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