A new method for matching objects in two different geospatial datasets based on the geographic context

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%.