index.md

DSCI 532: Data Visualization II

Time: 11am-12:20pm Mon/Wed, Mar 20 - Apr 12 2017
Location: ORCH 3058
Labs: Mon 2-4pm, ESB 1042
Quizzes: Mon Apr 3 2-2:30pm, Thu Apr 20 2-2:30pm
Office hours: Mon 5:30-6:30 Mar 20 - Apr 10; Tue 2-3pm Apr 18 ICICS/CS X661
Slack channel: https://ubc-mds.slack.com/messages/531_viz-2
Instructor: Tamara Munzner; github @munzner, slack @tmm
Teaching fellow: Vincenzo Coia; github/slack @vcoia
TA: Sam Hinshaw; github @hinshaws, slack @samhinshaw

This module continues with analysis, design, and implementation considerations for creating interactive visualizations including multiple linked views, in contrast to the focus of DSCI 531 (Data Visualization I) on the creation of single static figures.

Schedule

Lect Date Day Topic
1 2017-03-20 Mon Interaction design: reactivity, responsiveness, and selection.
2 2017-03-22 Wed Interactive navigation: panning, zooming, and other changes of viewpoint.
3 2017-03-27 Mon Design considerations for multiple views: juxtaposing and coordinating views.
4 2017-03-29 Wed Design considerations for multiple views: partitioning and layering.
5 2017-04-03 Mon Data reduction: filtering and aggregation of items.
6 2017-04-05 Wed Data reduction: filtering and aggregation of attributes.
7 2017-04-10 Mon Usability, user testing, and validation.
8 2017-04-12 Wed Case studies of appropriate design choices and their trade-offs.

Labs

Week/Assignment Due Lab topic
1 html, 1 md Mon 3/27/17 9am Interactive/reactive visualizations with shiny
2 html, 2 md Mon 4/3/17 9am Interactive multiple linked views with shiny and ggmaps, multiple views with ggplot2
3 html, 3 md Mon 4/10/17 9am Interactive data filtering and aggregation with shiny
4 html, 4 md Tue 4/18/17 9am Peer review of usability, iterative refinement of previous software

Quizzes

Time Date Location
1 html, 1 md 14:00 - 14:30 2017-04-03 ESB 1042
2 html, 2 md 14:00 - 14:30 2017-04-20 ESB 1042

Solutions

Reference Material

Week 4: Lectures 7/8, Mon Apr 10 & Wed Apr 12. Usability & Case Studies

Learning Outcomes

By the end of the course, students are expected to be able to:

  1. Analyze interactive visualizations in terms of approaches to handling complexity: dynamic change over time, partitioning into multiple views, and data reduction within a single view (in addition to the derivation of new data, as covered in Viz-1 module).
  2. Design new interactive visualizations for complex datasets.
  3. Implement interactive visualizations using existing toolkits and libraries.
  4. Explain the trade-offs of using animation vs juxtaposed views vs derived data.
  5. Explain and justify methods to validate visualization design effectiveness including computational benchmarks, field studies on deployed software, and qualitative discussion of visual results.

Lecture Learning Objectives

  1. Interaction design: reactivity, responsiveness, and selection.

By the end of the lecture, students are expected to be able to:

  • Explain costs and benefits of interactively changing views.
  • Reason about the design choices involved in selection and highlighting.
  • Characterize human response to latency into categories relevant to the design of responsive software.
  1. Interactive navigation: panning, zooming, and other changes of viewpoint.

By the end of the lecture, students are expected to be able to:

  • Explain the difference between geometric and semantic zooming and reason about when each is appropriate.
  • Explain the tradeoffs between constrained and unconstrained navigation.
  • Explain the tradeoffs between slicing, cutting, and projection.
  1. Design considerations for multiple views: juxtaposing and coordinating views.

By the end of the lecture, students are expected to be able to:

  • Analyze the use of multiple views in terms of shared versus different visual encoding and data shown.
  • Discuss the tradeoffs between a single view that changes over time and multiple linked views.
  • Explain the costs and benefits of different view coordination strategies.
  1. Design considerations for multiple views: partitioning between views and visual layering.

By the end of the lecture, students are expected to be able to:

  • Reason about the order in which to select attributes to partition a dataset into multiple views according to the target user's task.
  • Analyze the scalability of superimposed layers for static layering
  • Analyze the scalability of superimposed layers for dynamic layering
  1. Data reduction: filtering and aggregation of items

By the end of the lecture, students are expected to be able to:

  • Discuss the tradeoffs of filtering vs aggregation for reducing data.
  • Analyze static aggregation techniques in terms of what data was derived to support them
  • Analyze dynamic aggregation techniques in terms of derived data and interactive selection.
  1. Data reduction: filtering and aggregation of attributes

By the end of the lecture, students are expected to be able to:

  • Relate dimensionality reduction techniques to attribute aggregation goals
  • Analyze complex combinations of filtering and aggregation.
  1. Usability and user testing. By the end of the lecture, students are expected to be able to:
  • Conduct basic usability testing.
  • Reason about the costs and benefits of different forms of validation and user testing to make appropriate choices given time constraints and quality targets.
  1. Case studies of appropriate design choices and their trade-offs. By the end of the lecture, students are expected to be able to:
  • Analyze an existing visualization interface according to all previously discussed criteria
  • Relate the scalability of visualization design choices to the four major strategies for handling visual complexity: deriving new data, changing the view over time, partitioning into multiple views, and reducing the amount of data shown in a single view.
  • Propose a new visualization design appropriate for a specific data and task combination.