2020.10.23 Brainstorming for Time


Now that we've spent some time making visualizations, it's time to handle a bit harder challenge. In this activity we will brainstorm visualizations for dealing with data that have a temporal (time) component to them. Here are the rules:

Please follow this procedure:

Step 1 - 5 minutes - [GROUP] Identify Key Goals
  1. As a group, read the instructions.
  2. Together, take a look at the Domain section and identify between 3 and 5 domain tasks that you want to help users perform through your visualization.
  3. Rank those tasks in order of importance using any method you'd like

Step 2 - 12 minutes - [INDIV] Two sketches
  1. Now, working individually, begin making two very rough visualization sketches that best satisfy those tasks. Keep them low fidelity.
  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 - 20 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. You will need to pick a group member to record your final idea and present it to the class, but everyone should sketch as they go.
  3. We will reconvene at the end to check out everyone's ideas.
Note that you may design for mobile multi-touch devices or traditional desktop platforms. Pick one as a group.

Your dataset

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’re again 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. metal -> gothic metal -> sympho metal)
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
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.