GIS Data Models: Vector

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GIS Data Models Vector

• INLS 110-111
• GIS Digital Information
• Uses, Resources Software Tools

Prepared by Mary Ruvane PhD Candidate, SILS
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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

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Real World gt Data Needed

• Basic carrier of information entity
• Real-world phenomenon not divisible into
  phenomena of the same kind
• An entity consists of
• Type Classification
• Attributes
• Relationships

4
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

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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

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Real World gt Data Modeling
Source Bernhardsen, Tor. (1999). 2nd Ed.
Geographic Information Systems An Introduction.
p 38.
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Real World gt Modeling Process
Source Bernhardsen, Tor. (1999). 2nd Ed.
Geographic Information Systems An Introduction.
p 39. Fig 3.2.
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Modeling Geometric Attribute Data
Source Bernhardsen, Tor. (1999). 2nd Ed.
Geographic Information Systems An Introduction.
p. 40.
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Modeling Attribute Data
Source Bernhardsen, Tor. (1999). 2nd Ed.
Geographic Information Systems An Introduction.
pp 40.
10
Modeling Entity Relations
Source Bernhardsen, Tor. (1999). 2nd Ed.
Geographic Information Systems An Introduction.
pp 40.
11
Data Model gt Entities as Objects

• Real-world entities correspond to database
  objects
• carrier of information entity gt object(s)

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

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Object Spatial Component
Source Bernhardsen, Tor. (1999). 2nd Ed.
Geographic Information Systems An Introduction.
p 43.
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Object Attribute Component
Source Bernhardsen, Tor. (1999). 2nd Ed.
Geographic Information Systems An Introduction.
p. 43.
15
Basic Data Models (Graphics)

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

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

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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 gt depends on level of
  generalization/scale

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Variety of Vector Models

• Spaghetti model
• 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

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Vector Model Spaghetti
Source Lakhan, V. Chris. (1996). Introductory
Geographical Information Systems. p. 54.
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Vector Model Topological
Bernhardsen, Tor. (1999). 2nd Ed. Geographic
Information Systems An Introduction. p. 62. fig.
4.12.
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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.

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Vector Model TIN
tessellation a mosaic, typically consisting of
small square stones
Source Demers, Michael. N. (2000). 2nd Ed.
Fundamentals of Geographic Information Systems.
p. 117.
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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.
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Vector Model TIGER (US Census Bureau)
Image Source Clarke, Keith C. (2001). 3rd Ed.
Getting Started with Geographic Information
Systems. p 92.
26
Vector Graphic TIGER Example (Goleta, CA)
Image Source Clarke, Keith C. (2001). 3rd Ed.
Getting Started with Geographic Information
Systems. p 91.
27
Vector Model DLGs
Image Source Clarke, Keith C. (2001). 3rd Ed.
Getting Started with Geographic Information
Systems. p. 90
28
Vector Graphic DLG Example
Image Source Clarke, Keith C. (2001). 3rd Ed.
Getting Started with Geographic Information
Systems. p. 91
29
Vector Model Network
Source Heywood, Ian and Sarah Cornelius and
Steve Carver. An Introduction to Geographical
Information Systems. p. 60. fig. 3.14.
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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.
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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.
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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

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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.

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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

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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.