scholarly journals Evaluating the Capability of Geographically Weighted Regression in Improvement of Urban Growth Simulation Performance Using Cellular Automata

2018 ◽  
Vol 6 (2) ◽  
pp. 43-64
Author(s):  
Babak Mirbagheri ◽  
Abbas Alimohammadi ◽  
◽  
Keyword(s):  
Cellular Automata ◽  
Urban Growth ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Growth Simulation ◽  
Simulation Performance

scholarly journals Integrating Spatial Markov Chains and Geographically Weighted Regression-Based Cellular Automata to Simulate Urban Agglomeration Growth: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 633
Author(s):  
Yabo Zhao ◽  
Dixiang Xie ◽  
Xiwen Zhang ◽  
Shifa Ma
Keyword(s):  
Hong Kong ◽  
Markov Chain ◽  
Cellular Automata ◽  
Urban Growth ◽  
Geographically Weighted Regression ◽  
Spatial Organization ◽  
Urban Agglomeration ◽  
Rapid Expansion ◽  
Weighted Regression ◽  
Bay Area

Urban agglomeration is an important spatial organization mode in China’s attempts to attain an advanced (mature) stage of urbanization, and to understand its consequences, accurate simulation scenarios are needed. Compared to traditional urban growth simulations, which operate on the scale of a single city, urban agglomeration considers interactions among multiple cities. In this study, we combined a spatial Markov chain (SMC) (a quantitative composition module) with geographically weighted regression-based cellular automata (GWRCA) (a spatial allocation module) to predict urban growth in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), an internationally important urban agglomeration in southern China. The SMC method improves on the traditional Markov chain technique by taking into account the interaction and influence between each city to predict growth quantitatively, whereas the geographically weighted regression (GWR) gives an empirical estimate of urban growth suitability based on geospatial differentiation on the scale of an urban agglomeration. Using the SMC model to forecast growth in the GBA in the year 2050, our results indicated that the rate of smaller cities will increase, while that of larger cities will slow down. The coastal belt in the core areas of the GBA as well as the region’s peripheral cities are most likely to be areas of development by 2050, while established cities such as Shenzhen and Dongguan will no longer experience rapid expansion. Compared with traditional simulation models, the SMC-GWRCA was able to consider spatiotemporal interactions among cities when forecasting changes to a large region like the GBA. This study put forward a development scenario for the GBA for 2050 on the scale of an urban agglomeration to provide a more credible scenario for spatial planning. It also provided evidence in support of using integrated SMC-GWRCA models, which, we maintain, offer a more efficient approach for simulating urban agglomeration development than do traditional methods.

Modeling ESV losses caused by urban expansion using cellular automata and geographically weighted regression

2020 ◽  
Vol 712 ◽  
pp. 136509 ◽  
Author(s):  
Shurui Chen ◽  
Yongjiu Feng ◽  
Xiaohua Tong ◽  
Song Liu ◽  
Huan Xie ◽  
...  
Keyword(s):  
Cellular Automata ◽  
Geographically Weighted Regression ◽  
Urban Expansion ◽  
Weighted Regression

scholarly journals Implementation and calibration of a new irregular cellular automata-based model for local urban growth simulation: The MUGICA model

2017 ◽  
Vol 46 (2) ◽  
pp. 243-263 ◽  
Author(s):  
Pablo Barreira-González ◽  
Francisco Aguilera-Benavente ◽  
Montserrat Gómez-Delgado
Keyword(s):  
Cellular Automata ◽  
Urban Growth ◽  
Confusion Matrix ◽  
Good Choice ◽  
Random Component ◽  
Simulation Accuracy ◽  
Growth Simulation ◽  
Geographic Space ◽  
Irregular Structure ◽  
Geographical Environment

Cellular automata-based models have traditionally employed regular grids to represent the geographical environment when simulating urban growth or land use change. Over the last two decades, the scientific community has introduced the use of other spatial structures in an attempt to represent the processes simulated by these models more realistically. Cadastre parcels are a good choice when simulating urban growth at local scales, where pixels or regular cells do not represent the geographic space properly. Furthermore, the implementation and calibration of key factors such as accessibility and suitability have not been sufficiently explored in models employing irregular structures. This paper presents a fully calibrated model to simulate urban growth: Model for Urban Growth simulation using Irregular Cellular Automata. The model uses the irregular structure of the cadastre and its smallest unit: the cadastral parcel. The factors included are based on the traditional Neighbourhood, Accessibility, Suitability and Zoning Status modelling schema, frequently employed in other models. Each factor was implemented and calibrated for the irregular structure employed by the model, and a new approach was explored to introduce a random component that would reproduce illegal growth. Several versions of Model for Urban Growth simulation using Irregular Cellular Automata were produced to calibrate the model within the period 2000–2010. The results obtained from the simulations were compared against observed growth for 2010, adapting the traditional confusion matrix to irregular space. A new metric is proposed, called growth simulation accuracy, which measures how well the model locates urban growth.

A grey wolf optimizer-cellular automata integrated model for urban growth simulation and optimization

2019 ◽  
Vol 23 (4) ◽  
pp. 672-687 ◽  
Author(s):  
Min Cao ◽  
Mengxue Huang ◽  
Ruqi Xu ◽  
Guonian Lü ◽  
Min Chen
Keyword(s):  
Cellular Automata ◽  
Urban Growth ◽  
Integrated Model ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Growth Simulation ◽  
Simulation And Optimization
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