Ethan Nauman

Geog352.001

Final Retail Site Selection

 

Cabela’s Retail Site Selection for Minneapolis Area

Cabela’s Store:

                The store that I am choosing to do for this final retail site selection is a Cabela’s store for the Minneapolis area. The reason that I am choosing the Cabela’s store to find the retail site selection is because recently I have visited the Minneapolis area and every time I have passed the Cabela’s store in Woodbury Minnesota. I also thought that this was a funny place to have a big name brand store as it is on the east side of the twin cities just as you crossed the border to get into Minnesota. Now I am not from Minnesota so I don’t know the demographics for the Woodbury area or for the Minneapolis area for that matter, but I always thought that Cabela’s would pick a wealthy suburb of the twin cities to be able to support their store. I wanted to know if this was the only store in the Minneapolis area, because if that is the case I believe that this area could easy support if not one, but more stores. The Minneapolis area is a perfect location to support a Cabela’s store because it’s a store for outdoorsmen and women. With a thriving hunting and fishing community surrounding the Minneapolis area, I believe that Cabela’s is a must stop location for the surrounding population around the twin cities. With that being said, this allows for the Cabela’s store to have a large area to pull customers from. With just one store currently on the eastern side of the cities that I found this allowed for other outlet stores to cut into Cabela’s customer base. After doing some more research, I found that there is also another Cabela’s store on the northwestern edge of Minneapolis in Rogers MN. With Cabela’s having two stores now on opposite sides of the twin cities, it allowed for them to broaden their customer base while hopefully not competing with each other. I want to find that if another Cabela’s store came into the area if it would be able to thrive, or would it just be taking away from the two stores that currently occupy the area.

Demographics:

                What is being sold at Cabela’s stores suits the needs for the outdoorsmen and women alike. Although my study area, Ramsey, Hennepin, Anoka, Washington, and Dakota counties, are surrounding the twin cities with much of the area being suburbs, these two stores are able to survive in the area. My study area supports over 2.5 million people. Now not everyone is an outdoorsmen, but with 2.5 million people and only two Cabela’s stores to support those outdoorsmen, I believe that there is an area that another store would be able to be placed and not take away from the other two stores while still attracting enough customers and make a profit. The ideal customers for a Cabela’s store would be people living in the suburbs I believe. Although there are people living downtown the twin cities who are outdoorsmen, they need to get out of town to be able to do anything outdoors. With the two stores on major highway routes it is easy access for them. I want to concentrate on the suburb area that is outside of the twin cities that allows the customers to go straight from the store to the field and use the products.

Market Structure:

                With market structure always being a concern, the Cabela’s store market tends to be the top of the line head guy on the block. There are many competitors with the outdoors business especially in a study area so big. It seems that every hardware store now has an outdoors section, and you can’t forget about the Gander Mountain’s, Walmart’s, and small town business’s that are selling these outdoors products. I believe that many of these other stores are able to take away the customer base from Cabela’s because they are smaller stores and are usually more convenient within these cities. It’s less stress and much quicker to be able to run inside your hardware store five minutes from home and get what you need, instead of making a half hour drive to Cabela’s were you know it will be packed but you also do have a larger selection of product to choose from. One thing I would bring up to Cabela’s would be is to maybe think about opening more stores with smaller store locations. Cabela’s is known for being an enormous store full of outdoors displays and animal mounts with the top of the line product. It wouldn’t be a bad idea to look at opening smaller store locations within two large cities that allow customers to have the feel of a local hardware store. The other question would be is would these small store locations be able to survive with just selling outdoors products.

First map:

figure 1

                The first map that I created was of the two store locations of Cabela’s, along with the Gander Mountain stores that are within my area of interest. The reason I choose to show the Gander Mountain stores was because these are the biggest competitors to the Cabela’s brand. Along with showing the Gander Mountain stores, I also performed a proximity analysis on the Gander Mountain stores. I wanted to see the stores that were within a 20 miles drive distance to the two Cabela’s stores. There were four stores that were within that 20 mile distance. By being able to single these stores out, I thought that it would help to narrow down the three store locations of my new proposal sites for Cabela’s. It did help me narrow down my three store locations to a certain extent. I wanted to be able to keep these store selections on major highways as well. The three store selections that I proposed were in Eden Prairie, Bloomington, and Lino Lakes. Although each of these site selections would also be within 20 miles of Gander Mountains, I realized that I needed to propose a fourth site selection allowing me to place this fourth store location on a major highway yet far enough away from having two Gander Mountain stores as competitors. My fourth store location that I propose is just up the street from Eden Prairie. This will have one Gander Mountain within 20 miles, but that shouldn’t cause an issue compared to having two stores within that 20 miles.

