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What are the primitives of spatial data mining?
Spatial data mining is the application of data mining to spatial models. In spatial data mining, analysts use geographical or spatial data to make business intelligence or different results. This needed specific methods and resources to get the geographical data into relevant and beneficial formats.
There are several challenges involved in spatial data mining include recognizing patterns or discovering objects that are relevant to the questions that drive the research project. Analysts can be viewed in a large database area or other completely huge data set to discover only the relevant data, utilizing GIS/GPS tools or similar systems.
The primitives of spatial data mining are as follows −
Rules − There are several types of rules that can be found from databases in general. For example characteristic rules, discriminant rules, association rules, or deviation and evaluation rules can be mined.
A Spatial characteristic rule is a general representation of the spatial data. For instance, a rule defining the general cost range of houses in several geographic areas in a city is a spatial characteristic rule.
A discriminant rule is the usual representation of the features discriminating or contrasting a class of spatial records from different classes like the comparison of cost ranges of houses in several geographical areas.
A spatial association rule is a rule which defines the association of one group of features by another group of features in spatial databases. For instance, a rule associating the cost range of the houses with nearby spatial characteristics, such as beaches, is a spatial association rule.
Thematic Maps − Thematic map is a map generally designed to display a theme, an individual spatial distribution, or a pattern, using a definite map type. These maps display the distribution of features over limited geographical regions. Each map represents a partitioning of the area into a group of closed and disjoint areas; each contains all the points with a similar feature value.
Thematic maps show the spatial distribution of an individual or a few attributes. This differs from general or reference maps where the goal is to present the position of the object about different spatial objects. Thematic maps can be used for finding multiple rules.
For instance, it can look at a temperature thematic map while analyzing the general weather pattern of a geographic area. There are two methods to represent thematic maps including Raster, and Vector
In the raster image form, thematic maps have pixels related to the attribute values. For instance, a map can have the altitude of the spatial objects program as the depth of the pixel (or the color).
In the vector description, a spatial object is defined by its geometry, most generally being the boundary definition along with the thematic attributes. For example, a park may be represented by the boundary points and corresponding elevation values.
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