1. 1Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Data Enrichment for Adaptive Generalization from a Multiresolution Database Moritz Neun SNF-Project DEGEN 4/2004 - 4/2007

2. 2Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Context DEGEN = Data Enrichment for the Control of the Generalization Process (Stefan Steiniger) & Data Enrichment for Adaptive Generalization from a Multiresolution Database (Moritz Neun)

3. 3Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Summary 1.Introduction 2.Data Enrichment Defining Relations Classifying and Modeling Relations Extracting Relations Representing Relations Exploiting Relations 3.Time Table, Conferences & Publications 4.Conclusion Slides english Präsentation deutsch

4. 44Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 1. Introduction

5. 5Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Generalization

6. 6Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Generalization

7. 7Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Data Enrichment... data enrichment is necessary to equip the ”raw” spatial data with additional information which can be used for a variety of purposes within the overall generalization process: characterization (priority, groups, relationships) conflict detection algorithm and parameter selection

8. 8Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Multiresolution Databases (MRDB) Multiresolution ≠ Multirepresentation Different Levels of Detail (LOD) are stored in one Database. Common for web mapping services (zooming) Important for Generalization Objects on different LODs are linked Database Technologies Object Oriented (e.g. Gothic) (Object) Relational (e.g. ArcSDE)

9. 9Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Thematic Maps Most research in generalization on topographic maps  majority of maps are of thematic nature (categorical, GIS, facilities, networks, POI...)  focus on thematic maps with polygons in a generic approach Examples: geology, landuse, statistics, administration

10. 10Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Research Purpose The purpose of DEGEN is data enrichment, the modeling of the enriched data and the exploitation of this enriched data for generalizing thematic maps

11. 11 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Data Enrichment: 2.1 Defining Relations

12. 12Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Definitions Relations are a kind of property defined between two modifiable object types... A relation can be one-to-one, one-to-many or many-to-many... Map Objects are the representation of a real world objects in the map data model. We distinguish simple and complex map objects (groupings). Each map object consists of its semantics (name, attributes,...), its geometry and its topology.

13. 13Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Horizontal & Vertical Relations Horizontal relations of map objects exist within one specific scale or level of detail (LOD) and represent common structural properties. Vertical relations are links between single map objects or groups of map objects between different map scales and LODs.

14. 14Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Horizontal Relations Horizontal Relations GeometryTopologyStructureSemantics Statistics & Density Presented last semester by Stefan Steiniger  5 groups of measures for expressing horizontal relations

15. 15Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Vertical Relations changes between single map objects changes of properties for the whole LOD  link map objects across different LODs  enrich the links with additional information about their characteristics (properties) Vertical Relations map object relations identity relation (micro object) group relation (meso object) LOD relations

16. 16Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Using Relations Interpolation of intermediate scale levels (Cecconi 2003) e.g. in combination with morphing Incremental updating of lower detailed LODs (Kilpeläinen and Sarjakoski 1995) choice of appropirate algorithms more information about parameters for algorithms better evaluation of results Training and use of learning algorithms (inductive, neuronal) by analyzing relations and properties (Weibel et al. 1995)...

17. 17Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Working Hypothesis The integration of enriched information into a MRDB allows the use of more sophisticated generalization algorithms, accelerates adaptive generalization, and helps to determine and maintain important structures across different scale levels. This enriched information can be gained by analyzing, modeling and extracting relations between map objects. Vertical Relations, being links between map objects on two different LODs, are representing abstract knowledge about the generalization from the higher to the lower map scale. Classification of Relations Modeling of Relations Extraction of Relations Storage of Relations Exploitation of Relations

18. 18Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Research Questions What types of “vertical” relations between map objects on different levels of detail can be established? How can these relations effectively be modelled and represented in a multiresolution database? How can the map objects in two levels of detail be matched and the enriching relations and their attributes be gained? How can the relations and the matching process be managed and the relations be deployed? Can these vertical relations be used for the creation of intermediate levels of details? Can the same relations also be used for incremental Generalization?

19. 19 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 2.2 Classifying and Modeling Relations Classification of Relations Modeling of Relations Extraction of Relations Storage of Relations Exploitation of Relations

20. 20Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Vertical Relations procedural knowledege is bound to algorithm & scale  vertical relations = abstract knowledge  express the geometrical, topological and semantical outcome  formalize the outcome by parameterizing abstract generalization operators Vertical Relations map-object relations identity relation (micro object) group relation (meso object) LOD relations

21. 21Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Vertical Relations vertical relations map object relations identity relation 1:1 (micro object) simplification * smoothing * enlargement * exaggeration * collapse * symbolization * displacement * group relation n:m (meso object) aggregation * (alignment, cluster) amalgamation * (cluster) typification * (cluster, alignment) partitioning * (through e.g. alignments) LOD relations semantic similarity legend type priorities causal & logic structural neigbourhood matrix diversity configuration

22. 22Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Vertical Identity Relations 1:1 simplification smoothing enlargement exaggeration

23. 23Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Vertical Identity Relations 1:1 collapse symbolization displacement

24. 24Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 aggregation Vertical Group Relations n:m

25. 25Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 typification amalgamation Vertical Group Relations n:m

26. 26Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Relation Properties relation properties semantic properties statistics resistance / attraction configuration (island, landscape) containment (in, ring model) threshold level geometric properties size / position shape orientation type change topological properties neigbourhood intersection type structure change originator color codes for properties: valid for identity relations valid for group relations valid for all relations

27. 27Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Relation Properties Topology, compactness Frequency, distance, size Inter-thematic (river  soil) Orientation, meso structure

28. 28 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 2.3 Extracting Relations Classification of Relations Modeling of Relations Extraction of Relations Storage of Relations Exploitation of Relations

29. 29Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Matching 1:25‘0001:200‘000

30. 30Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Matching The main possibilities of the matching process: semantic matching (e.g. by object name or identifier) geometric matching (e.g. by location, size, surface description) topological matching (e.g. overlaps, neigbourhoods)

31. 31Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Matching – Properties

32. 32 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 2.4 Storing & Representing Relations Classification of Relations Modeling of Relations Extraction of Relations Storage of Relations Exploitation of Relations

33. 33Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Storing & Representing Relations How to … represent and store the vertical relations in a MRDB (relation objects, attributes …)? represent identity, group relations and special cases? establish links to the horizontal relations (Stefan Steiniger)? represent interdependencies with horizontal relations? make the relations (as support service) available to others? tree structure ?

