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Data Visualization Nikhil Srivastava, 2015
• What is Data Visualization?
• Thinking and Seeing
• From Data to Graphics
• Principles and Guidelines
• Highcharts and Javascript
• Class Project
introduction
foundation & theory
building blocks
design & critique
construction
Last time …
Data Visualization Nikhil Srivastava, 2015
From Data to Graphics
What kind
of data do
we have?
How can we
represent the
data visually?
How can we
organize this into
a visualization?
Athi River Machakos 139,380
Awasi Kisumu 93,369
Kangundo-Tala Machakos 218,557
Karuri Kiambu 129,934
Kiambu Kiambu 88,869
Kikuyu Kiambu 233,231
Kisumu Kisumu 409,928
Kitale Trans-Nzoia 106,187
Kitui Kitui 155,896
Limuru Kiambu 104,282
Machakos Machakos 150,041
Molo Nakuru 107,806
Mwingi Kitui 83,803
Naivasha Nakuru 181,966
Nakuru Nakuru 307,990
Nandi Hills Trans-Nzoia 73,626
Visual Encoding
Data Visualization Nikhil Srivastava, 2015
Data TypesCATEGORICAL ORDINAL NUMERICAL
Interval Ratio
Male / Female
Asia / Africa / Europe
True / False
Small / Med / Large
Low / High
Yes / Maybe / No
Time
Latitude/Longitude
Compass direction
Time
Length
Count
= = = =
< > < > < >
+ - + -
* /
Data Visualization Nikhil Srivastava, 2015
Scatter Plot – Applications
CORRELATION GROUPING OUTLIERS
Data Visualization Nikhil Srivastava, 2015
type mark channel data represented
Pie Chart area size (angle) 1 quantitative
Data Visualization Nikhil Srivastava, 2015
Advanced Chart Types
• Stacked/Grouped Charts
• Treemap
• Multi-level Pie
• Heat Map
• Choropleth/Cartogram
Data Visualization Nikhil Srivastava, 2015
• What is Data Visualization?
• Thinking and Seeing
• From Data to Graphics
• Principles and Guidelines
• Highcharts and Javascript
• Class Project
introduction
foundation & theory
building blocks
design & critique
construction
Data Visualization Nikhil Srivastava, 2015
From Science to Art
• Design principles*
• Style guidelines*
*dependent on visualization context
and objective (and author)
Data Visualization Nikhil Srivastava, 2015
Design Principles
• Integrity
– Tell the truth with data
• Effectiveness
– Achieve visualization objectives
• Aesthetics
– Be compelling, vivid, beautiful
Data Visualization Nikhil Srivastava, 2015
Integrity
Lie Ratio = size of effect in graphic
size of effect in data
Data Visualization Nikhil Srivastava, 2015
Effectiveness*
Data/Ink Ratio = ink representing data
total ink
*according to Tufte
Data Visualization Nikhil Srivastava, 2015
Effectiveness (Few)
Data Visualization Nikhil Srivastava, 2015
Practical Guidelines
• Avoid 3-D charts
• Focus on substance over graphics
• Avoid separate legends and keys
• Make grids/labeling faint
• Avoid unnecessary textures and colors
Data Visualization Nikhil Srivastava, 2015
Color Guidelines
• To label
• To emphasize
• To liven or decorate
Data Visualization Nikhil Srivastava, 2015
More Color Guidelines
• Use color only when necessary
• Use saturated colors for data labels, thin
lines, small areas
• Use less saturated colors for large areas,
backgrounds
• Use tools like ColorBrewer
Data Visualization Nikhil Srivastava, 2015
• What is Data Visualization?
• Thinking and Seeing
• From Data to Graphics
• Principles and Guidelines
• Highcharts and Javascript
• Class Project
introduction
foundation & theory
building blocks
design & critique
construction
Data Visualization Nikhil Srivastava, 2015
• What is Data Visualization?
• Thinking and Seeing
• From Data to Graphics
• Principles and Guidelines
• Highcharts and Javascript
• Class Project
introduction
foundation & theory
building blocks
design & critique
construction
Data Visualization Nikhil Srivastava, 2015
Homework #5 – Due Today
• What is the message of the visualization? What are the user
tasks or questions you are answering?
• What is the exact data source? This should not be a complex
database but only the specific fields and items that will be
included in your final visualization. (Email me a link to the full
data set or a sample.)
• Which visual channels and chart types do you plan on using
in your visualization? Provide a summary or sketch.
Data Visualization Nikhil Srivastava, 2015
Timeline
Today
– Clean, process, restructure data
– Import into Highcharts (JSON or otherwise)
Next Tuesday
– Working draft of visualization
Next Thursday
– Finalize visualization
– Presentations