1. GEOINFO 2006 Utilização da biblioteca TerraLib para algoritmos de agrupamento em Sistemas de Informações Geográficas Mauricio P. Guidini Carlos H. C. Ribeiro Nov 2006 Supervisor Use of the TerraLib library for clustering algorithms in Geographic Information Systems

2. 25/10/2004 “... 3000 unregistered flights, with origin and destiny unkown by authorities, invaded the Brazilian airspace in the first ten months of this year. The Air Force calculates that about 30% of these flights were related to drug dealing... Translated from note from

3. 3 Data Mining in GIS  Objetive To present the integration of a Data Mining algorithm (k-means) to TerraLib/TerraView, forming a Geographic Information System for Unknown Air Traffic analysis (GisTAD).

4. 4  Summary  Data Mining  Clustering Algorithms  Air Traffic  K-means Implementation  Results  Aplication Data Mining in GIS

5. 5 Data Mining Definition: “A non-trivial process of identification of valid, new, useful standards implicitly present in large volumes of data” Knowledge Discovery in Database (KDD) - Fayyad et al. (1996) Data Mining in GIS

6. 6  How proceed DM?  KDD process Data Mining in GIS

7. 7 Clustering Algorithms The clustering process tries to grouping the data into groups that have highly similar features, helping the understanding of the information that they hold. A good clustering algorithm is characterized by the production of high level classes, where the intraclass similarity is high, and the interclass similarity is low. [Han & Kamber 2001] Data Mining in GIS

8. 8  Major Categories  Partitioning – k-means, k-medoids  Hierarchical – CURE, BIRCH  Density-based – DBSCAN, OPTICS  Grid-based – STING  Model-based  Others  ANN – Kohonen network  Incremental - Leader

9. 9 Data Mining in GIS  Air Traffic Movement of aircraft, national or foreign, that fly over national territory.  Unkown Air Traffic To unidentified airplanes (flight plan), two lines of action can be taken[Bernabeu 2004]: 1.Intercept; or 2.Generate an Unkown Air Traffic Report

10. 10 Traffic Representation  Line segments  Latitude (decimal degrees)  Longitude (decimal degrees)  Distance (miles)  Heading Restrictions  Acceptable deviations Data Mining in GIS

11. 11 K-means algorithm Data Mining in GIS Precondition: set max deviation values to coordinates, distance and route Begin: K=0 While criterion condition not satisfied (deviation in clusters) Increase K Arbitrarily choose K centers (among data objects) While centers change (k-means) (re)assign routes in cluster based on weights update centers values end movement intergroups deviation in groups ok Save results End

12. 12 Distance Measure Data Mining in GIS Minimize deviations Improve cluster quality and

13. 13 GIS Integration  TerraLib  TerraView  k-means Data Mining in GIS

14. 14  Data preparation  8000 records  looking for information (what?) Data Mining in GIS Search space restrictions Search space restrictions

15. 15  Numeric Tests  to 500 records  GisTAD Tests  319 records  73 groups  Aprox. time = 40 sec. Data Mining in GIS

16. 16 TerraView

17. 17 TerraView

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19. 19 Applications  Air Operations  Improper use of air space Data Mining in GIS

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21. 21 Data Mining in GIS Conclusion Considering the problem proposed, the k-means algorithm is applicable, and returned a good set of clusters. However, the number of records that must be clustered can make the application of the algorithm very time consuming.

22. 22 Future Work Other partitioning algorithms should be implemented, to verify which one is the most efficient for the problem in analysis, considering any size of records to be clustered. The algorithms to be tested are:  Kohonen neural network;  Leader algorithm.