A balanced decomposition approach to real-time visualization of large vector maps in CyberGIS
Mingqiang GUO, Ying HUANG, Zhong XIE
A balanced decomposition approach to real-time visualization of large vector maps in CyberGIS
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 Cyber-GIS 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, vertical multistage” 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.
real-time visualization / large vector map / balanced decomposition / CyberGIS / load balance
[1] |
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
CrossRef
Google scholar
|
[2] |
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
CrossRef
Google scholar
|
[3] |
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
CrossRef
Google scholar
|
[4] |
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
CrossRef
Google scholar
|
[5] |
Wang S. CyberGIS: blueprint for integrated and scalable geospatial software ecosystems. International Journal of Geographical Information Science, 2013, 27(11): 2119-2121
CrossRef
Google scholar
|
[6] |
Kelly N M, Tuxen K. WebGIS for monitoring sudden oak death in coastal California. Computers, Environment and Urban Systems, 2003, 27(5): 527-547
CrossRef
Google scholar
|
[7] |
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
CrossRef
Google scholar
|
[8] |
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
CrossRef
Google scholar
|
[9] |
Pessina V, Meroni F. A WebGis tool for seismic hazard scenarios and risk analysis. Soil Dynamics and Earthquake Engineering, 2009, 29(9): 1274-1281
CrossRef
Google scholar
|
[10] |
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
CrossRef
Google scholar
|
[11] |
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
CrossRef
Google scholar
|
[12] |
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
CrossRef
Google scholar
|
[13] |
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
CrossRef
Google scholar
|
[14] |
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
|
[15] |
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
CrossRef
Google scholar
|
[16] |
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
CrossRef
Google scholar
|
[17] |
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
CrossRef
Google scholar
|
[18] |
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
CrossRef
Google scholar
|
[19] |
Zhang L, Yang C, Tong X, Rui X. Visualization of large spatial data in networking environments. Computers and Geosciences, 2007, 33(9): 1130-1139
CrossRef
Google scholar
|
[20] |
Zhang L, Ren Y, Guo Z. Transmission and visualization of large geographical maps. Journal of Photogrammetry and Remote Sensing, 2011, 66(1): 73-80
CrossRef
Google scholar
|
[21] |
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
CrossRef
Google scholar
|
[22] |
Yang B, Purves R S, Weibel R. Variable-resolution compression of vector data. GeoInformatica, 2008, 12(3): 357-376
CrossRef
Google scholar
|
[23] |
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
CrossRef
Google scholar
|
[24] |
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
CrossRef
Google scholar
|
[25] |
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
CrossRef
Google scholar
|
[26] |
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
CrossRef
Google scholar
|
[27] |
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
CrossRef
Google scholar
|
[28] |
Tang W. Parallel construction of large circular cartograms using graphics processing units. International Journal of Geographical Information Science, 2013, 27(11): 2182-2206
CrossRef
Google scholar
|
[29] |
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
CrossRef
Google scholar
|
[30] |
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
CrossRef
Google scholar
|
[31] |
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
CrossRef
Google scholar
|
[32] |
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
CrossRef
Google scholar
|
[33] |
Parry H R, Bithell M. Large scale agent-based modelling: a review and guidelines for model scaling. In: Proceedings of Agent-Based Models of Geographical Systems. 2012, 271-308
|
[34] |
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
CrossRef
Google scholar
|
[35] |
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
CrossRef
Google scholar
|
[36] |
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
CrossRef
Google scholar
|
[37] |
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
|
[38] |
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
CrossRef
Google scholar
|
[39] |
Wang S, Armstrong M P. A theoretical approach to the use of cyber-infrastructure in geographical analysis. International Journal of Geographical Information Science, 2009, 23(2): 169-193
CrossRef
Google scholar
|
[40] |
Çağdaş V, Stubkjær E. Design research for cadastral systems. Computers, Environment and Urban Systems, 2011, 35(1): 77-87
CrossRef
Google scholar
|
/
〈 | 〉 |