Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata

Yao, Yao; Liu, Xiaoping; Xia, Li; Liu, Penghua; Hong, Ye; Zhang, Yatao; Mai, Ke

doi:10.1080/13658816.2017.1360494

Cited by 83 publications

(54 citation statements)

References 84 publications

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“…The FoM indicates the model performance, and is focused on the change. It is defined as the ratio of the intersection and union of the actual result and the predicted result (Pontius et al, 2008;Yao et al, 2017), and is widely used in the comparison of the actual results and the simulation results of urban CA models. The RMSE is commonly used to measure the errors between the simulation outputs and the actual data (Al-Ahmadi, Alahmadi, & Alahmadi, 2016; Tong & Feng, 2020).…”

Section: Assessment Methodsmentioning

confidence: 99%

Analyzing the effects of stochastic perturbation and fuzzy distance transformation on Wuhan urban growth simulation

Bin

Wang

et al. 2020

Transactions in GIS

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Stochastic perturbation and fuzzy distance transformation (FDT) have been widely introduced into the derivation of transition rules to improve the simulation capability of urban cellular automata (CA) models. However, their effects on urban growth simulation have not been revealed. In this article, we compare their effects on urban growth simulation for the city of Wuhan by a sensitivity analysis of the simulation accuracy and simulated urban patterns. We reveal the relationship between the two components and propose an optimized stochastic variable to improve the simulation capability of urban CA models. The results show that the stochastic perturbation is the main factor affecting the simulation accuracy and simulated urban patterns of Wuhan, in that it significantly reduces the simulation accuracy and increases the degree of fragmentation and the proximity and shape complexity of the simulation results. In addition, the change caused by the stochastic perturbation increases with its intensity. Although FDT can restrain the effect of the stochastic perturbation, its inhibitory effect becomes negligible as the stochastic perturbation intensity increases. The optimized stochastic variable can significantly reduce the negative impact of the original stochastic variable, and its improvement is more obvious when the stochastic perturbation intensity is higher.

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Section: Assessment Methodsmentioning

confidence: 99%

Analyzing the effects of stochastic perturbation and fuzzy distance transformation on Wuhan urban growth simulation

Bin

Wang

et al. 2020

Transactions in GIS

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“…In 2016, the permanent resident population in Shenzhen was 12 million, with a gross domestic product (GDP) of approximately 300 billion USD [33]. There are ten districts in Shenzhen, namely: Luohu, Futian, Nanshan, Yantian, Baoan, Longgang, Longhua, Guangming, Pingshan, and Dapeng (Figure 1), among which Luohu, Futian, Nanshan, and Yantian are the four initial special economic zones in Shenzhen [34]. Presently, Futian and Luohu are the downtown areas and Nanshan is the high-technology zone in Shenzhen City [35].…”

Section: Study Areamentioning

confidence: 99%

Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, China

Wang

Qingsheng

2018

IJGI

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Urban land use and transportation are closely associated. Previous studies have investigated the spatial interrelationship between street centralities and land use intensities using land cover data, thus neglecting the social functions of urban land. Taking the city of Shenzhen, China, as a case study, we used reclassified points of interest (POI) data to represent commercial, public service, and residential land, and then investigated the varying interrelationships between the street centralities and different types of urban land use intensities. We calculated three global centralities (“closeness”, “betweenness”, and “straightness”) as well as local centralities (1-km, 2-km, 3-km, and 5-km searching radiuses), which were transformed into raster frameworks using kernel density estimation (KDE) for correlation analysis. Global closeness and straightness are high in the urban core area, and roads with high global betweenness outline the skeleton of the street network. The spatial patterns of the local centralities are distinguished from the global centralities, reflecting local location advantages. High intensities of commercial and public service land are concentrated in the urban core, while residential land is relatively scattered. The bivariate correlation analysis implies that commercial and public service land are more dependent on centralities than residential land. Closeness and straightness have stronger abilities in measuring the location advantages than betweenness. The centralities and intensities are more positively correlated on a larger scale (census block). These findings of the spatial patterns and interrelationships of the centralities and intensities have major implications for urban land use and transportation planning.

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“…As a result, cadastral land parcels have been widely used for the definition of cells in vector CA. In the past decade, the VecGCA, DLPS-VCA and MUGICA models have been developed at the cadastral land parcel scale, with globally-distributed study regions in Canada (Southwest Alberta), China (Shenzhen City) and Spain (Madrid region) (Barreira-González et al., 2017; Moreno et al., 2009; Yao et al., 2017). Generally speaking, for the purposes of land use modelling, land parcels are a better representation of the real world than raster cells (Long and Wu, 2017).…”

Section: Introductionmentioning

confidence: 99%

Land use change simulation and analysis using a vector cellular automata (CA) model: A case study of Ipswich City, Queensland, Australia

Laffan

Pettit

et al. 2019

Environment and Planning B: Urban Analytics and City Science

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The loss of accuracy in vector-raster conversion has always been an issue for land use change models, particularly for raster based Cellular Automata models. Here we describe a vector-based cellular automata (CA) model that uses land parcels as the basic unit of analysis, and compare its results with a raster CA model. Transition rules are calibrated using an artificial neural network (ANN) and historical land use data. Using Ipswich City in Queensland, Australia as the study area, the simulation results show that the vector and raster CA models achieve 96.64% and 93.88% producer’s spatial accuracy, respectively. In addition, the vector CA model achieves a higher kappa coefficient and more consistent frequency of misclassification, while also having faster processing times. Consequently, the vector-based CA model can be applied to explore regulations of land use transformation in urban growth process, and provide a better understanding of likely urban growth to inform city planners.

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Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata

Cited by 83 publications

References 84 publications

Analyzing the effects of stochastic perturbation and fuzzy distance transformation on Wuhan urban growth simulation

Analyzing the effects of stochastic perturbation and fuzzy distance transformation on Wuhan urban growth simulation

Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, China

Land use change simulation and analysis using a vector cellular automata (CA) model: A case study of Ipswich City, Queensland, Australia

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