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The USGS Center of Excellence for Geospatial Information Science (CEGIS) is advancing spatio-temporal data model research to enhance geospatial analysis and decision-making. This outline discusses the current state of research, including feature-based spatio-temporal data models that integrate spatial and temporal data from various sources. It highlights successes, ongoing challenges, and future research directions aimed at improving data models for applications such as hazard management and disaster recovery. Collaborations and publications reflect the project's commitment to long-term growth and innovation.
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The USGS Center of Excellence for Geospatial Information Science – Spatio-temporal Data Model Research E. Lynn Usery usery@usgs.gov http://cegis.usgs.gov
Outline • CEGIS Spatio-Temporal Research • What is solved (maybe what worked)? • What is almost solved (but not quite working) • What has failed? • What is missing? • What is next? • CEGIS future research in geospatial and geotemporal data models
Feature-based Spatio-Temporal Data Model • Developed Feature Library • Handles spatial and temporal data • Handles Vector and raster data • Feature-based • Works by loading from existing data, e.g., USGS NHD, ArcGIS Geodatabase, CSV files • Temporal data model for base category data • Work with Yanfen Le; published in CaGIS and ASPRS Manual of GIS
Feature-based Spatio-Temporal Data Model – What didn’t work • Production implementation for The National Map for spatio-temporal model • Interface to existing GIS/image processing systems for feature extraction • Long-term research project • Just beginning to build from other models
Missing? • Robust and effective spatio-temporal data model that works with databases and persistence • Temporal and event-based model for hazards, evacuation, and disaster recovery
The USGS Center of Excellence for Geospatial Information Science – Spatio-temporal Data Model Research E. Lynn Usery usery@usgs.gov http://cegis.usgs.gov