Visualizing Environmental Data

Slide 1

Slide 1 text

Visualizing Environmental Data Geoff McGhee Bill Lane Center for the American West, Stanford University Presentation at Reed College Oct. 24, 2016 

Slide 2

Slide 2 text

http://west.stanford.edu

Slide 3

Slide 3 text

No content

Slide 4

Slide 4 text

• 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

Slide 5

Slide 5 text

No content

Slide 6

Slide 6 text

No content

Slide 7

Slide 7 text

No content

Slide 8

Slide 8 text

Engage Explain Explore

Slide 9

Slide 9 text

Engage http://stanford.io/1sr74bp

Slide 10

Slide 10 text

Engage http://stanford.io/1lZ1vXO

Slide 11

Slide 11 text

Engage Explain Explore

Slide 12

Slide 12 text

Explain http://stanford.io/1UycU40

Slide 13

Slide 13 text

Explain http://stanford.io/1Uj0NFc

Slide 14

Slide 14 text

Explain http://stanford.io/1WyCDwy

Slide 15

Slide 15 text

Explain http://stanford.io/1r3bIL2

Slide 16

Slide 16 text

Explain http://stanford.io/1r3bIL2

Slide 17

Slide 17 text

Engage Explain Explore

Slide 18

Slide 18 text

Explore http://stanford.io/1r3bIL2

Slide 19

Slide 19 text

Explore http://stanford.io/1WyCDwy

Slide 20

Slide 20 text

Some background…

Slide 21

Slide 21 text

A Decade in Infographics and Multimedia

Slide 22

Slide 22 text

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

Slide 23

Slide 23 text

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

Slide 24

Slide 24 text

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

Slide 25

Slide 25 text

No content

Slide 26

Slide 26 text

• 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

Slide 27

Slide 27 text

No content

Slide 28

Slide 28 text

Data Visualization Back to Basics

Slide 29

Slide 29 text

Charles Minard, 1869

Slide 30

Slide 30 text

No content

Slide 31

Slide 31 text

THEN we drew pictures of data William Playfair, 1786

Slide 32

Slide 32 text

THEN we drew pictures of data NOW we use software code to generate visualizations of (possibly fluctuating) data using predefined rules

Slide 33

Slide 33 text

Visual Encoding of Information But the same fundamental process

Slide 34

Slide 34 text

• “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?

Slide 35

Slide 35 text

Literal

Slide 36

Slide 36 text

Fernanda Viegas and Martin Wattenberg

Slide 37

Slide 37 text

Metaphorical

Slide 38

Slide 38 text

The New York Times

Slide 39

Slide 39 text

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

Slide 40

Slide 40 text

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

Slide 41

Slide 41 text

No content

Slide 42

Slide 42 text

2014-15 2010-11

Slide 43

Slide 43 text

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

Slide 44

Slide 44 text

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

Slide 45

Slide 45 text

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

Slide 46

Slide 46 text

No content

Slide 47

Slide 47 text

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?

Slide 48

Slide 48 text

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

Slide 49

Slide 49 text

Drought Monitoring: Interactive

Slide 50

Slide 50 text

Monthly Precipitation: Static

Slide 51

Slide 51 text

Monthly Precipitation: Interactive

Slide 52

Slide 52 text

Wildﬁres: Static

Slide 53

Slide 53 text

Wildﬁres: Interactive

Slide 54

Slide 54 text

No content

Slide 55

Slide 55 text

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

Slide 56

Slide 56 text

No content

Slide 57

Slide 57 text

Data Journalism? Science Communication?

Slide 58

Slide 58 text

Sharability Designed for syndication

Slide 59

Slide 59 text

Slide 60

Slide 60 text

Thematic Dashboards

Slide 61

Slide 61 text

Slide 62

Slide 62 text

Adding a Narrative Layer

Slide 63

Slide 63 text

Adding a Narrative Layer

Slide 64

Slide 64 text

Adding a Narrative Layer

Slide 65

Slide 65 text

Adding a Narrative Layer

Slide 66

Slide 66 text

Adding a Narrative Layer

Slide 67

Slide 67 text

Clarity in Symbology

Slide 68

Slide 68 text

Clarity in Symbology Noise Reduction Rainbow palette is popular in scientific visualization But it lacks a visual hierarchy

Slide 69

Slide 69 text

Clarity in Symbology “True color” is more intuitive “False color” can show more subtle gradations

Slide 70

Slide 70 text

Clarity in Symbology Highlight only what’s most important

Slide 71

Slide 71 text

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

Slide 72

Slide 72 text

Looking Ahead

Slide 73

Slide 73 text

No content

Slide 74

Slide 74 text

No content

Slide 75

Slide 75 text

No content

Slide 76

Slide 76 text

Looking Ahead

Slide 77

Slide 77 text

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)