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10 Reasons Why We Visualise Data

  1. 10 Reasons Why We Visualize Data Francesco D’Orazio @abc3d ! CIO FACE
  2. STATISTIK “Analysis 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.
  3. “Whatever can be expressed in numbers, may be expressed by lines.” William Playfair, 1805
  4. We have come a long way 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.
  5. 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.
  6. All this still doesn’t explain why humans like to visualise data. Here’s 10 reasons.
  7. “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
  8. 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. Who’s influencing the News of the World’s debate on Twitter?
  9. Mountain Fear
  10. Weavrs Emotion Map OBJECTIFY abstract information in shapes, surfaces, volumes and colors.
  11. Social Media in Research Study CLASSIFY and COMPARE data, entities, distributions…
  12. 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.
  13. Density Design – The Geopolitics of Sharing “Our ability to identify PATTERNS and CORRELATIONS when dealing with numbers is incredibly poorer than our ability to recognize and compare shapes.”
  14. DIRECT-OBJECT-MANIPULATION on live data. Dynamic Audience Mapping – Brand Graph
  15. CONTINUOUS ITERATION: play with a wider range of hypothesis to solve a problem. A New Epistemology? Pulsar Social Data Mining platform 16
  16. CONTINUOUS ITERATION: starting with 0 hypothesis > finding out what I don’t know I don’t know 17
  17. 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
  18. 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
  19. 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.  
  20. The key tool in our Macroscope toolbox, helping us stay afloat in a sea of data that’s getting deeper everyday.
  21. Thanks @abc3d !!