Final Project

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

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.

Figure 1 Euclidean Distance function used on highway exists

Once the three rasters were created, I then began to reclassify the three raster images.
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,

www.archerytrade.org/news/survey-says-18.9-million-archers-are-active-in-u.s. 

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 …

www.bing.com/cr?IG=D8AC8A848647432998060BA8AAC357AE&CID=3FB4BD6C6

25469B0045DB647635268FD&rd=1&h=o0FWPzP1UUVGMzryYxdM48bRuuw9BhV

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.

Lab 3

The purpose of a geodatabase is to store the various pieces of data that is required to complete a given project. For the purpose of this project a hypothedical situation was created where a local business was looking to find optimal placement of a new retail store in the city of Eau Claire. For the this project a personal geodatabase was chosen because the data will only be accessed by one user. The geodatabase was created with the geographic extent of Eau Claire County and have a local coordinate system (NAD_1983_HARN_Adj_WI_EauClaire_Feet) that will minimize geographic distortion for the data.

The datasets that were included were the census tract shapefile from the Americanfactfinder to which the various excel sheets can be joined to. The excel sheets included are demographic information such as different sexes, ages and, income levels that will be chosen at different levels in order to create a suitability matrix.  I’ve also included streets for the county as well as highways and their exists. For the EC_Building layer, I merged two building shapefiles to create the single buildings feature. This will be used to select a building of a high enough square footage that meets the needs of the project. I did not create any subtypes of domains for my data as I did not create any new features other than the one above that required to be merged.

Figure 1. The geodatabase for this projetc

Lab 2

Part 1

The purpose of this part of the lab is to delineate the watersheds in the Adirondack Park in northeastern New York. Watersheds represent the area of land that all surface water is connected. Delineating these such watersheds allows for watershed managers to better monitor the quality and the quantity of water found within them. For the purpose of this lab the Adirondack shapefile was downloaded from the New York State GIS Clearinghouse (http://gis.ny.gov). The hydrology data was acquired from the Cornell University Geospatial Information Repository (http://cugir.mannlib.cornell.edu/index.jsp). The DEM was retrieved from ArcGIS Online.

Once the data was brought into ArcMap, the data needed to be preprocessed. All the data was placed into the Universal Transverse Mercator (UTM) Zone 18N NAD 1983 projected coordinate system. To begin, a 20 kilometer buffer the Adirondack boundary so that DEM could be processed more smoothly. Then the hydrology layer was clipped by the Adirondack shapefile, leaving only the hydrology in the park. Next, the DEM was clipped by the buffered Adirondack boundary.

Once all the preprocessing was completed, the watershed delineation process could be started. This was done in a series of steps. To begin, the a flow direction raster was created and the sinks were then filled (sinks disrupt the flow of water and therefore would negatively effect the waterflow modeling). Once the sinks were filled, another flow direction model was created a flow accumulation raster was created, leaving small stream channels. These small channels were then given a threshold of 50,000 cells. Then the streams were then connected so that the watersheds could then delineated (Figure. 1).

(Figure. 1)  Adirondack Watershed Map

Part 2

The second part of this lab was to use model bluespots (depressions prone to flooding because of a lack drainage) in the Denmark City of Copenhagen. Due to extreme rainfall events such as cloudbursts, the city has experienced large amounts of flood damage as a result. To identify areas in danger of flooding a model was run (Figure. 2) that identified bluespots and then intersected them with buildings (Figure. 3). After running the first model, a second model was run. This second model not only located bluespots but calculated the volume of these spots so that the bluspots can be ranked by their susceptibility for flooding (Figure. 4).

(Figure. 2) Model used to find bluespots and intersect them with buildings

(Figure 3) The map above shows bluespots and the buildings that are in contact with them

(Figure. 4)The Map above shows buildings that are touching bluespots as well as showing the drainage capacity of the bluespots

The map above shows the buildings that are at most risk of flooding in Genofte

The map above displays the relationship between watershed area and the bluespots found within them

The map above shows roads and rail ways that are least likely to be affected by flooding