Process Book:

Music Tours Visualization by Ian Katzman and Nikash Taskar

Overview and Motivation:

The main goal of this project is to create a visualization that shows useful data about how successful certain musical artists are on tour. We do this by displaying different information on a US Map and bar graphs (as of right now). We gathered this information from Wikipedia pages specific to the tours we are using (more about that later), and we created the visualization using d3 Javascript.
Our main motivation for this project comes from us being avid music fans. We really enjoy music, and going to concerts, but it seems that a lot of major artists tend to avoid coming to St. Louis. We were curious as to why artists avoid St. Louis, and some cities, and tend to gravitate towards other cities. We thought that if we could gather information about different artist's performances, like the attendance, revenue, etc, we can start to determine which cities artists have the most success in.

Related Work:

We were not directly inspired by a specific dataset or visualization, but as stated earlier we were inspired to answer a question we had noticed about a certain trend with tour stops by musical artists. That is, we were inspired to answer the question as to why artists tend to stop more at some cities rather than other cities.

Questions:

What stops were the most attended for each artist on their tour?
What stops were the least attended for each artist on their tour?
What stops made the most money for each artist on their tour?
What stops made the least money for each artist on their tour?

We began with the questions above as a baseline, but as we were working we realized we needed to look into data that involved all of the tours we were studying, so we formed some more questions like:

What stops were the most attended overall?
What stops were the least attended overall?
What stops made the most money overall?
What stops made the least money overall?
Were there any stops made by all artists? Only one artist? What is the crossover like?

We will most likely try to come up with more questions before the final project is due.

Data

We got our data from 5 Wikipedia pages with information about 5 specific artists' tours:

Kendrick Lamar: The Damn Tour - https://en.wikipedia.org/wiki/The_Damn_Tour
Beyonce: The Formation World Tour - https://en.wikipedia.org/wiki/The_Formation_World_Tour
The Weeknd: Starboy: Legend of the Fall Tour - https://en.wikipedia.org/wiki/Starboy:_Legend_of_the_Fall_Tour
Drake: Summer Sixteen Tour - https://en.wikipedia.org/wiki/Summer_Sixteen_Tour
Justin Bieber: Purpose World Tour - https://en.wikipedia.org/wiki/Purpose_World_Tour
Ariana Grande: Dangerous Woman Tour - https://en.wikipedia.org/wiki/Dangerous_Woman_Tour
Bruno Mars: 24K Magic World Tour - https://en.wikipedia.org/wiki/24K_Magic_World_Tour
Jay-Z: On the Run II Tour - https://en.wikipedia.org/wiki/On_the_Run_II_Tour
Shawn Mendes: Illuminate World Tour - https://en.wikipedia.org/wiki/Illuminate_World_Tour
Pitbull: Live Tour - https://en.wikipedia.org/wiki/Enrique_Iglesias_and_Pitbull_Live
Maluma: F.A.M.E Tour - https://en.wikipedia.org/wiki/F.A.M.E._Tour_(Maluma)

This data was gathered and cleaned using a webcrawler created in Python. We isolated the data from the charts that were on each Wikipedia page that displayed data about each stop on the tour, and put that data into separate JSON files for each tour. We got data about each tour stop in these categories:
- City of tour stop
- State of tour stop
- Abbreviation of that state
- Date of tour stop
- Country of tour stop (should be United States for all of them
- Venue of tour stop
- Attendance of tour stop
- Revenue of tour stop

Exploratory Data Analysis:

The first way that we saw our data was looking at the charts on the Wikipedia pages. An example of one of these charts is shown below. These charts were extremely helpful as they contained almost all of the information we needed in order to create the visualization we wanted to create. We struggled at first to find a reliable data source that wouldn't cost money or be illegal to access, so finding these charts was a relief. They helped us to create the final form of what we wanted our design to be, because they gave us specific information that were were looking for, like attendance and revenue for each spot in particular.
Example of one of the charts we used:

Design Evolution:

