GIS Data Models: Raster

1
GIS Data Models Raster
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
2
Basic Data Models (Graphics)

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

3
Vector vs. Raster Graphics
Image Source Burrough, Peter A. and Rachael A.
McDonnell. (1998). Principles of Geographic
Information Systems. p 27.
4
Vector vs. Raster Images
Source Delany p 18
5
Basic Raster Graphic Representation
The Raster structure illustrates points, lines,
and areas utilizing the confines of cells for
representing geographic areas. Raster models
dont provide explicit locational information.
Source Demers, Michael. N. (2000). 2nd Ed.
Fundamentals of Geographic Information Systems.
p. 99. fig. 4.10.
6
Real World gt Coded Grid Cells
Raster data can be visualized as a grid lying
over the real world terrain. Each grid cell has
a code stored in the database describing the
terrain within that particular cell.
Source Bernhardsen, Tor. (1999). 2nd Ed.
Geographic Information Systems An Introduction.
p 68. fig. 4.18
7
Raster Data Models/Structures

• One model for representing geographic space
• Spatial locations are implicit
• Relationships between entities/objects are
  explicit
• Points associated with single grid cell
• Lines are a connected sequence of cells
• Areas are a sequence of interconnected cells

8
One Object Multiple Attribute Layers
Only one attribute value may be assigned to each
cell. Objects with several attributes are
represented with a number of raster layers, one
for each attribute.
Source Bernhardsen, Tor. (1999). 2nd Ed.
Geographic Information Systems An Introduction.
p 70. fig. 4.20.
9
Raster Cells Coding
A line number and column number define the cells
position in the raster data. The data are then
stored in a table giving the number and attribute
value of each cell.
Source Bernhardsen, Tor. (1999). 2nd Ed.
Geographic Information Systems An Introduction.
p 70. fig. 4.19.
10
Raster Data Model Method GRID/LUNR/MAGI
Source Demers, Michael. N. (2000). 2nd Ed.
Fundamentals of Geographic Information Systems.
p. 104.fig. 4.12 (a)..
11
Raster Data Model Method IMGRID GIS
Source Demers, Michael. N. (2000). 2nd Ed.
Fundamentals of Geographic Information Systems.
p. 104.fig. 4.12 (b).
12
Raster Data Model Method Map Analysis Package
(MAP)
Source Demers, Michael. N. (2000). 2nd Ed.
Fundamentals of Geographic Information Systems.
p. 104.fig. 4.12 (c).
13
Raster Data Input Presence/Absence Method
Source Demers, Michael. N. (2000). 2nd Ed.
Fundamentals of Geographic Information Systems.
p. 145. fig. 5.8 (a).
14
Raster Data Input Centroid-of-Cell Method
Source Demers, Michael. N. (2000). 2nd Ed.
Fundamentals of Geographic Information Systems.
p. 145. fig. 5.8 (b).
15
Raster Data Input Dominant Type Method
Source Demers, Michael. N. (2000). 2nd Ed.
Fundamentals of Geographic Information Systems.
p. 145. fig. 5.8 (c).
16
Raster Data Input Percent Occurrence Method
Source Demers, Michael. N. (2000). 2nd Ed.
Fundamentals of Geographic Information Systems.
p. 145. fig. 5.8 (d).
17
Raster Data Methods of Compacting

• Four common methods of storing data
• Run-length codes
• Raster chain codes
• Block codes
• Quadtrees

18
Compacting Data Model Run-length Encoding
Run-length encoding is the method used to save
data storage space by reducing a row of cells
with the same value to a single unit having a
specific value and quantity.
Source Bernhardsen, Tor. (1999). 2nd Ed.
Geographic Information Systems An Introduction.
p 73. fig. 4.23.
19
Compacting Data Model Run-length Encoding
Source Demers, Michael. N. (2000). 2nd Ed.
Fundamentals of Geographic Information Systems.
p. 107. fig. 4.13 (a).
20
Compacting Data Models Raster Chain Block
Codes
Raster Chain Codes
Block Codes
Source Demers, Michael. N. (2000). 2nd Ed.
Fundamentals of Geographic Information Systems.
p. 107. fig. 4.13 (b) and (c).
21
Compacting Data Model Quadtrees
Source Demers, Michael. N. (2000). 2nd Ed.
Fundamentals of Geographic Information Systems.
p. 107. fig. 4.13 (d).
22
A Few GIS Processes Functions Analysis tools

• Conversions
• Overlays
• Addition
• Buffering
• Neighborhood analysis
• View shed analysis

