1. Geographic Information System (GIS) Dr. Taysir Hassan Abdel Hamid
Lecture 2

2. 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.
We will discuss database structures/models further in a separate presentation.

3. A look behind the scenes: Vector GIS data models
Spaghetti model
Topological model

4. The Spaghetti Model
The spaghetti model is the most simple vector data model
The model is a direct representation of a graphical image
NO explicit topological information

5. Spaghetti Model
Description: direct line for line translation of the paper map (often viewed as raw digital data)
Pros: easy to implement, good for fast drawing
Cons: storage and searches are sequential, storage of attribute data

6. Spaghetti model

7. Topology Branch of mathematics dealing with geometric properties
Geometry of objects remain invariant under transformations
Neighborhood relationships remain the same
Topology is the distinguishing basis for more complicated vector models

8. Topological Vector Model
Topological data models are provided with information that can help us in obtaining solutions to common operations in advanced GIS analytical techniques.
This is done by explicitly recording adjacency information into the data structure, eliminating the need to determine it for multiple operations.
Each line segment, the basic logical entity in topological data structures, begins and ends when it either contacts or intersects another line, or when there is a change in direction of the line.

9. Topological Vector Model
Each line has two sets of numbers, a pair of coordinates and an associated node number.
Each line segment has its identification number that is used as a pointer to indicate which set of nodes represent its beginning and ending.

10. Topological Vector Model
Polygons also have identification codes that relate back to the link numbers. Each link in the polygon now is capable of looking left and right at the polygon numbers to see which two polygons are also stored explicitly, so that even this tedious step is eliminated.
The Topological data model more closely approximates how we as map readers identify the spatial relationships contained in an analog map document.

11. Nevada
Utah
California
Arizona

12. Identify the polygons

13. Create the Polygon Attribute Table (PAT)
Poly-ID
Name
Population 1
California 2
Nevada 3
Utah 4
Arizona

14. Identify the nodes

15. Node table
Node ID
X-coord
Y-coord 1 2 3 4 5 6 7 8

16. Identify the links (arcs, lines)

17. Simplify this

18. Create the topology! Links table
FNode
TNode
LPoly
RPoly 1 2 3 4 5 6 7 8 9 10 11

19. Nodes First
Link#
FNode
TNode
LPoly
RPoly 1 2 3 6 4 5 7 8 9 10 11

20. Nodes First
Link#
FNode
TNode
LPoly
RPoly 1 2 3 6 4 5 7 8 9 10 11

21. Polygons
Link#
FNode
TNode
LPoly
RPoly 1 2 3 4 6 5 7 8 9 10 11

22. Polygons
Link#
FNode
TNode
LPoly
RPoly 1 2 3 4 6 5 7 8 9 10 11

23. Identify the points

24. Link List Link# List of points 1 1,2,3,4,5,6,7,8,9,10… etc 2 3 4 5 6 711

25. Point Coordinates ID
X-coord
Y-coord 1 2 3 4 5 6 7 8
9 (etc)

26. Putting it all together
Point-ID
X-coord
Y-coord 1 2 3 4 5 6 7 8
9 (etc)
Poly-ID
Name
Population 1
California 2
Nevada 3
Utah 4
Arizona
Link#
FNode
TNode
LPoly
RPoly 1 2 3 4 6 5 7 8 9 10 11
Link#
List of points 1
1,2,3,4,5,6,7,8,9,10… etc 2 3 4 5 6 7 8 9 10 11
Node ID
X-coord
Y-coord 1 2 3 4 5 6 7 8

27. Putting it all together
Poly-ID
Name
Population 1
California 2
Nevada 3
Utah 4
Arizona
Point-ID
X-coord
Y-coord 1 2 3 4 5 6 7 8
9 (etc)
Link#
FNode
TNode
LPoly
RPoly 1 2 3 4 6 5 7 8 9 10 11
Link#
List of points 1
1,2,3,4,5,6,7,8,9,10… etc 2 3 4 5 6 7 8 9 10 11
Node ID
X-coord
Y-coord 1 2 3 4 5 6 7 8

28. Putting it all together
Point-ID
X-coord
Y-coord 1 2 3 4 5 6 7 8
9 (etc)
Poly-ID
Name
Population 1
California 2
Nevada 3
Utah 4
Arizona
Link#
FNode
TNode
LPoly
RPoly 1 2 3 4 6 5 7 8 9 10 11
Link#
List of points 1
1,2,3,4,5,6,7,8,9,10… etc 2 3 4 5 6 7 8 9 10 11
Node ID
X-coord
Y-coord 1 2 3 4 5 6 7 8

29. Putting it all together
Point-ID
X-coord
Y-coord 1 2 3 4 5 6 7 8
9 (etc)
Poly-ID
Name
Population 1
California 2
Nevada 3
Utah 4
Arizona
Link#
FNode
TNode
LPoly
RPoly 1 2 3 4 6 5 7 8 9 10 11
Link#
List of points 1
1,2,3,4,5,6,7,8,9,10… etc 2 3 4 5 6 7 8 9 10 11
Node ID
X-coord
Y-coord 1 2 3 4 5 6 7 8

30. Putting it all together
Point-ID
X-coord
Y-coord 1 2 3 4 5 6 7 8
9 (etc)
Poly-ID
Name
Population 1
California 2
Nevada 3
Utah 4
Arizona
Link#
FNode
TNode
LPoly
RPoly 1 2 3 4 6 5 7 8 9 10 11
Link#
List of points 1
1,2,3,4,5,6,7,8,9,10… etc 2 3 4 5 6 7 8 9 10 11
Node ID
X-coord
Y-coord 1 2 3 4 5 6 7 8

