Driving force of urban growth and regional planning: A case study of China's Guangdong Province

Lu, Chen; Wu, Yuzhe; Shen, Geoffrey Qiping; Wang, Hao

doi:10.1016/j.habitatint.2013.01.006

Cited by 57 publications

(36 citation statements)

References 57 publications

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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%

Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction

Wang

Maduako

2018

European Journal of Remote Sensing

101

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An accurate information on the amount and location of Land use and land cover (LULC) changes is necessary to develop and implement a sustainable-urban planning.This research investigates the potential of an integrated Multi-Layer Perceptron and Markov Chain Analysis (MLP-MCA) to map and accurately predict the future LULC change scenarios in Lagos Metropolitan Region of Nigeria. Multi-temporal LULC datasets derived from remotely sensed Landsat images from 1984, 2000 and 2015 were used for modeling, validation and prediction. Predicted LULC changes for 2030 and 2050 were performed based on the LULC map of 2015 using MLP-MCA method. The result reveals a significant expansion of built-up areas during the whole study period. Analysis of LULC distribution in Lagos metropolitan region shows that about 50% of urban land expansion happened beyond the administrative boundary of Lagos State during the period of 2000-2015. It is predicted that more than 75% of future urban growth will occur across the border of Lagos State, in the neighbouring Ogun State by 2050. These results imply that a strong and consistent collaboration between different states is crucial to establish an effective regional planning framework and ensure a proper planned growth of the metropolitan region.ARTICLE HISTORY

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Section: Selection Of Variablesmentioning

confidence: 99%

Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction

Wang

Maduako

2018

European Journal of Remote Sensing

101

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“…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%

What Brings Youth to Recreational Parks?

Zainol

Au-Yong

2016

PMJ

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Locations of recreational parks are said to have a direct influence on youth physical activities. However, there is still a lack of studies on youth activities at recreational parks. Thus, this study examines the relationship between reasons and habit of youth going to recreational parks through a questionnaire survey. Variables that are measured include enjoying the outdoors, use a particular facility at the park, play sports, picnic and general activities, walk or ride a bicycle for exercise and meeting friends. Logistic regression analysis reveals youths are most likely to visit the park for playing sports, walking or riding a bicycle, provided the facilities and amenities are available. Hence, planning, implementation and maintenance of recreational park play a significant role in promoting outdoor activities and active lifestyle among the youth.

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“…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%

Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling

Naghibi

Delavar

2016

IJGI

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This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA) using the artificial bee colony (ABC) optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO) as intelligent approaches were performed to evaluate the potential of the proposed methods. According to previous studies, swarm intelligence algorithms for solving optimization problems such as discovering transition rules of CA in land use change/urban growth modeling can produce reasonable results. Modeling of urban growth as a dynamic process is not straightforward because of the existence of nonlinearity and heterogeneity among effective involved variables which can cause a number of challenges for traditional CA. ABC algorithm, the new powerful swarm based optimization algorithms, can be used to capture optimized transition rules of CA. This paper has proposed a methodology based on remote sensing data for modeling urban growth with CA calibrated by the ABC algorithm. The performance of ABC-CA, PSO-CA, and CA-logistic models in land use change detection is tested for the city of Urmia, Iran, between 2004 and 2014. Validations of the models based on statistical measures such as overall accuracy, figure of merit, and total operating characteristic were made. We showed that the overall accuracy of the ABC-CA model was 89%, which was 1.5% and 6.2% higher than those of the PSO-CA and CA-logistic model, respectively. Moreover, the allocation disagreement (simulation error) of the simulation results for the ABC-CA, PSO-CA, and CA-logistic models are 11%, 12.5%, and 17.2%, respectively. Finally, for all evaluation indices including running time, convergence capability, flexibility, statistical measurements, and the produced spatial patterns, the ABC-CA model performance showed relative improvement and therefore its superiority was confirmed.

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Driving force of urban growth and regional planning: A case study of China's Guangdong Province

Cited by 57 publications

References 57 publications

Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction

Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction

What Brings Youth to Recreational Parks?

Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling

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