GIS Data Models: Vector

INLS 110-111

GIS Digital Information:

Uses, Resources & Software Tools

Prepared by:

Mary Ruvane

PhD Candidate, SILS

GIS Data Models

The real world can only be depicted in a GIS through the use of models that define phenomena in a manner that computer systems can interpret, as well perform meaningful analysis

Real World > Data Needed

Basic carrier of information = entity– Real-world phenomenon not divisible into

phenomena of the same kind

An entity consists of:Type ClassificationAttributesRelationships

Entity: Type Classification

Assumes identical occurrences can be classified Each entity type must be unique (no ambiguity)

– e.g., detached house classified under house; not industrial building

Some entities may need to be categorized – e.g., roadways as a class: with categories for national highways,

urban roads, private roads

Entity type also known as qualitative data – or in statistics the ‘nominal scale’

Entity: Attributes

Each entity type may have one or more attributes– e.g., buildings may have attributes characterizing material (frame

or masonry), as well number of stories

Attributes may describe quantitative data ranked in three levels of accuracy

Ordinal (Ranks)

– Good– Better– Best

Interval (numeric)

– Age– Income

Ratio (scale)

– Length– Area

Real World > Data Modeling

Source: Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. p 38.

Real World > Modeling Process

Source: Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. p 39. Fig 3.2.

Modeling: Geometric & Attribute Data

Source: Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. p. 40.

Modeling: Attribute Data

Source: Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. pp 40.

Modeling: Entity Relations

Source: Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. pp 40.

Data Model > Entities as Objects

Real-world entities correspond to database objects– carrier of information = entity > object(s)

Image: Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. p 42.

Objects Characterized by:

Type (unique ID, type code/object class) Attributes (qualitative/quantitative data) Relations (calculable vs. attributable) Geometry (point, line, area/polygon) Quality (accuracy, resolution, coverage extent,

representation, etc.)

Object: Spatial Component

Source: Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. p 43.

Object: Attribute Component

Source: Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. p. 43.

Basic Data Models (Graphics)

There are two types of GIS Data Models:(models used for graphic representation of geographic space)

1. Vector

2. Raster

Note: A database structure need seldom be made to suit a data model. But a well prepared data model is vital for a successful GIS analysis.

Vector vs Raster Graphics

Image Source: Burrough, Peter A. and Rachael A. McDonnell. (1998). Principles of Geographic Information Systems. p 27.

Vector Data Models/Structures

One model for representing geographic space Spatial locations are explicit Relationships between entities/objects are implicit Points associated with single set of coordinates (X, Y)

Lines are a connected sequence of coordinate pairs Areas are a sequence of interconnected lines whose 1st

& last coordinate points are the same

Vector Data Models/Structures

Model most representative of dimensionality as it appears on a map

Entity data and attribute data kept in separate files, perhaps a DBMS, which links them

A line consists of 2 or more coordinate pairs, with its attributes stored separately

More complex lines made up of many line segments Exactness > depends on level of generalization/scale

Variety of Vector Models

Spaghetti model Topological model (most common)Topological model (most common) Triangulated irregular network (TIN) Dime files and TIGER files Network model Digital Line Graph (DLG) Shapefile (ArcView/ArcGIS; ESRI) Others: HPGL, PostScript/ASCII, CAD/.dxf

Vector Model: Spaghetti

Source: Lakhan, V. Chris. (1996). Introductory Geographical Information Systems. p. 54.

Vector Model: Topological

Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. p. 62. fig. 4.12.

Why Topology Matters

Connections & relationships between objects are independent of their coordinates

Overcomes major weakness of spaghetti model – allowing for GIS analysis (Overlaying, Network, Contiguity, Connectivity)

Requires all lines be connected, polygons closed, loose ends removed.

Vector Model: TIN

Source: Demers, Michael. N. (2000). 2nd Ed. Fundamentals of Geographic Information Systems. p. 117.

tessellation: a mosaic, typically consisting of small square stones

Vector Model: Dime files and TIGER files

GBF/DIME model

TIGER model

POLYVRT model

Image Source: Demers, Michael. N. (2000). 2nd Ed. Fundamentals of Geographic Information Systems. p. 113. fig 4.16.

Vector Model: TIGER (US Census Bureau)

Image Source: Clarke, Keith C. (2001). 3rd Ed. Getting Started with Geographic Information Systems. p 92.

Vector Graphic: TIGER Example (Goleta, CA)

Image Source: Clarke, Keith C. (2001). 3rd Ed. Getting Started with Geographic Information Systems. p 91.

Vector Model: DLGs

Image Source: Clarke, Keith C. (2001). 3rd Ed. Getting Started with Geographic Information Systems. p. 90

Vector Graphic: DLG Example

Image Source: Clarke, Keith C. (2001). 3rd Ed. Getting Started with Geographic Information Systems. p. 91

Vector Model: Network

Source: Heywood, Ian and Sarah Cornelius and Steve Carver. An Introduction to Geographical Information Systems. p. 60. fig. 3.14.

Vector Model: Shapefile (ArcGIS; ESRI)

This table represents examples of the shape types of geographic features in a data set for a shapefile

Demers, Michael. N. (2000). 2nd Ed. Fundamentals of Geographic Information Systems. p. 114. fig 4.17.

Vector Model: Others(HPGL, CAD/.dxf PostScript/ASCII,)

Source: Clarke, Keith C. (2001). 3rd Ed. Getting Started with Geographic Information Systems. p. 89. fig. 3.12.

Vector Data Structures/Models

Advantages– Good representation of entity data models– Compact data structure– Topology can be described explicitly – therefore

good for network analysis– Coordinate transformation & rubber sheeting is

easy– Accurate graphic representation at all scales– Retrieval, updating and generalization of graphics &

attributes are possible

Vector Data Structures/Models

Disadvantages– Complex data structures– Combining several polygon networks by intersection &

overlay is difficult; uses considerable computer power– Display & plotting often time consuming and expensive;

especially high quality drawings, coloring, and shading– Spatial analysis within basic units such as polygons is

impossible without extra data because they are considered to be internally homogeneous

– Simulation modeling of processes of spatial interaction over paths not defined by explicit topology is more difficult than with raster structures because each spatial entity has a different shape & form.

Raster Data Structures/Models

Advantages– Simple data structures– Location-specific manipulation of attribute data is

easy– Many kinds of spatial analysis and filtering may be

used– Mathematical modeling is easy because all spatial

entities have a simple, regular shape– The technology is cheap– Many forms of data are available

Raster Data Structures/Models

Disadvantages– Large data volumes– Using large grid cells to reduce data volumes reduces

spatial resolution; loss of information & inability to recognize phenomenologically defined structures

– Crude raster maps are inelegant though graphic elegance is becoming less of a problem

– Coordinate transformations are difficult & time consuming unless special algorithms & hardware are used and even then may result in loss of information or distortion of grid cell shape.