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Spatial access method for urban geospatial database management: An efficient approach of 3D vector data clustering technique

Abstract:

In the last few years, 3D urban data and its information are rapidly increased due to the growth of urban area and urbanization phenomenon. These datasets are then maintain and manage in 3D spatial database system. However, performance deterioration is likely to happen due to the massiveness of 3D datasets. As a solution, 3D spatial index structure is used as a booster to increase the performance of data retrieval. In commercial database, commonly and widely used index structure for 3D spatial database is 3D R-Tree. This is due to its simplicity and promising method in handling spatial data. However, 3D R-Tree produces serious overlapping among nodes. The overlapping factor is important for an efficient 3D R-Tree to avoid replicated data entry in a different node. Thus, an efficient and reliable method is required to reduce the overlapping nodes in 3D R-Tree nodes. In this paper, we proposed a 3D geospatial data clustering to be used in the construction of 3D R-Tree and respectively could reduce the overlapping among nodes. The proposed method is tested on 3D urban dataset for the application of urban infill development. By using several cases of data updating operations such as building infill, building demolition and building modification, the proposed method indicates that the percentage of overlapping coverage among nodes is reduced compared with other existing approaches.