also appear to be good places
to locate a business. After comparing the two results. I identified an area on
the northern section of Eau Claire (Figure 3). This was
an area that had high median income levels, medium population density and high accessibility
from the highway. This area was also on the other end of Eau Claire from the
cities only other competing archery store being Scheels.
 |
| Figure 2 First Boolean Overlay Created using Demographic Data |
 |
| Figure 3 Weighed overlay including highway accessibility, final location is area circled in red |
Sources
“18.9 million Americans Participate In
Archery.” Archery Trade Association, 9 Aug. 2013,
Abramovich, Adriana Alicia. “Using GIS to
Assist Location and Site Selection Decisions.”
Business Site Selection, Location Analysis
and GIS, 2012,
doi:10.1002/9780470432761.ch2.
GIS for Retail Business - Esri - Esri: GIS
Mapping …
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l-QBmzpNc8A&v=1&r=http%3a%2f%2fwww.esri.com%2flibrary%2fbestpractices
American FactFinder , United States Census
Bureau, 5 Oct. 2010,
factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t.
Suárez-Vega, Rafael, et al. “Location models and GIS
tools for retail site location.” Applied
Geography, vol.
35, no. 1-2, 2012, pp. 12–22., doi:10.1016/j.apgeog.2012.04.009.
Weber, Patrick, and Dave Chapman. “Location
Intelligence: An Innovative Approach to
Business Location Decision‐making.” Transactions
in GIS, Blackwell Publishing Ltd,
6 June 2011, onlinelibrary.wiley.com/doi/10.1111/j.1467-9671.2011.01253.x/abstract.
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