Urban expansion simulation and scenario prediction using cellular automata: comparison between individual and multiple influencing factors

Feng, Yongjiu; Wang, Jiafeng; Tong, Xiaohua; Shafizadeh‐Moghadam, Hossein; Cai, Zongbo; Chen, Shurui; Lei, Zhenkun; Gao, Chen

doi:10.1007/s10661-019-7451-y

Cited by 19 publications

(5 citation statements)

References 49 publications

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“…The major differences between these two scenarios are observed near water bodies, as shown by the line chart. This may be due to the fact that the same facilities or service points can cause very different contributions to the urban scenario prediction when they are reflected by different methods, such as proximity and accessibility (Feng, Wang, Tong, & Shafizadeh-Moghadam, Cai, et al, 2019). Meanwhile, in both models, changes in the urban centroid are found within a 3 × 3 km rectangle.…”

Section: Discussionmentioning

confidence: 99%

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A comparison of proximity and accessibility drivers in simulating dynamic urban growth

Wang

Feng

Wei

et al. 2020

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Dynamic urban growth is affected by various driving factors which are important for building cellular automata (CA) models. Two categories of urban-growth drivers (i.e., proximity and accessibility) have not been well differentiated in simulating urban patterns. We built two CA models (Pro-CA PSO and Acc-CA PSO ) using particle swarm optimization (PSO) incor-Cellular automata (CA) models are powerful tools for modeling dynamic spatial processes and geographical phenom-

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Section: Discussionmentioning

confidence: 99%

“…For instance, the railway's proximity factor promotes urban growth while its accessibility factor resists urban growth. In addition to the calculation methods of quantizing factors, the correlation among factors would cause the changes in their effects on urban growth (Feng, Wang, Tong, Shafizadeh-Moghadam, Cai, et al, 2019).…”

Section: Resistmentioning

confidence: 99%

A comparison of proximity and accessibility drivers in simulating dynamic urban growth

Wang

Feng

Wei

et al. 2020

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“…CA is considered the simplest type of dynamic spatial model (Sfa et al, 2020;White & Engelen, 2000) and is a grid-based modeling approach where each cell is in a specific state, in this case, specific land use or cover. Five important components prepared and determined in the CA process, including cell space, cell state, neighborhood, transition rules, and iteration time (Feng et al, 2019). Time runs in discrete time steps.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Land Cover Change Prediction Models: A Case Study in Kedungkandang District, Malang City

Hariyanto,

Yudono,

Wicaksono

2024

Geoplanning J. Geomatics Plann.

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The infrastructure of Malang City is currently being directed towards the eastern and southeastern parts, Kedungkandang District. Infrastructure plays an important role in the aspect of land cover change, which raises the complexity of the emergence of urban forms and dynamics. This study compares three models, Artificial Neural Network (ANN), Logistic Regression (LR), and Multi-Criteria Evaluation (MCE), to predict changes in land cover in the Kedungkandang District using the Cellular Automata (CA) approach. The prediction results indicate that the ANN and MCE models have the highest overall Kappa values (prediction accuracy), while the ANN and LR models have the highest location-specific Kappa values. However, overall, the ANN model demonstrates the highest accuracy and performance among the other two models. This research makes a significant contribution to urban planning by highlighting the importance of using machine learning-based technology to predict land cover changes in Malang City, particularly in the Kedungkandang District. Stakeholders can leverage this technology to design more effective and sustainable infrastructure policies and implement preventive measures to mitigate the negative impacts of uncontrolled urban growth.

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“…erefore, how to guarantee a continuous demand for urban land for socialeconomic development in the new era and how to clarify the formation mechanism of urban growth in order to reasonably control the urban scale, delimit the urban growth boundary, and optimize the spatial pattern of urban land have become the working emphases of current urban management [11,12]. Scholars have carried out extensive research to clarify those factors that drive urban expansion [13,14], strengthen the development and application of dynamic monitoring technologies for urban expansion [15,16], and accelerate sustainable urban spatial planning [17]. e scientific literature is an important and authoritative knowledge carrier.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Identifying Key and Common Themes Based on Text Mining: An Example in the Field of Urban Expansion

Zhang

Lin

et al. 2021

Discrete Dynamics in Nature and Society

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Urban land use is a core area of multidisciplinary research that involves geography, land science, and urban planning. With the rapid progress of global urbanization, urban expansion has become a research focus in recent years. Therefore, how to scientifically and accurately identify key and common themes in the urban expansion literature has become crucial for scientific research institutions in various countries. This paper proposes a new framework for identifying such themes based on an analysis of scientific literature and by using text mining and thematic evolutionary analysis. First, the latent Dirichlet allocation algorithm is used to capture the thematic clustering of scientific literature. Second, the key degree of the thematic node in the thematic evolution transfer network is used to represent the key feature of a theme, and the PageRank algorithm is employed to measure the critical score of this theme. When recognizing common themes, the common features of various themes are digitized and mapped to a specially selected quadratic function to measure the degree of commonness. Finally, the hidden Markov model is used to build a thematic prediction model. This method can efficiently identify key and common themes from the literature and provide theoretical and technical support for future research in related fields.

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Urban expansion simulation and scenario prediction using cellular automata: comparison between individual and multiple influencing factors

Cited by 19 publications

References 49 publications

A comparison of proximity and accessibility drivers in simulating dynamic urban growth

A comparison of proximity and accessibility drivers in simulating dynamic urban growth

Comparison of Land Cover Change Prediction Models: A Case Study in Kedungkandang District, Malang City

A New Method for Identifying Key and Common Themes Based on Text Mining: An Example in the Field of Urban Expansion

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