1.4 Task Taxonomies
Understanding and applying these taxonomies is crucial for translating a domain problem into a visualization design. They provide a common language and a systematic approach to task abstraction.
Brehmer and Munzner’s Typology (2013)
This taxonomy provides a high-level, human-centered view of visualization tasks. It categorizes tasks into three top-level goals:
- What does the user know?
- Lookup: Find a known item in a known location.
- Browse: Look for an item without a specific target.
- Locate: Find a known item in an unknown location.
- Explore: Look for any unknown items or patterns.
- Why does the user perform the task?
- Enjoy: For entertainment or aesthetic pleasure.
- Find Anomalies: To spot unexpected patterns.
- Synthesize: To combine information from different sources.
- Analyze: To draw conclusions and gain insights.
- How does the user interact?
- Annotate: Add notes or labels.
- Share: Export or distribute.
- Present: Create a presentation from a view.
For our bus delay case study, we focus on Explore (for finding unknown patterns) and Analyze (for drawing conclusions about delay causes).
Amar, Eagan, and Stasko’s Low-Level Task Taxonomy (2005)
This taxonomy provides a more granular, operational view of tasks that a visualization tool must support. It describes specific user actions.
- Retrieve Value: Find the value of a specific data point.
- Filter: Find data points that meet a condition.
- Find Extremum: Find the maximum or minimum value.
- Sort: Order data points based on a specific value.
- Determine Range: Find the range of values in a dataset.
- Characterize Distribution: Describe the overall shape of the data.
- Find Anomalies: Identify outliers.
- Cluster: Find groups of similar data points.
- Correlate: Find relationships between different data variables.
For our bus delay case study, we use these to specify our low-level tasks, as seen in the task abstraction log (e.g., Filter by day of the week, Correlate weather with delays).
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