tessellation data models

DM-11 - Hierarchical data models
• Describe alternatives to quadtrees for representing hierarchical tessellations (e.g., hextrees, rtrees, pyramids)
• Explain how quadtrees and other hierarchical tessellations can be used to index large volumes of raster or vector data
• Implement a format for encoding quadtrees in a data file
DM-10 - The Triangulated Irregular Network (TIN) model
• Describe how to generate a unique TIN solution using Delaunay triangulation
• Describe the architecture of the TIN model
• Construct a TIN manually from a set of spot elevations
• Delineate a set of break lines that improve the accuracy of a TIN
• Describe the conditions under which a TIN might be more practical than GRID
• Demonstrate the use of the TIN model for different statistical surfaces (e.g., terrain elevation, population density, disease incidence) in a GIS software application
DM-09 - The hexagonal model
• Illustrate the hexagonal model
• Explain the limitations of the grid model compared to the hexagonal model
• Exemplify the uses (past and potential) of the hexagonal model
DM-08 - Grid compression methods
• Illustrate the existing methods for compressing gridded data (e.g., run length encoding, Lempel-Ziv, wavelets)
• Explain the advantage of wavelet compression
• Evaluate the relative merits of grid compression methods for storage
• Differentiate between lossy and lossless compression methods
DM-07 - The Raster Data Model

The raster data model is a widely used method of storing geographic data. The model most commonly takes the form of a grid-like structure that holds values at regularly spaced intervals over the extent of the raster. Rasters are especially well suited for storing continuous data such as temperature and elevation values, but can hold discrete and categorical data such as land use as well.  The resolution of a raster is given in linear units (e.g., meters) or angular units (e.g., one arc second) and defines the extent along one side of the grid cell. High (or fine) resolution rasters have comparatively closer spacing and more grid cells than low (or coarse) resolution rasters, and require relatively more memory to store. Active research in the domain is oriented toward improving compression schemes and implementation for alternative cell shapes (such as hexagons), and better supporting multi-resolution raster storage and analysis functions.

DM-06 - Grid representations
• Explain how grid representations embody the field-based view
• Differentiate among a lattice, a tessellation, and a grid
• Explain how terrain elevation can be represented by a regular tessellation and by an irregular tessellation
• Identify the national framework datasets based on a grid model
DM-07 - The Raster Data Model

The raster data model is a widely used method of storing geographic data. The model most commonly takes the form of a grid-like structure that holds values at regularly spaced intervals over the extent of the raster. Rasters are especially well suited for storing continuous data such as temperature and elevation values, but can hold discrete and categorical data such as land use as well.  The resolution of a raster is given in linear units (e.g., meters) or angular units (e.g., one arc second) and defines the extent along one side of the grid cell. High (or fine) resolution rasters have comparatively closer spacing and more grid cells than low (or coarse) resolution rasters, and require relatively more memory to store. Active research in the domain is oriented toward improving compression schemes and implementation for alternative cell shapes (such as hexagons), and better supporting multi-resolution raster storage and analysis functions.

DM-09 - The hexagonal model
• Illustrate the hexagonal model
• Explain the limitations of the grid model compared to the hexagonal model
• Exemplify the uses (past and potential) of the hexagonal model