23
Conversion Vector to Raster
Conversion of vector data to raster data (a)
Coded polygons (b) a grid with the appropriate
cell size overlaid on top of the polygons (dots
represent the center of each grid cell (c) each
cell is assigned the attribute code of the
polygon to which it belongs.
Source Bernhardsen, Tor. (1999). 2nd Ed.
Geographic Information Systems An Introduction.
p 76. fig. 4.31.
24
Conversion Raster to Vector
Conversion of raster data to vector (a) each
raster cell is assigned an attribute value (b)
boundaries are set up between different attribute
classes (c) a polygon is created by storing x
and y coordinates for the points adjacent to the
boundaries.
Source Bernhardsen, Tor. (1999). 2nd Ed.
Geographic Information Systems An Introduction.
p 76. fig.4.30.
25
Conversion Errors
Image Source Clarke, Keith C. (2001). 3rd Ed.
Getting Started with Geographic Information
Systems. p 96.
26
Manual Overlay Site Suitability
Image Source Chrisman, Nicholas.(2002). 2nd Ed.
Exploring Geographic Information Systems. p 121.
fig. 5-1.
27
Raster Overlay Concept
Conceptual view of the raster overlay operation.
Any mathematical operator can be used on a pair
of values. Results become a new map layer suited
to the analysis at hand.
Image Source Chrisman, Nicholas.(2002). 2nd Ed.
Exploring Geographic Information Systems. p 124.
fig. 5-2.
28
Raster Overlay Boolean Combine
Sources in this image have been processed using a
centroid-to-cell rule. The output shows two
queries, each using two of the input sources.
Image Source Chrisman, Nicholas.(2002). 2nd Ed.
Exploring Geographic Information Systems. p 125.
fig. 5-3.
29
Raster Overlay Composite Combine
Image Source Chrisman, Nicholas.(2002). 2nd Ed.
Exploring Geographic Information Systems. p 126.
fig. 5-4.
30
Vector Overlay Composite Structure
A composite topological structure is constructed
by finding all intersections, then polygons are
labeled with a unique identifier linked to the
source attribute tables.
Image Source Chrisman, Nicholas.(2002). 2nd Ed.
Exploring Geographic Information Systems. p 127.
fig. 5-5.
31
Combining Attributes Rules
Image Source Chrisman, Nicholas.(2002). 2nd Ed.
Exploring Geographic Information Systems. p 132.
fig. 5-8.
32
Raster Layer Calculations Simple Addition
lt new map layer
Source Bernhardsen, Tor. (1999). 2nd Ed.
Geographic Information Systems An Introduction.
p 72. fig. 4.22.
33
Simple Addition Example
Ratings assigned to categories for a county Solid
Waste Plan. Ratings for grid cells were added to
obtain a composite ranking.
Image Source Chrisman, Nicholas.(2002). 2nd Ed.
Exploring Geographic Information Systems. p 144.
fig. 5-12.
34
Vector Distance Operation Buffers Setbacks
Diagram of simple buffers and a setback. NOTE
buffers go outward from lines or areas setbacks
run inside of areas (not lines).
Image Source Chrisman, Nicholas.(2002). 2nd Ed.
Exploring Geographic Information Systems. p 154.
fig. 6-1.
35
Buffer Creation Illustrated
Image Source Chrisman, Nicholas.(2002). 2nd Ed.
Exploring Geographic Information Systems. p 60.
fig. 6-3.
36
Raster Distance Operation Addition to cell
neighbors
Image Source Chrisman, Nicholas.(2002). 2nd Ed.
Exploring Geographic Information Systems. p161.
fig. 6-4.
37
Neighborhood Functions

• Total analysis - extended neighborhoods
• Targeted analysis - immediate neighborhoods
• Roving Windows Filters
• Depends on surface topology of the topography

38
View Shed Function
Image Source Chrisman, Nicholas.(2002). 2nd Ed.
Exploring Geographic Information Systems. p 198.
fig. 8-14.
39
DEMs
Image Source Clarke, Keith C. (2001). 3rd Ed.
Getting Started with Geographic Information
Systems. p 94. Fig. 3.17.
40
GIS Graphic Models Characteristic Differences
Source Bernhardsen, Tor. (1999). 2nd Ed.
Geographic Information Systems An Introduction.
p 7___. fig. 4.32.
41
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

42
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.

43
Spatial Resolution Selected Satellite Systems
Image SourceKorte GIS Book. p 77
44
Spectral Resolution Selected Satellite Systems
Image SourceKorte GIS Book. p 78
45
Digital Orthophoto Example
Image SourceKorte GIS Book. p 75
46
Extent/Scale/Resolution Selected Satellite
Systems
Image SourceKorte GIS Book. p 79