31. The definition of Topology
The spatial relationships can be interpreted
identification of connecting lines along a path
definition of the areas enclosed within these lines
identification of contiguous areas
In digital maps, these relationships are depicted using ‘Topology’
Topology = A mathematical procedure for explicitly defining spatial relationship
Topology is the description of how the spatial objects are related with spatial meaning

32. Topological data models
Three types of topological concepts
Arc, Node and polygon topologies
Arc
Arcs have directions and left and right polygons (=contiguity)
Node
Nodes link arcs with start and end nodes (=connectivity)
Polygon
Arcs that connect to surround an area define a polygon (=area definition)

33. Terms and concepts Connectivity - from and to nodes
Contiguity - Polygon Enclosure
Adjacency - from Direction To
Node
Arc
Right
Polygon
Left
Polygon
From
Node

34. Topology errors
There are different types of topological errors and they can be grouped according to whether the vector feature types are polygons or polylines.
Topological errors with polygon features can include
unclosed polygons,
gaps between polygon borders or overlapping polygon borders.
A common topological error with polyline features is that they do not meet perfectly at a point (node).
This type of error is called an undershoot if a gap exists between the lines, and an overshoot if a line ends
beyond the line it should connect to

35. Raster representation: Bathymetry
East Pacific Rise near 9-10N is currently our best-studied section of fast-spreading mid-ocean ridge
Decades of investigation by ridge geologists and geophysicists, as wellchemists and biologists.
wealth of observational data, results and data-driven theoretical (often numerical) studies that are very much under-utilized research scientists and educators. (state several reasons different formats, standards, availablility, tools incompatible or incomplete, some in their infancy, etc.)
Situation is improving but much data, results, and related theoretical models still exist either in an inert, non-interactive form (e.g. journal publications) or as unlinked and currently incompatible computer data or algorithms.
Infrastructure needed not just for ready access to data but linkage of disparate data sets (data with data) AND data with models quantitatively evaluate hypotheses, refine numerical simulations, and explore new relations between observables

36. Spatial Encoding - RASTER
POINT 1 5 5 3
AREA 1 3 3 1 1 2
LINE 1 1 1

37. Spatial Encoding - VECTOR
* a single node
with NO area
POINT
- x, y
- x1, y1
- x2, y2 .
- xN, yN
LINE
* a connection of
nodes (vertices)
beginning with a
“to” and ending
with a “from”
(Arcs)
Area
(Polygons)
* a series of arc(s)
that close around
a “label” point
- x1, y1
- x2, y2 .
- xN, yN (closure Point)

38. Raster Models
Quantizes or divides space into discrete packets (cells), each representing a part of the whole
Cells are of equal size square, rectangular, triangles
Loose the ability to represent exact locations (e.g., point represented as single cell)
Lines represented as a series of connected cells
Multiple cells joined at edges or corners, usually with only 1 or 2 neighbors, 1D objects represented in 2D
Areas represented as a series of connected cells
2D objects represented in 2D, cells distort area and shape - stairs-stepped appearance

39. Like the vector data model, the raster data model can represent discrete point, line and area features.
A point feature is represented as a value in a single cell, a linear feature as a series of connected cells that portray length, and an area feature as a group of connected cells portraying shape.

40. Generic structure for a grid
Grid extent
Grid
cell s w o R
Resolution
Columns

41. Because the raster data model is a regular grid, spatial relationships are implicit. Therefore, explicitly storing spatial relationships is not required as it is for the vector data model.

42. Vector to Raster 32 34 34

43. Raster Representation32 34 34

44. Vector Vs. Raster

45. PRO AND CONS OF RASTER MODEL
raster data is more affordable
simple data structure
very efficient overlay operation
cons
topology relationship difficult to implement
raster data requires large storage
not all world phenomena related directly with raster representation
raster data mainly is obtained from satellite images and scanning

46. PRO AND CONS OF VECTOR MODEL
more efficient data storage
topological encoding more efferent
suitable for most usage and compatible with data
good graphic presentation
cons
overlay operation not efficient
complex data structure

47. Tabular data Tabular data is information describing a
Raster or Vector?
While any feature type can be represented using either model, discrete features, such as customer locations, pole locations or others, and data summarized by area such as postal code areas or lakes, are usually represented using the vector model. Continuous categories, such as soil type, rainfall, or elevation,
are represented as either vector
or raster.
Tabular data Tabular data is information describing a
map feature. For example, a map of
customer locations may be linked
to demographic information about those
Customers Tabular data for use in a GIS can
be purchased already packaged
with spatial data or it can be found in
your own organization.

48. Attribute data The “I” in GIS GIS are often split into two components
Coordinate information (describes object geometry or spatial information)
Attribute information (describes other non-spatial properties associate with it)
Often referred as tabular data as they are presented in tabular form

49. Databases - cont. GIS data components - spatial & non-spatial
Bolstad, 2005
GIS data components - spatial & non-spatial

50. Attribute Information Presentation
In GIS, attribute information are typically entered, analyzed, and presented using a database management system (DBMS)

51. DBMS Functions
DBMS incorporates a special set of software tools to manage the GIS non-spatial tabular data
Efficient data storage
Data retrieval
Data indexing
Data reporting

52. Example of a real-world problem
Imagine that you are a retail analyst who wants to create a GIS to help with decisions about a new store location.
You have collected together maps, population statistics, aerial photographs and post-coded customer information.

53. End of Today’s Lecture