Gravity Model/ Point of Indifference:

figure 2

                The gravity model and point of indifference allows me to show the shopping characteristics of my given study area. It is an equation that shows the extremity of a city’s trading area where households would be indifferent between shopping in that city or going to an alternative shopping area in a different geographical location. With the point of indifference calculations that I performed, the trade area for the Minnetonka Cabela’s location look like a triangle. The trade area from Minnetonka to the Rogers Cabela’s would be 19.33 miles, the trade area from Minnetonka to the Woodbury location would be 22.02 miles, and the final trade area from Minnetonka to the proposed Cabela’s in Bloomington would be 13.47 miles. With a point of indifference being so large, the proposed Cabela’s site for the Minnetonka location would be the best fit.

Trade Areas:

                This is a map of the trade areas of the two existing Cabela’s, along with my fourth Cabela’s location proposal. I wanted to map the trade areas of 5 and 10 mile radius’s for these sites. This would allow me to see if there is any overlap when targeting customers. When looking at the map, there is a small overlap between trade areas for the fourth proposed site and the Rogers Cabela’s location. Although there is overlap, which is never good, it is so small that this won’t cause much of an issue. The only proposed location that wouldn’t have any overlap with the two existing Cabela’s would be the proposed site in Eden Prairie. All the other proposed sites would have a greater overlap then the Minnetonka proposed site.

figure 3

         



Assignment 4

Trader Joe's Site Selection

 

Introduction:

    This was a very unique assignment dealing with the site selection of Trader Joe's in the Minneapolis area. I was given five Trader Joe's locations in Hennepin and Ramsey Counties in Minnesota, and was asked to analyze them to see if another store is needed. For the assignment I was asked to look at market penetration, find the optimal store locations, locate ideal customers, and rank new sites for a new Trader Joe's store.

    Like I have with all the other assignments, the first order of business was to narrow my study area down to just Hennepin and Ramsey Counties. This allowed me to bring in the data for the five Trader Joe's stores along with all the customer data that was provided. After the data was brought in the first map I wanted to focus on was the Market Penetration Report. Market penetration is based on the number of customers in an area compared to a demographic variable such as total population. This is a useful tool to show the areas that you are doing well in sales, along with the ones that need improvement. Looking at figure one is the completed market penetration that I ran. You can see that the darker green areas are areas that show that Trader Joe's is doing a good job with reaching out to the market area. The light green areas show that Trader Joe's could do a better job of reaching out to customers in these areas. You can see from figure one that there are few customers out in the light green areas. One reason for this is due to a large commute time just to get back and fourth to a Trader Joe's. These light green areas are ideal places for a new store for Trader Joe's to expand in.

Figure 1

     The next process of finding a new site for a Trader Joe's store location was to find the mean center of all the consumers of Trader Joe's. By finding the mean center, this will allow me to analyze and see what direction Trader Joe's has the most pull in. By taking the pull of Trader Joe's into account I will be able to determine what direction expansion would be key to go in. In figure two the mean center of all the customers is shown by a pink dot. This dot is located just northwest of Minneapolis. By taking into account the customers that Trader Joe's already has, shown by the little blue dots, and the area's that these customers come from. It is very easy to see that there is lots of room for expansion of Trader Joe's in the Northwest and western part of the counties. With at least one more store on the western half of the counties, Trader Joe's would expand their customer base.

 Figure 2

    Now that I have the mean center for all the customers, I wanted to locate ideal customers or do customer prospecting. This located regions with ideal demographic characteristics for targeting new customers. I was looking at areas with the minimum population and annual income between $18,000 and $60,000. The reason I chose these values to use was because I wanted to locate areas outside of downtown Minneapolis and Saint Paul. I want to see the suburban areas that Trader Joe's can target to expand their retail with. Now I know that there is a large margin between the floor and ceiling of the price range, but without knowing the suburban areas around the cities well enough I wanted to keep I vague. In figure 3 you can see that Trader Joe's would be able to expand there retail store area to a larger range with the breakdown of the map. Looking closer at location of ideal customers in bright green, at first going back to the other maps my expansion was only set for the western area of the counties, but now taking into account figure three I believe that there is room for another store as well in the northeast corner.

