[Data Visualization] Why - Task Abstraction

Review: The Big Picture

• What data does the user see? (data abstraction)
• Why does the user use the system? (task abstraction)
• How are the visual encoding and interaction idioms constructed? (idiom abstraction)

The Big Picture

• What data does the user see? (data abstraction)
• Why does the user use the system? (task abstraction)
• How are the visual encoding and interaction idioms constructed? (idiom abstraction)

Why - Task Abstraction

• Why data abstraction?
  • There are an infinite number of datasets.
  • Thus, it would be inefficient to design a visualization system for every dataset.
• Why task abstraction?
  • There are an infinite number of tasks that users want to perform on the system.
  • Thus, it would be inefficient to design a visualization system for every task.

• Consider tasks in abstract form , rather than domain specific way
  • Otherwise, hard to make useful comparisons between domain situations
• Actually, below are two instances of “compare values between two

• The analysis framework has a small set of carefully chosen words to describe why people are using vis
  • Action: analyze, search, query, $\dots$
  • Targets: trends, outliers, distribution, $\dots$
• The same vis tool might be usable for many different goals.
  • To describe complex activities, you can specify a chained sequence of tasks, where the output of one becomes the input to the next.

Who : Designer or User

• Although who is not a part of the what why how framework, it is sometimes useful to specify who has a goal or makes a design choice.

Actions

• Three levels of actions:
• High level choice: Analyze
  • Q) How is the vis tool used to analyze data?
  • A) Consume existing data or produce additional data.
• Mid level choice: Search
  • Q) What kind of search is involved?
  • A) Lookup, browse, locate, or explore
• Low level choice: Query
  • Q) Does the user need to identify one target?
• Choices at the three levels are independent.
  • Usually, we describe actions at all three levels.

High level Choice: Analyze

• Why are possible goals of users who want to analyze data using a vis tool?
• Consume : The most common case for vis is for the user to consume information that has already been generated as data stored in a format amenable to computation.
  • This is the most common “why”.
• Produce : However, sometimes, we use vis to produce new materials! We will see examples later.

High level Choice: Analyze - Consume

• Three consume goals:
• Discover (= explore) explore): to find new knowledge that was not previously known
• Present (= explain): to communicate with others about the knowledge that is known
• Enjoy : visualization in casual encounters, e.g., infographic

High level Choice: Analyze - Consume - Discover

• The discover goal refers to using vis to find new knowledge that was not previously known.
• You want to find some insights from an unseen dataset. What will you do?
  • Usually, investigation is driven by existing theories, models, hypotheses, or hunches.
• Generate a new hypothesis or verify , or disconfirm, an existing hypothesis.

High level Choice: Analyze - Consume - Discover Example

• You have a periodic table data.

• What can you discover?
• You may want to explore the data using a vis tool.

• Plot a scatterplot melting point vs first ionization energy

• The distribution was bell shaped with an outlier (Carbon).

• Plot a scatterplot melting point vs boiling point

• My hypothesis was (boiling point) > (melting point), but there were a few outliers (e.g., Californium).

High level Choice: Analyze - Consume - Present

• The present goal refers to the use of vis for the succinct communication of information (= explain).
  • e.g., telling a story with data, or guiding an audience through a series of cognitive operations.
• One classic example: a diagram in a newspaper
• The knowledge communicated is already known to the presenter in advance.

High level Choice: Analyze - Consume - Present Example

High level Choice: Analyze - Consume - Enjoy

• The enjoy goal refers to casual encounters with vis.
  • Vis for fun!
• Sometimes, the goals of the eventual vis user might not be a match with the user goals conjectured by the vis designer!

High level Choice: Analyze - Consume - Enjoy Example

• Top 15 Best Global Brands Ranking (2000 2018)
  • Source: https://www.youtube.com/watch?v=BQovQUga0VE&ab_channel=TheRankings

High level Choice: Analyze - Produce

• In the produce goal, the intent of the user is to generate new material.
  • Sometimes, the user intends to use the new material for some other vis related tasks, such as discovery presentation.
• Annotate (~tag): adding graphical or textual annotations associated with one or more visualization elements
• Record : saving or capturing visualization elements as persistent artifacts
• Derive (= transform): producing new data elements based on existing data elements.

