Progress
So far, we have worked on cleaning the data that we received from the sports information office. We have also worked on adding coordinate data to our data sheet so we can import our data into ArcGIS. We have been using Google Maps and the Geocode by Awesome Table google sheets extension.
Problems and Solutions
We struggled a bit this week with standardizing names in our datasheet. We decided we wanted to make our map less cluttered by omitting “university” or “college” from the school names but when we used the Geocode extension it recognized some of these schools as cities and not as schools (ex. The town Beloit vs Beloit College). We troubleshooted this by adding the college/university back to the school’s name and then rerunning the extension. We then double checked these locations using Google Maps and found the correct coordinates if the extension gave inaccurate ones.
Tools and Techniques
We have used R so far for data cleaning. We have decided to put our final project in ArcGIS potentially both as a regular map and as a story map, but haven’t decided on the specifics.
Deliverables
We are a bit behind where we expected to be but we will still be able to finish everything on time. By the end of Tuesday we will have finished cleaning our data, decided what maps we will make, what symbology we will use, and we will have started to work on the first map. By the end of Thursday we hope we will have started on both maps and have started to talk through stylistic choices for the maps and the website. We will finish everything and iron out the rest of the details over the course of week 10.
Anya: I have been working on adding location data to our spreadsheet, like we had planned in the team charter. This will be very important when we input the file into ArcGIS.
Sydney: I have been helping with adding location data and plan on importing it to ArcGIS this week.
Marc: I have been doing the data cleaning using R so far and I plan to help create the maps we are going to use in ArcGIS.
Nate: I have been helping with cleaning location data and confirming which teams we played against. I plan to help Sydney this week with getting the data in ArcGIS.
9 thoughts on “Final Project Update — Football Map”
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I can’t wait to see how this project turns out! it seems you all have a firm grasp of the things you want to do. I also realized maps can be tricky when you have to keep checking back and forth with Google Maps. It can be quite draining and also a daunting task to complete. I’m curious what drove your team to use R? Was it just an overall familiarity with R? If so then I can understand I feel that the learning curve with OpenRefine is pretty high and can be hard to muddle through.
It looks like a great idea to incorporate Geocode by Awesome Table to speed up the process of translating location names into coordinates! It was very detail-oriented of you guys to have spotted some of the inaccuracies that Geocode produced and gone through the collating with Google Maps manually. As a Carl who knows little about our athletic programs, I am excited to see the final results of the maps—and be informed about the football team’s history and achievements. Also, I thought it would be interesting to do a network analysis on how strong the rivalry is between Carleton’s football team and their different opponents in the conference, but that would probably go beyond the scope and goal of your current project.
Wow your project seems very cool! It seems like your group has made a lot of project and I’m excited to see the final result. I think that using R for data cleaning is definitely the most efficient software for data cleaning. As someone who isn’t familiar with football, I am excited to see your football map. I’m also excited to see what details you’ll choose to incorporate while making the map, they are definitely many stylistic choices that can be made.
Your project is very creative and I think will fill in the summary and visual representation of this content currently in the Carleton library. Your data is very detailed and seems to be very actionable. The idea of using awesome table is also very clever. Your progress is very fast. I am very curious about how you use R for data cleaning. I hope to see this step demonstrated in your final project.
Sounds like a great topic! I thought you guys were in great progress as you guys already started to clean up your dataset. I am not familiar with Geocode and AwesomeTable but sounds like a useful tool that will help you guys with the project. I am glad you guys were able to come up with a solution for the ArcGIS and database issues. This project seems like it will require a lot of trial and error so keep up with work!
Your project looks really cool and i’m looking forward to seeing your map. I had some experience with Geocode add-on in Google sheets as I used it to map the birthplaces of Tate artists for midterm project. While most of the coordinates it generates are correct, some did need a second check and manually search in Google maps. For visualization, I found that Flourish can also be used to generate fantastic maps and interactive maps.
It sounds like you’ve made really good progress! We’re also planning on using ArchGIS, but with a much smaller data set, so I imagine we’ll have completely different processes! I also think it’s smart to abbreviate college names for clarity, but it does raise some questions about where to draw the line. Is it worth shortening things to make the data more digestible? Or is it better to keep longer names to avoid confusion?
Impressive progress! Your team’s meticulous data cleaning and integration of location coordinates demonstrate dedication and problem-solving skills. Overcoming challenges with practical solutions underscores your resilience. The strategic use of tools like R and ArcGIS showcases your thoughtful approach. Despite minor delays, your clear plan and collaborative effort ensure timely completion. Keep up the excellent work, Anya, Sydney, Marc, and Nate! Your commitment to excellence shines through.