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 • 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
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
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
Entity Definition Figure 3.2 Source: Heywood et al., 2011
Entity Definition • Surfaces • used to represent continuous features or phenomena Figure 3.3 Source: Heywood et al., 2011
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
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
Entity Definition • Features with fuzzy boundaries • Continuous canopy and open woodland Figure 3.7 Source: Heywood et al., 2011
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
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
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
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
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
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
Raster Data Model • Raster file structure for storing data on several entities of the same type Figure 3.11 Source: Heywood et al., 2011
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
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
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
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
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
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
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
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)
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
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
