Multi-scale Surface Entity Cascade Update Method based on Natural Grid Network Index

The incremental cascade updating of multi-scale geospatial data is one of the important means to enhance the real time of geospatial data. The incremental cascade updating includes multi-scale data matching, correlation relationship establishment, matching and change detection, incremental information generalization, and incremental cascade update. The adjacent scale data matching is the basic of multi-scale data correlation establishment and an important foundation for the implementation of incremental cascade update. Aiming at the low accuracy and efficiency of association matching between multi-scale entities with the same name in existing methods, an incremental cascade updating method of multi-scale surface entities based on natural grid index is proposed. By building the natural grid and coding, building the surface entity and assigning code, adjacent scale entity matching and associated relationship, the corresponding steps of the correlation relationship are established, and the natural grid index is introduced into the incremental cascade update. The specific steps include: firstly, incremental packets of the same source and same scale vector spatial data are detected; secondly, the grid location of the updating entity is determined by the natural grid index, and then deleted and updated automatically or manually; and finally, the cartographic generalization and edge joining are implemented. Taking the residential elements in Hushan villiage of Ganzhou City as an example, the experimental results show that: 1) when the matching threshold is set to 0.51, compared to the direct matching method, the time consumed by the natural grid index method proposed in this paper is reduced by 88.4%, and the recall and precision are increased by 5% and 4%, respectively, which demonstrates the effectiveness of the natural grid index method in matching geographical elements between adjacent scales; 2) compared with the traditional Hausdorff distance, Euclidean distance, and centroid methods, the method proposed in this paper performs best with low time cost (1.34s), and high recall (66.5%) and precision rates (94.1%), which demonstrates the effectiveness and efficiency of the cascade update method proposed in this paper; 3) the proposed method can avoid the influence of the difference of associated residential areas, thus greatly reducing the operation time of manual editing, and effectively improving the accuracy and efficiency of multi-scale incremental cascade updating of residential entities. © 2022, Science Press. All right reserved.