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Data models Vector data model Raster data model (x,y) (x,y) (x,y) (x,y) (x,y) (x,y) (x,y) (x,y) (x,y) (x,y) point line polygon(area) The Vector Model of Real world The vector data model represent geographic features similar to the way maps do

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data models
Data models
  • Vector data model
  • Raster data model
slide2

(x,y)

(x,y)

(x,y)

(x,y)

(x,y)

(x,y)

(x,y)

(x,y)

(x,y)

(x,y)

point

line

polygon(area)

The Vector Model of Real world

The vector data model represent geographic features similar to the way maps do

A point: recorded by a pair of (x,y) coordinates, representing a feature that is

too small to have length and area.

A line: recorded by joining two points, representing features too narrow to have

areas

A polygon: recorded by a joining multiple points that enclose an area

spaghetti vector data model
Spaghetti Vector Data Model
  • Each point, line, or polygon is stored as a record
  • in a file that consists ID and a list of coordinates.

Data Storage

Points

Point ID Coordinates

+1

+2

1 1, 1

2 4, 2

3 5, 2

4 2, 4

+3

+4

spaghetti vector data model4
Spaghetti Vector Data Model

Lines:

  • ID Coordinates
  • (0,1), (3,4), (5,6)
  • (3,1), (5,2), (4,3)

1

2

spaghetti vector model
Spaghetti Vector Model
  • Uses a single line to represent the boundary of a polygon
    • Boundaries shared by two polygons are stored twice
    • Sliver polygons
spaghetti vector model6
Spaghetti Vector Model

Advantages

1. Simple

2. Relatively efficient as a method of cartographic display

spaghetti vector model7
Spaghetti Vector Model

1. Unstructured, lines often do not connect when they should

2. Spaghetti model severely limits spatial data analysis (e.g., area calculation)

Disadvantages

topological vector models
Topological vector models
  • In addition to coordinate locations, Topological vector model explicitly record topological relationships (Polygon adjacency is an example)

“Topology: Spatial relationships between points, lines & polygons”

slide9

Arc # Start Node Vertices End Node

  • 2 1,6 5
  • 2 3,4 5
  • 2 5

Polygon arc list

A①, ③

B②, ③

The Arc-Node Data Structure

2

Arc: ①, ②, ③

Nodes: 2, 5

Vertices: 1, 6 for arc ①

3, 4 for arc ②

3

1

B

A

4

5

6

  • Points
  • 1 x1,y1
  • 2 x2,y2
  • 3 x3,y3
  • x4,y4
  • 5 x5,y5
  • 6 x6,y6
slide10

Topology

Topology defines spatial relationships. The arc-node data structure supports

three major topological concepts:

Connectivity:

Arcs connect to each other at nodes

Area definition:

Arcs that connect to surround an area define a polygon

Contiguity:

Arcs have direction and left and right sides

slide11

Topology: Connectivity

Connected arcs are determined by searching through the list for common

node numbers.

Arc-node list

10

11

12

  • Arc From-Node To-Node
  • 10 11
  • 11 12
  • 11 13
  • 13 15
  • 13 14

13

14

15

Because of the common node 11, arcs 1, 2, and 3 all intersect. The computer

can determine that it is possible to travel along arc 1 and turn onto arc 3. But it

is not possible to turn directly from arc 1 to arc 5.

slide12

A

1

8

B

5

C

4

2

E

9

D

6

7

3

Topology: Area Definition

Polygon-Arc Topology

Polygon Arc List

B 1,5,8,4

C 2,6,9,5

D ?

E ?

Polygons are simply the list of arcs defining its boundary, arc coordinates

are stored only once, therefore, reducing the amount of data and ensuring

that the boundaries of adjacent polygons don’t overlap

slide13

Topology: Contiguity

An Arc

left

From-Node

To-Node

right

Direction

1

8

  • Arc Left Right
  • Polygon Polygon
  • C B
  • E C
  • ? ?
  • 1 ? ?

B

5

C

4

2

E

9

D

6

7

Two geographic features which share a boundary are called

adjacent. Contiguity is the topological concept which allows

the vector data model to determine adjacency.

3

advantages of topological model
Advantages of topological model
  • Spatial relationships between features are explicitly encoded, making it very easy to determine if polygons are adjacent, if arcs connect, etc.
  • Highly desirable model if spatial analysis is to be done on the data
limitations of topological model
Limitations of topological model
  • Data must be very “clean”

all lines must begin and end with a node

all lines must connect correctly

all polygons must be closed

  • Computational cost