Spatial databases


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Spatial Data Mining

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Spatial databases

  1. 1. SpatialDatabasesPresented By:Nazir
  2. 2. Outline Introduction Spatial data mining Spatial Data warehouse and OLAP Mining Spatial Association Patterns Spatial Clustering Spatial Classification Spatial Trend Analysis
  3. 3. Spatial Databases: Introduction A Spatial Database large amount of space-relateddata. data, such as maps, preprocessed remotesensing or medical imaging data, and VLSI chip layoutdata. It offers spatial data types (SDTs) in its data model andquery language; e.g. POINT, LINE, REGION.
  4. 4. What is represented Objects in space e.g. forests , rivers Space
  5. 5. Spatial Data Mining Spatial data mining refers to the extraction ofknowledge, spatial relationships, or other interestingpatterns not explicitly stored in spatial databases. Suchmining demands an integration of data mining withspatial database technologies. It can be used for understanding spatial data, discoveringspatial relationships and relationships between spatial andnon-spatial data, constructing spatial knowledgebases, reorganizing spatial databases, and optimizingspatial queries.
  6. 6. Spatial Data WarehouseSchema and spatial OLAP A spatial data warehouse is a subject-oriented, integrated, time-variant, and nonvolatilecollection of both spatial and non-spatial data insupport of spatial data mining and spatial-datarelated decision-making processes.
  7. 7.  Star schema is a good choice for modeling spatialdata warehouse. However, in a spatialwarehouse, both dimensions and measures maycontain spatial components.
  8. 8. There are three types of dimensions in a spatial data cube:o A non-spatial dimension contains only non-spatial data.E.g. Temperature , Precipitation. A spatial-to-nonspatial dimension is a dimension whoseprimitive-level data are spatial but whosegeneralization, starting at a certain high level, becomesnon-spatial. A spatial-to-spatial dimension is a dimension whoseprimitive level and all of its high level generalized data arespatial.
  9. 9. two types of measures in a spatial data cube: A numerical measure contains only numerical data. Forexample, one measure in a spatial data warehouse couldbe the monthly revenue of a region, so that a roll-up maycompute the total revenue by year, by county, and so on. A spatial measure contains a collection of pointers tospatial objects.
  10. 10. Mining Spatial AssociationPatterns A spatial association rule is of the form A)B [s%;c%], whereA and B are sets of spatial or non-spatial predicates, s% isthe support of the rule, and c% is the confidence of therule. For example, the following is a spatial associationrule:is_a(X; “school”)^close_to(X; “sports center”)=> close_to(X;“park”) [0.5%, 80%].This rule states that 80% of schools that are close to sports centersare also close to parks, and 0.5% of the data belongs to such acase.
  11. 11. Progressive Refinement It is a mining Optimization method which can be adoptedin spatial analysis. The method first mines large data sets roughly using a fastalgorithm and then improves the quality of mining in apruned data set using a more expensive algorithm.
  12. 12.  To ensure that the pruned data set covers thecomplete set of answers when applying the high-quality data mining algorithms at a later stage, animportant requirement for the rough miningalgorithm applied in the early stage is the supersetcoverage property: that is, it preserves all of thepotential answers. In other words, it should allow afalse-positive test.
  13. 13. Clustering Spatial data clustering identifies clusters, or denselypopulated regions, according to some distancemeasurement in a large, multidimensional data set.
  14. 14. Spatial Classification Spatial classification analyzes spatial objects to deriveclassification schemes in relevance to certain spatialproperties, such as the neighborhood of adistrict, highway, or river. Spatial
  15. 15. Spatial Trend Analysis Spatial trend analysis deals with another issue: the detectionof changes and trends along a spatial dimension.Typically, trend analysis detects changes with time, such asthe changes of temporal patterns in time-series data. Spatial trend analysis replaces time with space and studiesthe trend of nonspatial or spatial data changing withspace. E.g. you may observe the trend of changes of theclimate or vegetation with the increasing distance from anocean.
  16. 16. Thank You?