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Open AccessArticle

Spatial Multi-Objective Land Use Optimization toward Livability Based on Boundary-Based Genetic Algorithm: A Case Study in Singapore

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Department of Geography, National University of Singapore, Singapore 119077, Singapore
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School of Information Systems, Singapore Management University, Singapore 188065, Singapore
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College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
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Department of Civil and Environmental Engineering, National University of Singapore, Singapore 119077, Singapore
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Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(1), 40; https://doi.org/10.3390/ijgi9010040
Received: 8 December 2019 / Revised: 28 December 2019 / Accepted: 30 December 2019 / Published: 14 January 2020
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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. View Full-Text
Keywords: spatial multi-objective land use optimization; boundary-based genetic algorithm; livability; accessibility; smart planning; Singapore spatial multi-objective land use optimization; boundary-based genetic algorithm; livability; accessibility; smart planning; Singapore
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Cao, K.; Liu, M.; Wang, S.; Liu, M.; Zhang, W.; Meng, Q.; Huang, B. Spatial Multi-Objective Land Use Optimization toward Livability Based on Boundary-Based Genetic Algorithm: A Case Study in Singapore. ISPRS Int. J. Geo-Inf. 2020, 9, 40.

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