2013
DOI: 10.1016/j.habitatint.2013.01.006
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Driving force of urban growth and regional planning: A case study of China's Guangdong Province

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Cited by 59 publications
(36 citation statements)
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“…Roads and socioeconomic centers always play a significant role in urbanization as they provide residents higher accessibility to daily needs and resources. Besides, population and GDP are also considered as macro-factors that influence urban expansion (Lu, Wu, Shen, & Wang, 2013;Wu & Zhang, 2012). Neighbourhood effects usually illustrates that a nonbuilt-up cell is more likely to be converted into builtup land if it is surrounded by built-up land.…”
Section: Selection Of Variablesmentioning
confidence: 99%
“…Roads and socioeconomic centers always play a significant role in urbanization as they provide residents higher accessibility to daily needs and resources. Besides, population and GDP are also considered as macro-factors that influence urban expansion (Lu, Wu, Shen, & Wang, 2013;Wu & Zhang, 2012). Neighbourhood effects usually illustrates that a nonbuilt-up cell is more likely to be converted into builtup land if it is surrounded by built-up land.…”
Section: Selection Of Variablesmentioning
confidence: 99%
“…Industrialisation or commercialisation which implies economic factor is also one of the important determinants in promoting urban development (Lu, Wu, Shen & Wang, 2013;Liu, Wang & Long, 2008;Lo & Yang, 2002). It offers many job opportunities which attract employees to stay in the vicinity and also influences road network development to stimulate economic factor in regional trade (Lu et.…”
Section: Background Of the Studymentioning
confidence: 99%
“…However, determining transition rules (or calibrating CA) becomes challenging and complicated when CA modeling is used to simulate a dynamic process such as urban growth modeling because of dealing with several heterogeneous and nonlinear variables and the complex relationships of effective land use conversion parameters [57,[60][61][62]. Among various methods to calibrate CA models, researchers have been able to solve the complex problems by SI algorithms such as ABC and PSO in order to overcome their inherent limitations such as subjectivity, black box nature, and linearity assumptions among effective urban growth factors [34].…”
Section: Urban Growth Modeling By Camentioning
confidence: 99%