Abstract
Compared with static spatial data, spatiotemporal data can better represent dynamic real-world phenomena. Many models and temporal GIS applications have been proposed to process, manage and analyze spatiotemporal data. However, most of these works mainly focus on static representation and storage in database and lack enough support for dynamic visualization. Furthermore, most spatiotemporal data models are usually specially designed for specific problems, making it difficult for interdisciplinary data integration. In this paper, we propose a model-driven approach for dynamically visualizing spatiotemporal data in animation, with a property-pluggable model and a modular web-based GIS. Demonstration on 2 real-world datasets shows that our method can integrate multi-source heterogeneous data, while keeping simple, pluggable structure for future customization and extension.
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Acknowledgments
This work was supported by the Strategy Priority Research Program of Chinese Academy of Sciences (No. XDA20080200), the National Key Research and Development Program of China (No. 2018YFB1005002), the National Natural Science Foundation of China (No. 61572479), the National Natural Science Foundation of China together with the National Research Foundation of Singapore (No. 61661146002).
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Kong, W., Yang, L., Ren, J., Zuo, C., Zhang, F. (2019). Model-Driven Dynamic Visualization of Spatiotemporal Data in GIS. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_20
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DOI: https://doi.org/10.1007/978-981-13-9917-6_20
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