Geospatial grid management: A comprehensive framework and systematic review of subdivision, encoding, indexing and storage
•Conducts a comprehensive discussion on geospatial grid management.•Explains the relationships among geospatial grid subdivision, encoding, indexing, and storage.•Provides a categorized analysis of geospatial grid subdivision methods.•Offers an in-depth review of geospatial grid encoding techniques....
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| Vydané v: | International journal of applied earth observation and geoinformation Ročník 144; s. 104860 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.11.2025
Elsevier |
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| ISSN: | 1569-8432 |
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| Abstract | •Conducts a comprehensive discussion on geospatial grid management.•Explains the relationships among geospatial grid subdivision, encoding, indexing, and storage.•Provides a categorized analysis of geospatial grid subdivision methods.•Offers an in-depth review of geospatial grid encoding techniques.•Discusses structured and unstructured databases storage and grid indexing integration.
With the rapid advancement of the Internet of Things (IoT), sensor technologies, and remote sensing, spatiotemporal data has emerged as a crucial data source across diverse industries, extensively utilized in environmental monitoring, intelligent transportation, socio-economic analysis, and other domains. Spatiotemporal data encompasses the locations, states, and interrelationships of objects within specific temporal and spatial contexts. It is characterized by dynamic properties, high dimensionality, and large data volumes, which pose significant challenges for storage, querying, and analysis. To address the challenges associated with managing large-scale spatiotemporal data, geospatial grid subdivision, grid encoding, grid indexing, and grid storage technologies offer essential support and have demonstrated remarkable effectiveness. In the past, existing reviews have typically focused on a single aspect, such as the fundamental methods of grid subdivision, the implementation details of encoding and indexing techniques, or solely on spatiotemporal databases. Although these reviews provide in-depth discussions of specific technologies, they lack a systematic analysis of the interrelationships among multiple technical modules, resulting in an inability to fully reveal the collaborative potential between modules. Additionally, current research provides limited comprehensive discussions on grid indexing technologies. This paper aims to address this gap by providing a systematic review of the development status of grid indexing technologies. Furthermore, it reviews and summarizes the key technologies, interrelationships, research advancements, and future directions of grid subdivision, grid encoding, grid indexing, and grid storage, thereby providing references for enhancing the storage and querying efficiency of spatiotemporal data. |
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| AbstractList | With the rapid advancement of the Internet of Things (IoT), sensor technologies, and remote sensing, spatiotemporal data has emerged as a crucial data source across diverse industries, extensively utilized in environmental monitoring, intelligent transportation, socio-economic analysis, and other domains. Spatiotemporal data encompasses the locations, states, and interrelationships of objects within specific temporal and spatial contexts. It is characterized by dynamic properties, high dimensionality, and large data volumes, which pose significant challenges for storage, querying, and analysis. To address the challenges associated with managing large-scale spatiotemporal data, geospatial grid subdivision, grid encoding, grid indexing, and grid storage technologies offer essential support and have demonstrated remarkable effectiveness. In the past, existing reviews have typically focused on a single aspect, such as the fundamental methods of grid subdivision, the implementation details of encoding and indexing techniques, or solely on spatiotemporal databases. Although these reviews provide in-depth discussions of specific technologies, they lack a systematic analysis of the interrelationships among multiple technical modules, resulting in an inability to fully reveal the collaborative potential between modules. Additionally, current research provides limited comprehensive discussions on grid indexing technologies. This paper aims to address this gap by providing a systematic review of the development status of grid indexing technologies. Furthermore, it reviews and summarizes the key technologies, interrelationships, research advancements, and future directions of grid subdivision, grid encoding, grid indexing, and grid storage, thereby providing references for enhancing the storage and querying efficiency of spatiotemporal data. •Conducts a comprehensive discussion on geospatial grid management.