This document discusses raster and vector data models. It provides details on the raster data model including that it uses a grid structure with cells that can each hold a value. Features are represented by single cells for points, sequences of cells for lines, and collections of cells for areas. The document also discusses raster coding methods like presence/absence and dominant area, types of raster data like DEMs and DOQs, raster data structures including cell-by-cell and run length encoding, and the storage space versus resolution tradeoff of rasters.

1. Lecture 06
Raster and Vector Data Models
Part (1)
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Common Data Models
Vector Raster
Y Points Points
( x,y )
Area Area
Line Line
X
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2. • Raster uses a grid cell structure
3 • Vector is more like a drawn map
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3. 5
Raster Data Model
A raster represents a continuous
surface, but for data storage and
analysis, a raster is divided into rows,
columns, and cells.
Raster data represent points by single
cells, lines by sequences of neighboring
cells, and areas by collections of
contiguous cells.
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4. A continuous elevation raster with darker shades for higher elevations.
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Representation of
point, line, and
area features:
raster format on
the left and vector
format on the right.
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5. Raster Data Model
• A raster data model uses a grid.
• One grid cell is one unit or holds one attribute.
• Every cell has a value, even if it is “missing.”
• A cell can hold a number or an index value
standing for an attribute.
• A cell has a resolution, given as the cell size in
ground units.
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Generic structure for a grid
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6. Elements of Raster Data Model
1. Cell value. Each cell in a raster carries a value, which
represents the characteristic of a spatial phenomenon at
the location denoted by its row and column. The cell
value can be integer or floating-point.
2. Cell size. The cell size determines the resolution of the
raster data model.
3. Raster bands. A raster may have a single band or
multiple bands.
4. Spatial reference. Raster data must have the spatial
reference information so that they can align spatially with
other data sets in a GIS.
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Raster – The Mixed Pixel Problem
Land cover map –
Two classes, land
or water
Cell A is
straightforward
What category to
assign for B, C, or
D?
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7. Raster Coding
In the data entry process, maps can be
digitized or scanned at a selected cell size
and each cell assigned a code or a value.
The methods for assigning cell codes are:
1. Presence/Absence
2. Cell Center
3. Dominant Area
4. Percent Coverage (advanced method)
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Presence/Absence
The most basic method is to record a feature
if some of it occurs in the cell space.
The only practical way of coding point and
line features, because they don’t take up much
area of a cell.
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8. Cell Center
This method involves reading only the
center of the cell and assigning the code
accordingly.
When there are multiple features in a cell
area, the one in the center wins the code.
This is no a good scheme for points or
lines, because they don’t necessarily pass
through the exact center.
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Dominant Area
A common method is to assign the cell
code to the feature with the largest
(dominant) share of the cell.
This is suitable primarily for polygons,
although line features could be assigned
according to which one has the most linear
distance in a cell.
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Types of Raster Data
1. Satellite Imagery: Imagery acquired from
satellites such as Landsat and Spot.
2. Digital Elevation Models (DEMs): The
representation of continuous elevation values
over a topographic surface by a regular array of
z-values referenced to a common datum.
3. Digital Orthophotos Quadrangles (DOQ): The
scanned photographic images that have been
corrected for distortions due to camera tilt,
terrain displacement, and other factors.
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10. Types of Raster Data (continued)
4. Bi-Level Scanned Files: The scanned images
containing values of 1 and 0, they are usually
scanned from paper maps.
5. Digital Raster Graphics (DRGs): The scanned
images of USGS topographic maps.
6. Graphic Files: Maps, photographs, and images
can be stored as raster graphic files such as
TIFF (tagged image file format),
GIDF( graphics interchange format), and
JPEG( joint photographic expert group).
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Types of Raster Data (continued)
7. GIS Software-Specific Raster Data: GIS
packages use raster data that are
imported from DEMs, satellite images,
scanned images, graphic files, and ASCII
files or are converted from vector data.
The GIS software packages store raster
data in different formats, for example,
ArcGIS stores raster data in the ESRI
Grid format.
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11. DEMs at three resolutions: 30 meters, 10 meters, and 3 meters. The 30-m and
10-m DEMs are USGS DEMs. The 3-m DEM is a derived product from LIDAR
data.
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USGS 1-meter black-and-white DOQ for Sun Valley, Idaho.
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12. A bi-level scanned file showing soil lines.
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USGS DRG for Sun Valley, Idaho.
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13. Raster Data Structures
– Raster data stored as an array of values
• Georeferencing is implicit in the structure
• Usually defined by one corner of the image and
the cell size
• Attributes are defined by the cell values (no
character data!)
• One attribute for each raster file
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Raster Data Structure (continued)
Raster data structure refers to the method or
format for storing raster data.
The three common methods for storing raster
data are:
1. Cell-by-Cell Encoding
2. Run Length Encoding
3. Quad Tree
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14. Cell-by-Cell Encoding
The cell-by-cell encoding
method provides the
simplest raster data
structure.
A raster is stored as a
matrix, and its cell values
are written into a file by
row and column.
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Run- Length Encoding (RLE)
The run length encoding method
is a raster data structure that
records the cell values by row and
by group.
This method records the cell
values in runs. Row 1, for example,
has two adjacent cells in columns 5
and 6 that are gray or have the
value of 1. Row 1 is therefore
encoded with one run, beginning in
column 5 and ending in column 6.
The same method is used to record
other rows.
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15. Quad Tree
The regional quad tree
method divides a raster into
a hierarchy of quadrants.
The division stops when a
quadrant is made of cells of
the same value (gray or
white).
A quadrant that cannot be
subdivided is called a leaf
node.
In the diagram, the
quadrants are indexed
spatially: 0 for NW, 1 for
SW, 2 for SE, and 3 for NE.
Using the spatial indexing
method and the hierarchical
quad tree structure, the
gray cells can be coded as
02, 032, and so on.
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Rasters: The Storage Space/Resolution Tradeoff
Decreasing the cell size by one-fourth causes a
four-fold increase in the storage space
required for the raster data.
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16. Raster Data Compression
Data compression refers to the reduction of data
volume.
A variety of techniques are available for image
compression. Compression techniques can be
lossless or lossy.
Lossless Compression: One type of data
compression that allows the original image to be
precisely reconstructed.
Lossy Compression: One type of data
compression that can achieve high compression
ratios but cannot reconstruct fully the original
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Raster – Advantages and
Disadvantages
• Advantages
– Simple data structure
– Easy overlay
– Various kinds of spatial analysis
– Uniform size and shape
– Cheaper technology
• Disadvantages
– Large amount of data
– Less “pretty”
– Projection transformation is difficult
– Different scales between layers can be a nightmare
– May lose information due to generalization
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