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Spatio-temporal asset management in a database of glacier change. David Percy(1) Geospatial Data Manager PSU Geology Department.
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Spatio-temporal asset management in a database of glacier change David Percy(1) Geospatial Data Manager PSU Geology Department Co-authors: Eric Hanson(2), Darrell Fuhriman, Cris Holm(2), William Garrick(2), Chris LeDoux(1), Matt Hoffman(1) and Andrew Fountain (1)(1)Dept of Geology, Portland State University(2)Academic and Research Computing, Portland State University Funding by NSF, NASA, USGS and Portland State University
Three main points • Hierarchical spatial structure: spatial ontology? Initially avoided due to complexity of relations, eventually imposed top-down • RDF for database • Temporal topology
The challenges Glaciers may have many sources that describe their state. Glaciers change over time and these various data sets need to be connected to the currently displayed outline. Over 8000 polygons showing extent of perrenial ice and snow. No one wants to touch each of these to associate them with assets. Need a solution to link space and time with data base of assets.
Components of Open Source Web MappingThe new “LAMP” • L – Linux • A – Apache • M – MySQL • P – PHP • L – Linux • A – Apache • M – MapServer • P – PostGIS
Vector Data Asset id (PK) Asset type desc Metadata id (FK) Asset name Shape File name File location Source scale Year start Year end Metadata Glac_num Centroid lat Centroid long Max elev Min elev Mean elev Mean elev area weighted Mean aspect Region State HUC Metadata id (FK) File name File location Source scale Cell size Year start Year end Glacier asset mm Glac_num (PK) Asset id (PK) Aster Asset id (PK) Asset type desc Metadata id (FK) Asset name File name File location Year start Year end Cell size Quality Img acquired month Various Oblique photo Asset id (PK) Asset type desc Metadata id (FK) Asset name File name File location Source scale Cell size Year start Year end Asset id (PK) Asset type desc Metadata id (FK) Asset name File name File location Year start Year end References Ref id <fields> Glacier database Web version Aggregated data Glacier – polygon Moraine – polyline HUC – polygon GNIS – point Raster Data DRG DOQ DEM
Glacier Asset Glac_num Centroid lat Centroid long Max elev Min elev Mean elev Mean elev area weighted Mean aspect Region State HUC Metadata id (FK) File name File location Source scale Cell size Year start Year end Asset id (PK) Region_id Asset type desc Metadata id (FK) Asset name File name File location Source scale Cell size Year start Year end Metadata Asset_Props Regions Prop_Id Asset id (PK) Name Value Region_Id Geometry Description Region_level Parent_id
Temporal topology Past 1907 1924 1907 1924 1936 1924 1936 1936 1946 1946 1956 1956 1986 Future
User says "show me all glaciers from 1940" By having stored the temporal topology, ie prior year date and subsequent year date, we can find the closest object in the following three ways: Past only ( [Year] <= 1940) and ([Subsqunt_yr] > 1940 ) Closest Temporal buffer (( [Year] + ([Subsqunt_yr] - [Year]/2)) >= 1940) and (([Year] - ([Year] - [Prior_yr]/2)) <= 1940) Gives the temporal mid point between two spatially referenced objects Conservative Temporal buffer (( [Year] + ([Subsqunt_yr] - [Year]/1.5)) >= 1940) and (([Year] - ([Year] - [Prior_yr]/3)) <= 1940) Gives you 2/3 past, 1/3 future This can be parameterized!
Conclusions • Hybrid RDF databases are viable, useful and in production NOW. • The big question is how granular to get with RDF? For example individual vector data? Performance issues... • Temporal topology may be the way to solve certain problems WRT spatio-temporal asset management
Future • WMS and WFS services to expose “stovepipe” to outside access • http://geospatial.research.pdx.edu/cgi-bin/mapserv?map=/disk/mapserver/maps/percy/glaciers_wms.map&service=wms&request=getcapabilities • Google Earth? • Query results dynamically to KML... • Example: http://geospatial.research.pdx.edu/~bjpd/usgs/ngm_quicksearch.html