34. 34Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Representing Relations Current MRDB approaches usually work with strictly hierarchical data structures such as aggregation trees  not flexible enough  evaluation of non-taxonomic and partonomic relations Database technology: OODBMS vs. RDBMS elegance vs. performance directed acyclic graph (DAG) ? RDBMS OODBMS from www.gitta.info

35. 35Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Managing and Deploying Relations Open Generalization Platform with Web-Services technology Auto-Carto 2005

36. 36Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Application scenarios Web Feature Service Middleware solution Generalization Service Web Map Service GEO Database http:// GIS Client / Browser clustering allows real time typification of symbolized foreground objects (e.g. points of interest) applications- adaptive zooming for web mapping - dynamic mapping for mobile applications limits: only applicable for simple generalization operations Generalization platform GIS, map production Generalization Service standalone generalization services interactive solution, generalization service as toolbox practicable for complex generalization applicable in advance, e.g. semi automated update Auto-Carto 2005

37. 37Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Open Research Platform map production possibility for small companies to offer generalization solutions, new business models customers can keep their production lines open research platform for generalization allows techniques and code to be shared supports benchmarks and comparison of different implementation complex generalization task like orchestration of generalization operators can be addressed at the last meetings of “ICA Commission on Map Generalization and Multiple Representation” (Paris 2003 and Leicester 2004) University Zurich got responsibility to bring forward the idea of a common open research platform for generalization Auto-Carto 2005

38. 38Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Open Research Platform Registry for Generalization Services Generic XML Interface Descriptions Auto-Carto 2005

39. 39 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 2.5 Exploiting Relations Classification of Relations Modeling of Relations Extraction of Relations Storage of Relations Exploitation of Relations

40. 40Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Exploiting Relations interpolation of intermediate scale levels (e.g. Morphing) incremental generalization and updating...

41. 41Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Morphing Morphing of single points along linear or weighted transformation paths: Every point in LOD1 has a transformation path to the final point in LOD2 The intermediate point is created by simple interpolation along the transformation path Interpolation can be realized directly in the database (stored procedures)

54. 54Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Morphing Combining vector morphing with scaleless storage of the geometry.

55. 55 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Time Table, Conferences & Publications

56. 56Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Conferences & Publications ICA Workshop 2004: Neun, M., R. Weibel and D. Burghardt, Data Enrichment for Adaptive Generalisation Auto-Carto 2005: Burghardt, D., M. Neun and R. Weibel, Generalization Services on the Web – A Classification and an Initial Prototype Implementation ICA Book 2005: Edwardes, A., D. Burghardt and M. Neun, Experiments to build an open generalisation system  also in a CaGIS Special Issue ISGI Symposium 2005: Edwardes, A., D. Burghardt and M. Neun, Interoperability in Map Generalisation Research ICA Workshop 2005: Neun, M. and D. Burghardt, Web Services for an Open Generalisation Research Platform ICA Conference 2005: Neun, M. and S. Steiniger, Modelling Relations for Categorical Maps

57. 57Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Time Table

58. 58 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Conclusion

59. 59Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Conclusion Purpose:data enrichment modeling of enriched data exploitation of enriched data Focus: thematic vector maps Goals/Questions:types of “vertical” relations between map objects on different LODs? modelling and representing in a MRDB? matching of map objects in two LODs and acquisition relations and their attributes? management and deployment of relations? usefulness of vertical relations for the creation of intermediate LODs? usefulness of the same relations for incremental generalization?

60. 60Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Thanks for your attention! Any questions, suggestions or comments? Bibliography: Cecconi, A. (2003) Integration of Cartographic Generalization and Multi-Scale Databases for Enhanced Web Mapping Galanda, M. (2003) Automated Polygon Generalization in a Multi Agent System Kilpelainen, T. and T. Sarjakoski (1995) Incremental Generalization for Multiple Representations of Geographical Objects Ruas, A. (1999) Modèle de généralisation de données géographiques à base de contraintes et d‘autonomie Weibel, R., S. Keller and T. Reichenbacher (1995) Overcoming the Knowledge Acquisition Bottleneck in Map Generalization: The Role of Interactive Systems and Computational Intelligence.

61. 61Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 Full Bibliography Bobzien, M. and D. Morgenstern (2002), Geometry Change in Model Generalization – A Geometrical or a Topological Problem Kilpelainen, T., Sarjakoski, T. Incremental Generalization for Multiple Representations of Geographical Objects. In Muller, J. C., Lagrange, J. P., Weibel, R. (editors) GIS and Generalization: Methodology and Practice, Taylor & Francis, 1995. Weibel, R., S. Keller and T. Reichenbacher (1995). Overcoming the Knowledge Acquisition Bottleneck in Map Generalization: The Role of Interactive Systems and Computational Intelligence. In: Frank, A.U.; Kuhn, W. (eds.): Spatial Information Theory: A Theoretical Basis for GIS. Lecture Notes on Computer Science, Berlin: Springer-Verlag, Vol 988: pp. 139-156