 Figure 3

    The final process I wanted to run was to pick three locations based off my maps to add a new Trader Joe's. The first step for me to choosing a new Trader Joe's was to analyze my three other maps. Both figure 1 and 2 lead me to believe that a new location should be put in the western part of the counties, with figure 3 leading me to that as well. Figure 3 also shows that I could put a store location in the northeast corner. I would look at putting a store there if the population would justify for having two stores relatively close and pulling from the same customer base. I decided to go off of  figure 1 and 2 instead. The three locations I chose that would support a Trader Joe's expansion store were: 21550 S Diamond Lake Rd, Rodgers, MN. The second store location was 270 St John St, Loretto MN, and the third store location was 5575 Shoreline Dr, Mound MN. Instantly after looking closer at first store location in Rodgers MN, I knew that a Trader Joe's wouldn't be a good idea there. The reason I came to that conclusion was that there is already a Trader Joe's just down the highway in Maple Grove. Although for a new store location you want easy accessibility with a highway, I feel that the stores are to close together and would be pulling from the same customers more then likely. This left me with the new locations in Loretto and Mound MN. When looking on goggle maps at the Loretto store, the surrounding area was mostly farming community. This would be ideal if there were enough customers in the region. When looking at the potential store location in Mound MN, the surrounding area was suburbs and also had a large chain of lakes surrounding the area. Usually houses on lakes allow for a higher customer potential along with a bigger wallet. The store location that I feel that would best support a new store for Trader Joe's would be the location in Mound MN.

 Figure 4

    When taking into account of figures 1 through 3, it was easy to justify were a potential new store could be located. Looking at all the customers on all the maps I could see that Trader Joe's had a significant pull to allow me to put a new store location not on a major highway. This allowed me to narrow my new store locations to Loretto and Mound MN. Taking into account Loretto being a farming community I realized that Mound was the best place out of the three that I had for a new Trader Joe's location. 





                                                                                                       Ethan Nauman

                                                                                              Geog 352

 

234 Roosevelt Ave.

Introduction

    When it comes to selling and purchasing houses in a given area there are many factors. Often times knowing the area around the house in a given area is just as important as knowing the property itself. Realtors have to come up with a selling strategy most fitting for the given area and target buyers who fit that strategy the best. Buyers, especially people looking at purchasing investment properties also have to come up with a strategy that is good for their business. These buyers are not purchasing so much for themselves and what they like in a property but more so what they think others will like and be willing to shell out monthly rent to live there.

Case Study

    In the case of our property, 234 Roosevelt Ave. this situation is no different. We are selling a 4 bedroom, 2.5 bathroom home in the Third Ward very close to the UW - Eau Claire campus. The home is over 3,500 sq ft, central cooling and forced air heat, has garage parking for 3 vehicles and is a minute walk away from being on campus. In other words, everything a group of students could want and more.

    A house like this is highly sought after by students who want to live off campus but not necessarily feel like they are living in a typical rental property. Which opens up other opportunities. With a property like this you are not limited to just students. Other properties for rent in the area are bareboned, out of date and not necessarily family friendly. This house would be perfect for a family new to the area who wants the downtown experience without fully committing to home ownership.

    To establish what kind of strategy we decided to use we had to look at the Third Ward as a whole as well as the surrounding area. To do so we used Business Analyst by ESRI and extension of ArcMap. We established a couple of things. In the Third Ward there are over 450 Renter Occupied Units (Figure 1). Going off this map alone we can see this is an area students are looking to rent in. Over half the houses in the Third Ward are being rented out, (52%.) We also wanted to see what the median age for the surrounding area was knowing that the college is a minute away. For the area our house is in the median age is 25 (Figure 2).This tells us a lot of traditional college aged students live there with maybe some younger families also located in the area.

We also wanted to see how the Third Ward compared to the surrounding area.  In the surrounding area (Figure 1) 66% of households are occupied by rents. This area of town is occupied with a pretty significant population of renters.

We were curious to as what “type” of people were occupying the Third Ward and Surrounding Area and fortunately Business Analyst has a feature called Tapestry that lays out the demographic of any area with a brief explanation. Our Third Ward property is located in an area considered “College Towns” and the nearby University is considered “Dorms to Diplomas”. (Figure 3) This group is focused on their education, with 59% being enrolled in college or graduate school. Since many of these people are students, median household income tends to be low. This is due to most of the employed residents only work part time. If they aren’t living in a dorm on campus, this group tends to live in low income apartment rentals off campus. “Dorms to Diplomas” is very similar, this group is focused on their education, with 59% being enrolled in college or graduate school. Since many of these people are students, median household income tends to be low. This is due to most of the employed residents only work part time. If they aren’t living in a dorm on campus, this group tends to live in low income apartment rentals off campus. The last relevant group is the group on the east side of the third ward is known as “Old and Newcomers”. This is a transitional area, with people either just starting their careers or retiring. Educational attainment is above average. Although this is a different age class than the “Dorms to Diplomas” and “College Town”, more than 60% of “Old and Newcomers” are renters.

