Soccer Complex Potential in Minnesota
The purpose of this project is to create a database that analyzes whether adequate soccer complexes and parks exist in Minnesota. Before moving to Minnesota, I grew up in Montana. Soccer became a popular youth sport in my time there. We traveled the state in pursuit of soccer competition. Other Montana cities foresaw far earlier than Great Falls saw the potential for youth soccer and developed soccer complexes. As we traveled to out of state competition in Albuquerque, Spokane, the Twin Cities, and Boise I realized that I couldn’t stay in Montana. I eventually chose Minnesota to pursue my education further and leaving Montana behind. I was inspired to leave because of all the opportunities that existed in Minnesota. Though I would give up soccer once in college, I always came back to that reason for why I left Montana. It has inspired me to pursue a career in Urban and Regional Planning.
The purpose of this project is to explore whether areas the size of Great Falls have adequate areas to play soccer in Minnesota. This limits the overall size of the cities I’m exploring in Minnesota to 19 cities. Data that has been gathered will hopefully answer the following questions:
Which city has the most places to play soccer in Minnesota
Which school district has the most access to areas to play soccer
What potential land parcels exist where a complex could be implemented
Before continuing on some background information is needed to explain the necessity for why more fields are necessary. According to the Sporting Goods Manufacturers Association over 14 million people play soccer in the United States. With the participation in soccer on the rise and the decline in popularity in terms of participation in baseball and football, areas suitable for playing have to be adopted. I predict that in Minnesota the need for fields isn’t particularly dire considering the cluster of cities that lie in the southern portion of the state. Many parks exist the surrounding area of the Twin Cities. However the question of St Cloud, Duluth, and Rochester are the areas that I’m most curios about. The areas to be studied are indicated by the map below
I have also included a raster dataset of the state to indicate the amount of available land that is present.
Related Work
When considering why soccer complexes are being implemented it is vital to consider the American Youth Soccer Association’s reasons for why soccer fields and complexes are a built and why they’re a necessity. Many studies exist on how cities and towns have conducted research in building these complexes. Some include complexes that were built in Waverly, Iowa, or Berkeley California. These studies are more centered towards the administrative side of how soccer complexes and parks got started in these areas. Basically, it has been an area where the community gages the necessary need and then a number of volunteers go into action in accomplishing the task. This is obviously neglects many things that my database answers including the impact it can show with school districts. However, these case studies and AYSO’s guide are important to not ignore because these models can help show how these complexes are built and where. They just a geodatabase to go with them which is the purpose of this project. My project will also be more centered towards using GIS to understand similarities to these places and keep my focus towards exploring how census tracts, available land parcels, and school districts to determine possible locations.
Methods and Data Sources
The data gathered for this project’s database was taken from Minnesota Geospatial Commons Office, ESRI, Minnesota Department of Transportation, United States Census Bureau, USA Soccer Foundation, SAIPE Data from American Fact Finder, and lastly I-Sport which listed every soccer field in United States on google maps.
The main data tables used in this project consisted of state, cities, school districts, fields and open areas. I’ve created a table showing the purposes, data type, and source for each that will eventually be implemented into SQL Server table.
E/R Diagram
Before I could proceed in implementing these tables into SQL Studio Server I needed to do some data acquisition. Using the sources listed in the table I created maps of Minnesota that indicated the most populated cities, unified school districts, census tracts, and land cover.
The following bullet points then were implemented into Microsoft SQL Server:
Cities: Name, County, Population, Schools, In_Park ID, and Traffic_Flow.
Schools: Name, In_City, Student Body, Varsity Sports, To_Park, and Soccer Accessible.
Soccer Fields: Name, Address Total_Fields, Private, Public, School District Fields To_Schools, and In_City.
use austinan_final
create tables State (Name text, POP int)
Cities (Name text, County text, POP int, Schools text, Parks text)
School Districts (Name text, Students int, Schools int)
Soccer Fields(Name text, Address nvarchar, Total_Fields int, Private int, Public int, Training Grounds int, Complexes int)
Some of the data I had to implement through editing the table’s rows. After these steps were completed I needed to establish the one to many relationships within the diagrams.
