Abstract
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.
Similar content being viewed by others
References
Yang C, Wong DW, Yang R, Kafatos M, Li Q. Performance-improving techniques in web-based GIS. International Journal of Geographical Information Science, 2005, 19(3): 319–342
Wang S. A CyberGIS framework for the synthesis of cyberinfrastructure, GIS, and spatial analysis. Annals of the Association of American Geographers, 2010, 100(3): 535–557
Yang C, Nebert D, Taylor D R F. Establishing a sustainable and crossboundary geospatial cyberinfrastructure to enable polar research. Computers and Geosciences, 2011, 37(11): 1721–1726
Wang S, Anselin L, Bhaduri B, Crosby C, Goodchild M F, Liu Y, Nyerges T L. CyberGIS software: a synthetic review and integration roadmap. International Journal of Geographical Information Science, 2013, 27(11): 2122–2145
Wang S. CyberGIS: blueprint for integrated and scalable geospatial software ecosystems. International Journal of Geographical Information Science, 2013, 27(11): 2119–2121
Kelly N M, Tuxen K. WebGIS for monitoring sudden oak death in coastal California. Computers, Environment and Urban Systems, 2003, 27(5): 527–547
Mathiyalagan V, Grunwald S, Reddy K R, Bloom S A. A WebGIS and geodatabase for Florida’s wetlands. Computers and Electronics in Agriculture, 2005, 47(1): 69–75
Jia Y W, Zhao H L, Niu C W, Jiang Y Z, Gan H, Xing Z, Zhao X L, Zhao Z X. A WebGIS-based system for rainfall-runoff prediction and real-time water resources assessment for Beijing. Computers and Geosciences, 2009, 35(7): 1517–1528
Pessina V, Meroni F. A WebGis tool for seismic hazard scenarios and risk analysis. Soil Dynamics and Earthquake Engineering, 2009, 29(9): 1274–1281
Liu Z Q, Qin Y S, Yang Y, Qiao Y M, Li J, Tao R. Research on visualized information system of reservoir ecotope based on WebGIS. Procedia Environmental Sciences, 2011, 10: 2354–2359
Dong S, Wang X, Yin H, Xu S, Xu R. Semantic enhanced WebGIS approach to visualize Chinese historical natural hazards. Journal of Cultural Heritage, 2013, 14(3): 181–189
Hou S, Li A, Han B, Zhou P. An early warning system for regional raininduced landslide hazard. International Journal of Geosciences, 2013, 4: 584–587
Wu H, Li Z, Zhang H, Yang C, Shen S. Monitoring and evaluating the quality of Web Map Service resources for optimizing map composition over the internet to support decision making. Computers and Geosciences, 2010, 37(4): 485–494
Ming L Y, Chang Z W. A model of caching Geo-data sharing based on computer cluster technology. Advanced Materials Research, 2012, 532: 902–907
Guo M, Xie Z, Huang Y.WebGIS model based on response ratio priority schedule algorithm. In: Proceedings of the 5th International Symposium on Computational Intelligence and Design. 2012, 1: 184–187
Guo L, Gong J, Sun J, Wei X. Study on GIS architecture based on SO A and RIA. In: Proceedings of the 3rd International Conference on Information Sciences and Interaction Sciences. 2010, 620–625
Ahmad W, Zia A, Khalid U. A Google Map based social network (GMBSN) for exploring information about a specific territory. Journal of Software Engineering and Applications, 2013, 6(07): 343–348
Mustafa N H, Krishnan S, Varadhan G, Venkatasubramanian S. Dynamic simplification and visualization of large maps. International Journal of Geographical Information Science, 2006, 20(3): 273–302
Zhang L, Yang C, Tong X, Rui X. Visualization of large spatial data in networking environments. Computers and Geosciences, 2007, 33(9): 1130–1139
Zhang L, Ren Y, Guo Z. Transmission and visualization of large geographical maps. Journal of Photogrammetry and Remote Sensing, 2011, 66(1): 73–80
Yang B, Purves R, Weibel R. Efficient transmission of vector data over the Internet. International Journal of Geographical Information Science, 2007, 21(2): 215–237
Yang B, Purves R S, Weibel R. Variable-resolution compression of vector data. GeoInformatica, 2008, 12(3): 357–376
Hawick K A, Coddington P D, James H A. Distributed frameworks and parallel algorithms for processing large-scale geographic data. Parallel Computing, 2003, 29(10): 1297–1333
Gao J, Wang C, Li L, Shen H W. A parallel multiresolution volume rendering algorithm for large data visualization. Parallel Computing, 2005, 31(2): 185–204
Li J, Jiang Y, Yang C, Huang Q, Rice M. Visualizing 3D/4D environmental data using many-core graphics processing units (GPUs) and multi-core central processing units (CPUs). Computers and Geosciences, 2013, 59: 78–89