Summary

There are many features about this property that make it valuable for people from all walks of life. It would be perfect for a new family starting out, plenty of bedrooms, wonderful interior and located close to many amenities they may desire. Our house is surrounded by people that are accustomed to renting their home, and this home has that potential. It could also easily be converted into a wonderful rental property. Its close proximity to campus, off street parking with two garages would make this desirable to students attending the university. Based off of surrounding rental properties, with similar characteristics, our house could be rented for a base price of $2,000.00 per month. This property has endless options, it is up to you as to what you would like to do with it.

Assignment 2

Study Areas, Geocoding, Customers, and Trade Areas

 

 

    The purpose of this assignment was to introduce us to spatial tools in business analyst. We started with the basics of study areas, geocoding, customers, and trade areas. These were all tied into our assignment dealing with coffee shops and doughnut shops. The question we were looking at was about two friends in the San Francisco area who owned coffee and doughnut shops. They wanted to maximize their trade areas in order to benefit both businesses. The store owners asked for a map illustrating where their customers are located, along with a map showing all other competing coffee/doughnut businesses in the area. They would also like two other maps showing their trade areas and one of driving time from their customers addresses to their businesses. These questions could pose a problem but luckily I was given plenty of data. The most crucial data I was given was the addresses of the customers.

    The first process of making my maps was to illustrate the study area of San Francisco. This was easily done in business analysts and allowed for me to just pull data from San Francisco county. I now had to place their two separate coffee shops on the map along with all their respective customers. I was able to geocode all the customers and stores on the map along with using tools in arctoolbox to show the mean centers of the stores. I was lucky to have detailed information on the customers addresses when geocoding. The more specific the address is when geocoding the better.

    The next step was to locate and map the similar business. In business analyst I was able to go into the menu and add business listings. This allowed for me to select businesses within my study area that were either coffee or doughnut shops. This is important for the owners to see exactly where their competition is at and how they can better their business to keep having their customers coming back rather then leave for another business. When looking at the map pictured below you can see the northern store has much more competition then the southern store. This store could look to add more to their menu or do something unique to keep their customers coming back.

      The last two maps I wanted to create were the maps dealing with trade area and drive time. This is important because you can see how large of an area you are pulling your customer base from. In business analysts menu there is a tool called trade area. This function allowed for me to compute all this information. When working with trade area I also made a report. This report allowed for me to show community profiling and retail goods and services expenditures. Also, my trade area was broken into three sections. My three rings around the business were broken into 40% of customers, 60% of customers, and 80% of customers falling within my three rings. I could change these rings or add more if I wanted. With the trade area map the owners can now see exactly how far away they are pulling their customers from.

    My final map was showing customer drive time within how many miles away they were coming from. San Francisco isn't a large city so by being able to pull customers from over a mile away is very pleasing for any business, especially a coffee or doughnut shop. I computed my data just like before for the trade area in business analysts except instead of showing 40,60,80 percentages I changed the rings to showing miles. My rings were setup as half a mile, one mile, and a mile and a half. This would give the owners an area of how far a majority of their customers are willing to travel to come to their business.

    After computing all the data and analyzing it, it was easy to see that these two business were doing well in their markets. Looking at the competitors map (map 2) you can see that the northern store had more competition yet they were still pulling customers away from their competitors areas. When looking at the customer trade areas map (map 3) you can see that the northern store has a smaller trade area. Now this can be both good and bad. The good of it is that their customers locations are located closer to the store allowing for easier access of the business. The bad would be that it is not covering as large of an area for their customer base. The northern part of San Francisco could be more densely populated and have more living quarters compared to the southern business trade area. 




Population Dynamics

Assignment 1

    The purpose of this assignment was to introduce us to ways of looking at population through the census. Using terms like population pyramid, dependency ratio, and location quotient, these calculations and help drastically when looking at starting a new business. All these terms along with being able to read and comprehend the United States census would allow for a business geographer to make a calculated decision on where a business could thrive along with the view of people that the business would be geared towards. 

    The first part of this assignment dealt with the creation of a population pyramid. To be able to do this I had to download information from the census website. I was asked by a business investor to steer him in the right direction allowing him to understand the population structure of Colorado Springs as well as the overall service sectors of the city. 

