GIS Data Models: Vector INLS 110-111 GIS Digital Information: Uses, Resources & Software Tools...
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Transcript of GIS Data Models: Vector INLS 110-111 GIS Digital Information: Uses, Resources & Software Tools...
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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 > Data Needed
Basic carrier of information = entity– Real-world phenomenon not divisible into
phenomena of the same kind
An entity consists of:Type ClassificationAttributesRelationships
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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
Ordinal (Ranks)
– Good– Better– Best
Interval (numeric)
– Age– Income
Ratio (scale)
– Length– Area
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Real World > Data Modeling
Source: Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. p 38.
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Real World > 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.
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Modeling: Entity Relations
Source: Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. pp 40.
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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.
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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.
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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.
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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 > depends on level of generalization/scale
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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
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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
Source: Demers, Michael. N. (2000). 2nd Ed. Fundamentals of Geographic Information Systems. p. 117.
tessellation: a mosaic, typically consisting of small square stones
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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.
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Vector Graphic: TIGER Example (Goleta, CA)
Image Source: Clarke, Keith C. (2001). 3rd Ed. Getting Started with Geographic Information Systems. p 91.
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Vector Model: DLGs
Image Source: Clarke, Keith C. (2001). 3rd Ed. Getting Started with Geographic Information Systems. p. 90
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Vector Graphic: DLG Example
Image Source: Clarke, Keith C. (2001). 3rd Ed. Getting Started with Geographic Information Systems. p. 91
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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.