Geospatial data models

Helena Mitasova

GIS/MEA582 Geospatial Modeling and Analysis NCSU

Learning objectives

• understand and perform raster data resampling
• modify vector data representation
• convert between raster and vector data models
• find and use data repositories, wms services

Geospatial data models

Mapped data, results of modeling or analysis are represented in GIS using

• raster (regular grid) data model
• vector (feature) data model
• specialized representations: meshes

Modifications of data representation

• Changing raster resolution, e.g., when model inputs are rasters at different resolutions

• Changing vector geometry type, e.g., when model input requires different geometry than given data (points instead of lines)

Raster data - changing resolution

Resolution: size of the grid cell (pixel) in map units (m)

• continuous fields: interpolation
  • the higher resolution raster values are interpolated using the values of the neighboring lower resolution cells
  • methods: bi-linear, bi-cubic, spline.

• discrete raster data: nearest neighbor resampling
  • assigns the higher resolution cell the same value as the nearest lower resolution cell
  • resulting raster has only the values present in the input raster

Increasing resolution: continuous

Elevation at 30m resolution resampled to 10m resolution

Nearest neighbor creates "flats" in the resampled DEM, interpolation preserves smooth surface.
See equations for bi-linear interpolation

Increasing resolution: discrete

Geology at 30m resolution resampled to 10m resolution

Raster values are classes of observed geology

Increasing resolution: compare

Effect of resampling / reinterpolation on the results
More complex downscaling techniques using additional variables and machine learning may be needed if the difference in resolution is large

Decreasing resolution

Nearest neighbor resampling of 10m DEM to 30m and 20m DEMs

Decreasing resolution

Nearest neighbor resampling of 10m soil typemap to 30m and 20m maps

Modifying vector data

• Converting vector data type
  • lines to points, areas to lines or points
  • points to lines: network building or interpolation may be needed
  • usually preserves the shape

• Generalization
  • simplifying geometry while preserving important information
  • both data geometry and type can be modified
  • line to simplified line, polygon to simplified polygon or point
  • selecting subset of features
  • important when combining local, state and national scale data

Changing vector data type

Data geometry is not modified, but subset is extracted and stored in a different data structure

Topology building is required for conversions point to line, line to polygon

Conversion between data models

• vector to raster
  • continuous: spatial interpolation (covered by a separate topic)
  • discrete: nearest neighbor

• raster to vector
  • continuous: point sampling, isolines
  • discrete: nearest neighbor, grid center or boundary

Continuous: vector to raster data

Discrete: vector to raster data

• lines, areas: nearest neighbor
• areas: attribute value applies to the entire polygon

Raster to vector data

Continuous data: isolines, sampling points

Raster to vector data

• points – centers of grid cells
• lines, polygon border lines: connected grid cell centers
• thinning and smoothing is often performed for lines

Raster to vector data

• areas – boundary, centroid, requires building topology
• connects points on grid cell boundary

Data repositories

• Web mapping Service
• Web Processing Service

See Webpage with links to relevant services

Summary

• raster and vector data models
• modifying raster and vector data representation
• converting between raster and vector data models
• data repositories, wms services