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