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Interoperating with GIS and Statistical Environment for an Interactive Spatial Data Mining

Interoperating with GIS and Statistical Environment for an Interactive Spatial Data Mining. Didier Josselin, THEMA, UPRESA 6049 du CNRS, Besançon, GDR CASSINI didier.josselin@univ-fcomte.fr http://thema.univ-fcomte.fr/didier.htm Xlisp-Stat programming :

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Interoperating with GIS and Statistical Environment for an Interactive Spatial Data Mining

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  1. Interoperating with GIS and Statistical Environmentfor an Interactive Spatial Data Mining Didier Josselin, THEMA, UPRESA 6049 du CNRS, Besançon, GDR CASSINI didier.josselin@univ-fcomte.fr http://thema.univ-fcomte.fr/didier.htm Xlisp-Statprogramming : @ D. Betz, L. Tierney, C. Brunsdon, D. Josselin, L. Guerre, B. Dancuo

  2. French Research Group about GIS(1990-2000 : GDR CASSINI, 2000...?)

  3. The spatial data mining quest Finding significant relations between geographical objects in order to cluster them

  4. Examples of geographical purpose

  5. Sub-objectives at geographical entityscale • 1st door : the statistical dependency some entities have common characteristics... • 2nd door : the spatial relation some entities are contiguous, closed from each others… • 3rd door : the combinationof spatial and statistical relation some entities are similar and closed...

  6. Sub-objectives at territory and geographical space scale • 1st door : the spatial cutting out and data aggregation : a succession of deriving ... Analysing spatial repartition, Identifiing gradients, Detecting discontinuities... • 2nd door : the spatial auto-correlation measure Global and local • 3rd door : the identification of geographical composite (heterogeneous) entities

  7. Geographical agricultural flows analysis

  8. Agricultural flows between French communes Commune A Commune B

  9. Various flows status

  10. Outgoing flows in Franche-Comté

  11. Commune aggregate with its key and boundary Commune described by an attribute Commune couple flow What are we looking for ?

  12. Which softwares may be available and convenient ?

  13. Geographical InformationSystems

  14. + • Various structured query languages • Existing tools to build clean structured databases • Graphical and mapping functionalities • generally open to other softwares

  15. - • Poor in statistical functions • Rarely integrate Exploratory Data Analysis • Need to write queries rather execute them in a graphic way

  16. ESDA Environment

  17. + • Numerous statistical functions • Numerous graphic representations • Ease to select objects on screen • Dynamic link between objects • generally open to development by programming

  18. - • Poor in geographical and semiologic functionalities • Does not integrate structured databases functions • Does not include geometrical or topological models

  19. Any solutions ?

  20. Modifying existing softwares

  21. First methodological choice Adding to a statistical environment some mapping and relational functionalitiesARPEGE’ : a tool to Analyse Robustly in Practice and Explore Geographical Environment (XlispStat)

  22. The « visioner » in ARPEGE’

  23. Using ARPEGE’ to analyse flows

  24. + • Dynamic link between multiple objects • Relative fastness to support expert decision making • Facilities to implement relations and triggers between objects • Possibility to focus on many crossed selections

  25. - • Difficult to manage with multiscaling • Users may miss some synthetic statistical indicators or automatic methods • Application must be quite simple (RAM limitations) • Combinatory explosion risk !

  26. Coupling two complementary softwares

  27. Second methodological choice Interoperating with a GIS and a statistical environment softwareLAVSTAT : a dynamic Link between ArcView and XlispSTAT

  28. Interaction

  29. executing LAVSTAT principles Services, DDE Dynamic link with AVLINK Server connecting XlispStat importing ArcView modifying

  30. + • Dynamic link between GIS and Statistical software • The whole functionalities access to both systems • Increases the ways to investigate spatial data

  31. - • A screen is not enough to explore data • A few time loss to make interoperating the two softwares • Not already stable (memory conflicts)

  32. CONCLUSION

  33. A few advices for spatial analysis to take reliant decisions in order to shape the future ...

  34. If you have some objectives to reach with data to explore...

  35. Choose the appropriate methods ... 0

  36. Keep a critical look on tools and methods ... 1

  37. Choose most robusts methods to analyse your data ... 2

  38. Check hypothesis withouttoo tight assumptions 3

  39. Try to dominate time during anaysis and to be inside learning process ... 4

  40. Keep in touch with all individual data 5

  41. Bring to light all aspects of your problem by multiple representations 6

  42. Use dynamic links and interactivity 7

  43. Study the fringe as the trend... 8

  44. ...and model deviation, residuals ... 9 7

  45. … and relations between geographical objects through different scales... 10

  46. … which may be well defined (semantic,topology, structural, functional ...) 11

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