•Explains the relationships among geospatial grid subdivision, encoding, indexing, and storage.•Provides a categorized analysis of geospatial grid subdivision methods.•Offers an in-depth review of geospatial grid encoding techniques.•Discusses structured and unstructured databases storage and grid indexing integration. With the rapid advancement of the Internet of Things (IoT), sensor technologies, and remote sensing, spatiotemporal data has emerged as a crucial data source across diverse industries, extensively utilized in environmental monitoring, intelligent transportation, socio-economic analysis, and other domains. Spatiotemporal data encompasses the locations, states, and interrelationships of objects within specific temporal and spatial contexts. It is characterized by dynamic properties, high dimensionality, and large data volumes, which pose significant challenges for storage, querying, and analysis. To address the challenges associated with managing large-scale spatiotemporal data, geospatial grid subdivision, grid encoding, grid indexing, and grid storage technologies offer essential support and have demonstrated remarkable effectiveness. In the past, existing reviews have typically focused on a single aspect, such as the fundamental methods of grid subdivision, the implementation details of encoding and indexing techniques, or solely on spatiotemporal databases. Although these reviews provide in-depth discussions of specific technologies, they lack a systematic analysis of the interrelationships among multiple technical modules, resulting in an inability to fully reveal the collaborative potential between modules. Additionally, current research provides limited comprehensive discussions on grid indexing technologies. This paper aims to address this gap by providing a systematic review of the development status of grid indexing technologies. Furthermore, it reviews and summarizes the key technologies, interrelationships, research advancements, and future directions of grid subdivision, grid encoding, grid indexing, and grid storage, thereby providing references for enhancing the storage and querying efficiency of spatiotemporal data. |
| ArticleNumber | 104860 |
| Author | Zhu, Daoye Xiao, Boyong Li, Shuang Cheng, Chengqi Qu, Tengteng Su, Yuanhao Zhai, Weixin |
| Author_xml | – sequence: 1 givenname: Yuanhao surname: Su fullname: Su, Yuanhao email: 832303114@fzu.edu.cn organization: College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China – sequence: 2 givenname: Daoye surname: Zhu fullname: Zhu, Daoye email: zhudaoye@pku.edu.cn organization: College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China – sequence: 3 givenname: Boyong surname: Xiao fullname: Xiao, Boyong email: 231027185@fzu.edu.cn organization: College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China – sequence: 4 givenname: Shuang surname: Li fullname: Li, Shuang email: li_shuang@fudan.edu.cn organization: Institute of Chinese Historical Geography, Fudan University, Shanghai 200433, China – sequence: 5 givenname: Tengteng surname: Qu fullname: Qu, Tengteng email: tengteng.qu@pku.edu.cn organization: College of Engineering, Peking University, Beijing 100871, China – sequence: 6 givenname: Weixin surname: Zhai fullname: Zhai, Weixin email: zhaiweixin@cau.edu.cn organization: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China – sequence: 7 givenname: Chengqi surname: Cheng fullname: Cheng, Chengqi email: ccq@pku.edu.cn organization: College of Engineering, Peking University, Beijing 100871, China |
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| Keywords | Spatiotemporal data Storage Distributed database Encoding Subdivision Geospatial grid management Indexing |
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| Snippet | •Conducts a comprehensive discussion on geospatial grid management.•Explains the relationships among geospatial grid subdivision, encoding, indexing, and... With the rapid advancement of the Internet of Things (IoT), sensor technologies, and remote sensing, spatiotemporal data has emerged as a crucial data source... |
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| StartPage | 104860 |
| SubjectTerms | Distributed database Encoding Geospatial grid management Indexing Spatiotemporal data Storage Subdivision |
| Title | Geospatial grid management: A comprehensive framework and systematic review of subdivision, encoding, indexing and storage |
| URI | https://dx.doi.org/10.1016/j.jag.2025.104860 https://doaj.org/article/58e18bfc26d44e9085f0a080353ca213 |
| Volume | 144 |
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