A georeferenced graph model for geospatial data matching by optimising measures of similarity across multiple scales

doi:10.1080/13658816.2020.1858301

	A georeferenced graph model for geospatial data matching by optimising measures of similarity across multiple scales
	Zhang, Wen-Bin 1,2; Ge, Yong 1,2; Leung, Yee 3,4; Zhou, Yu 4
刊名	INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
	2020-12-14
页码	17
关键词	Geospatial data matching georeferenced graph multiscale similarity relationships object matching optimisation
ISSN号	1365-8816
DOI	10.1080/13658816.2020.1858301
通讯作者	Ge, Yong(gey@lreis.ac.cn)
英文摘要	The growth of georeferenced data sources calls for advanced matching methods to improve the reliability of geospatial data processing, such as map conflation. Existing matching methods mainly focus on similarity measures at the entity scale or area scale. A measure that combines entity-scale and area-scale similarities can provide sound matching results under various circumstances. In this paper, we propose a georeferenced-graph model that integrates multiscale similarities for data matching. Specifically, a match of correspondent data objects is identified by the entity-scale measure under the constraint of the area-scale measure. Nodes in the proposed georeferenced graph model represent polygons by their centroids, whereas the links in the graph connect the nodes (i.e. centroids) according to pre-defined rules. Then, we develop an algorithm to identify many-to-many matches. We demonstrate the proposed graph model and algorithm in real-world experiments using OpenStreetMap data. The experimental results show that the proposed georeferenced-graph model can effectively integrate the context and the location-and-form distance of geospatial data matches across different datasets.
资助项目	National Science Foundation of China[41421001] ; National Key Research and Development Program[2017YFB0503501] ; National Natural Science Foundation for Distinguished Young Scholars of China[41725006] ; Hong Kong Research Grants Council[CUHK 14406514]
WOS研究方向	Computer Science ; Geography ; Physical Geography ; Information Science & Library Science
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000598600300001
资助机构	National Science Foundation of China ; National Key Research and Development Program ; National Natural Science Foundation for Distinguished Young Scholars of China ; Hong Kong Research Grants Council
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/137704]
专题	中国科学院地理科学与资源研究所
通讯作者	Ge, Yong
作者单位	1.Univ Acad Sci, Coll Resources & Environm, Beijing, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 3.Chinese Univ Hong Kong, Dept Geog & Resource Management, Hong Kong, Peoples R China 4.Chinese Univ Hong Kong, Inst Future Cities, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Wen-Bin,Ge, Yong,Leung, Yee,et al. A georeferenced graph model for geospatial data matching by optimising measures of similarity across multiple scales[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2020:17.
APA	Zhang, Wen-Bin,Ge, Yong,Leung, Yee,&Zhou, Yu.(2020).A georeferenced graph model for geospatial data matching by optimising measures of similarity across multiple scales.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,17.
MLA	Zhang, Wen-Bin,et al."A georeferenced graph model for geospatial data matching by optimising measures of similarity across multiple scales".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2020):17.