Efficient management of ubiquitous location information using geospatial grid region name
• Introduced geospatial grid region name (GGRN) for managing ubiquitous location information (ULI). • Proposed a ULI management method based on GGRN (UMMG). • UMMG improves location cognition between humans and machines. • Applied UMMG to mainstream databases, showing enhanced retrieval efficiency. • UMMG reduces spatial database indexing complexity. With the increasing popularity of sensors and the rapid advancement of network infrastructure and communication technology, managing, retrieving, and applying ubiquitous location information (ULI) poses a significant challenge. This study introduces the concept of the geospatial grid region name (GGRN) and proposes a ULI management method based on the GGRN (UMMG). To evaluate the feasibility and retrieval efficiency of the UMMG, it was applied to mainstream databases and compared with their spatial expansion modules. The experimental results demonstrate that the UMMG effectively addresses the challenge of precise location cognition between humans and machines while also reducing the complexity of spatial database indexing, with an overall performance improvement of 43.00 % compared to Oracle Spatial and 33.30 % compared to PostgreSQL + PostGIS.
Authors
- Min Huang (ORCID: https://orcid.org/0000-0003-3793-968X)
- Chengqi Cheng
- Qifeng Lin (ORCID: https://orcid.org/0000-0001-5334-4067)
- Daoye Zhu (ORCID: https://orcid.org/0000-0003-4351-2359)
- Yanyu Wang (ORCID: https://orcid.org/0000-0002-5369-6860)
- Shuang Li (ORCID: https://orcid.org/0000-0003-4544-1101)
Institutions
- Peking University (CN)
- Fudan University (CN)
- Wuhan University (CN)
- 3v Geomatics (Canada) (CA)
- State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN)
- Jiangxi Normal University (CN)
- Zhejiang University (CN)
- Fuzhou University (CN)
Publication Details
- Journal
- International Journal of Applied Earth Observation and Geoinformation
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1016/j.jag.2025.104400
- Citations
- 1
- Primary Topic
- Data Management and Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 1.49
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Fujian Province