Use of Rough Sets Theory in Point Cluster and River Network Selection


In this paper, we applied the rough sets to the point cluster and river network selection. In order to meet the requirements of rough sets, first, we structuralize and quantify the spatial information of objects by convex hull, triangulated irregular network (TIN), Voronoi diagram, etc.; second, we manually assign decisional attributes to the information table according to conditional attributes. In doing so, the spatial information and attribute information are integrated together to evaluate the importance of points and rivers by rough sets theory. Finally, we select the point cluster and the river network in a progressive manner. The experimental results show that our method is valid and effective. In comparison with previous work, our method has the advantage to adaptively consider the spatial and attribute information at the same time without any a priori knowledge.

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Qiu, J. , Wang, R. and Li, W. (2014) Use of Rough Sets Theory in Point Cluster and River Network Selection. Journal of Geographic Information System, 6, 209-219. doi: 10.4236/jgis.2014.63020.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Song, Y., Yu, D. and Shen, C. (2009) Knowledge Acquisition Model of Map Generalization Based on Granular Com-puting. Proceedings of SPIE International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 7492, South Africa.
[2] Li, W., Wang, H., Lin, Z., et al. (2009) A Study of Point Objects Generalization Based on Rough Sets Technology. Proceedings of 17th International Conference on Geoinformatics, Fairfax, 12-14 August 2009, 1-6.
[3] Qiu, J. and Li, W. (2010) River Network Dynamic Selection with Spatial Data and Attribute Data Based on Rough Set. Proceedings 18th International Conference on Geoinformatics, Beijing, 18-20 June 2010, 1-5.
[4] Li, W., Qiu, J., Wu, Z., et al. (2011) Application of Rough Sets in GIS Generalization. Proceedings of 6th International Conference on Rough Sets and Knowledge Technology, 6954, 347-353.
[5] Yan, H. and Weibel, R. (2008) An Algorithm for Point Cluster Generalization Based on the Voronoi Diagram. Com-puters & Geosciences, 34, 939-954.
[6] Cai, Y. and Guo, Q. (2012) Point Set Generalization Based on the Kohonen Net. Geospatial Information Science, 11, 221-227.
[7] De Berg, M., Bose, P., Cheong, O., et al. (2004) On Simplifying Dot Maps. Computational Geometry: Theory and Ap-plications, 27, 43-62.
[8] Bereuter, P. and Weibel, R. (2013) Real-Time Generalization of Point Data in Mobile and Web Mapping Using Qua-dtrees. Cartography and Geographic Information Science, 40, 271-281.
[9] Bereuter, P., Weibel, R. and Burghardt, D. (2012) Content Zooming and Exploration for Mobile Maps. Proceedings of the AGILE'2012 International Conference on Geographic Information Science, Avignon, 24-27 April, 2012
[10] Ai, T., Ai, B. and Huang, Y. (2009) Multi-Scale Representaion of Hydrographic Network Data for Progressive Trans-mission Over Web. Proceedings of 24th International Cartographic Conference, Santiago, Chile.
[11] Ai, T., Liu, Y. and Chen, J. (2006) The Hierarchical Watershed Partitioning and Data Simplification of River Network. Proceedings of 12th International Symposium on Spatial Data Handling, Vienna, 617-632.
[12] Buttenfield, B.P., Stanislawski, L.V. and Brewer, C.A. (2013) Adapting Generalization Tools to Physiographic Diversity for the United States National Hydrography Dataset. Cartography and Geographic Information Science, 38, 289-301.
[13] Stanislawski, L.V. and Buttenfield, B.P. (2013) Hydrographic Generalization Tailored to Dry Mountainous Regions. Cartography and Geographic Information Science, 38, 117-125.
[14] Stanislawski, L.V. (2009) Feature Pruning by Upstream Drainage Area to Support Automated Generalization of the United States National Hydrography Dataset. Computer, Environment and Urban Systems, 33, 325-333.
[15] Sen, A. and Gokgoz, T. (2012) Clustering Approaches for Hydrographic Generalization. Proceedings of GIS Ostrava 2012—Surface Models for Geoscience, Ostrava.
[16] Sen, A., Gokgoz, T. and Sester, M. (2014) Model Generalization of Two Different Drainage Patterns by Self-Organizing Maps. Cartography and Geographic Information Science, 41, 151-165.
[17] Dahinden, T. and Sester, M. (2009) Categorization of Linear Objects for Map Generalization Using Geocoded Articles of a Knowledge Repository. Proceedings of 9th International Conference on Spatial Information Theory, Aber Wrach’h, 21-25 September 2009.
[18] Tinker, M., Anthamatten, P., Simley, J. and Finn, M.P. (2013) A Method to Generalize Stream Flowlines in Small-Scale Maps by a Variable Flow-based Pruning Threshold. Cartography and Geographic Information Science, 40, 444-457.
[19] Pawlak, Z. (1997) Rough Set Approach to Knowledge-Based Decision Support. European Journal of Operational Research, 99, 48-57.
[20] Wu, H. (1997) Structured Approach to Implementing Automatic Cartographic Generalization. Proceedings of 18th International Cartographic Conference, Stockholm, 23-27 June 1997, 349-356.
[21] Li, J. and Ai, T. (2010) A Triangulated Spatial Model for Detection of Spatial Characteristics of GIS Data. Proceedings of 2010 IEEE International Conference on Progress in Informatics and Computing, Shanghai, 10-12 December 2010, 155-159.
[22] Liu, X., Zhan, F.B. and Ai, T. (2010) Road Selection Based on Voronoi Diagrams and “Strokes” in Map Generalization. International Journal of Applied Earth Observation and Geoinformation, 12, S194-S202.
[23] Horton, R.E. (1945) Erosional Development of Streams and Their Drainage Basins; Hydrophysical Approach to Quantitative Morphology. Geological Society of America Bulletin, 56, 275-370.[275:EDOSAT]2.0.CO;2
[24] Topfer, F. and Pillewizer, W. (1966) The Principles of Selection. The Cartographic Journal, 3, 10-16.
[25] Zhu, G. (2004) Cartography. Wuhan University Press, Wuhan.
[26] Thomson, R.C. (2006) The “Stroke” Concept in Geographic Network Generalization and Analysis. Proceedings of 12th International Symposium on Spatial Data Handling, Vienna, 12-14 July 2006, 681-697.
[27] Gulgen, F. and Gokgoz, T. (2010) A Block-Based Selection Method for Road Network Generalization. International Journal of Digital Earth, 4, 133-153.
[28] Li, W., Qiu, J., Lin, Z., et al. (2013) Approach of Curve Bends Recognition and Contour Cluster Structuralization. Acta Geodaetica et Cartographica Sinica, 42, 295-303.

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