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Data modeling in GIS

GI Systems and Science January 23, 2012. Data modeling in GIS. Points to Cover. What is spatial data modeling? Entity definition Topology Spatial data models Raster data model Vector data model Representing surfaces using Raster approach Vector approach. Spatial Data Modeling.

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Data modeling in GIS

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  1. GI Systems and Science January 23, 2012 Data modeling in GIS

  2. Points to Cover • What is spatial data modeling? • Entity definition • Topology • Spatial data models • Raster data model • Vector data model • Representing surfaces using • Raster approach • Vector approach

  3. Spatial Data Modeling • GIS are computer representations of the real world • These representations are necessarily simplified • Only those aspects that are deemed important are included • The simplified representation of the real world adopted by GIS is a model • Set of rules about how the spatial objects and relationships between them should be represented

  4. Spatial Data Modeling • A GIS model can be conceptualized in terms of two aspects: • A model of spatial form: how geographical features are represented • A model of spatial processes: how relationships between these features are represented • Building a model of the world for your GIS is a key stage in any GIS project

  5. Spatial Data Modeling • Creating a data model involves going through a series of stages of data abstraction: • Indentifying the spatial features form the real world that are of interest in the context of the research question • Choosing how to represent the features (i.e., as points, lines or areas) • Choosing an appropriate spatial data model (i.e., raster or vector) • Selecting an appropriate spatial data structure to store the model within the computer

  6. Entity Definition Figure 3.2 Source: Heywood et al., 2011

  7. Entity Definition • Surfaces • used to represent continuous features or phenomena Figure 3.3 Source: Heywood et al., 2011

  8. Entity Definition • Networks • used to represent a series of interconnected lines along which there a flow of data, objects or materials Figure 3.5 Source: Heywood et al., 2011

  9. Entity Definition • Issues associated with simplifying the complexities of the real world • Identification of the proper scale for representation • How much detail is required? • Dynamic nature of the real world • How to select the most appropriate representation of the feature? • How to model change over time? • Identification of discrete and continuous features • Fuzzy boundaries

  10. Entity Definition • Features with fuzzy boundaries • Continuous canopy and open woodland Figure 3.7 Source: Heywood et al., 2011

  11. Topology • A geometric relationship between objects located in space • Adjacency • Features share a common boundary • Containment • A feature is completely located within another feature • Connectivity • A features is linked to another feature • Independent of a coordinate system • Independent of scale

  12. Spatial Data Modeling • Creating a data model involves going through a series of stages of data abstraction: • Indentifying the spatial features form the real world that are of interest in the context of the research question • Choosing how to represent the features (i.e., as points, lines or areas) • Choosing an appropriate spatial data model (i.e., raster or vector) • Selecting an appropriate spatial data structure to store the model within the computer

  13. Spatial Data Models • Data models and corresponding data structures provide the information the computer requires to construct the spatial data model in digital form • Two main ways in which computers can handle and display spatial entities: • Raster approach • Vector approach

  14. Spatial Data Models • The raster data model • Based on principles of tessellation • Cells are used as building blocks to create images of features • The size of the cell defines the resolution (degree of precision) with which entities are represented Figure 3.8 Source: Heywood et al., 2011

  15. Spatial Data Models • The vector data model • The real world is represented using two-dimensional Cartesian co-ordinate space • Points are basic building blocks • The more complex the shape of a feature the greater number of points is required to represent it Figure 3.8 Source: Heywood et al., 2011

  16. Raster Data Model • Basic raster data structure • One layer stores and represents one feature • Presence-absence principle Figure 3.10 Source: Heywood et al., 2011

  17. Raster Data Model • Raster file structure for storing data on several entities of the same type Figure 3.11 Source: Heywood et al., 2011

  18. Raster Data Model • One of the major problems with raster datasets is their size • A value must be recorded and stored for each cell in an image regardless of the complexity of the image • To address this problem a range of data compaction methods have been developed • Run length encoding • Block coding • Chain coding • Quadtree data structures

  19. Raster Data Model • Raster structure for storing data on several entities of the same type • Reduces data volume on a row by row basis Figure 3.12(a) Source: Heywood et al., 2011

  20. Vector Data Model • Basic vector data structure • A file containing (x,y) co-ordinate pairs that represent the location of individual points Figure 3.14(a) Source: Heywood et al., 2011

  21. Vector Data Model • Point dictionary vector data structure • Allows to avoid redundancy when areal features share a boundary (are adjacent) • But does not really store information on topology Figure 3.14(b) Source: Heywood et al., 2011

  22. Vector Data Model • Topological vector data structure • Informs the computer where one feature is in respect to its neighbours • Withstands transformations well Figure 3.15 Source: Heywood et al., 2011

  23. Vector Data Model • All topological vector data structures are designed to ensure that: • Nodes and lines segments (arcs) are not duplicated • Arcs and nodes can be referenced to more than one polygon • All polygons have unique identifiers • Island and hole polygons can be adequately represented

  24. Modeling Surfaces • Surfaces represent continuous features of phenomena • Theoretically have an infinite number of data points • A model of a surface approximates continuous surface using a finite number of observations • The issue of selecting a sufficient number observations

  25. Modeling Surfaces • Digital Terrain Models (DTMs) are digital datasets recreating topographic surfaces • Created from a series of (x,y,z) data points • Resolution is determined by the frequency of observations used • Are derived from a number of data sources • Maps (low to moderate accuracy, all scales, selected coverage) • GPS (high accuracy, small areas) • Aerial photographs (high accuracy, large areas)

  26. Modeling Surfaces • Raster approach • DTM is a grid of height values Figure 3.21 Source: Heywood et al., 2011 • Also known as Digital Elevation Model (DEM) • Each cell contains a value representing the height of the terrain covered by the cell • Accuracy depends on the size of the cell and complexity of the surface

  27. Modeling Surfaces • Vector approach • Grid • Triangulated Irregular Network (TIN) • Triangles provide area, gradient and aspect of terrain • TINs use only surface significant points to reproduce a terrain surface Figure 3.22 Source: Heywood et al., 2011

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