GIS & RS Applications
Geographic information and spatial data types
Geographic phenomenon

Is a manifestation of an entity /body/ or process of interest that
 Can be named or described
 Can be georeferenced
 Can be a assigned a time (interval) at which it is/was present
 Geographic phenomena: exists in the real world, using GIS this phenomena can be
represented by a computer
Three ways in which we can look at the objects of study in a GIS application
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Geographic information and spatial data
types

Representation of a phenomena in a GIS requires us to
state:
what it is?, and
where it is?
We must provide a description or at least a name on the
one hand, and a georeference on the other hand.

Some phenomena manifest themselves essentially
everywhere in the study area, while others only do so in
certain locations
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Geographic information and spatial data
types
Types of geographic phenomena

A geographic field: for every point in the study area, a
value can be determined. Some common examples are air
temperature, barometric pressure and elevation.
 Geographic objects: populate the study area, and are
usually well-distinguished, discrete, and bounded entities.
Ex’s are building, road network, lakes, rivers etc.
 Generally: man-made phenomena are usually geographic
objects. Many exceptions to this rule actually exists, so one
must be careful in applying it.
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Geographic information and spatial data
types
Geographic field
Has a value ‘everywhere’ in the study area.
 Like a mathematical function f(x,y), any position in the study
area a specific value can be found.
 Geographic field can be discrete or continuous
Continuous field: the underlying function is assumed to be
mathematically smooth /don't change abruptly, only gradual
air temperature, barometric pressure, soil salinity and elevation.
Discrete fields: cutup the study area in mutually exclusive bounded
parts, which all location in one part have the same value
Land classification, geological class, soil type, land use etc

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Geographic field
A continuous field example of
Lake Tana rainfall
A discrete field example of Lake
Tana soil map
Geographic information and spatial data
types
Geographic object
Geographic phenomena is not present
everywhere in the study area, but somehow
sparsely populates it.
 Easily distinguished and named , there position in
space is determined by:





Location (where?)
Shape (what form?)
Size (how big?)
Orientation (which direction?)
Example: Road network, petrol station, land parcels, metrological
stations, rivers, lakes etc
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Geographic object
Main and minor road network
Metrological stations in lake
Tana basin
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DATA Representation
►Vector
data
►Raster
data
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Vector Data
►Information
for these features can
be divided in three parts:
 Information about the geographical
objects – X, Y and Z co-ordinates
 Attribute data
 Additional information – Accuracy,
Scale, Producer, Production year, etc
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Vector Data
►In
vector GIS, geographical data are
points, lines and polygons
►Features are separated from each
other by defined borderlines
►Example
are rainfall
station, streams in a
watershed, agricultural
plots in a given piece of
land
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Vector representation
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Vector Data
►The
point has no spatial extent
►Represented by a single pair of coordinates
►For simple line the vector model stores
the start point and the end point
►For complex lines several pairs of
coordinates, one at each breakpoint,
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Topological structure
(x , y)
►Point
►Simple
line
►Complex
►Storing
line
(x1 ,y1)
(x1 ,y1)
(x2 ,y2)
(x2 ,y2)
(x4 ,y4)
(x3 ,y3)
of polygon features is similar
to line storing
What feature will you
expect if you know that
►there
is only one pair of coordinates ???
►the
co-ordinates for the start node
and stop node are the same???
►the object is composed of several
pairs of co-ordinates ???
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Vector Data
►Depending
on the map scale, an
object in the real world may be
represented by different geometric
objects
Think about




City on a world map
City on a more detailed map
Highway on a national atlas
Highway on city map
Generalization
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Vector Data
Advantages are:
►Exact
►Fast for processing
►Good for visualization
►Compact in terms of data structure
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Raster Data
►A
raster data represents the real world
A
with no distinct boarder lines
►Same
features represented in
vector structure can also be
represented by raster structure
Rainfall station, streams in a
watershed, agricultural plots in
a given piece of land
A
B
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C
Raster Data
►Suitable
for
continuous data,
representing
phenomena such
as temperature,
elevation, or
spectral data such
as satellite images
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Raster representation
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Raster data
►A
raster database is made of
columns and rows
►Intersection of a
row with a column
forms the cell
►Cell resolution
implies
dimensions of
the cell
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Creating raster data
►When
creating a raster database,
the first step is to decide the
resolution of the grid (the size of
the cells)
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Data size issues
►Raster
data requires a lot of
storing space in your computer
because the raster structure
does not allow for “empty” cells
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Deficiencies
►Information
about the exact
location of linear objects is lost
►Fuzzy boundaries
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Typical example
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Real world
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Raster Data
Advantages are:
►Way of storing is quite simple and
easy to understand,
►Is very efficient for representing
continuous surfaces
►Particularly suitable for combining
with remote sensing data
►Enable overlay analysis
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Example on overlay analysis
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Example on
overlay analysis
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End of chapter
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