High level Choice: Analyze - Produce - Annotate & Record

• The difference between annotate and record
  • The annotate choice attaches information temporality (can be subsequently recorded)
  • The record choice saves a persistent artifact (e.g., screen shots, videos, etc.)
  • But, it seems that these two are interchangeable in most contexts.

High level Choice: Analyze - Produce - Derive

• The derive goal is to produce new data elements based on existing data elements.
  • What can be derived? an attribute or a new dataset
• Let’s recall the InfoVis Reference Model

• How would you derive a new attribute (creating derived attributes)?
  • By changing the type of an attribute (can lose some information)
    • Grade (O) to score (Q): A+ 98, A0 93, B+ 88, …
    • Temperature (Q) to category (N): 30 hot, 20 warm, 0 cold
  • By augmenting external data (adding information)
    • City name (N) to latitude (Q) and longitude (Q)
  • By applying mathematical operations
    • Computing the difference between two attributes
    • Log scale
    • Min-max normalization
    • One hot vector encoding

High level Choice: Analyze - Produce - Derive Example

#### High level Choice: Analyze - Produce - Derive

• Sometimes, we derive an entirely new dataset.
  • e.g., reshaping operations in pandas

• Sometimes, we derive an entirely new dataset.
  • e.g., group by operations in pandas

• Another example of deriving a new dataset is to change the dataset type.
  • e.g., building K Nearest Neighbor (KNN) Graph (a table to a network)

Mid-level Choice: Search

• All of the high level analyze cases require users to search for elements of interest within the vis as the mid level goal.
• Search can be classified into four cases depending on
  • 1. whether the identity of the search target is known or not and
  • 1. whether the location of the search target is known or not.

• Lookup : looking up human (target) knowing that it belongs to mammals ( location
• Locate : locating rabbits target ) not knowing where it belongs to
  • Commonly, we call just this specific task a “search”.
  • e.g., search “abc.txt” on your disk
• Browse : browsing all leaves of the mammal subtree location
• Explore : exploring for a family having the largest number of species
  • Note: “explore” was a synonym of “discover”.

Low-level Choice: Query

• After searching, you will find a target or set of targets.
• Then, you may want to investigate the targets by querying some information.
• Identify : returns the characteristic of a single target
• Compare : returns the characteristics of multiple targets
• Summarize (= overview): returns a comprehensive view of everything
  • Extremely common in vis systems as a startup view!

Low-level Choice: Query Example

• Identify : identifying the election result of one state
• Compare : comparing the election result of one state to another
• Summarize : summarizing the election results cross all states to determine how many favored one candidate

Targets

• So far, we have learned actions (verbs) that users want to perform on vis.
• Targets (nouns) mean some aspect of data that is of interest to users.
• What does your vis do?
  • Action + Target
  • Discover Trends
  • Present Distribution
  • Compare Topology

• All data level
  • Trends : a high level characterization of data
    • e.g., increase, peaks, troughs, plateaus
  • Outliers : elements do not fit well with trends
  • Features : particular structures of interest
    • e.g., clique in graph theory

• Attribute level
  • Distribution : the distribution of study time
  • Extremes : the student who study longest per week
  • Dependency : does grade depend on study time
    • In general, such a causal relationship is really hard to prove!
  • Correlation : are grade and study time positively correlated?
  • Similarity : similarity between students in terms of study time grade

• Network datasets: topology and paths
• Spatial datasets: shapes
• The last two pertain to specific types of datasets.

So, How?

• So far, we have learned the terms for describing what to be visualized (data abstraction) and why we visualize (task abstraction)
• Then, how do we visualize data?
  • “visualization
• There are many useful idioms and studying those idioms is the main goal of this course!
• Let me give you a brief overview first…

How - Idiom Abstraction

Visualization Analysis Example

• SpaceTree vs. TreeJuxtaposer
• SpaceTree : https://www.youtube.com/watch?v=B4vuSLVCJtw&t=112s&ab_channel=HCILUMD
• TreeJuxtaposer : https://www.youtube.com/watch?v=eIK3ItXyMi0&ab_channel=HCILUMD

Deriving Attribute Example

• When a tree is too big, we need a way to summarize the tree.
• The Strahler Number : a measure for node importance
• Original: 500,000 nodes, Simplified: 5,000 nodes
• Can be described by a chained sequence of two instances

Summary: Task Abstraction