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Spatial Data Mining- Applications

Spatial Data Mining- Applications. Hemant Kumar Jerath, B.Tech. MS Project Student Mangalore University Advisors: Dr. B.K Mohan & Dr.(Mrs.).P. Venkatachalam CSRE, IIT Bombay. Beyond Conventional GIS Analysis: Spatial Analysis, Geospatial Data Mining, and Knowledge Discovery.

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Spatial Data Mining- Applications

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  1. Spatial Data Mining-Applications Hemant Kumar Jerath,B.Tech. MS Project Student Mangalore University Advisors: Dr. B.K Mohan & Dr.(Mrs.).P. Venkatachalam CSRE, IIT Bombay

  2. Beyond Conventional GIS Analysis: Spatial Analysis, Geospatial Data Mining, and Knowledge Discovery

  3. Conventional GIS Operations Retrieve Spatial and Attribute Data Measure Descriptive Statistics Classification Overlay Buffer Network Analyses

  4. Conventional GIS Operations: Attribute Query Where do minorities live in New Jersey? Which census tracts have percent minority > 25%

  5. Conventional GIS Operations: Attribute Query SQL Structured (Standard) Query Language Formal language for interacting with relational databases SELECT <fields> FROM <tables> WHERE <condition> SELECT %Min FROM CensusTracts WHERE %Min > 0.25

  6. Tougher GIS Operations: Finding Spatial Relationships in Very Large, Spatio-Temporal, Multi-Dimensional Data Sets

  7. Some Tougher Questions… • Are hazardous facilities clustered in certain areas? If so, what are the socioeconomic characteristics associated with these clusters? • What is the influence of degree of urbanization, proximity to transportation, and industrial land use on the relationship between locations of hazardous facilities and the racial and other socioeconomic characteristics of the communities that host these facilities?

  8. Exploratory Spatial Data Analysis (ESDA) • The sophisticated quantitative analysis of spatial and spatio-temporal data, typically involving interactive and dynamic interfaces. • Related terms: • Spatial Statistics • Spatial Analysis

  9. Geospatial Data Mining (DM) and Knowledge Discovery (KD) • A process to discover hidden facts and useful information contained in databases. • Related terms: • Machine Learning • Pattern Recognition

  10. Some ESDA, DM, and KD Operations Finding Spatial and Multi-Dimensional Clusters Summarizing Variables by Other Variables Finding Associations Among Attributes Predicting Values Feature Extraction

  11. GeneralizationDr.Hans,SMU • 500 weather probes • Monthly mean temp and precipitation • Weather pattern of season 1990 • 18,000 tuples • Attribute Induction • Non-spatial(merging of tuples based on non-spatial concept hierarchies) • Spatial(merging of spatial objects based on the concept hierarchies-spatial region merging and/or spatial approximation)

  12. Spatial Associative Rules • Finding large towns and nearby objects like mines, country boundary, water(sea, lake, or river) and major highways. • Generalisation of the above spatial objects

  13. Spatial Clustering • A request to all expensive houses in an area. • Finding down the relationships of the clusters with other spatial objects like roads, different land-use area

  14. Clustering<house type,price,size> Distribution

  15. Spatial Data Mining: A Database ApproachMartin Ester, Hans-Peter Kriegel, Jorg Sander • Step I: Discover centers based on some non-spatial attribute[clustering-descriptive mining] • Step II: determine the (theoretical) trend of some non-spatial attribute. • Step III: discover the deviation of the theoretical trends • Step IV: explain the deviation by the spatial object, e.g. may be presence of some infrastructure.

  16. Spatial Classification • Non-spatial attribute e.g. no. of salespersons in a store • Spatially related attribute with non-spatial values, like population living within 1km from store • Spatial predicates, like • Distance_less_than_10km(X,a) • Spatial function, like driving_distance(X,beach)

  17. Decision Tree Description of census block group Buffers are defined For Trade Area Description of classified objects

  18. Classification is developed using ID3 algorithm

  19. High_profit=N High_profit=Y

  20. Geo-spatial Data Mining and KDD using Decision Tree Algorithm-A case study of soil data setsJianting Zang, Diansheng Guo, Qing Wan

  21. Association Rule Mining on RSI using P-TreesQin Ding, Qiang Ding, William Perrizo • Identification of high and low crop yield • Insect and weed infestations • Nutrient requirement • Flood damage assess

  22. ILP, SPADA(Spatial Pattern Discovery Algorithm)Donalto Malerba, Francesca A Lisi • Find associations between • Reference objects (Towns) • Task Relevant Objects ( road network, hydrography and administration layers) • Stockport Census data • Socio-Economic phenomenon • Census data (80 tables, 120 attributes)

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