Reference Hub11
Spatial Clustering in SOLAP Systems to Enhance Map Visualization

Spatial Clustering in SOLAP Systems to Enhance Map Visualization

Ricardo Silva (Universidade Nova de Lisboa, Portugal), João Moura-Pires (Universidade Nova de Lisboa, Portugal) and Maribel Yasmina Santos (Universidade do Minho, Portugal)
Copyright: © 2012 |Volume: 8 |Issue: 2 |Article: 2 |Pages: 21
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781466610422|DOI: 10.4018/jdwm.2012040102
Cite Article Cite Article

MLA

Silva, Ricardo, João Moura-Pires and Maribel Yasmina Santos. "Spatial Clustering in SOLAP Systems to Enhance Map Visualization." IJDWM 8.2 (2012): 23-43. Web. 27 Mar. 2020. doi:10.4018/jdwm.2012040102

APA

Silva, R., Moura-Pires, J., & Santos, M. Y. (2012). Spatial Clustering in SOLAP Systems to Enhance Map Visualization. International Journal of Data Warehousing and Mining (IJDWM), 8(2), 23-43. doi:10.4018/jdwm.2012040102

Chicago

Silva, Ricardo, João Moura-Pires and Maribel Yasmina Santos. "Spatial Clustering in SOLAP Systems to Enhance Map Visualization," International Journal of Data Warehousing and Mining (IJDWM) 8 (2012): 2, accessed (March 27, 2020), doi:10.4018/jdwm.2012040102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The emergence of the SOLAP concept supports map visualization for improving data analysis, enhancing the decision making process. However, in this environment, maps can easily become cluttered losing the benefits that triggered the appearance of this concept. In order to overcome this problem, a post-processing model is proposed, which relies on Geovisual Analytics principles. Namely, it takes advantage from the user interaction and the spatial clustering approach in order to reduce the number of elements to be visualized when this number is inadequate to a proper map analysis. Moreover, a novel heuristic to identify the threshold value from which the clusters must be generated was developed. The proposed post-processing model takes into account the query performed, i.e., the number of spatial attributes, the number of spatial dimensions, and the type of spatial objects selected from dimensions. The results obtained so far show: (i) the novel approach to support queries with two spatial attributes from different dimensions allows useful analysis; (ii) the proposed post-processing model is very effective in maintaining a map suitable to the user’s cognitive process; and, (iii) the heuristic proposed provide the user participation in the clustering process, in a user-friendly way.

