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Evaluating map design of ecosystem services models - NACIS 2017

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Talk presented at NACIS 2017 on cartographic representations in ecosystem services models

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Evaluating map design of ecosystem services models - NACIS 2017

  1. 1. Evaluating map design and funding of ecosystem services models Eric Nost University of Wisconsin-Madison nost@wisc.edu | @ericnost
  2. 2. Goals •Survey ecosystem services models for the kinds of cartographic representations they produce •Ask: • Do models live up to their expectation as “decision support”? • Even if so, what kinds of decisions do they support?
  3. 3. What are ecosystem services? (ES) •“The benefits nature provides to society” •Example: storm surge •Why would decision-makers want to measure ES? • To restore or conserve them •Why model ES? • To determine location, value, tradeoffs
  4. 4. How do decision-makers learn with ES models? • Maps! • What kinds of maps? • Time, expertise, tools, and (mis)communication shape decision-makers’ use and interpretations • Differences maps! • But absolute or relative differences? Animation, interaction, or static?
  5. 5. Source: 2017 Master Plan Modeling Update Webinar. CPRA.
  6. 6. InVEST Source: Natural Capital Project
  7. 7. Source: http://aries.integratedmodelling.org/wp-content/uploads/2015/11/new-interface-vancouver-aesthetics.jpg
  8. 8. What do we already know about decision-support tools for ES? •All ES researchers acknowledge communication is key •Yet, emphasis is on spatial analysis (e.g. 2013 special issue of Ecosystem Services) •Less on representation and considerations like how to display absolute vs. relative differences • Even though representation choices shape policy (e.g. McKendry and Machlis 2009; Neset et al. 2016)
  9. 9. Research questions •What kinds of maps do ecosystem services models produce? • What can decision-makers learn from them? •How is their development funded?
  10. 10. Survey • Find as many ES DSTs as possible. Start very broadly - frameworks for helping decision-makers act on ES • Note that this excludes many research models (e.g. GUMBO) • But it still includes many kinds of DSTs: conceptual, databases, desktop/standalone, ArcGIS plug-ins, and interactive webmaps • Aimed widely – explicitly ES but also coastal planning tools that may deal with ES
  11. 11. Sources • Ecosystem-Based Management Tools Network • “Tools for Coastal Climate Adaptation Planning” • “Tools for Landscape-Level Assessment and Planning” • BSR “Analytical Tools for Assessing Business Impacts & Dependencies Upon Ecosystem Services” report for 2013, 2014, 2015 • “Payments for Ecosystem Services: Catalog of Online Tools and Resources” (Oregon State) • esp-mapping.net/home (Drakou et al. 2015) • Previous surveys (e.g. Bagstad et al. 2013)
  12. 12. Sources • 74 “tools” in the coastal planning, adaptation, and ecosystem services space, ranging from conceptual to databases to engineering-level computer models to webmaps • 39 explicitly (29) or implicitly (10) calculated ES • 7 of the 29 were conceptual decision-making frameworks • 19 of the remaining 22 involved mapping, usually through an ArcGIS plug-in • Excluded: 1 is proprietary, 2 are still in development • 7 out of 16 were webmaps or had a webmapping component (!)
  13. 13. Webmaps sample • Focused on 6 out of the 7 as one requires use of an iPad • 1 of the 6 is explicitly coastal, 2 were forest-oriented 1. InForest 2. LandServer 3. iTree 4. Co$ting Nature (unlicensed version) 5. TNC Coastal Resilience (Gulf of Mexico) 6. InVEST Coastal Vulnerability and Habitat Risk Assessment dashboards
  14. 14. What kinds of things would we want to know about these maps? Based on interviews with decision-makers, experience in using these tools, and the cartographic literature, I proposed 4 factors: 1. Scale 2. Uncertainties 3. Color 4. Change
  15. 15. Results - summary • All 6 webmaps were tools designed to inform planning decisions • Spatial focus ranged from site-based assessment to regional/global assessment • Applicability varied from specific states (e.g. Virginia) to countries (US) to anywhere on the globe • 5/6 enabled prioritization-type decisions (where is the best place to protect this ES?) • 4/6 allowed scenarios (what if sea level rise was 2m?) • 5/6 were developed by or with the involvement of nonprofits • 4/6 ... by government entities • 2 focused on > 5 ES, the rest < 5 • Only 1 operated on a fee model
  16. 16. Results - cartography • Scale • 1 webmap enabled changing the unit of analysis (e.g. from Census block group to county) • 1 (re)aggregated data at different zoom levels (more shortly) • Uncertainty • Only 2 visualized uncertainty, either by providing access to different (climate) scenarios or displaying pre-modeled uncertainties (e.g. for sea-level rise) • Color • 1 allowed users to change color ramps • 2 allowed changing transparency (to see basemap/other overlays) • Change • Only 2 enabled any kind of differences map (change in ES value between now and some future)
  17. 17. Case – InVEST dashboards • InVEST: 18 different ES models, all of which can produce maps as outputs, at local, regional, or global scale. • Was an ArcGIS plug-in; now open source standalone programs. • Users can upload model outputs into interactive map- based dashboards (for coastal vulnerability and habitat risk models only) • Both only visualize one scenario/output at a time
  18. 18. Case – InVEST coastal vulnerability dashboard
  19. 19. An experiment I built a webmap that takes two or more scenarios and allows for comparison – side by side or valued
  20. 20. Conclusions • Even with the models that don’t enable map representations, the data could be mapped with some other tool. But there are real challenges to that: time, expertise, miscommunications. • Interactive maps offer a solution. • But you can’t do everything in a webmap. Tradeoffs. • Decision-makers would be better served with tools for comparing scenarios and change in different ways.

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