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Spatial Data Integration Deana D. Pennington, PhD University of New Mexico

Spatial Data Integration Deana D. Pennington, PhD University of New Mexico. Land Use Tracts Roads Streams Vegetation Species occurrence. What is data integration?. Spatial Structures Projections/datums Spatial Scales Example. Combining datasets by resolving differences in:

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Spatial Data Integration Deana D. Pennington, PhD University of New Mexico

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  1. Spatial Data IntegrationDeana D. Pennington, PhDUniversity of New Mexico

  2. Land Use Tracts Roads Streams Vegetation Species occurrence What is data integration? • Spatial Structures • Projections/datums • Spatial Scales • Example • Combining datasets by resolving • differences in: • Data structures – text vs database • Spatial data:vector, raster, tin, contour map • Units – inches vs meters • Spatial data:plus projections and datums • Spatial scales – grain, extent, focus • Temporal scales – hourly vs monthly samples • Semantics – call the same things different names, or call different things by the same name • Context – harmonizing different things that are related

  3. Metadata, Metadata, Metadata!

  4. Hay et al., 2001 Data Structures: Fields vs Objects Field perspective Every location has a value Elevation Temperature % vegetation Object perspective Some locations are within the bounds Species occurrence Sample site Streams

  5. Data Structures: GPS points, lines, polygons Most field data Satellite data Air photos Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project, Department of Geography, The University of Colorado at Boulder

  6. Hay et al., 2001 Data Structures: Converting raster data to vector data (vectorize) • Problems: • Fuzzy edges • Overlapping objects • Error and uncertainty

  7. Veg Water Band 2 Band 2 Band 3 Soil Band 1 Band 1 Classification

  8. Spatial Dependence & Error False color composite Maximum Likelihood 89.44%

  9. Hay et al., 2001 Data Structures: Converting vector data to raster data: categorical Nearest neighbor

  10. Data Structures: Converting vector data to raster data: numerical • Proximal (nearest point) • Linear averaging • Non-linear function • Kriging (semi-variogram)

  11. Next: • Spatial Structures • Projections/datums • Spatial Scales • Example

  12. CoordinateSystems There are many different coordinate systems, based on a variety of reference systems, projections, geodetic datums, and units in use today Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project, Department of Geography, The University of Colorado at Boulder

  13. Projections Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project, Department of Geography, The University of Colorado at Boulder

  14. Projections Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project, Department of Geography, The University of Colorado at Boulder

  15. Projections Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project, Department of Geography, The University of Colorado at Boulder

  16. Reference Ellipsoids • Ellipsoidal models define an ellipsoid with an equatorial radius and a polar radius. • The best of these models can represent the shape of the earth over the smoothed, averaged sea-surface to within about one-hundred meters. • Reference ellipsoids are defined by semi-major (equatorial radius) and semi-minor (polar radius) axes. Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project, Department of Geography, The University of Colorado at Boulder

  17. Datums

  18. Ellipsoids & Datums ***Referencing geodetic coordinates to the wrong datum can result in position errors of hundreds of meters*** Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project, Department of Geography, The University of Colorado at Boulder

  19. Next: • Spatial Structures • Projections/datums • Spatial Scales: Grain & Extent • Example

  20. Study Grain & Extent Hay et al., 2001

  21. Grain in vector data Plot average biomass Site average biomass Biome average biomass State average biomass

  22. Next: • Spatial Structures • Projections/datums • Spatial Scales • Example

  23. Example: Integrating Species Occurrence Points and Images Excel File Sample 1, lat, long, presence Access File Sample 3, lat, long, absence Vegetation cover type Sample 2, lat, long, presence Integrated data: Elevation (m) P, juniper, 2200m, 16C P, pinyon, 2320m, 14C A, creosote, 1535m, 22C Mean annual temperature (C) • Semantics • Compatible scales • Reproject • Resample grain • Clip extent • Sample occurrence points

  24. Lab #11 • Raster/vector conversions • Projections • Scale change

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