USIViz: An Efficient Visualization System for Multi-Source Urban Spatial Information Data
Comprehensive visualization of urban geographic information is critical for understanding and recognizing urban data. However, because of the diverse sources, different types, varying volumes and significant differences in temporal and spatial scales of urban geographic information, achieving comprehensive visualization on a single platform is challenging. The current urban geographic information application platform has problems with incomplete visualization data types and insufficient efficiency of visualization services. This paper summarized the data types of urban geographic information. For static data types such as vector, raster and chart, the digital earth-driven visualization process is sorted out and technical solutions for visualizing different urban geographic data are provided. Then, a dynamic visualization method based on CZML multi-temporal processing is proposed to meet the visualization requirements of dynamic display. Finally, an efficient visualization rendering method for large-scale vector data is proposed for the improvement of visualization efficiency. We conduct a series of experiments using a large amount of urban geographic data based on the digital earth engine. The experiment results verify the effectiveness of the visualization method proposed in this paper and the feasibility of the overall visualization solution for urban geographic information data. This study can provide a reference for the visualization analysis of urban spatial data, and also provide a demonstration and inspiration for related researchers.
Authors
- Xiaohui Huang (ORCID: https://orcid.org/0000-0002-0394-2357)
- Ximin Cui (ORCID: https://orcid.org/0000-0002-2736-9102)
- Rongrong Duan
- Zhaoyang Zhang (ORCID: https://orcid.org/0000-0002-0656-366X)
- Jiabao Li
- Yuewei Wang (ORCID: https://orcid.org/0000-0001-8429-4795)
Institutions
- China University of Geosciences (CN)
Publication Details
- Journal
- Journal of Circuits Systems and Computers
- Published
- 2025-07-18
- DOI
- https://doi.org/10.1142/s0218126625504146
- Primary Topic
- Data Management and Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.00