Embedding and Extending GIS for Exploratory Analysis of Large-Scale Species Distribution Data

1. Embedding and Extending GIS for Exploratory Analysis of Large-Scale Species Distribution Data Jianting Zhang, Dept. of Computer Science The City College of the City University of New York Le Gruenwald, School of Computer Science The University of Oklahoma

2. Outline • Background and Motivation • Modeling/Representation for Data Integration • LEEASP: The Prototype System for Visual Exploration • Related Works and Discussions

3. NEON Infrastructure Overview William K. Michener Deborah Estrin, http://www.projectscience.org/workshop7/talks/estrin.pdf

4. Terrestrial Arrays 4 Aquatic Arrays

5. Background • Species distribution analysis • Quantifying the relationship between species distributions and the environment • Central to ecology/biogeography theories and conservation practices • Incorporating climate change and human impact scenarios • Enabling Technologies • GPS technology in modern field survey • Geo-referring technology in transforming descriptive museum records to geographical coordinates • Internet and the cyber-infrastructure for distributed data access/integration • Spatial databases and GIS for data management and analysis

6. Background Guisan, A. and N. E. Zimmermann (2000). Predictive habitat distribution models in ecology. Ecological Modelling 135(2-3): 147-186. Waide, R. B., M. R. Willig, et al. (1999). The relationship between productivity and species richness. Annual Review of Ecology and Systematics, 30, pp. 257-300. Stockwell, D. R. B. and D. P. Peters (1999). The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographic Information Systems 13(2): 143-158. Hirzel, A. H., Hausser, J., Chessel, D.,Perrin, N., 2002. Ecological-niche factor analysis: How to compute habitat-suitability maps without absence data? Ecology, 83(7), 2027-2036.

7. Background The availability of compiled digital datasets • USDA PLANT Database • 89759 plant species in 3141 US counties • WWF Wildfinder database: • 29112 species, 4815 genus, 445 families, 69 orders in 4 classes (amphibians, reptiles, birds, and mammals) among the world’s 845 ecoregions • 350045 species-ecoregion records • USGS • Little tree species distribution data: 679 • NatureServe species distribution maps • 5743 amphibians species worldwide • 4273 birds species of the western hemisphere • 1786 mammals species of the western hemisphere

8. Background Community – Ecosystem – Biome – Biosphere Species Taxonomic (Linnaean ranks) Kingdom Phylum Class Order Family Genus Species SubSpecies Phylogenentic Environment Area Environmental Gradient Water-Energy Latitude Altitude Productivity Phylogeography

9. Environmental Taxonomic Correlation Distribution Distribution Configuration Geographical Background

10. Motivations • We aim at developing an integrated data model/representation that seamlessly links geographical, taxonomic and environmental data. • We utilize state-of-the-art visualization techniques to build a prototype to allow visual explorations between and among relevant data: • Embedding GIS for visualizing geographical maps • Incorporating Graph/tree visualization for taxonomic trees and ecoregion hierarchies • Using Sortable Table, Parallel Coordinate Plot (PCP) and other techniques for multivariate environmental data

11. GIS Data Model Layer 1 Species 1 Layer 2 Species 2 Layer n Species n Data Modeling/Representation Using Traditional GIS Data Model

12. Data Modeling/Representation Problems • The relationships among the geographical units in different layers are not a part of the traditional GIS data models. • To use the layer-based GIS data model for managing multiple species distribution data, thegeographical and the environmental data need to be joined for each layer, either permanently or dynamically. • While it is possible to arrange the species layers into groups in modern GIS to mimic the taxonomic hierarchy, it is difficult toidentify/visualize query results that involve multiple layers back in the layer list.

13. The Integrated Data Model GIS Data Model Data Modeling/Representation

14. Environmental Relational (RDBMS) From/To Taxonomic Geographical Environmental Geographical GIS Taxonomic Data Modeling/Representation • Object-Relational Framework • Taxonomic data is now first-class citizen

15. Data Modeling/Representation Supported Operations Environmental Taxonomic E->G(+T) T->G(+E) G->T Geographical G->E Operations need to be formally defined!

16. Environmental View Geographical View Taxonomic View Ecoregion View LEEASP: Prototypehttp://www-cs.ccny.cuny.edu/~jzhang/tech/LEEASPV10.zip Linked Environment for Exploratory Analysis of Large-Scale Species Distribution Data

17. LEEASP: Prototype Example Data • USGS NA Little dataset: 679 tree species, 90 Megabytes in ESRI Shapefile format • WorldClim 10 minutes altitude and 18 bioclimate variables • EPA NA Ecoregion data: up to Level III • Resolution: 0.5*0.5 Deg • 11777 valid cells

18. On-screen digitizing to specify environmental gradients LEEASP: Prototype Geographical View • Embedding GIS • Based on open source JUMP GIS from Vividsolutions • Designed to present the distribution information • Follows “Focus+Context” principle

19. Overview Control Summary Details LEEASP: Prototype Environmental View

20. 1 1 1 0 0 0 2 0 1 1 1 1 3 OR 0 0 0 0 0 1 0 1 0 1 1 0 0 0 …… …… …… LEEASP: Prototype Taxonomic View G->T T->G

21. LEEASP: Prototype Ecoregion View • Using the same API for Taxonomic view • Based on Prefuse (Jeffrey et al, 2005) • Efficient Tree Layout algorithms • Advanced information visualization functions (zoom/animation)

22. LEEASP: Prototype • Coordinated Multiple View • Overview+Detail • Focus+Context

23. Related Works/Discussions • USGS (1990s): Climate-Vegetation Atlas of the North America (http://pubs.usgs.gov/pp/p1650-a/) • Prasad and Iverson (1999-ongoing):A Climate Change Atlas for 80 Forest Tree Species of the Eastern United States http://www.fs.fed.us/ne/delaware/atlas/ (Forest Service) • Spatiotemporal data modeling and visualization (Andrienko et al 2003, Guo et al 2006) • Tree and graph visualization research (Bongshin et al 2004, Hillis et al 2005, Graham and Kennedy 2005, Parr et al 2007)

24. Related Works/Discussions • LEEASP focuses on dynamic visualizations through user interactions rather than delivering static mapping results. • LEEASP provides multi-way mapping among geographical, ecoregion, environmental and taxonomic data • Views in LEEASP represent the four types of data are coordinated: when a subset of data in one view is selected through the graphic user interfaces, the subset of data will be identified and highlighted in other views.

25. Related Works/Discussions • Future work • Better formalization of the integrated data model • Conduct more thorough user evaluations by domain scientists • Distributed data integration based SOA • Explore “mashup” technologies

26. Acknowledgements • Prefuse and JUMP GIS open source development teams. • This work is supported in part by NSF grant ITR #0225665 SEEK and NSF grant ATM #0619139 CEO:P-COMET. • Thanks to Profs. Robert K. Peet (UNC) and Jessie Kennedy (Napier University, UK) for taxonomy help. • Thanks to Dr. Weimin Xi (TAUM) and Anantha M. Prasad (USDA Forest Service) for evaluating the prototype and providing constructive suggestions. • Special thanks to three anoymous ACM-GIS conference reviewers for their comments and suggestions. • Conference travel is supported by faculty startup fund from the Grove School of Engineering, the City College of the City University of New York.

27. Q&A Jzhang@cs.ccny.cuny.edu http://www-cs.ccny.cuny.edu/~jzhang/tech/LEEASPV10.zip 27