An Efficiently Computable Metric for Comparing Polygonal Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficiently Locating Objects Using the Hausdorff Distance
International Journal of Computer Vision
Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A rubber sheeting algorithm for non-rectangular maps
Computers & Geosciences
Finding corresponding objects when integrating several geo-spatial datasets
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Adaptive Blocking: Learning to Scale Up Record Linkage
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
GeoDDupe: A Novel Interface for Interactive Entity Resolution in Geospatial Data
IV '07 Proceedings of the 11th International Conference Information Visualization
| Hi-index | 0.00 |
Although several methods of handling object matching problems across different datasets have been developed, there is still a need to design new approaches to address the diverse matching applications. Such cases include those where the coordinate differences in datasets are significant, where the shapes of the same objects are dissimilar, or even where the shapes are too similar for different objects. This is especially true, as many large portals worldwide are opening their spatial databases to public access by providing an open application programming interface (API). With this understanding, we propose in this paper a new method for matching objects in different datasets based on geographic context similarity measures. The proposed method employs and combines a set of concepts such as buffer growing, Voronoi diagrams, triangulation, and geometric measurements. This approach is simple in its algorithm but powerful in resolving situations when two datasets have significant coordinate discrepancies. In addition, the concept is highly effective regardless of the shapes of objects. After testing the method for the two major digital datasets in Korea, we found that the matching success rate reached 99.4%.