Water Educator Network Symposiumopenwaterfoundation.org/wp-content/uploads/2016/11/... ·...
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Water Educator Network Symposium
Innovative Water Education in the 21st Century
Visual storytelling, using technology to share
Colorado’s Water StoriesNovember 17, 2016
Steve Malers, Open Water Foundation
www.openwaterfoundation.org
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Open Water FoundationSocial enterprise 501(c)3 nonprofit focusing on developing open source software tools to help make better decisions about water resources. Water is a public resource, and water data and software tools should also be public.
openwaterfoundation.org
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Telling Your Story with Data
“There are two goals
when presenting data:
convey your story and
establish credibility.”
-Edward Tufte
https://en.wikipedia.org/wiki/Edward_Tufte
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Make the Complicated…Simple
“Making the simple
complicated is
commonplace.
Making the
complicated simple,
awesomely simple,
that’s creativity”
-Charles Mingushttps://en.wikiquote.org/wiki/Charles_Mingus
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Resource: 2015 Presentation“Water Education through Stories Using Open and Big Data”
2015 Sustaining Colorado’s Watersheds, October 8, 2015
http://openwaterfoundation.org/wp-content/uploads/2016/04/SCW-2015-Malers.pdf
(1854) London Cholera outbreak (1858) Where does Paris get its beef?
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Resource: 2016 SMP Presentation“Step 5 – Existing Data Collection / Data as a Platform”
Stream Management Plan Workshop, Aug 23-24, 2016
http://openwaterfoundation.org/wp-content/uploads/2016/08/CWC-2016-SMP-Workshop-Step5-Malers.pdf
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Overview of Publishing Story for Web
Your dataset
Someone else’s dataset
Derived dataset
Cloud-hosted dataset
Get your datasets
in order
Context / Credibility / Visualization / Story / (Re)Action
Visualize / Tell Story
Raw Data View
Analytics
Dashboard
Story
Infographic
What is the complexity and
cost (money, effort)?
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Nothing is ever easy…until it is
Simple approach:
“Just tell me what I
need to do”
…but… “you mean I can’t do THAT?”
80/20 rule
Complex approach:
“Learn the
technologies”
…and… “I can do whatever I want”
(until you need to learn more)
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Cloud Data Hosting
• Dropbox
• Google Drive
• Amazon
• Microsoft Azure
• Box
• Many others
• Hosting integrated with visualization
• How to do I load datasets?• How does software get to the datasets?• What are features and limitations?• How much does it cost?• Can I change my mind later?
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data.colorado.gov
http://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0002N4“dataset” = often a table or map layer
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opencolorado.org
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coloradodata.statesales.opendata.arcgis.com
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Cloud Data Hosting for Spatial Data
• DataBasin.org
• ArcGIS Online
• Carto
• MapBox
• Mango
• Google Maps
• Many others
• Can treat as files using file hosting services• How to do I load datasets?• How does software get to the datasets?• What are features and limitations?• How much does it cost?• Can I change my mind later?
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DataBasin.org
National-level
resource
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CDSS and CWCB Map Viewers
Good for locating data, ad hoc queries
Limitation – limited data download, lack of automation
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Stream Mile Representation Framework
Stream mile + Excel = simple
E&R Attributes
Instream Flow Reaches
Projects & Methods
Stream Gages
Diversion Headgates
Organization Extent
Master Plans
and more…
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Citizen Science Platforms
http://pubs.usgs.gov/sir/2015/5015/
http://www.cocorahs.org
Also see citsci.org
High Groundwater
CoCoRaHS
Citizen-reported
precipitation
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Raw Data View
• Graph
• Table
• Simple map
• Static image/page or interactive
• Perhaps with download link
Goal is data access
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Raw Data Example
Annotations go a
long way towards
providing context
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Raw Data Example
Sometimes the data
speaks for itself
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Analytics
“Analytics is the discovery, interpretation, and
communication of meaningful patterns in
data.” - Wikipedia
“Data visualization
communicates insight.”
• Tableau• Zeppelinhub.com• D3.js• Others
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Analytics Example
“raster graph” or
“heat map”
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Analytics Example
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Analytics Example
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Dashboard
• “Dashboards often provide at-a-glance views
of key performance indicators (KPIs) relevant
to a particular objective or business
process.” – Wikipedia
• Often a combination of visualizations.
= real-time-ish data?
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Dashboard Example
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Telling Stories
• Characters – roles, perspective
• Plot
• Narrative point of view
• Purpose: share values, pass
knowledge, generate outcome
http://www.compassonline.org/
https://en.wikipedia.org/wiki/Storytelling
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Example Story
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The Most Common Jobs in Each State
1978 1990
2000 2014
http://apps.npr.org/dailygraphics/graphics/hist-job-map-90/child.html
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Irrigated Agriculture Dry Up
1956 1976
1987 2010Open Water Foundation
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Infographics
“Information graphics or infographics
are graphic, visual representations of
information, data, or knowledge
intended to present information
quickly and clearly.” - Wikipedia
Often include annotations and
clever visualization techniques
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“What is urban water use efficiency?”
Groundwater withdrawals
Wholesale
provider
(raw/treated)
Springs
Water
treatment
plant
Wastewater
treatment
plant
Non-potable water
Potable water
Wastewater
Treated wastewater
Distributed
non-potable water
Distributed potable water
Municipal/
Utility
Facility
Commercial,
Industrial,
and
Institutional
(CII)
Residential-Single-family
-Multi-family
Irrigation
only
Resale
entity
Wholesale & agricultural sales
Treated wastewater effluent
Raw water supply
2016-01-31
Urban Water
Provider
System Components
Downstream
Effluent
treatment
plant
System
losses
Treated effluent reuse
Sewage collection system
Surface water diversions
Stormwater
/
stormsewer
Irrigation return flows
Precipitation
Precipitation
or
Upstream
Surface and subsurface (delayed) flows
Surface and subsurface (delayed) flows
Irrigated
Agriculture
Collection /
Detention /
Runoff
Environment
& Recreation
Storage
Self-Supplied
Industry
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The “Snake” Diagram
http://www.nytimes.com/2010/04/27/world/27powerpoint.html
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Colorado Water Economy
http://www.denverwater.org/docs/assets/4bea7503-0237-e833-64a3f4c3447f588c/frwc_econ_report.pdf
“Water and
the Colorado
Economy”,
Front Range
Water Council,
2009
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Rain Barrels
Denver Water
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Virtual Water Balance
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Summary
• Get your data house in order…figure out the
datasets and the data flow.
• Determine the story you want to tell and what
datasets are needed…do you have the datasets you
need?
• Select technologies.
• Visualize dataset(s). Provide context.
• Organize visualizations for impact…as dashboard,
story, etc.
• Keep it relevant…keep investing in new data, better
visualization, technology updates.
• There will be a cost.
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