DocumentCode :
1990060
Title :
Generating On-Demand Web Mapping through Progressive Generalization
Author :
Weihua, Dong
Author_Institution :
Inst. of Geogr. & Remote Sensing, Beijing Normal Univ., Beijing
Volume :
2
fYear :
2008
fDate :
21-22 Dec. 2008
Firstpage :
163
Lastpage :
166
Abstract :
Because web and wireless mobile users of geographical information systems require information that is directly relevant to the specific task in which they are engaged, Traditional generalization techniques produce maps that are general-purpose presents a new set of challenges with the growth of web and wireless of geographical information systems. Therefore, a possible solution to the problem relies on applying task-oriented generalization techniques including simplification adaptive to the diverse requirements for users of web or wireless mobile GIS. In this paper, the level of detail in the generated web mapping is adapted by progressive generalization algorithm, which remains or reduces online web map description data according to the geometric and semantic of the features represented. At the same time, we research topological checking method for web mapping to avoid topological conflicts. Based on this, we generate web mapping on demand and prove effective generalization methods by concrete experiments.
Keywords :
Internet; geographic information systems; mobile computing; task analysis; GIS; geographical information systems; on-demand Web mapping; progressive generalization; task-oriented generalization; wireless mobile users; Displays; Educational technology; Geographic Information Systems; Geography; Geoscience and remote sensing; Information systems; Internet; Ontologies; Roads; Wireless sensor networks; On-demand; cartographic generalization; progressive simplification; web mapping;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Education Technology and Training, 2008. and 2008 International Workshop on Geoscience and Remote Sensing. ETT and GRS 2008. International Workshop on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3563-0
Type :
conf
DOI :
10.1109/ETTandGRS.2008.234
Filename :
5070332
Link To Document :
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