4a: Task abstraction

Why do data viz?

Why: An overview

Visualization Analysis and Design, chapter 3, Munzner

Why task abstraction?

Thinking about why in an abstract form, rather than the domain specific way users often talk about data viz.

This will allow us a framework to discuss use cases, which may on surface look different.

Actions / Targets

This proposed taxonomy is from Munzner's framework (Visualization Analysis and Design, chapter 3)

Actions in this case is a verb, and targets are nouns.

Reading: A multi-level typology of abstract visualization tasks, Bremmer, Munzner

Reading: Taxonomy of interactive dynamics for visual analysis, Schneiderman, Heer

Viz designer or user?

Are you consuming the visualization or producing it?

Viz tools fall somewhere along a continuum from specific to general.

On the general side, tools are flexible and allow users many choices what to make.

On the specific side, the tool is curated and choices are limited in how an end user can interact with the data set.

Exploratory vs Explainatory

Storytelling with data, chapter 1, Nussbaumer

Why: 3-levels of actions

Visualization Analysis and Design, chapter 3, Munzner

For more specifics read up her white paper linked here.

Why: 3-levels of actions

A framework for why a task is performed, and includes multiple levels of specificity, a narrowing of scope from high-level (consume vs. produce) to midlevel (search) to low-level (query).

Analyze: Consume data

  • Exploratory Analysis

  • Presentation

  • Enjoyment

Produce data

  • Annotate, e.g. adding text labels to existing charts

  • Record, e.g. screenshots, video recordings of visualizations

  • Derive, e.g. create new datasets based on existing ones.

Search

Target known Target unknown
Location known

Lookup

Browse
Location Unknown

Locate

Explore

Query

  • Identify

  • Compare

  • Summarize

Targets

Visualization Analysis and Design, chapter 3, Munzner

All together: task breakdown

Example: Which carpark is the one with the highest utilization in SG on Monday mornings?

  • Analyze: Exploratory analysis
  • Search: Locate
  • Query: Identify
  • Target: One attribute, utilization

All together: task breakdown 2

Example: SG election. What interesting trends can we see overall?

  • Analyze: Presentation
  • Search: Explore
  • Query: Compare, Summarization
  • Target: Trend, Outliers

Industry perspective on why data viz

Sample job Why data viz Tools

Data scientist

 Getting insights Python, R. Quick and dirty charts

Business analyst

 Insights + simple dashboards for client / management Tableau, PowerBI, Qlik

Data journalist

 Storytelling. Presentation to make things clear Graphics / animation tools and libraries

Code artist

 Presentation to impress 3D / Animation / sound / interaction libraries

Product frontend

 Building a custom product for a specific usecase D3, JS and CSS frameworks

Product backend (e.g. regulator)

 Building a custom product for a specific usecase tech architecture, database and warehousing, backend and devops

How: An overview

We'll cover definitions and sample idioms later in the course.

Questions?

Chi-Loong | V/R