Spatial Data Integration Deana D. Pennington, PhD University of New Mexico.
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Transcript of Spatial Data Integration Deana D. Pennington, PhD University of New Mexico.
Spatial Data IntegrationSpatial Data Integration
Deana D. Pennington, PhDDeana D. Pennington, PhDUniversity of New MexicoUniversity of New Mexico
What is data integration?What is data integration?
Combining datasets by resolving differences in:•Data structures – text vs database
Spatial data: vector, raster, tin, contour map
•Units – inches vs metersSpatial 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
1. Spatial Structures2. Projections/datums3. Spatial Scales4. Example
Land Use TractsRoadsStreamsVegetationSpecies occurrence
Metadata, Metadata, Metadata, Metadata, Metadata!Metadata!
Data Structures: Fields vs ObjectsData Structures: Fields vs Objects
Hay et al., 2001
Field perspectiveEvery location has a value
ElevationTemperature% vegetation
Object perspectiveSome locations are within the bounds
Species occurrenceSample siteStreams
Data Structures: Data Structures:
Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project, Department of Geography, The University of Colorado at Boulder
GPS points, lines, polygonsMost field data
Satellite dataAir photos
Data Structures: Converting raster Data Structures: Converting raster data to vector data (vectorize)data to vector data (vectorize)
Hay et al., 2001
Problems:1. Fuzzy edges2. Overlapping objects3. Error and uncertainty
ClassificationClassification
Band 1
Ban
d 2
Band
3
Soil
VegWater
Band 1
Ban
d 2
Spatial Dependence & ErrorSpatial Dependence & Error
False colorcomposite
Maximum Likelihood89.44%
Data Structures: Converting vector Data Structures: Converting vector data to raster data: categoricaldata to raster data: categorical
Hay et al., 2001
Nearest neighbor
Data Structures: Converting vector Data Structures: Converting vector data to raster data: numericaldata to raster data: numerical
•Proximal (nearest point)•Linear averaging•Non-linear function•Kriging (semi-variogram)
Next:Next:
1. Spatial Structures2. Projections/datums3. Spatial Scales4. Example
CoordinateCoordinateSystemsSystems
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
Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project, Department of Geography, The University of Colorado at Boulder
ProjectionsProjections
Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project, Department of Geography, The University of Colorado at Boulder
ProjectionsProjections
Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project, Department of Geography, The University of Colorado at Boulder
ProjectionsProjections
Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project, Department of Geography, The University of Colorado at Boulder
Reference Reference EllipsoidsEllipsoids
•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.
DatumsDatums
Kenneth E. Foote and Donald J. Huebner, The Geographer's Craft Project, Department of Geography, The University of Colorado at Boulder
Ellipsoids & DatumsEllipsoids & Datums
***Referencing geodetic coordinates to the wrong datum can result in position errors of
hundreds of meters***
Next:Next:
1. Spatial Structures2. Projections/datums3. Spatial Scales:
Grain & Extent4. Example
Study Grain & ExtentStudy Grain & Extent
Hay et al., 2001
Grain in vector dataGrain in vector data
Plot average biomass
Site average biomass
Biome average biomass
State average biomass
Next:Next:
1. Spatial Structures2. Projections/datums3. Spatial Scales4. Example
Elevation (m)
Vegetation cover type
P, juniper, 2200m, 16CP, pinyon, 2320m, 14CA, creosote, 1535m, 22C
Sample 3, lat, long, absence
Mean annual temperature (C)
Access File
Excel File
Integrated data:
Sample 2, lat, long, presence
Sample 1, lat, long, presence
Example: Integrating Example: Integrating Species Occurrence Points Species Occurrence Points
and Imagesand Images
1. Semantics2. Compatible scales3. Reproject4. Resample grain5. Clip extent6. Sample occurrence points
Lab #11Lab #11
1. Raster/vector conversions2. Projections3. Scale change