A cartographic generalization method for 3D visualization of trajectories in space–time cubes: case study of epidemic spread
The widespread adoption of positioning technology and location-based services has resulted in the continuous generation of substantial volumes of accessible spatiotemporal trajectory data. While many studies focus on 2D trajectory visualization, research on visual overload in 3D space remains limite...
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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/10.1080/17538947.2025.2474190 |
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| author | Fei Yang Jie Shen Fengzhen Zhu Junrui Zhang |
| author_facet | Fei Yang Jie Shen Fengzhen Zhu Junrui Zhang |
| author_sort | Fei Yang |
| collection | DOAJ |
| description | The widespread adoption of positioning technology and location-based services has resulted in the continuous generation of substantial volumes of accessible spatiotemporal trajectory data. While many studies focus on 2D trajectory visualization, research on visual overload in 3D space remains limited. Thus, there is a need to balance the presentation of spatiotemporal information and to minimize visual occlusions in the 3D representation of trajectories. To address this gap, we propose a global-local cooperative optimization method based on cognitive load theory, which utilizes cartographic generalization to emphasize local features and clarity, while treating 3D visualization as a global opacity optimization problem to enhance visibility and reduce occlusions. We take the spread trajectories of infectious diseases as our research subject, due to their characteristic spatiotemporal patterns, and employ a space–time cube as the visualization tool. The proposed method incorporates a 3D generalization algorithm that mitigates visual stickiness, while leveraging a 3D line field visualization technique to optimize opacity, thereby minimizing visual occlusion and spatial clutter. The experimental results validate the method's effectiveness in reducing occlusion, resolving visual entanglement, and lowering cognitive load, which in turn improving the clarity and usability of epidemic trajectory visualization. |
| format | Article |
| id | doaj-art-0c7cd97e45b4458792a33bc2eee44095 |
| 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-0c7cd97e45b4458792a33bc2eee440952025-08-25T11:28:27ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2474190A cartographic generalization method for 3D visualization of trajectories in space–time cubes: case study of epidemic spreadFei Yang0Jie Shen1Fengzhen Zhu2Junrui Zhang3Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, People’s Republic of ChinaJiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, People’s Republic of ChinaJiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, People’s Republic of ChinaJiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, People’s Republic of ChinaThe widespread adoption of positioning technology and location-based services has resulted in the continuous generation of substantial volumes of accessible spatiotemporal trajectory data. While many studies focus on 2D trajectory visualization, research on visual overload in 3D space remains limited. Thus, there is a need to balance the presentation of spatiotemporal information and to minimize visual occlusions in the 3D representation of trajectories. To address this gap, we propose a global-local cooperative optimization method based on cognitive load theory, which utilizes cartographic generalization to emphasize local features and clarity, while treating 3D visualization as a global opacity optimization problem to enhance visibility and reduce occlusions. We take the spread trajectories of infectious diseases as our research subject, due to their characteristic spatiotemporal patterns, and employ a space–time cube as the visualization tool. The proposed method incorporates a 3D generalization algorithm that mitigates visual stickiness, while leveraging a 3D line field visualization technique to optimize opacity, thereby minimizing visual occlusion and spatial clutter. The experimental results validate the method's effectiveness in reducing occlusion, resolving visual entanglement, and lowering cognitive load, which in turn improving the clarity and usability of epidemic trajectory visualization.https://www.tandfonline.com/doi/10.1080/17538947.2025.2474190Spatiotemporal visualizationspace–time cubecognitive load theorycartographic generalizationepidemic spread |
| spellingShingle | Fei Yang Jie Shen Fengzhen Zhu Junrui Zhang A cartographic generalization method for 3D visualization of trajectories in space–time cubes: case study of epidemic spread International Journal of Digital Earth Spatiotemporal visualization space–time cube cognitive load theory cartographic generalization epidemic spread |
| title | A cartographic generalization method for 3D visualization of trajectories in space–time cubes: case study of epidemic spread |
| title_full | A cartographic generalization method for 3D visualization of trajectories in space–time cubes: case study of epidemic spread |
| title_fullStr | A cartographic generalization method for 3D visualization of trajectories in space–time cubes: case study of epidemic spread |
| title_full_unstemmed | A cartographic generalization method for 3D visualization of trajectories in space–time cubes: case study of epidemic spread |
| title_short | A cartographic generalization method for 3D visualization of trajectories in space–time cubes: case study of epidemic spread |
| title_sort | cartographic generalization method for 3d visualization of trajectories in space time cubes case study of epidemic spread |
| topic | Spatiotemporal visualization space–time cube cognitive load theory cartographic generalization epidemic spread |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2474190 |
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