Francesco DOrazio @abc3d !CIO FACE facegroup.com
10 Reasons Why We Visualize Data
STATISTIKAnalysis of Data about the State to sustain the need of modern states to base policy on
demographic and economic data. Emerges at the intersection of a revolution in measurement and the rise of the Modern State.
Whatever can be expressed in numbers, may be expressedby lines.
William Playfair, 1805
Spatial organization in the 17th and 18th century
Discrete comparison in the 18th and early 19th century
Continuous distribution in the 19th century
Multivariate distribution and correlation in the late 19th and 20th century.
We have come a long way
William Brinton, in his Graphic Presentation in 1939, explaining why data visualization has been so tardy in being developed and widely adopted, despite being extremely useful.
All this still doesnt explain why humans like to visualise data. Heres 10 reasons.
There is a magic in graphs. The profile of a curve reveals in a flash a whole situation the life history of an epidemic, a panic, or an era of prosperity. The curve informs the mind, awakens the imagination, convinces. Henry D. Hubbard, 1939
SPATIALISE information, making it tangible and allowing us to think with eyes and hands.
We like it because our perception and cognition of the world is inherently informed by space.
Whos influencing the News of the Worlds debate on Twitter?
OBJECTIFY abstract information in shapes, surfaces, volumes and colors.
Weavrs Emotion Map
CLASSIFY and COMPARE data, entities, distributions
Social Media in Research Study
They act as an EXTERNAL MEMORY, external scaffolding of the mind, allowing us to take into account a greater number of variables and hypothesis and to move seamlessly between focused reasoning and free associations.
Our ability to identify PATTERNS and
CORRELATIONS when dealing with numbers is
incredibly poorer than our ability to recognize and compare shapes.
Density Design The Geopolitics of Sharing
DIRECT-OBJECT-MANIPULATION on live data.
Dynamic Audience Mapping Brand Graph
CONTINUOUS ITERATION: play with a wider range of hypothesis to solve a problem.
A New Epistemology?
Pulsar Social Data Mining platform
CONTINUOUS ITERATION: starting with 0 hypothesis > finding out what I dont know I dont know
CONTEXT and NARRATIVE: data visualization redefines and encompasses the entire problem field, allowing us to grasp an holistic understanding of the problem, not just a fraction of it.
Predicting the Oscars 2012 with multiple datasets
Represent PROCESS, not only structure. Condensed dynamic images picture time into spatial terms making any transformative process visible and tangible.
50 Years of Space Exploration
The telescope helped us
understand the infinitely great.
The microscope helped understand the infinitely small.
Today we are confronted with
another infinite: the infinitely complex.
The key tool in our Macroscope toolbox, helping us stay afloat in a sea of data thats getting deeper everyday.