A Network Approach for Discovering Spatially Associated Objects
Discovering spatially associated objects involves measuring objects’ similarities and retrieving associated objects. The integration of spatial topology and network models for discovering associated objects remains largely unexplored. Here, the concept of a maximum topological accessibility path was...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/6/226 |
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| author | Changfeng Jing Tao Liang Yunlong Feng Jianing Li Sensen Wu Jiale Ding Gaoran Xu Yang Hu |
| author_facet | Changfeng Jing Tao Liang Yunlong Feng Jianing Li Sensen Wu Jiale Ding Gaoran Xu Yang Hu |
| author_sort | Changfeng Jing |
| collection | DOAJ |
| description | Discovering spatially associated objects involves measuring objects’ similarities and retrieving associated objects. The integration of spatial topology and network models for discovering associated objects remains largely unexplored. Here, the concept of a maximum topological accessibility path was developed to quantify objects’ similarity attenuation. Considering the topological accessibility and spatial feature similarity of network nodes, an approach named the Weighted Similarity measure method considering Topological Accessibility (WSTA) is proposed to measure object association. The WSTA can capture both spatial interaction patterns and topological relationships in complex urban environments, thereby improving the accuracy of spatially associated object discovery. The proposed approach is validated using real-world point-of-interest (POI) datasets from Beijing city. The results suggest that integrating topological relationship approaches yields significant accuracy improvements in existing baseline methods, thereby enriching geospatial data retrieval in the era of big geospatial data. |
| format | Article |
| id | doaj-art-7cc9e182e17c4ea193cf0ffa10eb20d3 |
| institution | Kabale University |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-7cc9e182e17c4ea193cf0ffa10eb20d32025-08-20T03:27:18ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-06-0114622610.3390/ijgi14060226A Network Approach for Discovering Spatially Associated ObjectsChangfeng Jing0Tao Liang1Yunlong Feng2Jianing Li3Sensen Wu4Jiale Ding5Gaoran Xu6Yang Hu7School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310027, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310027, ChinaSchool of Information Engineering, China University of Geosciences Beijing, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences Beijing, Beijing 100083, ChinaDiscovering spatially associated objects involves measuring objects’ similarities and retrieving associated objects. The integration of spatial topology and network models for discovering associated objects remains largely unexplored. Here, the concept of a maximum topological accessibility path was developed to quantify objects’ similarity attenuation. Considering the topological accessibility and spatial feature similarity of network nodes, an approach named the Weighted Similarity measure method considering Topological Accessibility (WSTA) is proposed to measure object association. The WSTA can capture both spatial interaction patterns and topological relationships in complex urban environments, thereby improving the accuracy of spatially associated object discovery. The proposed approach is validated using real-world point-of-interest (POI) datasets from Beijing city. The results suggest that integrating topological relationship approaches yields significant accuracy improvements in existing baseline methods, thereby enriching geospatial data retrieval in the era of big geospatial data.https://www.mdpi.com/2220-9964/14/6/226spatial objectspatially associated objectstopological similarity |
| spellingShingle | Changfeng Jing Tao Liang Yunlong Feng Jianing Li Sensen Wu Jiale Ding Gaoran Xu Yang Hu A Network Approach for Discovering Spatially Associated Objects ISPRS International Journal of Geo-Information spatial object spatially associated objects topological similarity |
| title | A Network Approach for Discovering Spatially Associated Objects |
| title_full | A Network Approach for Discovering Spatially Associated Objects |
| title_fullStr | A Network Approach for Discovering Spatially Associated Objects |
| title_full_unstemmed | A Network Approach for Discovering Spatially Associated Objects |
| title_short | A Network Approach for Discovering Spatially Associated Objects |
| title_sort | network approach for discovering spatially associated objects |
| topic | spatial object spatially associated objects topological similarity |
| url | https://www.mdpi.com/2220-9964/14/6/226 |
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