## tessellation data models

##### DM-10 - Triangular Irregular Network (TIN) Models

A Triangular Irregular Network (TIN) is a way of storing continuous surfaces. It is vector based, and works in such a way that it connects known data points with straight lines to create triangles, often called facets. These facets are planes that have the same slope and aspect over the facet. Collectively, these hypothetical lines form a network covering the whole surface. TINs are efficient when storing heterogeneous surfaces, since homogenous areas are stored using few data points, while areas with more variability are stored in detail using a larger number of data points. In other words, a TIN can be more detailed where the surface is complex (high variation) by using smaller facets, and less detailed where the surface is more homogeneous by using larger facets. TINs also have a high modelling potential, e.g. in topography and hydrology. However, the unique way of storing data an a TIN often makes it difficult to combine with other spatial data formats. Instead, the TIN data would usually be converted to other suitable formats.

##### 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-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-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-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-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-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