Exploring trans-regional harvesting operation patterns based on multi-scale spatiotemporal partition using GNSS trajectory data
Identifying agricultural machinery operations is crucial for enhancing agricultural productivity and promoting the transition to data-driven agriculture. Current research focuses solely on administrative divisions, overlooking the links between machinery movement, natural spatial patterns, and spati...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2466027 |
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| author | Daoye Zhu Boyong Xiao Haoling Xie Dong Li Haitong He Weixin Zhai |
| author_facet | Daoye Zhu Boyong Xiao Haoling Xie Dong Li Haitong He Weixin Zhai |
| author_sort | Daoye Zhu |
| collection | DOAJ |
| description | Identifying agricultural machinery operations is crucial for enhancing agricultural productivity and promoting the transition to data-driven agriculture. Current research focuses solely on administrative divisions, overlooking the links between machinery movement, natural spatial patterns, and spatiotemporal dependencies. The direct clustering of GNSS points is inefficient and incurs substantial computational costs. In response to these challenges, we introduce an unsupervised clustering method based on multiscale spatiotemporal partitioning, which systematically integrates spatial and temporal dimensions to analyze GNSS trajectory data. By designing multiscale grids and temporal partitions, we efficiently processed high-dimensional trajectory data by employing t-SNE and K-means++ algorithms for dimensionality reduction and clustering, and the visualization validated the clustering effectiveness. When applied to GNSS data from the wheat harvest season in China, the results revealed distinct patterns of harvester movement, including trans-regional movement trends. The geogrids are clustered into four groups, each of which exhibits a distinct spatiotemporal relationship. A combined geogrid analysis with administrative regions identified Anhui as having the highest flow density, whereas Henan had the most concentrated areas of trans-regional harvester flow. These findings offer valuable insights for planning harvester operations, particularly in trans-regional harvester management, by understanding complex spatiotemporal dynamics. |
| format | Article |
| id | doaj-art-5d446c817f604a03b812cd687aa2ff15 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-5d446c817f604a03b812cd687aa2ff152025-08-25T11:31:53ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2466027Exploring trans-regional harvesting operation patterns based on multi-scale spatiotemporal partition using GNSS trajectory dataDaoye Zhu0Boyong Xiao1Haoling Xie2Dong Li3Haitong He4Weixin Zhai5College of Computer and Data Science, Fuzhou University, Fuzhou, People’s Republic of ChinaCollege of Computer and Data Science, Fuzhou University, Fuzhou, People’s Republic of ChinaMaynooth International Engineering College, Fuzhou University, Fuzhou, People’s Republic of ChinaAcademy of Artifcial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, People’s Republic of ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing, People’s Republic of ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing, People’s Republic of ChinaIdentifying agricultural machinery operations is crucial for enhancing agricultural productivity and promoting the transition to data-driven agriculture. Current research focuses solely on administrative divisions, overlooking the links between machinery movement, natural spatial patterns, and spatiotemporal dependencies. The direct clustering of GNSS points is inefficient and incurs substantial computational costs. In response to these challenges, we introduce an unsupervised clustering method based on multiscale spatiotemporal partitioning, which systematically integrates spatial and temporal dimensions to analyze GNSS trajectory data. By designing multiscale grids and temporal partitions, we efficiently processed high-dimensional trajectory data by employing t-SNE and K-means++ algorithms for dimensionality reduction and clustering, and the visualization validated the clustering effectiveness. When applied to GNSS data from the wheat harvest season in China, the results revealed distinct patterns of harvester movement, including trans-regional movement trends. The geogrids are clustered into four groups, each of which exhibits a distinct spatiotemporal relationship. A combined geogrid analysis with administrative regions identified Anhui as having the highest flow density, whereas Henan had the most concentrated areas of trans-regional harvester flow. These findings offer valuable insights for planning harvester operations, particularly in trans-regional harvester management, by understanding complex spatiotemporal dynamics.https://www.tandfonline.com/doi/10.1080/17538947.2025.2466027Spatiotemporal partitiongeogridagricultural machineryharvester flowcluster analysis |
| spellingShingle | Daoye Zhu Boyong Xiao Haoling Xie Dong Li Haitong He Weixin Zhai Exploring trans-regional harvesting operation patterns based on multi-scale spatiotemporal partition using GNSS trajectory data International Journal of Digital Earth Spatiotemporal partition geogrid agricultural machinery harvester flow cluster analysis |
| title | Exploring trans-regional harvesting operation patterns based on multi-scale spatiotemporal partition using GNSS trajectory data |
| title_full | Exploring trans-regional harvesting operation patterns based on multi-scale spatiotemporal partition using GNSS trajectory data |
| title_fullStr | Exploring trans-regional harvesting operation patterns based on multi-scale spatiotemporal partition using GNSS trajectory data |
| title_full_unstemmed | Exploring trans-regional harvesting operation patterns based on multi-scale spatiotemporal partition using GNSS trajectory data |
| title_short | Exploring trans-regional harvesting operation patterns based on multi-scale spatiotemporal partition using GNSS trajectory data |
| title_sort | exploring trans regional harvesting operation patterns based on multi scale spatiotemporal partition using gnss trajectory data |
| topic | Spatiotemporal partition geogrid agricultural machinery harvester flow cluster analysis |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2466027 |
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