Xia Y J, Kuang L, Li X M. Accelerating geospatial analysis on GPUs using CUDA. Journal of Zhejiang University SCIENCE C, 2011, 12(12): 990–999
Zhao Y, Padmanabhan A, Wang S. A parallel computing approach to viewshed analysis of large terrain data using graphics processing units. International Journal of Geographical Information Science, 2013, 27(2): 363–384
Tang W. Parallel construction of large circular cartograms using graphics processing units. International Journal of Geographical Information Science, 2013, 27(11): 2182–2206
Wang S, Armstrong M P. A quadtree approach to domain decomposition for spatial interpolation in Grid computing environments. Parallel Computing, 2003, 29(10): 1481–1504
Yang CW, Goodchild M, Huang Q Y, Nebert D, Raskin R, Xu Y, Bambacus M, Fay D. Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing? International Journal of Digital Earth, 2011, 4(4): 305–329
Yang C, Xu Y, Nebert D. Redefining the possibility of Digital Earth and geosciences with spatial cloud computing. International Journal of Digital Earth, 2013, 6(4): 297–312
Kim I, Tsou M. Enabling digital earth simulation models using cloud computing or grid computing — two approaches supporting highperformance GIS simulation frameworks. International Journal of Digital Earth, 2013, 6(4): 383–403
Parry H R, Bithell M. Large scale agent-based modelling: a review and guidelines for model scaling. In: Proceedings of Agent-Based Modelsof Geographical Systems. 2012, 271–308
Wang D, Berry M W, Carr E A, Cross L J. A parallel fish landscape model for ecosystem modeling. Simulation, 2006, 82(7): 451–465
Parker J, Epstein J M. A distributed platform for global-scale agentbased models of disease transmission. ACM Transactions on Modeling and Computer Simulation, 2011, 22(1): 2
Wang D, Berry M W, Gross L J. On parallelization of a spatiallyexplicit structured ecological model for integrated ecosystem simulation. International Journal of High Performance Computing Applications, 2006, 20(4): 571–581
Quinn M J, Metoyer R A, Hunter-Zaworski K. Parallel implementation of the social forces model. In: Proceedings of the 2nd International Conference in Pedestrian and Evacuation Dynamics. 2003, 63–74
Shook E, Wang S, Tang W. A communication-aware framework for parallel spatially explicit agent-based models. International Journal of Geographical Information Science, 2013, 27(11): 2160–2181
Wang S, Armstrong M P. A theoretical approach to the use of cyberinfrastructure in geographical analysis. International Journal of Geographical Information Science, 2009, 23(2): 169–193
Cağdaş V, Stubkjær E. Design research for cadastral systems. Computers, Environment and Urban Systems, 2011, 35(1): 77–87
Author information
Authors and Affiliations
Corresponding author
Additional information
Mingqiang Guo is a postdoctoral fellow in the China University of Geosciences. He received his PhD degree in geomatics from China University of Geosciences (Wuhan) in 2013, from where he also received his BS degree in computer science in 2007. His research focuses on key techniques for Cyber-GIS performance optimization, parallel spatial computing, computational intensity representation, and load balancing algorithms.
Ying Huang is a postdoctoral fellow, in the Information & Engineering deportment, China University of Geosciences (Wuhan) where she received her PhD degree in 2008. Her research focuses on key techniques for Cyber- GIS framework, concurrent processing performance optimization, big spatial data, spatial cloud computing, web services, OGC services, and load balancing algorithms.
Zhong Xie is a professor of the China University of Geosciences (Wuhan), where he received his PhD and BS degrees in 2002 and 1990, respectively. His research focuses on key techniques for geographical information systems, parallel spatial computing, CyberGIS framework, parallel spatial computing, computational intensity representation, and load balancing algorithms.
Rights and permissions
About this article
Cite this article
Guo, M., Huang, Y. & Xie, Z. A balanced decomposition approach to real-time visualization of large vector maps in CyberGIS. Front. Comput. Sci. 9, 442–455 (2015). https://doi.org/10.1007/s11704-014-3498-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11704-014-3498-7