Spatial multi-objective land use optimization toward livability based on boundary-based genetic algorithm: A case study in Singapore

In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-...

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Main Authors: CAO, Kai, LIU, Muyang, WANG, Shu, LIU, Mengqi, ZHANG, Wenting, MENG, Qiang, HUANG, Bo
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Language:English
Published: Institutional Knowledge at Singapore Management University 2020
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Online Access:https://ink.library.smu.edu.sg/sis_research/5119
https://ink.library.smu.edu.sg/context/sis_research/article/6122/viewcontent/Spatial_Multi_Objective_Land_Use_Optimization_towa.pdf
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spelling sg-smu-ink.sis_research-61222023-10-30T07:02:21Z Spatial multi-objective land use optimization toward livability based on boundary-based genetic algorithm: A case study in Singapore CAO, Kai LIU, Muyang WANG, Shu LIU, Mengqi ZHANG, Wenting MENG, Qiang HUANG, Bo In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have been successfully conducted, which can demonstrate the effectiveness of the spatial multi-objective land use optimization model developed in this research as well as the robustness and reliability of computer-generated solutions. In addition, the comparison between the computer-generated solutions and the two real planned scenarios has also clearly demonstrated that our generated solutions are much better in terms of fitness values. Lastly, the limitation and future direction of this research have been discussed. 2020-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5119 info:doi/10.3390/ijgi9010040 https://ink.library.smu.edu.sg/context/sis_research/article/6122/viewcontent/Spatial_Multi_Objective_Land_Use_Optimization_towa.pdf http://creativecommons.org/licenses/by/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Accessibility Boundary-based genetic algorithm Livability Singapore Smart planning Spatial multi-objective land use optimization Asian Studies Geographic Information Sciences Theory and Algorithms Urban Studies and Planning
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Accessibility
Boundary-based genetic algorithm
Livability
Singapore
Smart planning
Spatial multi-objective land use optimization
Asian Studies
Geographic Information Sciences
Theory and Algorithms
Urban Studies and Planning
spellingShingle Accessibility
Boundary-based genetic algorithm
Livability
Singapore
Smart planning
Spatial multi-objective land use optimization
Asian Studies
Geographic Information Sciences
Theory and Algorithms
Urban Studies and Planning
CAO, Kai
LIU, Muyang
WANG, Shu
LIU, Mengqi
ZHANG, Wenting
MENG, Qiang
HUANG, Bo
Spatial multi-objective land use optimization toward livability based on boundary-based genetic algorithm: A case study in Singapore
description In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have been successfully conducted, which can demonstrate the effectiveness of the spatial multi-objective land use optimization model developed in this research as well as the robustness and reliability of computer-generated solutions. In addition, the comparison between the computer-generated solutions and the two real planned scenarios has also clearly demonstrated that our generated solutions are much better in terms of fitness values. Lastly, the limitation and future direction of this research have been discussed.
format text
author CAO, Kai
LIU, Muyang
WANG, Shu
LIU, Mengqi
ZHANG, Wenting
MENG, Qiang
HUANG, Bo
author_facet CAO, Kai
LIU, Muyang
WANG, Shu
LIU, Mengqi
ZHANG, Wenting
MENG, Qiang
HUANG, Bo
author_sort CAO, Kai
title Spatial multi-objective land use optimization toward livability based on boundary-based genetic algorithm: A case study in Singapore
title_short Spatial multi-objective land use optimization toward livability based on boundary-based genetic algorithm: A case study in Singapore
title_full Spatial multi-objective land use optimization toward livability based on boundary-based genetic algorithm: A case study in Singapore
title_fullStr Spatial multi-objective land use optimization toward livability based on boundary-based genetic algorithm: A case study in Singapore
title_full_unstemmed Spatial multi-objective land use optimization toward livability based on boundary-based genetic algorithm: A case study in Singapore
title_sort spatial multi-objective land use optimization toward livability based on boundary-based genetic algorithm: a case study in singapore
publisher Institutional Knowledge at Singapore Management University
publishDate 2020
url https://ink.library.smu.edu.sg/sis_research/5119
https://ink.library.smu.edu.sg/context/sis_research/article/6122/viewcontent/Spatial_Multi_Objective_Land_Use_Optimization_towa.pdf
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