References

Agrawal, R. G. (1998). Automatic subspace clustering of high dimensional data for data mining applications. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 94-105).
Andrienko G. L. Andrienko N. V. Jankowski P. Keim D. A. Kraak M. J. MacEachren A. M. (2007). Geovisual analytics for spatial decision support: Setting the research agenda.International Journal of Geographical Information Science, 839–857. 10.1080/13658810701349011
Andrienko G. L. Andrienko N. V. Keim D. MacEachren A. Wrobel S. (2011). Challenging problems of geospatial visual analytics.Journal of Visual Languages and Computing, 251–256. 10.1016/j.jvlc.2011.04.001
Atallah M. J. (1983). A linear time algorithm for the hausdorff distance between convex polygons.Information Processing Letters, 207–209. 10.1016/0020-0190(83)90042-X
Bédard Y. Rivest S. Proulx M. J. (2006). Spatial on-line analytical processing (SOLAP): Concepts, architectures, and solutions from a geomatics engineering perspective. In WrembelR.KonciliaC. (Eds.), Data warehouses and OLAP: Concepts, architecture (pp. 298–319). Hershey, PA: IGI Global. 10.4018/987-1-59904-364-7.ch013
Bimonte S. (2010). On modeling and analysis of multidimensional geographic databases. In BellatrecheL. (Ed.), Data warehousing design and advanced engineering applications: Methods for complex construction,6(4) (pp. 96–112). Hershey, PA: IGI Global. 10.4018/978-1-60566-756-0.ch006
Bimonte S. Tchounikine A. Pinet F. (2010). When spatial analysis meets OLAP: Multidimensional model and operators.International Journal of Data Warehousing and Mining, 33–60. 10.4018/jdwm.2010100103
Davide De Chiara V. D. (2011). A Chorem-based approach for visually analyzing spatial data.Journal of Visual Languages and Computing, 173–193. 10.1016/j.jvlc.2011.02.001
Ertöz, L., Steinbach, M., & Kumar, V. (2003). Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data. In Proceedings of the Third SIAM International Conference on Data Mining (Vol. 112, pp. 47-59).
Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery (pp. 226-231).
Gómez L. Kuijpers B. Moelans B. Vaisman A. (2009). A survey of spatio-temporal data warehousing.International Journal of Data Warehousing and Mining, 5(3), 28–55. 10.4018/jdwm.2009070102
Guha, S., Rastogi, R., & Shim, K. (1998). CURE: an efficient clustering algorithm for large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 73-84).
Hartigan J. A. Wong M. A. (1979). A K-means clustering algorithm.Applied Statistics, 28, 100–108. 10.2307/2346830
Jorge, R. (2009). SOLAP+: Extending the interaction model (Unpublished master's thesis). Universidade Nova de Lisboa, Lisbon, Portugal.
Joshi, D., Samal, A., & Soh, L. K. (2009). Density-based clustering of polygons. In Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (pp. 171-178).
Karypis G. Han E. H. Kumar V. (1999). Chameleon: hierarchical clustering using dynamic modeling.Computer, 32(8), 68–75. 10.1109/2.781637
Kaufman L. Rousseeuw P. (1990). Finding groups in data: An introduction to cluster analysis. New York, NY: Wiley Interscience.
Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual analytics: Definition, process, and challenges. In A. Kerren, J. T. Stasko, J.-D. Fekete, & C. North (Eds.), Human-Centered Issues and Perspectives (LNCS 4950, pp. 154-175).
Kolatch E. (2001). Clustering algorithms for spatial databases: A survey (Tech. Rep.). Baltimore, MD: University of Maryland.
Malinowski, E., & Zimányi, E. (2005). Spatial hierarchies and topological relationships in the spatial MultiDimER model. In Proceedings of the British National Conference on Databases (pp. 17-28).
Malinowski E. Zimányi E. (2007). Logical representation of a conceptual model for spatial data warehouses.GeoInformatica, 11(4), 431–457. 10.1007/s10707-007-0022-3
Miller H. J. Han J. (2009). Geographic data mining and knowledge discovery (2nd ed.). Boca Raton, FL: CRC Press.
Moreira, A., & Santos, M. Y. (2007). Concave hull: A k-nearest neighbours approach for the computation of the region occupied by a set of points. In Proceedings of the Second International Conference on Computer Graphics Theory and Applications (pp. 61-68).
Ng R. T. Han J. (2002). Clarans: A method for clustering objects for spatial data mining.IEEE Transactions on Knowledge and Data Engineering, 1003–1016. 10.1109/TKDE.2002.1033770
Rivest, S., Bédard, Y., & March, P. (2001). Towards better support for spatial decision-making: defining the characteristics. Geomatica: the Journal of the Canadian Institute of Geomatics, 539-555.
Rivest, S., Bédard, Y., Proulx, M. J., & Nadeau, M. (2003). Solap: a new type of user interface to support spatio-temporal multidimensional data exploration and analysis. In Proceedings of the ISPRS Joint Workshop on Spatial, Temporal and Multi Dimensional Data Modelling and Analysis.
Rivest S. Bédard Y. Proulx M. J. Nadeau M. Hubert F. Pastor J. (2005). SOLAP technology: Merging business intelligence with geospatial technology for interactive spatio-temporal exploration and analysis of data.ISPRS Journal of Photogrammetry and Remote Sensing, 17–33. 10.1016/j.isprsjprs.2005.10.002
Sander J. Ester M. Kriegel H. P. Xu X. (1998). Density-based clustering in spatial databases: The algorithm gdbscan and its applications.Data Mining and Knowledge Discovery, 2(2), 169–194. 10.1023/A:1009745219419
Sheikholeslami, G., Chatterjee, S., & Zhang, A. (2000). WaveCluster: a wavelet-based clustering approach for spatial data in very large databases. Very Large Data Base Journal, 289-304.
Silva, R. (2010). SOLAP+ (Unpublished master's thesis). Universidade Nova de Lisboa, Lisbon, Portugal.
Silva, R., Moura-Pires, J., & Santos, M. Y. (2011). Spatial clustering to uncluttering map visualization in SOLAP. In Proceedings of the International Conference on Computational Ccience and its Applications - Volume Part I, Santander, Espanha.
Sips M. Schneidewind J. Keim D. (2007). Highlighting space-time patterns: Effective visual encodings for interactive decision making.International Journal of Geographical Information Science, 879–893. 10.1080/13658810701362147
Yildizli C. B. Pedersen T. Saygin Y. Savas E. Levi A. (2011). Distributed privacy preserving clustering via homomorphic secret sharing and its application to (vertically) partitioned spatio-temporal data.International Journal of Data Warehousing and Mining, 7(1), 46–66. 10.4018/jdwm.2011010103
Zhang, T., Ramakrishnan, R., & Livny, M. (1996). BIRCH: an efficient data clustering method for very large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 103-114).

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.