# Lesson 14: Tasks and Domains Understanding tasks and domains, drawing information design to user centered design. ## Objective Deepen understanding of how to approach data visualization design through the lens of user tasks and domain contexts, introducing Tamara Munzner's What/Why/How framework as an optional structured approach. ## Outline This lecture expands on the idea of domains and tasks with examples before looking at the What / Why / How framework from Munzner. It provides more information on the second perspective introduced in Lecture 1, focusing on visualization as task alongside representation, narrative, and dialogue perspectives. ### Understanding domains and contexts Understanding the user's context is essential for effective visualization design. - Who is the user and what is their expertise level? - What social context is that user in (professional, academic, public)? - What are the concepts the user is interacting with and their mental models? - What prior knowledge or understanding might the user be interacting with? - How are the data produced (collection methods, measurement instruments, sampling)? ### Methods for understanding users Three fundamental approaches to understanding users and their needs. - Talk to them through user interviews and conversations. - Ask them to share their work with you through contextual inquiry. - Share prototypes with them for iterative feedback and testing. There are structured appraoches but these act as general principles. ### Understanding tasks and questions What users need to accomplish is critical for design decisions. - What questions are the users trying to answer or actions they need to take? - What will the user do with that answer or action? - Why is the task important (business value, scientific contribution, policy implications)? - What information is needed to answer that question? - Understanding tasks tells you what to encode and with what priority. ### The What / Why / How framework This framework from Tamara Munzner provides a structured approach to visualization design through three iterative questions operating in a nested manner. - What (data abstraction): datasets, attributes, dataset availability. - Why (task abstraction): actions (analyze, search, query) and targets (trends, outliers, features). - How (visual encoding and interaction): encode, map, manipulate, facet, reduce. Note that this can be a useful framework but is optional in your projects for the course. ### Data abstraction Understanding what we are visualizing through data structure and types. - Data types: items, attributes, links, positions, grids. - Dataset types: tables, networks and trees, fields, geometry (spatial), clusters, sets, lists. - Attribute types: categorical, ordered (ordinal, quantitative). - Ordering direction: sequential, diverging, cyclic. - Dataset availability: static or dynamic. ### Task abstraction Understanding why users examine data through actions and targets. - Analyze actions: consume (discover, present, enjoy) or produce (annotate, record, derive). - Search understanding: lookup, browse, locate, explore based on what is known. - Query actions: identify, compare, summarize. - Targets for all data: trends, outliers, features. - Targets for attributes: distribution, extremes, dependency, correlation, similarity. - Targets for networks and spatial: topology, paths, shape. ### Visual encoding and interaction How the visualization helps achieve goals through arrangement and manipulation. - Encode and arrange: express, separate, order, align, use marks effectively. - Map from categorical and ordered attributes: color (hue, saturation, luminance), size, angle, curvature, shape, motion. - Manipulate: change viewpoint or parameters, select items or regions, navigate through data space. - Facet: juxtapose (side-by-side), partition (divide), superimpose (overlay). - Reduce: filter unwanted data, aggregate and summarize, embed into other contexts. ### Case study: NASA spacecraft operations Rachel Binx's Vortex project revealed periodicity of events users had never seen visually before. - Domain: NASA spacecraft operators monitoring real-time spacecraft with prior work of manual log review. - Tasks: monitor spacecraft status, identify anomalies, understand event patterns, track periodic events. - Design: visual representation of temporal patterns supporting identification of rhythms through multiple coordinated views. ### Case study: Jigsaw visual analytics An intelligence analysis tool for making sense of large document collections. - Domain: intelligence analysts and researchers in investigative analysis contexts. - Tasks: explore connections between entities, identify patterns across documents, compare different data views, generate and test hypotheses. - Design: multiple coordinated views (list, graph, scatter plot, text), interactive linking, iterative exploration, focus on user journey through questions. ## Take Aways Effective visualization design requires understanding both the user's domain context and their specific tasks, with the What/Why/How framework offering an optional structured approach to design decisions. - Talk to users, share work with them, and prototype iteratively to understand domain and context. - Understanding tasks directly informs what to encode and with what priority. - The What/Why/How framework provides a nested, iterative approach through data abstraction, task abstraction, and visual encoding. - Apply these concepts to familiar examples through analysis of domain, context, and tasks. ## Note This outline was expanded after the video was recorded and may include additional details. ## Citations [1] T. Munzner, "Visualization Analysis & Design," University of British Columbia, 2021. Available: https://www.youtube.com/watch?v=pHljd-cgICY [2] T. Romanowski, "Visualisation Analysis & Design by Tamara Munzner or the What-Why-How of data viz," Datarocks, 2023. Available: https://www.datarocks.co.nz/post/data-viz-bookshelf_visualization-analysis-design-tamara-munzner [3] J. Stasko, C. Gorg, Z. Liu, and K. Singhal, "Jigsaw – Visual Analytics," Georgia Institute of Technology, 2009. Available at: https://youtu.be/2CMw4i9DiaM?feature=shared [4] R. Binx, "Designing for Realtime Spacecraft Operations," OpenVisConf, 2016. Available at: https://youtu.be/HuYKhSHcRSQ?feature=shared ## License This lesson is part of [Interactive Data Science and Visualization](https://mooc.interactivedatascience.courses/) and is released under a [CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/deed.en) license.