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Proceedings Paper

Interactive decision-making support model in MOSD
Author(s): Yang Liu; Ze-ying Lan; Yao-lin Liu; Liang-jun Qin
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Paper Abstract

The Multi-objective spatial optimization problem is common in the word, which usually has a set of non-dominated resolutions, also called Pareto resolutions, instead of a single ideal one. So, the Multi-objectives spatial decision support system (MOSDSS) has two vital basements: how to acquire all the Pareto resolutions by Multi-objective optimization arithmetic, and how to analysis and appraise the candidates to determinate the final satisfying solution. At present, there are abundant research fruit for the former problem, however the latter one hasn't attracted abroad attention in the field. Nowadays, the findings about analyzing and evaluating the Pareto resolutions mainly focus on three aspects: the visual expression of candidates, appraising the comparability among the solutions, and designing the prototype system of visual support tools, which are lack of systemic conclusion and summarization. Hence, this paper emphasizes the latter problem of MOSDSS and puts up an interactive decision-making support model to largely improve the efficiency of analyzing and evaluating the Pareto resolutions. This model is composed of 3 pivotal parts: the geographic brush mechanism, the similarity querying operators as well as the interactive searching method. Then, the paper designs a prototype system on the base of the model, which is successfully tested in the exam.

Paper Details

Date Published: 16 October 2009
PDF: 12 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74924G (16 October 2009); doi: 10.1117/12.838443
Show Author Affiliations
Yang Liu, Guangzhou Urban Planning and Design Survey Research Institute (China)
Ze-ying Lan, Wuhan Univ. (China)
Yao-lin Liu, Wuhan Univ. (China)
Liang-jun Qin, Guangzhou Urban Planning and Design Survey Research Institute (China)


Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining
Yaolin Liu; Xinming Tang, Editor(s)

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