基于自然格网索引的多尺度面实体增量级联更新方法

doi:10.12082/dqxxkx.2022.210350

摘要/Abstract

摘要：

多尺度地理空间数据增量级联更新方法是增强地理空间数据的现势性的重要手段之一。级联更新包括多尺度数据匹配、关联关系建立、匹配与变化检测、增量信息制图综合、级联更新等步骤,其中,相邻比例尺数据匹配是多尺度数据关联关系建立的基本方法,也是实现级联更新的重要基础。针对现有方法中多尺度同名实体间的关联关系匹配的精度和效率问题,本文提出了基于自然格网索引的多尺度面实体增量级联更新方法。首先,检测出同源同尺度矢量空间数据发生变化的增量包;其次通过自然格网索引确定更新实体所在格网位置,并进行删除、自动或手动更新;最后实施制图综合、接边以及多尺度面实体增量级联更新信息传递操作。以赣州市虎山乡部分区域居民地要素为例,实验结果表明：① 在匹配阈值为0.51的情况下,相较于直接匹配的方法,采用本文提出的自然格网索引方法进行匹配所消耗的时间减少了88.4%,查全率和查准率分别提升了5%和4%,充分说明了自然格网索引方法在匹配相邻尺度间地理要素时的必要性; ② 相较于传统的Hausdorff距离、欧式距离和质心包含3种匹配方法,本文所提出的方法在耗时（1.34 s）、查全率（66.5%）和查准率（94.1%）方面处于领先地位,这充分说明了本文提出的级联更新方法的优越性; ③ 本文提出的方法可免除关联居民地差异的影响,从而大大减少人工编辑的作业时间,并可有效提高多尺度居民地实体增量级联更新的精度和效率。

关键词: 自然网格索引, 多尺度, 面实体, 增量级联更新, 制图综合, 接边, 匹配方法, 变化检测

Abstract:

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.

Key words: natural grid index, multi-scale, polygon entity, incremental cascading update, cartographic generalization, edge connection, matching method, change detection

焦洋洋, 刘平芝, 熊顺, 徐道柱. 基于自然格网索引的多尺度面实体增量级联更新方法[J]. 地球信息科学学报, 2022, 24(5): 851-863.DOI:10.12082/dqxxkx.2022.210350

JIAO Yangyang, LIU Pingzhi, XIONG Shun, XU Daozhu. Multi-scale Surface Entity Cascade Update Method based on Natural Grid Network Index[J]. Journal of Geo-information Science, 2022, 24(5): 851-863.DOI:10.12082/dqxxkx.2022.210350

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序号	实体编码
1	G001132013000232-130204-1550
2	G001132013000232-130102-1551
3	G001132013000232-130206-1572
4	G001132013000232-130102-1598
5	G001132013000232-130204-1652
6	G001132013000232-130204-1653
7	G001132013000232-130102-1704
8	G001132013000232-130102-1705
9	G001132013000232-130102-1707
10	G001132013000232-130204-1874

匹配方法	耗时/s	匹配数/个	正确数/个	查全率/%	查准率/%
直接匹配	11.55	346	312	61.5	90.1
基于自然格网索引匹配	1.34 （构建格网索引耗时0.53）	358	337	66.5	94.1

匹配方法	耗时/s	匹配数/个	正确数/个	查全率/%	查准率/%
Hausdorff距离	7.59	305	277	52.7	87.5
欧式距离	8.18	318	242	47.7	76.1
质心包含	4.12	325	293	57.8	90.2