INFO 3300 - Visualization Brainstorm Activity


In this activity we will brainstorm visualizations for dealing with some complex data related to streaming music habits over time. Here are the rules:

Please follow this procedure:

Step 1 - 7 minutes - [GROUP] Identify Key Goals
  1. As a group, read the instructions and Brief included below.
  2. Together, take a look at the Domain Task section and identify between 3 and 5 domain tasks that you want to help users do through your visualization.
  3. Rank those tasks in order of importance using any method you'd like (e.g. voting, by consensus)

Step 2 - 10 minutes - [INDIVIDUAL] Two sketches
  1. Now, working individually, begin making two very rough visualization sketches that best satisfy those tasks. Keep them low fidelity. You can use any medium you like (e.g.pen and paper, tablets, UI prototyping software, presentation tools, a whiteboard).
  2. Refer back to the dataset and think about what you want to show.
  3. Keep polishing your ideas until your group reconvenes.

Step 3 - 23 minutes - [GROUP] Final design
  1. As a group, go around and discuss each person's sketches. Keep it to 1 minute per sketch.
  2. Working together, create a consensus sketch.
  3. We will reconvene at the end to check out everyone's ideas.

Your brief:

Your goal is to build an interactive visualization that helps people understand how their music listening habits have changed over time. A person might have started their web streaming account by listening mopey goth metal out of tween-age rebellion, only to transition to popular radio hits as they grew up. Maybe their tastes changed as particular close friends or their partners introduced them to new bands. Their tastes might also have just changed with the time and trends. There are any number of reasons someone might want to visualize their listening history. User goals might be to identify preferred artists/genres of the year, describe how someone’s tastes have changed, highlight the influence of others in shaping their music interests, reminisce about past bad listening decisions, see deeper connections between the musicians they choose, identify potential new music to which to listen, or find other people who have similar tastes to theirs over time. Feel free to focus on individual-, group- or country-level info. Choose whatever granularity of time you feel is appropriate as well.

For this task we are using an imaginary dataset. Imagine that you have ultra-precise analytics from a platform like Spotify for a person's lifetime of music listening habits, even including things like geography and music that was played in their environment. You can aggregate information, for example getting time spans where a genre was played often or the average popularity of the music a person listened to each year. You also have access to the same data for social networking service friends and can compute intersections to see where and when tastes might have overlapped on any of those metrics. You do not need to use all of the different data components.

Songs Streamed:
Time stamp when streaming started
Song duration
% of song listened
Genre (hierarchical data, e.g. rock -> punk rock -> new wave)
Release date
Popularity by date (could be rankings, number of records sold, etc)
Review ratings
List of songs w/timestamps & albums
Demographics (age, gender identity, country of origin, etc)
Number of listens / popularity by date
Years active (w/popularity over the years)
Active genres
Connections to related artists
Songs and artists in genre
Number of listens / popularity by date
Subgenres, hierarchical data
Time period where genre active
Timestamped song listens in area
Popular artists, genres etc.
Dwell time for person in area
Type of events held in area
SNS Friends:
Tie strength to subject
Their song data
Overlap in songs, genres etc

Remember: Do not try to visualize all of these attributes.

Domain tasks

Choose between 3 and 5 domain tasks that are most important for making sense of trends over time and rank them in order of importance.