    I first began by downloading the age and sex data for Colorado Springs from the census website. This gave me the breakdown of different age groups and the sex. My data was based of off the ACS 5- year estimates for the city. Once I found the data on the census website I then downloaded it and brought it into excel for further processing. The next step was to calculate the percentage for both male and female for Colorado Springs. This was an easy process as I was able to compute the equation right in excel. My final table once it was neat and all the equations were computed looked like this. 

    The next step was to actually create the population pyramid. Again I was able to create this right in excel as I was able to highlight the data that I wanted to be on the pyramid. I chose the age column along with the %Male and %Female columns. When I had these three columns highlighted I selected a 2-d bar chart. This created a bar chart layout for me. After tinkering with the chart I was able to make it pleasing to the eye along with being able to be read easily. 

    The last thing I wanted to find with the population pyramid and data was the dependency ratio. The dependency ratio is calculated by comparing the youth and elderly populations to the working population. 

Dependency Ratio:

DR= 100* (p0-14+ p<65)/ p15-64

p0-14= Youth dependency 

p<65= Elderly dependency

p15-64= working class 

My equation looked like this: 100*(41.2+22)/136.4= 46.3

The dependency ratio measures the pressure on the working class to provide for both the youth and elderly who are dependent. This shows that it's around the normal for a city to have a dependency ratio such as 46.3. 

    After collecting data for Colorado Springs, I was then asked to get data on the state of Colorado. I did the same steps that I did when collecting and computing data for Colorado Springs. My data looked like this for the state of Colorado. 

Once again I calculated the dependency ratio for the state of Colorado. Both Colorado Springs and the state of Colorado were very close to normal for a dependency ratio. My equation for dependency ratio for the state of Colorado looked like this.

DR= 100*(40.2+22.8)/137=45.9

The DR for Colorado Springs was 46.3, While for Colorado was 45.9. These are only separated by 0.4. 

    The next process I wanted to find dealt with location Quotients. Location quotient measures concentration of an area. It represents the geographical concentration of particular region compared to another geographical area. If the location quotient is a 1, it means that that area has the exact makeup as the average U.S. town, county, or state depending on what area you are looking at. A number more then 1 means it has a higher average, a number lower then 1 means it has a lower average. 

    To calculate the location quotient I first had to find total population, total pop. 0-14, percent pop. 0-14, total pop. 65+, percent pop. 65+, total hispanic, percent hispanic, total white, and percent white for Colorado Springs, El Paso County, Colorado, and the United States. By finding all of this data on the census website, it would allow for me to calculate location quotients very easily. 

    Pictured above is a chart I made with all of the data I needed to find to be able to calculate location quotients. Location quotients are based off of the percentage for each category for the United States. To find the LQ for population 0-14, I took the pop. 0-14 of Colorado Springs and dived it by the pop 0-14 of the U.S. This would give me the location quotient. I would then repeat this process for El Paso County and the state of Colorado. I was asked to find the location quotients of pop. 0-14, 65+, Hispanic, and white. 

    The final process I wanted to look at was the the tertiary and quaternary sectors. I wanted to find the location quotients of these sectors as well for the state of Colorado along with Colorado Springs. The six sectors I wanted to compute location quotient on were: agriculture and forestry, construction, manufacturing, education, information, and public administration. When looking on the census website at Colorado and Colorado Springs, I wanted to looked at the Select Economic Characteristics census. This would give me the percent of the six industries for the state and the city. Once I had the percent for both the state and city, I was able to calculate the location quotient by taking the percent from the city and dividing it by the percent of the states industry. Again a number close to one meant it was the same as the states. 

The only industry that was the same as the states was the information industry. Education and public administration were above the average while agriculture and forestry were way below the average. This could be rationalized because there won't be very much forestry going on in a city really. 

    After looking at the location quotient and comparing Colorado Springs to the state of Colorado, I would advise my investors to go into the public administration industry. I would also advise my investors to going into an industry for young children or hispanic based off of my first findings on location quotients. The population of 65 and greater is to minimal to invest in a business that is worthwhile and thrive and make money. The best bet is the age grouping of 0-14 as it has the highest LQ. One thing I would throw towards my investors would potentially be that rather then just gearing there business towards children 0-14 just in Colorado Springs, test the market our here first then look at expansion. By finding another city with a LQ of the same or greater then Colorado Springs, the company could potentially open another branch if the one in Colorado Springs thrives and makes money.