A balanced decomposition approach to real-time visualization of large vector maps in CyberGIS

摘要

With the dramatic development of spatial data infrastructure, CyberGIS has become significant for geospatial data sharing. Due to the large number of concurrent users and large volume of vector data, CyberGIS faces a great challenge in how to improve performance. The real-time visualization of vector maps is themost common function in CyberGIS applications, and it is time-consuming especially when the data volume becomes large. So, how to improve the efficiency of visualization of large vector maps is still a significant research direction for GIScience scientists. In this research, we review the existing three optimization strategies, and determine that the third category strategy (i.e., parallel optimization) is appropriate for the real-time visualization of large vector maps. One of the key issues of parallel optimization is how to decompose the real-time visualization tasks into balanced sub tasks while taking into consideration the spatial heterogeneous characteristics. We put forward some rules that the decomposition should conform to, and design a real-time visualization framework for large vector maps. We focus on a balanced decomposition approach that can assure efficiency and effectiveness. Considering the spatial heterogeneous characteristic of vector data, we use a “horizontal grid, verticalmultistage” approach to construct a spatial point distribution information grid. The load balancer analyzes the spatial characteristics of the map requests and decomposes the real-time viewshed into multiple balanced sub viewsheds. Then, all the sub viewsheds are distributed to multiple server nodes to be executed in parallel, so as to improve the realtime visualization efficiency of large vector maps. A group of experiments have been conducted by us. The analysis results demonstrate that the approach proposed in this research has the ability of balanced decomposition, and it is efficient and effective for all geometry types of vector data.