Full process
*An abridged version of my process can be found here: https://medium.com/p/e3ac81d2d972/edit
Challenge
“Your challenge is to visualize an ephemeral phenomenon in the complex system of the city of Pittsburgh.
You will also propose one positive change to the system that could come about if what you visualized is used to make system-wide change.
The form of this visualization is purposefully open-ended, however it should consider user interaction.”
“Carve out an area of the city and spend time Deep Hanging Out. Ride a bus to a place you’ve never been. Follow an urban walking or bike trail. Spend at least one hour on a single city block and then move 5 streets down and do it again.”
Mac Miller’s album Blue Slide Park is an homage to Pittsburgh locations that shaped his upbringing. I was interested in seeing the relationship between said places and his music and decided to do my deep hanging at:
- Party on Fifth Ave. —> Fifth Ave.
- Blue Slide Park —> Blue Slide Playground
- Frick Park Market —> Frick Park Market
One thing that stood out to me while deep hanging and analyzing the songs, was that the lyrics had little to no reference to the physical locations. Instead, Miller wrote about associated memories and feelings related to places. In a way, his album was an emotional map as opposed to a physical one, which the titles/theme superficially suggested.
I wanted to collect some information at this point about why people might visit Blue Slide Playground despite the song’s non-physical relationship with it. Through observation, I noted that people at the park left flowers near the repainted slide. Through online research, I noted that many photos posted with the park’s location tagged showed fans posing with the slide. In a way, it became a physical representation of an intangible feeling that Miller left behind.
Initial ideas
- Look into phenomena between artist’s song and location (how each affects the other).
- Focus on Mac Miller’s album: he talks specifically about locations in Pittsburgh
- Evaluate each song and its ties to the mentioned location
- Identified phenomenon: How people in Pittsburgh come together to mourn his death and celebrate his life because of their cultural/personal identity relations
- How gathering in specific locations hold meaning to his fans
- How he used music in relation to Pittsburgh locations to do things like “bridge the gap between the new generation and old generation” (Party on Fifth Ave. commentary).
- Inspired by GOAT Black Friday interactive map: users learn about Miller’s music through an interactive map and accumulate points per location that they can later use to release a song or some sort of information.
Additional research
- “Born Malcolm McCormick, Miller was raised in the Point Breeze neighborhood of Pittsburgh, just a couple of miles from Carnegie Mellon. He was Bar Mitzvahed at Rodef Shalom, a reform synagogue located on Fifth Avenue. At age 15, while attending Taylor Allderdice High School, he recorded his first mixtape on his laptop.” (http://thetartan.org/2018/9/17/news/mac-miller)
- This person’s relation is apparent as seen by the mention of how close Miller’s home, school, etc. were to CMU, the author’s school
- In 2010 he told the Pittsburgh Jewish Chronicle, “I wanted to create a voice for a generation of regular kids who can relate to this music, like a soundtrack to their lives.”
- Highlight: soundtrack to their lives.
- Lucas Ochoa, a senior in Human-Computer Interaction and Design at Carnegie Mellon who also attended the vigil voiced that “[Miller] sung about Pittsburgh in a way that made me really proud to have grown up here.”
Critique notes
- It seems difficult to collect data based on my initial idea to study the phenomenon of Miller’s music and its relationship with the album’s mentioned locations (culturally, personally, etc.).
- Daphne’s suggestion: Instead, create a memory map where people can store memories they associate with locations in Pittsburgh.
- Not necessary to have a consumer-based reward at end.
- Focus on creating the experience of learning.
Research
Focusing on Daphne’s suggestion, I conducted a few interviews on why and how people associate their memories with locations. The results:
Revised ideas
- Fans and residents can learn about Mac Miller to celebrate his life on his anniversary of death (Sept. 7) by knowing more about the locations that held meaning to him, and the concepts he created in relation to these locations.
- For the songs with no explicit location, how may listeners interpret and think of Pittsburgh locations based on their similar experiences (similarity in background, culture, etc.)
- Data to be collected: who is listening to what songs most in specific areas.
More notes + feedback
- Instead of collecting data about who listens to which songs and where (might be too broad), collect (non-numerical) data about people’s memories at locations
- People’s associated memories with specific locations mentioned in song titles and songs that don’t mention physical locations
- People’s interpreted locations, feelings, etc. upon hearing the songs without categorizing (songs w locations, songs w/o locations)
- What does this data manifest to? What’s the larger (community) purpose it serves?
- Danny’s idea: On Soundcloud you make comments on specific parts of the song. What if when you click a specific emotion at one part of a song, it directs you to a different song with similar emotions?
- How do I map emotions in a song?
Ideas on mapping emotions
Critique feedback + notes
- Maybe axes showing different ranges of emotion.
- Idea: Each person gets something like a ribbon pattern (maybe visual parallel between highs and lows of a song and highs and lows of people’s emotions).
- Make the information more related to something that can be mapped geographically, not just emotionally.
Research
I looked into different projects to find inspiration on 1) how I might re-organize the information/data I aimed to show to be more geographical and 2) what kind of interfaces might be suitable. Links I noted:
https://radio-locator.com/cgi-bin/locate?select=city&city=Pittsburgh&state=PA
http://everyday-practice.com/page/8/
http://everyday-practice.com/portfolio/forged-into-collective-memory/
http://everyday-practice.com/portfolio/uosvd-workshop/
http://everyday-practice.com/portfolio/park-geunhye-gate/
http://everyday-practice.com/portfolio/poster-generator-1962-2018/
http://everyday-practice.com/portfolio/midoriterashima/
http://everyday-practice.com/portfolio/ciic_showcase/
http://everyday-practice.com/portfolio/mmcaf/
http://everyday-practice.com/portfolio/bbalgangee-2/
Research
I spoke with our college radio club and looked more into available data on music trends based on location, as this was the route my peers suggested during critique, but was informed by the radio club that this type of data did not exist.
Adapted idea
So, I re-routed and found a middle ground between A) general music trends per location (too broad, data not available) and B) a personal music memory bank (data is too specific): An interface where people can input demographic and music preference information and see how their profiles relate to other Pittsburghers’. Basically, relationships between people in Pittsburgh through music. My community goal was to show those relationships in the interface in order to push the idea that despite the different Pittsburgh communities (demographic information), people could find connectedness in music.
Inspiration
The interface in my inspiration was meant for generating a poster design based on people’s voices, but my idea was to design an interface where people could input their demographic and music preference information and receive some sort of mappable symbol which could be added to the interface as a profile once completed. The bottom row made me think of using symbols to represent each profile’s information in my map. I wanted my interface to include a gridded map as inspired by one of the books Daphne brought in (to display all the symbols).
Update on comparing profiles
- Not showing technical % similarity
- Showing instead relationships between songs, artist, etc. in tiers (ranked based on how indicative of relation each category is)
- Tier 1: same songs
- Tier 2: same artists
- Tier 3: related artists
- Tier 4: same genre
- Tier 5: same age
- The point is not to show relations in demographics but that info was collected to show variety of participants forming relationships
Design process
Initial style, layout and symbol coding iterations
More iterations + style/mood boards
Final iteration helpers
Final interface
