Spatial databases contain spatial-related information. Examples include geographic
(map) databases, very large-scale integration (VLSI) or computed-aided design
databases, and medical and satellite image databases. Spatial data may be
represented in raster format, consisting of n-dimensional bit maps or pixel maps.
For example, a 2-D satellite image may be represented as raster data, where each
pixel registers the rainfall in a given area. Maps can be represented in vector
format, where roads, bridges, buildings, and lakes are represented as unions or
overlays of basic geometric constructs, such as points, lines, polygons, and the
partitions and networks formed by these components.
Geographic databases have numerous applications, ranging from forestry and ecology
planning to providing public service information regarding the location of telephone
and electric cables, pipes, and sewage systems. In addition, geographic databases
are commonly used in vehicle navigation and dispatching systems. An example of
such a system for taxis would store a city map with information regarding one-way
streets, suggested routes for moving from region A to region B during rush hour, and
the location of restaurants and hospitals, as well as the current location of each driver.
“What kind of data mining can be performed on spatial databases?” you may ask.
Data mining may uncover patterns describing the characteristics of houses located
near a specified kind of location, such as a park, for instance. Other patterns
may describe the climate of mountainous areas located at various altitudes, or
describe the change in trend of metropolitan poverty rates based on city distances
from major highways. The relationships among a set of spatial objects can be
examined in order to discover which subsets of objects are spatially auto
correlated or associated. Clusters and outliers can be identified by spatial
cluster analysis.Moreover, spatial classification can be performed to construct
models for prediction based on the relevant set of features of the spatial objects.
Furthermore, “spatial data cubes” may be constructed to organize data into
multidimensional structures and hierarchies, on which OLAP operations
(such as drill-down and roll-up) can be performed.
A spatial database that stores spatial objects that change with time is called a
spatiotemporal database, from which interesting information can be mined. For
example, we may be able to group the trends of moving objects and identify
some strangely moving vehicles, or distinguish a bioterrorist attack from a
normal outbreak of the flu based on the geographic spread of a disease with time.
(map) databases, very large-scale integration (VLSI) or computed-aided design
databases, and medical and satellite image databases. Spatial data may be
represented in raster format, consisting of n-dimensional bit maps or pixel maps.
For example, a 2-D satellite image may be represented as raster data, where each
pixel registers the rainfall in a given area. Maps can be represented in vector
format, where roads, bridges, buildings, and lakes are represented as unions or
overlays of basic geometric constructs, such as points, lines, polygons, and the
partitions and networks formed by these components.
Geographic databases have numerous applications, ranging from forestry and ecology
planning to providing public service information regarding the location of telephone
and electric cables, pipes, and sewage systems. In addition, geographic databases
are commonly used in vehicle navigation and dispatching systems. An example of
such a system for taxis would store a city map with information regarding one-way
streets, suggested routes for moving from region A to region B during rush hour, and
the location of restaurants and hospitals, as well as the current location of each driver.
“What kind of data mining can be performed on spatial databases?” you may ask.
Data mining may uncover patterns describing the characteristics of houses located
near a specified kind of location, such as a park, for instance. Other patterns
may describe the climate of mountainous areas located at various altitudes, or
describe the change in trend of metropolitan poverty rates based on city distances
from major highways. The relationships among a set of spatial objects can be
examined in order to discover which subsets of objects are spatially auto
correlated or associated. Clusters and outliers can be identified by spatial
cluster analysis.Moreover, spatial classification can be performed to construct
models for prediction based on the relevant set of features of the spatial objects.
Furthermore, “spatial data cubes” may be constructed to organize data into
multidimensional structures and hierarchies, on which OLAP operations
(such as drill-down and roll-up) can be performed.
A spatial database that stores spatial objects that change with time is called a
spatiotemporal database, from which interesting information can be mined. For
example, we may be able to group the trends of moving objects and identify
some strangely moving vehicles, or distinguish a bioterrorist attack from a
normal outbreak of the flu based on the geographic spread of a disease with time.
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