Spatial Clustering to Uncluttering Map Visualization in SOLAP Ricardo Silva, João Moura-Pires - New University of Lisbon Maribel Yasmina Santos - University of Minho

1. Spatial Clustering To Uncluttering Map Visualization in SOLAP Authors: Ricardo Silva (1) João Moura-Pires (1) Maribel Yasmina Santos (2) (1) Universidade Nova de Lisboa - Faculdade de Ciências e Tecnologias Departamento de Informática (2) Universidade do Minho Departamento de Sistemas de Informação

2. Content Context and Motivation Approach Overview Details Conclusions and Future Work

4. Analysis of huge amount of data

7. The user can change on-the-fly:

8. Dimensions

9. Measures

10. The level of granularity

11. The user can:

12. Perform uni, bi, multivariate analysis

13. Slices, spatial slices

14. View contextual informationCartographic Can we always ensure that the maps offer a better and faster perception of query results? No! Why?

18. Few data

19. Point data

20. One spatial attribute

21. One numerical measure

23. Not so much data

24. One spatial attribute

25. One numerical measure

26. One semantic attribute

28. Approach Overview Conclusions and Future Work Context and Motivation Details How to approach this problem? To avoid the representations overlapping We need to summarize the data from the query How identify the overlapping groups? through Spatial clustering

29. Approach Overview Conclusions and Future Work Context and Motivation Details But… there other contexts that already use spatial clustering Examples Not too complex problem SOLAP context More complex

31. Able to handle with point data and polygon data

32. DBSCAN

33. P-DBSCAN

34. Maintain the synchronization between the displays (tables, maps)

35. Clusters represented on the map (depending on type of spatial objects)

36. Data at a multi-granularity (roll-up creation to each cluster)

37. The user is able to:

38. control the intensity of summarization

39. to constraint the clusters by a spatial hierarchy level

40. change the cluster representation

42. Approach Overview Conclusions and Future Work Context and Motivation Details Synchronization between map and tabular display A Spatial clustering algorithm A E D 5 B G1 5 B 15 15 F C >25 25 Stores Total Profit Stores Total Profit A 5 A 5 B 5 B 5 Data aggregated Group170 C 5 D 25 E 25 New representation F 15

43. Approach Overview Conclusions and Future Work Context and Motivation Details Synchronization between map and tabular display The semantic attribute (Type attribute) is at a same or at a higher level than the spatial attribute (Store attribute ) Store Type Total Profit Store Type Total Profit Cluster 1 X,Y 15 A X 5 Close objects Cluster 2 W 65 B Y 5 C X 5 Store Type Total Profit D W 25 Close objects Cluster 1 X 10 E W 25 B Y 5 F W 15 Cluster 2 W 65 Each cluster must share the semantic attribute value

44. Approach Overview Conclusions and Future Work Context and Motivation Details Synchronization between map and tabular display The semantic attribute (Type attribute) is at a incomparable or at a lower level than the spatial attribute (County attribute ) The semantic attribute comes from other dimension Type Type X Y X Y County Total Profit Total Profit County Total Profit Total Profit A 15 5 Cluster 1 45 17 Close objects B 5 10 C 25 2 Straight summarization

45. Approach Overview Conclusions and Future Work Context and Motivation Details Synchronization between map and tabular display Example

46. Approach Overview Conclusions and Future Work Context and Motivation Details Synchronization between map and tabular display Example Concave Hull

47. Control the intensity of summarization

48. Approach Overview Conclusions and Future Work Context and Motivation Details Control the intensity of summarization Spatial clustering algorithm G1 mmm… i want more clusters G1 G2 Interact with the system in an easy way

49. Approach Overview Conclusions and Future Work Context and Motivation Details Control the intensity of summarization Example Moving to the right or to the left

50. Approach Overview Conclusions and Future Work Context and Motivation Details DBSCAN Eps = radius MinPts = 3 Sorted 3.distance neighborhood Mapping each object to the distance from its 3-th nearest neighbor

52. Looks for gaps in 3.distance function

54. Rely on spatial clustering technique

55. Takes into account the spatial information to be displayed

56. The possible overlapping between representations

57. Adhoc/Region-based clustering

58. Query-aware

59. Novel heuristic to estimate epsDBSCAN/P-DBSCAN algorithm

60. The user has the ability to control the existence, or not, of the post-processing stage Help to maintain the benefits from map visualization in a SOLAP environment

62. Comparative analysis between several spatial clustering algorithms

63. DBSCAN authors heuristic versus our novel heuristic

64. The level of users’ satisfaction

65. Heuristic to detect the need to summarize the data

66. To consider datasets with the line as a spatial object

67. Spatial clustering applied to the map representations instead real coordinate space