DATA MODELING IN GIS

GI Systems and ScienceJanuary 23, 2012

Points to Cover What is spatial data modeling? Entity definition Topology Spatial data models

Raster data modelVector data model

Representing surfaces usingRaster approachVector approach

Spatial Data Modeling GIS are computer representations of the

real world These representations are necessarily

simplifiedOnly those aspects that are deemed important

are included The simplified representation of the real

world adopted by GIS is a modelSet 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

Formulating

research question

Collecting data

Creating data

model

Entering data into

a GIS

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

Formulating

research question

Collecting data

Creating data

model

Entering data into

a GIS

Entity Definition

Figure 3.2Source: Heywood et al., 2011

Entity Definition Surfaces

used to represent continuous features or phenomena

Figure 3.3Source: 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 3.5Source: Heywood et al., 2011

Entity Definition Issues associated with simplifying the

complexities of the real worldIdentification 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 boundariesContinuous

canopy and open woodland

Figure 3.7Source: Heywood et al., 2011

Topology A geometric relationship between

objects located in spaceAdjacency

Features share a common boundaryContainment

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

Formulating

research question

Collecting data

Creating data

model

Entering data into

a GIS

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 approachVector approach

Spatial Data Models

Figure 3.8Source: Heywood et al., 2011

The raster data modelBased on principles of

tessellationCells 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

Spatial Data Models

Figure 3.8Source: Heywood et al., 2011

The vector data modelThe 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

Raster Data Model Basic raster data structure

One layer stores and represents one featurePresence-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 sizeA 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 developedRun length encodingBlock codingChain codingQuadtree data structures

Raster Data Model Raster structure for storing data on several entities

of the same typeReduces 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 structureAllows 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 structureInforms the

computer where one feature is in respect to its neighbours

Withstands transformations well

Figure 3.15Source: Heywood et al., 2011

Vector Data Model All topological vector data structures

are designed to ensure that:Nodes and lines segments (arcs) are not

duplicatedArcs and nodes can be referenced to

more than one polygonAll polygons have unique identifiersIsland and hole polygons can be

adequately represented

Modeling Surfaces Surfaces represent continuous

features of phenomenaTheoretically have an infinite number of

data points A model of a surface approximates

continuous surface using a finite number of observationsThe issue of selecting a sufficient number

observations

Modeling Surfaces Digital Terrain Models (DTMs) are digital

datasets recreating topographic surfacesCreated from a series of (x,y,z) data pointsResolution is determined by the frequency of

observations usedAre 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 approachDTM is a grid of

height values 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

Figure 3.21Source: Heywood et al., 2011

Modeling Surfaces Vector approach

GridTriangulated

Irregular Network (TIN)○ Triangles provide

area, gradient and aspect of terrain

○ TINs use only surface significant points to reproduce a terrain surface

Figure 3.22Source: Heywood et al., 2011