Basic Data Visualization

Humans process visual info with pre-attentive perception and Gestalt principles.
– White spaces create groupings (group into rows? columns? separate? bunch similar?). Anything that moves together is also grouped as a unit.

Definition – making sense data, facilitate consummation in an easy way; display abstract info; shift the balance between perception and cognition.

How to make successful – quantities need to be selected accurately, clear, allow comparison, “this number is ___ compared to what?”. Support decisions.

Operational Principles

  1. Define goals.
  2. Fix the data. (overlap on what the data represents?)
  3. Highlight the important stuff
  4. Eliminate distractions
  5. Provide clear information hierarchy
  6. Provide filters

Challenge

  1. Assess info users really need
  2. Understand their goals
  3. Select and aggregate the data properly

Anatomy of Quantitative Data

  • Measures – raw data
  • Dimensions – organize the data
  • Categorical – divisions
  • Relationship/Filters – sorting, cluster data

What is the story?

  • Variations within measures (SD, mean, normal distribution)
    • Relationship between measures (correlation, i.e.)
  • Variations in time matter
  • Variations within dimensions
  • Variations across Time
  • Variations across Space

Line and bar charts – show the zero point

Measures

  • Raw measures – typically money and time
  • Normalized measures (designed) (like sales per sales person, sales per advertising dollar spent, sales per square foot of store, etc._
  • Summarized measures (statistics like subtotals, averages, etc)

Dimensions Organize the Data

  • Hierarchical or Categorical
  • Exact schema (IA)
  • Natural dimensions (scalar values)
  • Synthetic dimensions (designed, dynamic)

Ways to slice up the data

  • Clustering
  • Conversions
  • Normalization (comparison or baseline)

Filters

  • reduce noise
  • focus
  • compare
  • filter on measures or dimensions
  • sort

Big three Data Design tasks

  • comparison
  • categorization
  • normalization

Note that comparison to business goals (subjective target) is not a signal.

Visualization -relationships within charts (slide 42)
-Dimension relationships (nominal, ordinal, interval, hierarchical).
You show the measure relationships:

  • rank
  • ratio
  • correlation

Bar Charts – tell a story about differences. good to show rank
Lines – for trends

 

 

 

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