In recent decades, compact urban development and smart cities are recognized as most sustainable urban form in an effort to protect natural environment and decrease air and water pollution. Therefore, evaluation of existing compactness and sustainability of an area is an essential task before the real development takes place. There are few studies regarding city compactness assessment and most of them have considered only a few aspects of compact development analysis. This paper, analyzed urban sustainability of Kajang city (Malaysia) through a comprehensive city compactness assessment using Geographical Information System and latest remote sensing technology. Mixed land use development, urban density and intensity were the main indicators of the analysis. Finally multicriteria decision-making and Bayes theorem was applied for overall compactness assessment. The results classified the zones of the Kajang city in the range of least to most compact zones with the compactness. These promising results can help local government to improve the compactness of least compact zones to make Kajang city more sustainable. Furthermore, the results revealed that efficient public transportation and proper community facilities are the key factors to achieve sustainable urban development.
Abdullahi, S. (2019). City compactness assessment based on Multi-criteria decision making and Bayes theorem. Natural Resource Management, GIS & Remote Sensing, 1(1), 45-59. doi: 10.22121/ngis.2019.85267
MLA
Saleh Abdullahi. "City compactness assessment based on Multi-criteria decision making and Bayes theorem". Natural Resource Management, GIS & Remote Sensing, 1, 1, 2019, 45-59. doi: 10.22121/ngis.2019.85267
HARVARD
Abdullahi, S. (2019). 'City compactness assessment based on Multi-criteria decision making and Bayes theorem', Natural Resource Management, GIS & Remote Sensing, 1(1), pp. 45-59. doi: 10.22121/ngis.2019.85267
VANCOUVER
Abdullahi, S. City compactness assessment based on Multi-criteria decision making and Bayes theorem. Natural Resource Management, GIS & Remote Sensing, 2019; 1(1): 45-59. doi: 10.22121/ngis.2019.85267