Siting Pizza Delivery Services in Omaha, Nebraska


I started with a question: What is the best location for a pizza delivery restaurant in Omaha? I based my project on this question and quickly came up with other questions almost as important. What demographic factors are most important in determining the ideal location? What would be the best platform in which to analyze the data? What is the best way to graphically present the results?

After some initial investigation, I determined that ArcView would be the best format in which to undertake this project. The next phase was determining the optimum demographic cross-section of the average delivery customer.

From my experience, I found that income, age and number of young children, age of adults, as well as the proximity to high density housing, hotels and major thoroughfares, highways and interstates, were some of the most important factors.

Using this information, the next step was to create a basemap of the area with the basic data already in place. I started with a basic street map of Douglas County, Nebraska and cropped it down to include only the city of Omaha. To this I added the major highways, both county and state, as well as the Federal Interstate system giving me a nice map on which to add more data. To this base, I added block-group data from the Department of the Census Tiger Data. The street map was also from Tiger.

The next step was to locate all the other pizza delivery restaurants, apartments and hotels. I had previously entered these as a spreadsheet, then imported them into ArcView. Address matching the restaurants, hotels and apartments was made easy when a friend loaned me a CD with Douglas County Geocoding Data. This data automatically matched the Excel spreadsheet data with latitude and longitude coordinates and located them on the basemap.

To finish preparing the map for analysis, I visited the CIESIN Internet site and asked them to e-mail the most-recent US Census Data. From this data, I chose the characteristics that would be most beneficial in my analysis: average income, percentage of population-newborn to age nineteen, and percentage of population age twenty to age thirty-nine. My logic for choosing these last two categories was that children want more often pizza than adults and that adults between the ages of 20 and 39 have more children than adults in any other age category.

Using the spatial analysis tools provided with ArcView, I chose a location that looked suitable and analyzed it. This map is the original basemap with an average income map overlay. The area I chose is circle on the second map(above). The circle is a roughly two-mile radius around what looked to be a good location. The number of apartments(green dots) is high while the number of other delivery restaurants servicing the area is low. Also the average income for the block-groups within this circle appears to fall in the middle to upper-middle income range. No other area on the map has this combination of positive factors.

The next map shows the original basemap with the average number of children under the age of twenty. Again the same two-mile radius circle is drawn in the same place. Within this circle, the average number of children under the age of 19 is again in the middle to upper-middle range. Another positive sign in my search.

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The final map depicts the basemap with the average number of adults age twenty to thirty-nine. The two-mile radius circle in the same location once again shows the average number to be in the middle to upper-middle percentile.

Some issues arose throughout this project. I had debated with the idea of creating buffers around existing restaurants and targeting areas that lacked buffer coverage. However, I felt that the proximity to other establishments was less important because each brand of pizza has its own flavor and thus has its own following. People do not always go to the closest restaurant, they go to their favorite restaurant. The age of the Census data, being from 1990, also presents an quality issue. It is probably not as accurate as I would like. Also, when using block group data, the average is used but the average may not be indicative of the area as a whole. For instance, in a block group measuring two square miles, all the people may live in one corner measuring one-half square mile, but giving a false value to the area as a whole.

My final analysis in looking at the area as a whole, is that my first impression was correct. The area in the circle is the best area for locating a pizza delivery restaurant in Omaha. This area presents the combination of the most apartments, the fewest other delivery restaurants, the best access routes and the best demographic profile for an establishment of this kind. The next step is to start looking at property values, rent prices and the feasibility of locating in this area of Omaha.