Visualizing Environmental Data

E7ab9c918935168aae0bd07b503b9284?s=47 Geoff McGhee
October 24, 2016
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Visualizing Environmental Data

Talk at Reed College in October 2016.

E7ab9c918935168aae0bd07b503b9284?s=128

Geoff McGhee

October 24, 2016
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Transcript

  1. Visualizing Environmental Data Geoff McGhee Bill Lane Center for the

    American West, Stanford University Presentation at Reed College Oct. 24, 2016

  2. http://west.stanford.edu

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  4. • Environment and Resources • Economy and Public Policy •

    History and Culture of the West • Data visualization and multimedia for scholarship, outreach, journalism Core Issue Areas http://west.stanford.edu
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  8. Engage Explain Explore

  9. Engage http://stanford.io/1sr74bp

  10. Engage http://stanford.io/1lZ1vXO

  11. Engage Explain Explore

  12. Explain http://stanford.io/1UycU40

  13. Explain http://stanford.io/1Uj0NFc

  14. Explain http://stanford.io/1WyCDwy

  15. Explain http://stanford.io/1r3bIL2

  16. Explain http://stanford.io/1r3bIL2

  17. Engage Explain Explore

  18. Explore http://stanford.io/1r3bIL2

  19. Explore http://stanford.io/1WyCDwy

  20. Some background…

  21. A Decade in Infographics and Multimedia

  22. flight patterns data art- something in this… but what? http://www.aaronkoblin.com/project/flight-patterns/

  23. http://www.bewitched.com/historyflow.html

  24. http://hint.fm/projects/flickr/

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  26. • Big data and data visualization are changing news reporting

    and presentation • New players and skillsets are joining newsrooms • We need to learn more about telling stories with data • Good tools for narrative visualization don’t exist yet Journalism in the Age of Data (2010) datajournalism.stanford.edu
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  28. Data Visualization Back to Basics

  29. Charles Minard, 1869

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  31. THEN we drew pictures of data William Playfair, 1786

  32. THEN we drew pictures of data NOW we use software

    code to generate visualizations of (possibly fluctuating) data using predefined rules
  33. Visual Encoding of Information But the same fundamental process

  34. • “Utilizes one of the channels to our brain that

    have the highest bandwidths: our eyes”
 – Robert Kosara • Bypass language centers, go direct to the visual cortex • Leverage ability to recognize patterns, visual sense-making • Create mental models of phenomena… both literal and metaphorical Map of New Brainland by Unit Seven via Flickr How Visualization Works Why Visualize Information?
  35. Literal

  36. Fernanda Viegas and Martin Wattenberg

  37. Metaphorical

  38. The New York Times

  39. Visualizing Environmental Data • Way more information available than ever

    before • Government data portals, commercial and nonprofit data aggregators and services
 Data.gov, USGS, DataBasin, ArcGIS Online, Enigma • Explosion of cheap, free, and open-source GIS
 R, QGis, Python libraries • New sources of data and imagery
 Remote sensing, new devices and methods, modelling, new analytics on sensor data • Increased interest in environmental concerns in wake of droughts, extreme weather events The Opportunity
  40. Visualizing Environmental Data • Finding the right data can be

    hard • Who is the best source… data source of reference?
 Originating agency of data, partner agency, third party aggregator, news organization’s information graphic? • Is the data up to date?
 How long ago was it produced? is it refreshed regularly? Is there a feed/api? • Challenge of combining data sets
 Matching formats, symbology, scale, projection, data columns • … How does this turn into an engaging story? The Challenge
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  42. 2014-15 2010-11

  43. Visualizing Environmental Data EcoWest.org portal 
 
 Developed by Mitch

    Tobin, environmental consultant and journalist Supported by David and Lucile Packard Foundation’s Western Conservation subprogram Find the best data… and bring it together in one place EcoWest: Curating the Best Available Data
  44. Visualizing Environmental Data Presentation Decks and Clips 
 
 Curate

    best available information as static slides and graphics Ready to incorporate in Powerpoint decks - useful for teachers, researchers, NGOs Include extensive sourcing and background notes EcoWest: Powerpoint and Narrated Video to Go… Topics: Water, Biodiversity, Wildfires, Land, Climate, Politics
  45. Visualizing Environmental Data PROS
 