Our first and original design (from milestone 1) was a USA map that displayed information about each state based on whether or not the selected artist had a tour stop in that state
We then added the information by using tooltips for each state, that either displayed the tour stop information or a message stating there was no stop for that tour in that state
Then we added bar charts below the USA Map that displayed the venues from highest to lowest based on attendance, and venues from highest to lowest based on revenue (in two separate bar charts)
Our most recent change is adding color to our USA map to signify the highest revenue and attendance for each tour, and for all the tours. Purple designated the highest state in both revenue and attendance, red is for the state with the highest revenue, and blue is for the state with the highest attendance.
We plan on adding more features for the final submit.
After doing user studies in class, we gained some great insight. We first realized that the color coding on the map was fairly confusing, so we added a legend and also made the data more clear. We also discovered that the two separate bar charts were not necessarily great for readability, so we combined them into a double bar chart that can be sorted by attendance or revenue, or both. We also added a navigation bar at the top the page so that the user can access our process book, and see our github page. Lastly, we added an artist description for each artist when you choose them in the selector, so that the user can have some more context about the tours they are looking at the data for. These changes will be shown below in the implementation section.
We had three outside participants complete the tasks outlined in our usability study. From the three participants, we received a lot of insightful advice. Some of our participant thought that the colors in the bar charts were a bit difficult to distinguish and also felt as thought the added use of color to emphasize certain details would be useful. Most people felt as though the web page and visualizations were intuitive and were easily able to navigate the page to complete the tasks we asked of them.
After doing our presentation we got some more good feedback. The first thing we did was make sure there was more contrast on the US map between states that had concerts in them and states that did not. We also put the explanation for the color coding of the map into a box that can be toggled, so we can add more detailed explanations without taking up too much space.
We also decided to add some more features, so that the site is more appealing to the user. The first thing we added was an on-click function for each state on the map for each artist, which displays a pie chart that shows the percentage of the total state's revenue based on the different venues in that state. This helps to show what venues bring in more money for artists in each state.
We also added a function to compare artist bar charts to each other. Underneath the main selected artist's bar chart, there is another selection box, and if you choose an artist from that you can see their bar chart right underneath the main artist bar chart. This is an important feature when it comes to comparing who performs better in which cities.
Lastly, we added modal pop-up boxes to display information about our visualizations, so that we can put more explanation without taking up too much space on the main page.

Implementation:

The USA map for a specific artist:

The USA map for a specific artist when you hover your mouse over a state:

The revenue bar chart for a specific artist, which is displayed when an artist is chosen in the selector:

The attendance bar chart for a specific artist, which is displayed when an artist is chosen in the selector:

The revenue bar chart for a specific artist, when you hover over one of the bars:

The attendance bar chart for a specific artist, when you hover over one of the bars:

This image shows one of the artist descriptions, and the navigation bar we added at the top of the screen:

The double bar chart with both attendance and revenue displayed, information on hover:

The double bar chart sorted by attendance (also info on hover):

The double bar chart sorted by revenue (also info on hover):

This image shows the map explanation modal pop up, the others are very similar in style and structure:

This image shows the newly updated map, with better contrast and colors:

This image shows the pie charts with the revenue information that shows up on click of a state:

This image shows the main artist bar chart, with updated colors and sizing:

This image shows the selected compared artist bar chart, with updated colors and sizing:

Evaluation:

From this project we were able to determine what states and cities usually provide the most success for artists. We were able to find trends about certain cities that tend to not perform well for artists. We hope to dive deeper into trends for our final version of the project, to truly find the answers we set out to find in this project.
Our visualization works pretty well. There are some bugs we still have to fix, and some styling to fix, but the general functionality is exactly how we wanted it to be at this stage. Overall we are satisfied with our work so far.
As we prepare to finish up our project, we are generally very satisfied with what we have done. A lot of our work came from data collection and data cleaning, and we did a great job getting data that was very hard to access and making it manageable for our visualization. The data we gathered and the visualizations we created have been informative and told us exactly what we set out to find out, that is information about artists tour performance numbers.
A lot of what we found out seemed pretty obvious, but if we are able to add smaller and more various artists, our visualization can be very eye opening. We definitely plan to do this, as this is something that is interesting to us both beyond a school assignment.