1. Major League Baseball Expansion
Group Members:
Matt Moore, Jason Norris, Kevin Price, Kelby Pedery-Edwards

2. Recap of Project Motivations
Determine the most suitable city in the United States for the location, or relocation, of a baseball team.
Within selected city, locate optimal site for team headquarters and stadium.

3. 1st Attempt
Originally we intended to emphasize the second of our goals and put most of the spatial analysis into the city level of the project.
Using the Analytical Hierarchy Process (AHP), our group assigned weights to a number of variables to determine which city would be an optimal hoist to a baseball team.
Process worked, the AHP produced an output that specified San Antonio as the most hospitable location. Problem: no spatial analysis.
Meeting with Josh, back to drawing board.

4. 2nd Attempt
Meeting with Josh helped us reorder our priorities and shift the spatial analysis emphasis to the 1st leg of the project, determining the optimal city location within the United States.
Unfortunately most of what was originally produced had to be scraped, the only thing we could salvage where the basic variables we choose to help determine the city location: Proximity to an existing franchise, metropolitan population, temperature averages and precipitation patterns.
Goal was to produce a map that would have ‘hotspots’ throughout the US that would help us determine which general areas would be hospitable to a MLB team.

5. Proximity to Existing Franchise
This map layer was designed to produce a buffer set from existing franchises. This is in place of the original 100 mile exclusionary buffer. This would be one of our four inputs to produce a final hotspot map.
Five buffer rings encompassing the entire US that were each 213 km in width.

7. Temperature
This layer is extremely important because baseball is a warm weather, summer sport and it needs to played in a suitable climate.
Temperature data was gathered and scaled on a 1-5 scale with a value of 1 being the most inhospitable and 5 being the most suitable to having a franchise.

9. Precipitation Patterns
Precipitation patterns are important when analyzing potential sites because baseball cannot be played in rainy climates.
Precipitation data was acquired and once again a scaled map, 1-5, was made that would be an input to the final hotspot map

11. Population Analysis
For this layer we broke the US into a set of 26 Thiessen Polygons, with the centroids being existing cities with MLB teams. Each Thiessen Polygon then had its total population calculated and the data was scaled relative to the other polygons to find suitable areas for a team.
Cities with multiple teams (Chicago, New York, etc.) were lumped together as one polygon

13. Hot Spot Map
Once we created our four layers we used the join function to create one map with one attribute table. Then we created a new field and multiplied the weights we had given each variable by the scaled ranking we had created in the map layer. When combined the output map created patches of hotspots throughout the US that were the most hospitable to hosting a MLB team.

15. Analysis / Questions
Positives: spatial analysis of the entire US, has set values, no vagueness.
Negatives: Leaves out many variables, a very coarse analysis.
How could this have been done better?
More variables
Professional Input

16. The City….
When choosing our city we overlaid all cities in the contiguous US with a population over 200,000 onto our hotspot map to help us decide which to use. It ended up that no city with a large population resided within the most desirable zone. The city that fit the best was….

18. City Level Analysis
For the city level of New Orleans we used the same method but this time, because of time constraints, we only had 3 input layers for our hotspot map: flood risk, income level, and total population
Output was a hotspot map on the city level for New Orleans, giving us a starting point to search for suitable sites via Google Earth.