Soccer Field totals are also connected to Minnesota Cities but I changed my data frequently with that table and SQL would not accept the relationship. However, Fields_Total is a one to many relationship in that many fields can be in one city but not several cities.
These tables and diagrams were intended to organize the city, school district, and soccer field data as well as I could. I wanted to show where the most soccer fields were located and which city and school district benefited the most in that regard. These questions can be answered with my tables. I can also indicate which city has the most private areas for playing soccer and which areas are the most public. The attempt of these tables is to simplify whether school districts have enough locations for participation which can be answered based on looking at the 19 cities. The question of which area needs a complex can also be answered by consulting the tables.
Conclusion
This project’s conclusion shows that the St Cloud area was the area that proved to be lacking in terms of supplying a soccer complex. I reached this rationale with identifying that they only had 1 field with a population of 67109. This proves my hypothesis to be somewhat accurate. However certain issues arose that were surprising. For example, St Paul had only 7 fields compared to Minneapolis’s 44 seems peculiar. That is until you consider that St Paul and Minneapolis are very close and surrounding suburbs like Roseville and Vadnais Heights supply fields as well. If Roseville, Vadnais, and St Paul are combined, the number of fields in Ramsey county aren’t that bad. Still, the 19 largest cities in Minnesota having 225 fields was somewhat shocking. Although maybe I shouldn’t have been that surprised based on visual observation and hindsight when I think back over the last 12 years that I have lived here. Driving around these 19 cities and playing soccer at various locations should have prepared me for these results.
This project proved to be very difficult in terms of the data collection. Finding soccer data proved especially complicated after finding where people can play. This can be discussed with the number of fields in each location. As I suggested in the video, of the 225 fields founded, most didn’t supply a proper field count which left the question up to debate. Obviously, field count determines how big these fields can be. I felt that this was an important attribute because it can give an indication as to whether the park can be used for a variety of things in the neighborhood besides soccer. Seeing 225 fields as a total was slightly intimidating and overwhelming in that I wasn’t exactly sure how to proceed and I felt like I started over numerous times. I couldn’t decide how to use the data or how to implement it. All the parcel data that I gathered from Minnesota Geospatial Commons and Minnesota DNR didn’t accurately show all the parks either because of the various other areas within this data. Other issues also arose in that I wasn’t able to show the fields on a map. I missed a step somewhere in this venture. I made sure that the addresses were coordinate data from google maps and connected the USC Microsoft SQL Server to ArcGIS. After that, I added the fields and matched the XY data but it failed to be implemented into ArcMap.I plan to work this issue out further.
Besides that technical issue certain alternative ideas caught my focus as I continued on with project. For example, clearing up city data seemed to be more of the key than before I started. In the future one way I could clear up that data would be to create a table for each city. This could include the soccer fields in place as well as possible open land and the school districts involved. These are just some of the improvements I would have made to the database. The amount of information that I discovered proved to be daunting with how much is going on the metro area. It was difficult to weed out the information that I desired and wanted to present versus the information that was available. An example of this would be types of soccer fields. Some of the types including private and public seemed to be less important but I appreciated the place they had in classification and went forward with making them an attributes in my tables. This made the project bigger than what the focus should have been. If I were to limit this project I think I would conduct it in smaller locations and city regions. I think would have proven to be a lot easier but this is a taste of what bigger cities and towns face when they want to do something big like create a new park which is something I appreciated.
References
About SFIA Overview. Accessed April 28, 2017. https://www.sfia.org/about/overview.
"AYSO." PLAY SOCCER. Accessed April 28, 2017. http://www.ayso.org/site3.aspx.
Bureau, US Census. Census.gov. Accessed April 28, 2017. https://www.census.gov/.
“Improving services statewide through the coordinated, affordable, reliable and effective use of GIS." MnGeo: Minnesota Geospatial Information Office. Accessed April 28, 2017. http://www.mngeo.state.mn.us/.
"Pioneering ArcGIS, the world's most powerful mapping and analytics software." Esri RSS News. Accessed April 28, 2017. http://www.esri.com/.
“Soccer Players." Soccer | iSport.com. Accessed April 28, 2017. http://football.isport.com/.