The method for estimating the scale of vector spatial data based on spatial granularity

doi:10.13474/j.cnki.11-2246.2022.0242

Abstract

Abstract: Volunteered geographic information has been widely used in recent years. The data from some sources may lacks scale description. Therefore, it is necessary to estimate the scale of vector spatial data. This paper proposes a method to estimate the scale by calculating and counting the spatial granularity of vector spatial data. The road elements are used as examples,and this paper selects a variety of spatial granularity indicators, including the shortest straight segment length of road spatial object with the single-category and the smallest bend area of the same spatial object. The function of estimating vector spatial data scale is fitted by a linear interpolation. The process of estimating scale based on spatial granularity is described in detail, and the data sets include 1∶250000 scale data, 1∶1000000 scale data and 1∶4000000 scale data in Beijing city. In addition, we also count the number of different spatial granularity units, and establish the relationships between the number of spatial granularity unit and the scale based on the radical law. Finally, experiments verify the feasibility and effectiveness of the proposed method in this paper, which is conducive to the fusion and application of multi-scale and multi-source data.

Key words: map scale, spatial granularity, linear interpolation, radical law

CLC Number:

P208

ZHONG Qiyang, GUO Qingsheng, WANG Yong, LUO An. The method for estimating the scale of vector spatial data based on spatial granularity[J]. Bulletin of Surveying and Mapping, 2022, 0(8): 117-122.

References

[1] 李志林,王继成,谭诗腾,等.地理信息科学中尺度问题的30年研究现状[J].武汉大学学报(信息科学版),2018,43(12): 2233-2242.
[2] GOODCHILD M F,PROCTOR J.Scale in a digital geographic world[J].Geographical and Environmental Modelling,1997,1(1):5-24.
[3] ZHAO Jiayan,KLIPPEL A.Scale-unexplored opportunities for immersive technologies in place-based learning[C]//Proceedings of 2019 IEEE Conference on Virtual Reality and 3D User Interfaces.Osaka,Japan:IEEE,2019.
[4] LAURINI R.A conceptual framework for geographic knowledge engineering[J].Journal of Visual Languages& Computing,2014,25(1): 2-19.
[5] GOODCHILD M F.Scale in GIS:an overview[J].Geomorphology,2011,130(1/2): 5-9.
[6] 陈春希,凌子燕,廖超明.基于遥感与GIS技术的景观格局自相似性的尺度效应研究: 以防城港市为例[J].测绘通报,2012(5): 50-52.
[7] 李志林.地理空间数据处理的尺度理论[J].地理信息世界,2005,12(2): 1-5.
[8] 安晓亚,成晓强.矢量曲线的视觉清晰度及在网络地图综合中的应用[J].测绘学报,2020,49(2): 245-255.
[9] 刘鹏程,李精忠,艾廷华.数字地图要素的细节层次一致性评价模型研究[J].中国矿业大学学报,2019,48(2): 437-444.
[10] 李雯静,毋河海.地图信息衰减中关键比例尺的研究[J].武汉大学学报(信息科学版),2006,31(12): 1116-1119.
[11] LI Zhilin,OPENSHAW S.Algorithms for automated line generalization1 based on a natural principle of objective generalization[J].International Journal of Geographical Information Systems,1992,6(5): 373-389.
[12] 刘凯,龙毅,秦耀辰.论地理信息的空间粒度[J].地理与地理信息科学,2014,30(1): 8-12.
[13] 徐薇,黄厚宽,秦勇.时空本体研究及在地理信息系统中的应用[J].铁道学报,2005,27(4): 119-124.
[14] 郭庆胜,刘洋,李萌,等.基于网格模型的导航道路图渐进式化简方法[J].测绘学报,2019,48(11): 1357-1368.
[15] WANG Zeshen,MÜLLER J C.Line generalization based on analysis of shape characteristics[J].Cartography and Geographic Information Systems,1998,25(1): 3-15.
[16] 郭庆胜.空间推理与渐进式地图综合[M].武汉: 武汉大学出版社,2007.
[17] 黄窈蕙,范文涛,刘柳杨.利用轨迹数据进行道路网更新及轨迹融合[J].测绘通报,2018(8): 119-123.
[18] 黄蔚,蒋捷.基于1∶50000地形要素数据的居民地分级方法研究[J].测绘通报,2009(2): 66-69.