How we develop our story?
In our group, at beginning, we planned to analyze how the number of squirrels would connect with climate changes, and also be open-minded for other topics, like music, because Boston’s history and its developed entertainment industry. We thought it could be something we can tell based on databases. After we aimed our goals, we emailed a reporter, Andres Picon, who reported a relevant article on the Boston Globe to ask possible resources, and he told us that Marion Larson, spokeswoman for the Massachusetts Division of Fisheries and Wildlife, was his source. We emailed, called Ms. Larson for two weeks, however, we heard nothing from. At the same time, we contacted with Fenway ticket office, and eventually got the data information about music performances from a Fenway’s marketing manager. Thus, we formally decided to analyze music genres at local venues of Boston to see what type of music is Bostonians’ favorite and what reasons could cause the result. We chose TD Garden, Fenway, House of Blue, Brighton Music Hall and Boch Center as the local five venues to explore more. We also plan to head to these places to talk to the people in there to see what they think, then take the pictures in the fields.
It’s definitely hard for us to get all the data at beginning. We tried to call, email these venues besides Fenway to get datasets. We think that as the marketing department of each venue, they will have the relevant information based on scanning guests’ tickets, but we didn’t get anything that was useful; we also used Twitter, newspapers to talk to some editorials who did the similar works, and we finally got a website, which records and publishes venues’ shows based on dates and geography. Using this website Setlist.fm, we found all venues where we planned to explore and narrow down the date within these five years, which means from 2014 to 2018. We put the data information into Excel by ourselves and categorized them to “Artist”, “Date”, “Genre”, and “Venue”. We separated datasets based on different venues, and also combined them to one total dataset.
After setting up our databases, we moved on to clean data. We used OpenRefine and Pivot table of Excel in this process. Using OpenRefine, we narrowed down “date” as we mentioned before, which only focused on 2014 to 2018 these recent five years because we wanted to know a current trend of performed genres in Boston. Thus, we facet “date” this category. This was an easy part. However, during the data cleaning process, the most difficulty one is how to define music/concert genres. It was also a challenge for us when we collected data. As we know, how to separate music genres is tough because some music mixed many genres’ characteristics, like R & B based on rhythmed Blues, pop, Hip-Hop, and Hip-Hop belongs to pop music. Some of the music bands play many types of music or their music belongs to some subcategory of rock or folk, which was very hard to define genres. We talked to Tim Riley, our professor who is an expert of music as well. He recommended us that only separate main categories out into three subsets each, for example, Jazz: traditional, modern, free; Rock: classic, alternative, avant-garde, Folk: traditional, modern; Country: tradition, pop, alternative. Based on Tim’s suggestion and double checked “Artist” category via OpenRefine as well, we produced cleaned up datasets and put Pivot Table to see the relevant of each category. We found that, rock music is the most popular genre in Boston from a total database, but this trend was not very obvious at Brighton Music Center and Boch Center. Compared the size and the level of the venues, Brighton Music Center and Boch Center are different with TD Garden or Fenway these kind of huge studios.
After talking with professor Tim Reliy, he recommended us to narrow down the genres’ categories, because every main genre has many sub-categories, like alternative rock, punk, street rock, metal rock and more. If we put every categories in and visualize them, it’s too much for readers to understand, which the information is invalid based on the visual and the communicate angles.
We used Tableau to produce our visualization of data. Each venue has their own “personality”. TD Garden usually hosts very famous or nationwide even globalized celebrities, like U2, Bruno Mars, Katy Perry and more, on the contrary, Boch Center hosts more local, small bands, but the types of the music are so much diversity compared with Fenway, TD Garden, and House of Blue. Brighton Music Hall is a small theater, and it focuses on local rock bands a lot, many small local bands played their shows in here which is Brighton Music Hall’s unique part. Due to we curioused about the most popular genre in Boston, pie chart, line chart, packed bubbles, bars are all good visualized graphics’ formats. What’s more, we also considered that each format has each requirement, for example, when we use pie chart to show which the proportion based on genres, too many genre categories can make the graphic invalid to do effective visual communication. Readers would not easily and quickly grab the visual information. Thus, we will combine the genres to four main genres to narrow down the information. We also paid attention on our charts’ tittles, because a valid title should explain or summarize a visualized graphic.
Tools of production
- Tableau to visualize data information. A bar chart to show which genre is the most performed at four main venues in Boston. A line chart to show trends of four different performed genres of concerts within five years in TD Garden. A bubble chart, to show how many different genres at one smaller local venue.
- Photoshop. Combined three compared pie charts together to show outdoor venue, like Fenway, hosts more country music than indoor venues.
- We used WordPress to format and produce our whole media materials, including data graphics, pictures and text.
How will we tell the story?
Database will illustrate the wholistic conditions, and individual interviews will show the personal stories and interesting thoughts. We will use all the media to help the story be easy and interested to understand.
Group members’ contribution
Kristen: Contacting with interviewees, finding data, developing article content, format media materials, final revisions and review.
Nathatlie: Contacting sources, finding data, doing interviews, transcript.
Niovas: Collecting data, doing interviews, taking pictures.
Yaling: Collecting data, cleaning data, visualization data, format media materials, taking pictures, writing ‘methods’ and final revisions to the project.