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Building an Online System for Research, Outreach, and Education of Geospatial Environmental Research Jim Graham Colorado State University Fort Collins,

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Presentation on theme: "Building an Online System for Research, Outreach, and Education of Geospatial Environmental Research Jim Graham Colorado State University Fort Collins,"— Presentation transcript:

1 Building an Online System for Research, Outreach, and Education of Geospatial Environmental Research Jim Graham Colorado State University Fort Collins, Colorado

2 National Institute of Invasive Species Science

3 Forecasting at Various Scales Regional NationalGlobal Local

4 Data Management Challenges LatLonTempPrecip -105.50440.358195.3258.4 -107.47240.4986.3147.6 Example : Potential habitat distribution of invasive plant dalmation toadflax (Linaria dalmatica) in Colorado, USA Hierarchical Vector data 0 100 Precipitation Temperature Modeling Algorithm VariableCoefficientP-Value Intercept-1.520.064 Annual Precip -0.050.0 Annual Temp. 0.610.0 Map Generation Model-Specific Data Geo Referenced Rasters Spreadsheets Geo Referenced Rasters

5 General Imaging Issues Resolution and coverage of available data Acquisition costs Hardware and software performance Data quality File Format Compatibility

6 Goals Create an online system for geospatial- ecological science End-users: researchers, resource managers, teachers, and the public End-Users can add spatial data –Vector data (text and Shapefiles) –Raster data

7 Points, Polylines, Polygons

8 Vector Data A few very large, complex shapes –National parks –Countries –States Lots of small, simple shapes –Individual surveys –Observation points Some regions have very high densities of spatial coordinates

9 P – Projection time per coordinate L – Loading time per coordinate R – Rendering time per coordinate N – Number of coordinates

10 Approaches Access only data within viewing area 4 – Maintain All Required Projections –Geographic –3 UTM Zones –All are WGS84 Optimal use of an indexed, relational, enterprise-level database

11 MZ – Maximum point density

12 Topology

13 Rendering from Grid Cells

14 Equation 7: low resolution –NC – Number of cells Equation 8: high resolution –MH – Maximum point density at high resolution Q1 = maximum time to access indexed data in the database Maximum Rendering Times

15 Limiting Data Quantity

16 www.NIISS.org

17 Field Data Collection The Past The Future + Manual entry+ Automatic upload

18 Acknowledgements: www.NIISS.org NIISS: Tom Stohlgren, Mohammed Kalkhan, Greg Newman, Alycia Crall, Catherine Jarnevich, Tracey Davern, Paul Evangelista, Sunil Kumar, Sara Simonson NSF Grant #OCI-0636210 Volunteer Groups

19 System Architecture Database Browser HTML Pages Plug-in Images Web Server PHP Pages Raster Layers Images Spatial Library Job Controller Data Server Jobs Internet Multi- Processor ServerClient


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