2023.03.03 Geographic Data Brainstorm

In the past two weeks we have discussed papers about how tool interactions help analysts to make sense of data. Ben Shneiderman outlined a visualization mantra, "Overview first, zoom and filter, then details-on-demand." This week's papers discussed the users' need for context, describing alternative strategies for navigating complex data spaces. In this activity you will work together to brainstorm visualizations for a dataset that has both geographic and network components. You can choose the specific use case your group wants to pursue, but your design idea must provide the following functionality:


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 - 10 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.

Your dataset

Today you will be working with global aircraft flight data. As in previous exercises, you will only have a general data schema. Feel free to invent specific data attributes or other plausible data you might need.

Each row of data is an individual flight containing:
Flight number AA329, GTI585, etc.
Scheduled take-off timestamp
Actual take-off timestamp
Scheduled landing timestamp
Actual landing timestamp
Was on time boolean
Total delayed time minutes
Total time in the air minutes
Taxi time at both airports
Number of miles traveled
Whether cargo or passengers were carried
Amount of cargo / passengers
Altitude data Alt over flight time, highest point, etc
Departing airport code KITH, EGLL, RKSI
Arriving airport code
Departing and arriving airport metadata Lat/lng, state/country, avg ontime, weather at the time, etc.
You can also construct aggregate data for the flights so that users don't have to count things up or average. For example:
Number of flights between two airports, states, or countries.
Average number of flights per hour taking off between two airports
Min, max, and median lateness for an airport, state, or country
Histogram of take-off times to show popular days for flights
Finally, you can construct network data from this dataset. For example:
Each airport is a node and each edge is a single flight.
Each state is a node and there is an edge if a flight happens between the two states. Edges are weighted based on number of flights.
Each country is a node and weighted edges show the total amount of cargo per week between them.
Domain tasks

Choose between 3 and 5 domain tasks that are most important for making sense of geographic or network relationships between airline flights and rank them in order of importance.