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Building an Online System for Research, Outreach, and Education of Geospatial Environmental Research. Jim Graham Colorado State University Fort Collins, Colorado. National Institute of Invasive Species Science. Forecasting at Various Scales. Local. Regional. Global. National. 100. 0. - PowerPoint PPT Presentation
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Building an Online System for Research, Outreach, and Education of Geospatial Environmental Research
Jim GrahamColorado State UniversityFort Collins, Colorado
National Institute of Invasive Species Science
Forecasting at Various Scales
Regional
NationalGlobal
Local
Data Management Challenges
Lat Lon Temp Precip
-105.504 40.35819 5.32 58.4
-107.472 40.498 6.31 47.6
Example: Potential habitat distribution of invasive plant dalmation toadflax (Linaria dalmatica) in Colorado, USA
Hierarchical Vector data
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Legend
predict1
Value
High : 100
Low : 00
100
Precipitation
Temperature
ModelingAlgorithm
Variable Coefficient P-Value
Intercept -1.52 0.064
Annual Precip
-0.05 0.0
Annual Temp.
0.61 0.0
Map Generation
Model-Specific Data
Geo Referenced Rasters
Spreadsheets
Geo Referenced Rasters
General Imaging Issues
• Resolution and coverage of available data
• Acquisition costs
• Hardware and software performance
• Data quality
• File Format Compatibility
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
Points, Polylines, Polygons
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
• P – Projection time per coordinate• L – Loading time per coordinate• R – Rendering time per coordinate• N – Number of coordinates
T1 ( ) P L R N
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
• MZ – Maximum point density
T5 4 Q NA ( )L R MZ
Topology
Rendering from Grid Cells
• 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
T7 Q1 ( )L R NC
T8 Q1 ( )L R MH
Limiting Data Quantity
1 meter per pixel1 degree per pixel
Viewing Resolution
100% 100%
0% 0%
% Rendered as coordinates
% Rendered as grid-pixels
1 meter per pixel1 degree per pixel
Viewing Resolution
100% 100%
0% 0%
% Rendered as coordinates
% Rendered as grid-pixels
www.NIISS.org
Field Data Collection
The Past The Future
+ Manual entry + Automatic upload
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
System Architecture
DatabaseBrowser
HTMLPages
Plug-in
Images
WebServer
PHPPages
RasterLayers
Images
SpatialLibrary
JobController
DataServer
Jobs
Internet
Multi-Processor
ServerClient