Shape Similarity Analysis for Verification of Hazard Map for Storm Surge : Shape Criterion

폭풍해일 침수예상도 검증을 위한 형상유사도 분석 : 형상기준

  • Kim, Young In (Department of Civil Engineering, Hongik University) ;
  • Kim, Dong Hyun (Department of Civil Engineering, Hongik University) ;
  • Lee, Seung Oh (Department of Civil Engineering, Hongik University)
  • 김영인 (홍익대학교 토목공학과) ;
  • 김동현 (홍익대학교 토목공학과) ;
  • 이승오 (홍익대학교 토목공학과)
  • Received : 2019.09.05
  • Accepted : 2019.09.23
  • Published : 2019.09.30


The concept of shape similarity has been applied to verify the accuracy of the SIND model, the real-time prediction model for disaster risk. However, the CRITIC method, one of the most widely used in geometric methodology, is definitely limited to apply to complex shape such as hazard map for coastal disaster. Therefore, we suggested the modified CRITIC method of which we added the shape factors such as RCCI and TF to consider complicated shapes. The matching pairs were manually divided into exact-matching pairs and mis-matching pairs to evaluate the applicability of the new method for shape similarity into hazard maps for storm surges. And the shape similarity of each matching pair was calculated by changing the weights of each shape factor and criteria. Newly proposed methodology and the calculated weights were applied to the objects of the existent hazard map and the results from SIND model. About 90% of exact-matching pairs had the shape similarity of 0.5 or higher, and about 70% of mis-matching pairs were it below 0.5. As future works, if we would calibrate narrowly and adjust carefully multi-objects corresponding to one object, it would be expected that the shape similarity of the exact-matching pairs will increase overall while it of the mis-matching pairs will decrease.


  1. Ali, A. B. H. (2001). Positional and Shape Quality of Areal Entities In Geographic Databases: Quality Information Aggregation Versus Measures Classification. Proceeding of ECSQARU '2001 Workshop on Spatio-Temporal Reasoning and Geographic Information Systems. Toulouse. 1-16.
  2. Arkin, E. M., Chew, L. P., Huttenlocher, D. P., Kedem, K., and Mitchell, J. S. B. (1991). An Efficiently Computable Metric for Comparing Polygonal Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence. 13(3): 209-216
  3. Burghardt, D. and Steiniger, S. (2005). Usage of Pricipal Component Analysis in the Process of Automated Generalisation. Proceedings of ICA Conference. A Coruiia. SPAIN.
  4. Fu, Z. and Wu, J. (2008). Entity Matching in Vector Spatial Data. Proceeding of the XXIth ISPRS Congress. 3-11 Jul 2008. Beijing. China. 1467-1472.
  5. Huang, L., Wang, S., Ye, Y., Wang, B., and Wu, L. (2010). Feature Matching in Cadastral Map Integration with a Case Study of Beijing. Proceedings of 2010 18th International Conference on Geoinformatics. Peking University. Beijing. China. 1-4.
  6. Huh, Y. and Yoo, K. Y. (2012). Shape Similarity Measure for M:N Areal Object Pairs using the Zernike Moment Descriptor. Journal of the Korean Society of Surveying, Geodesy, Photgrammetry, and Cartography. 30(2): 153-162.
  7. Kim, D. H., Yoo, H. J., Jeong, S. I., and Lee, S. O. (2018). Development for Prediction Model of Disaster Risk Through Try and Error Method : Storm Surge. Journal of Korean Society of Disaster & Security. 11(2): 37-43
  8. Kim, J. H., Cho, C. M., and Chae, M. K. (2006). A Study on the Application of Land Form Indices to the Standardization of Development Available Lands, Using GIS. Journal of the Korean Society of Surveying, Geodesy, Photgrammetry, and Cartography. 24(1): 99-110.
  9. Kim, J. Y., Huh, Y., Kim, D. S., and Yoo, K. Y. (2011). A New Method for Automatic Areal Feature Matching Based on shape similarity using CRITIC method. Journal of the Korean Society of Surveying, Geodesy, Photgrammetry, and Cartography. 29(2): 113-121.
  10. Korea Hydrographic and Oceanographic Agency. (2014). Establishment of the Coastal Inundation Maps in the Islands Region. Ocean Research Division.
  11. Samal, A., Seth, S., and Cueto, K. (2004). A Feature-based Approach to Conflation of Geospatial Sources. International Journal of Geographical Information Science. 18(5): 459-489.
  12. Son, H. M., Huh, Y., and Yoo, K. Y. (2010). Geometric Shape Similarity Measure between Polygon Pairs Using the Normalized Central Moments. Conference of Korean Society for Geospatial Information. 161-162.
  13. Tong, X., Shi, W., and Deng, S. (2009). A Probability-based Multi-measure Feature Matching Method in Map Conflation. International Journal of Remote Sensing. 30(20): 5453-5472.
  14. Wenjing, T., Yanling, H., Yuxin, Z., and Ning, L. (2008). Research on Areal Feature Matching Algorithm Based on Spatial Similarity. Proceedings of Control and Decision Conference (CCDC 2008). China. 3326-3330.