 Excellent conceptual framework: six main

    topic areas with sub-topics Mixture of spatial, temporal, and qualitative data Relatively easy to add new information Very portable - *everybody* uses Powerpoint, right? Pros and Cons of Powerpoint/PDF/Video Format CONS 
 
 Files can be large and unwieldy Information is static and needs to be refreshed periodically Lack of animation, interactivity Because so much 3rd party material, inconsistency of projection, symbology, scale etc – makes it harder to follow Because so much 3rd party material, inconsistency of projection, symbology, scale etc – makes it harder to follow
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  47. Question: Can we present in more engaging, web-friendly way? PRESENTATION

    Place spatial data on a zoomable web map Enable navigation through time series Connect to live data sets to keep information fresh? Add a narrative layer to engage audience? USER EXPERIENCE Work across platforms: web, tablet, and mobile Shareable and embeddable? Enable users to link to specific view and share it? Aggregate multiple graphics into thematic dashboards?
  48. Drought Monitoring: Static L L S SL S S L

    S L L S S S SL The Drought Monitor focuses on broad- scale conditions. Local conditions may vary. See accompanying text summary for forecast statements. S http://droughtmonitor.unl.edu/ U.S. Drought Monitor February 16, 2016 Valid 7 a.m. EST (Released Thursday, Feb. 18, 2016) Intensity: D0 Abnormally Dry D1 Moderate Drought D2 Severe Drought D3 Extreme Drought D4 Exceptional Drought Author: Eric Luebehusen Drought Impact Types: S = Short-Term, typically less than 6 months (e.g. agriculture, grasslands) L = Long-Term, typically greater than 6 months (e.g. hydrology, ecology) Delineates dominant impacts U.S. Department of Agriculture
  49. Drought Monitoring: Interactive

  50. Monthly Precipitation: Static

  51. Monthly Precipitation: Interactive

  52. Wildfires: Static

  53. Wildfires: Interactive

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  55. Updates fire perimeters and smoke estimates 3x day Thousands of

    fire animations dating back to 2003 Can zoom to and embed fire animation or state overview Mobile-tolerant responsive layout works at multiple sizes
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  57. Data Journalism? Science Communication?

  58. Sharability Designed for syndication

  59. Question: Can we present in more engaging, web-friendly way? PRESENTATION

    Place spatial data on a zoomable web map Enable navigation through time series Connect to live data sets to keep information fresh? Add a narrative layer to engage audience? USER EXPERIENCE Work across platforms: web, tablet, and mobile Shareable and embeddable? Enable users to link to specific view and share it? Aggregate multiple graphics into thematic dashboards? working on it… Aggregate multiple graphics into thematic dashboards?
  60. Thematic Dashboards

  61. Question: Can we present in more engaging, web-friendly way? PRESENTATION

    Place spatial data on a zoomable web map Enable navigation through time series Connect to live data sets to keep information fresh? Add a narrative layer to engage audience? USER EXPERIENCE Work across platforms: web, tablet, and mobile Shareable and embeddable? Enable users to link to specific view and share it? Aggregate multiple graphics into thematic dashboards? working on it…
  62. Adding a Narrative Layer

  63. Adding a Narrative Layer

  64. Adding a Narrative Layer

  65. Adding a Narrative Layer

  66. Adding a Narrative Layer

  67. Clarity in Symbology

  68. Clarity in Symbology Noise Reduction Rainbow palette is popular in

    scientific visualization But it lacks a visual hierarchy
  69. Clarity in Symbology “True color” is more intuitive “False color”

    can show more subtle gradations
  70. Clarity in Symbology Highlight only what’s most important

  71. Summation Engaging Users in Environmental Data Seek to publish information

    in a way that is well-tailored to the medium Leverage interaction to allow free movement and exploration Refresh data regularly, automatically if possible Add a narrative layer to engage audience Clarify symbology, use intuitive sense as much as possible
  72. Looking Ahead

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  76. Looking Ahead

  77. Uses Twitter lists as groupings Provides context – who’s talking?

    Constantly updates Archive on the fly IN THE WORKS: Filter analytics by time period NLP filters for relevancy (trained by moderators)
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  81. Thanks! @mcgeoff on Twitter gmcghee@stanford.edu west.stanford.edu vis.ecowest.org