Advanced search
Publication Cover
International Journal of Digital Earth Volume 10, 2017 - Issue 9
Submit an article Journal homepage
1,020
Views
16
CrossRef citations to date
0
Altmetric
Articles

Multidimensional and quantitative interlinking approach for Linked Geospatial Data

Yunqiang ZhuState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, People’s Republic of China;Department of Geography, University of Wisconsin–Madison, Madison, WI, USAView further author information
,
A-Xing ZhuState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, People’s Republic of China;Department of Geography, University of Wisconsin–Madison, Madison, WI, USAView further author information
,
Jia SongState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, People’s Republic of ChinaCorrespondence[email protected]
[email protected]
ORCID Iconhttp://orcid.org/0000-0002-9051-1925View further author information
ORCID Icon,
Jie YangState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of China;College of Resource and Environment, University of Chinese Academy of Sciences, Beijing, People’s Republic of ChinaView further author information
,
Min FengSchool of Computer, Global Land Cover Facility, Department of Geography, University of Maryland, College Park, MD, USAView further author information
,
Kai SunState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of China;College of Resource and Environment, University of Chinese Academy of Sciences, Beijing, People’s Republic of ChinaView further author information
,
Jingqu ZhangSouth China Normal University, Guangzhou, People’s Republic of ChinaView further author information
,
Zhiwei HouState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of China;College of Resource and Environment, University of Chinese Academy of Sciences, Beijing, People’s Republic of ChinaView further author information
&
Hongwei ZhaoState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of China;College of Resource and Environment, University of Chinese Academy of Sciences, Beijing, People’s Republic of ChinaView further author information
 show all
Pages 923-943 | Received 10 Oct 2016, Accepted 22 Nov 2016, Published online: 06 Jan 2017

Figures & data

Figure 1. Comparison of existing methods and our approach of geospatial dataset interlinking.

Figure 1. Comparison of existing methods and our approach of geospatial dataset interlinking.

Table 1. Geospatial data characteristics and corresponding data interlinking types.

Table 2. Data interlinking relation instances, predicates, and properties: S refers to symmetry, T means transitivity, and I indicates inverse.

Table 3. Data type transformation instances and their similarities.

Table 4. Openness classification of data format sustainability factors.

Table 5. Weights for compound similarities and overall similarity calculation.

Figure 2. The overall workflow of geospatial data interlinking.

Figure 2. The overall workflow of geospatial data interlinking.

Figure 3. LGD network graph of GEO1 with GEO2, GEO3, GEO4, GEO5, and GEO36.

Figure 3. LGD network graph of GEO1 with GEO2, GEO3, GEO4, GEO5, and GEO36.

Table 6. The top five intra-links of NSTI-GEO and interlinks of NSTI-GEO with NSTI-CAM and NSTI-PH ordered by the overall data similarities.

Table 7. The top five intra-links with ‘GEO1: China 1:4,000,000 Basic Geospatial Dataset (1970s–1990s)’ in NSTI-GEO ordered by the overall data similarity (Basic geospatial data include boundary, topography, geomorphology, river, transportation, soil, land cover data, etc.).

Table 8. The amount of data interlinking with different data similarities threshold values.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.