VISualizing Terrestrial-Aquatic Systems (VISTAS)

Transcription

1 VISualizing Terrestrial-Aquatic Systems (VISTAS) software & visualization to better understand & communicate Grand Challenge Environmental Science Motivation The project Prior work leading to award Barbara Bond, Denise Lach, Mike Bailey John Bolte, Christoph Thomas Oregon State University M. Stiegliz, Georgia Tech Ed Rastetter, Woods Hole Evergreen Faculty? Susan Stafford University of Minnesota, Computer Scientists from UW, PSU, OSU, Willamette. July , BIO/ABI

2 VISualizing Terrestrial-Aquatic Systems (VISTAS) Project Overview (Motivation & Goals) Current Status / Visualizations Ancillary Activities and Broader Impacts. Prior Work Leading to the Award. CanopyDB &CanopyView (Why Evergreen as VISTAS Home Institution?) *BIO/ABI

3 VISualizing Terrestrial-Aquatic Systems (VISTAS) HJAs Cyber- and Digital- Forests create a LOT of data. not unlike the data deluge facing all scientists. These data have tremendous research potential, But scientists are (already) drowning in data. Could data visualization help? intuit testable hypotheses? refine models? validate data? communicate results? BIO/ABI

4 VISualizing Terrestrial-Aquatic Systems VISTAS Map-based visualizations and charts communicate results well to scientists, but this is less true for other stakeholders [dl2]. Duncan, Sally and Denise Lach Privileged Knowledge and Social Change: Effects of Using GIS in Natural Resource Management. Environmental Management 38(2):

5 VISualizing Terrestrial-Aquatic Systems (VISTAS) Eco-hydrologic modeling on Complex Topography: Integrate & Scale Up Data from Plots to Region, from Days to Centuries BIO/ABI

6 VISTAS Project Overview

7 VISTAS Research Goals Computer Science R&D Env.Science Research Social Science Inquiry Judith Cushing, Evergreen Michael Bailey, OSU Barbara Bond, HJA Bob McKane, EPA Marc Stieglitz, Ga. Tech Denise Lach, OSU Christophe Thomas, OSU Ed Rastetter, Woods Hole. Co-develop Visualizations and Software Outreach to Practitioners Study Co-development & Ask: Which Visualizations Work? With whom? Why? Northwest Computer Science Consortium to Enhance the Study of Climate Change Burnett, Delcambre, Maier, Orr, Shapiro, Suciu Susan Stafford, U. Minn

8 VISTAS Current Status / Visualizations forest stand catchment basin

9 VISTAS Current Status / Visualizations Soil moisture March 2, 1994 March 10, 1994 High values are blue, low values, red. soil moisture & dissolved inorganic nitrogen side-by-side.

10 VISTAS Current Status / Architecture

11 Ancillary Activities & Broader Impacts. Survey Visual Analytics at LTER Sites Integrate use of LTER Ecology Data into Teaching QM Use visualizations for science outreach? Deliver interdisciplinary CS/Ecology Undergrad Curriculum Interest Middle School Girls by Ecology Visualization Work with Native Americans and Science Stories Raise CS Awareness of Interesting Ecology Problems Bring recent CS research to bear on this problem Describe dynamics of scientific software development.

12 Prior work (inspiring &) leading to award CanopyDB, CanopyView Grasslands Data Integration, EEON Nalini Nadkarni, Lee Zeman, Nik Molnar, Anne McIntosh, Dylan Fischer, Carri Leroy And, Evergreen Interdisciplinary Prob lem Oriented Curricular Approach 12

13 Data Collection Ecology Data Management (traditionally) Data Archiving Long Term Data for Climate Change Research & Mitigation, & Resource Management 13

14 ECOLOGICAL DATA MANAGEMENT alternatively ARCHIVE Data Metadata Data Collection/Generation Stockpile 14

15 Information Entropy over Time Time of publication Information usefulness at 10 years, 20 years, 30 years Information Con ntent Accident Specific details General details Retirement or career change Death Time after Michener et al., 1997 From knb.ecoinformatics.or

16 Information Entropy over Time Time of publication Information usefulness at 10 years, 20 years, 30 years Information Con ntent Accident Specific details General details Retirement or career change Death Time after Michener et al., 1997 From knb.ecoinformatics.or

17 Canopy Database Project Vision Database technology can ease your data management, data entry, validation, archiving, metadata provision, and data mining for synthesis BUT Researchers aren t programmers, so The tools must be easy to use & research productivity. Access is pretty easy.. 17

18 A Relational Database Example Each place has one or more stems (1..m relationship) Each stem has one or more branches. How do you know which place a particular stem is associated with?

19 Our Database 19

20 An Even More Complex Example! (real world dataset)

21 Canopy DataBank: From Database Technology aimed at archiving Study Design Field Work Data Entry & Verification Data Analysis Data Sharing (w/in Group) Journal Publication Data Archive Data Mining Metadata Generation Archive in Lab(common types) Data Visualization Statistical analysis Data validation (against metadata) Data and metadata capture Database and Protocol Design Research Reference Tools Information Synthesis 21

22 to CanopyView: Data Visualization & Analysis: Data Analytics Study Design Field Work Data Entry & Verification Data Analysis Data Sharing (w/in Group) Journal Publication Data Archive Data Mining Metadata Generation Archive in Lab(common types) Visual analytics Data validation (against metadata) Data and metadata capture Database and Protocol Design Research Reference Tools Visual analytics Information Synthesis 22

23 CanopyView Canopy View 23

24 CanopyView sometimes 2D is better... Canopy View 24

25 Yosemite Animations. What s different? Modeled Data, and LOTS of it Field Data over time Complex Topography (DEMs) Visualizing Processes. 25