Simulating urban expansion using cellular automata model with spatiotemporally explicit representation of urban demand

Yang, Jianxin; Tang, Wenwu; Gong, Jian; Shi, Rui; Zheng, Minrui; Dai, Yunzhe

doi:10.1016/j.landurbplan.2022.104640

Cited by 22 publications

(4 citation statements)

References 49 publications

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“…This process-oriented rule regarding human activities and urban population is particularly helpful in agent-based modeling of urban dynamics. Additionally, the spatiotemporal Gaussian model can guide the urban modelers to spatialize the urban demand of a time period into space (each ring) and time, which allows for subtle control of the macrolevel morphology of simulated urban landscapes from a process-oriented paradigm if integrated with a bottom-up simulation tool, such as the cellular automata model [52,53].…”

Section: Major Contributions Of This Studymentioning

confidence: 99%

Spatial Diffusion Waves of Human Activities: Evidence from Harmonized Nighttime Light Data during 1992–2018 in 234 Cities of China

et al. 2023

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This study investigates whether the intensity of human activities conducted by urban populations and carried by urban land follows a wave-shaped diffusion rule using a harmonized DMSP-like NTL dataset during 1992–2018 in 234 cities of China. The results show that variations in the intensity of human activities are diffused in a wave-shaped manner from the urban center to the periphery in cities of different sizes and structures. The results demonstrate that variations in the intensity of human activity also exhibit a wave-shaped diffusion pattern, which is best modeled by a Gaussian function with an average R2 of 0.79 and standard deviation of 0.36 across all fitted functions. The outward movement of these waves in monocentric cities with an urban population <8 million occurred at a pace of ~0.5–1.0 km per year, reaching an average distance of ~18 km from the urban centers. While the pace decreased to ~0.2–0.6 km per year in larger or polycentric cities, the average distance of the waves from the urban centers increased to ~22–25 km in these larger cities. In addition, a process-pattern link between the distance-decayed rule and the wave-shaped rule of human activity dynamics was established. Moreover, a spatiotemporal Gaussian function was further discussed to enable modelers to forecast future variations in the intensity of human activities. The disclosed wave-shape rule and model can benefit the simulation of urban dynamics if integrated with other simulation technologies, such as agent-based models and cellular automata.

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Section: Major Contributions Of This Studymentioning

confidence: 99%

Spatial Diffusion Waves of Human Activities: Evidence from Harmonized Nighttime Light Data during 1992–2018 in 234 Cities of China

et al. 2023

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“…It employs a nonlinear stochastic process to estimate pixel locations and generates a complex matrix of LULC alterations [34]. Cellular automata models have rapidly evolved in several environmental studies and offer valuable insights into the spatiotemporal complexities of land uses in urban areas [35,36].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis and Prediction of Urban Land Use/Land Cover Changes Using a Cellular Automata and Novel Patch-Generating Land Use Simulation Model: A Study of Zhejiang Province, China

Koko,

Han,

et al. 2023

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Urban land use/land cover (LULC) monitoring and prediction are vital for understanding the spatiotemporal change dynamics of future land uses. They provide the necessary data for effectively planning and managing natural land resources. In this study, we analyzed and simulated the changes in urban LULC within Zhejiang Province, a region in China experiencing rapid urbanization. By exploring the historical change dynamics of the region, we observed substantial transformations in the extent of built-up areas, forests, and agricultural land from 1995 to 2020. Specifically, the study area witnessed the expansion in urban built-up areas by approximately 6126.93 km2, while forests and agricultural land witnessed decreases of 3252.47 km2 and 2885.13 km2, respectively. To predict the study area’s future LULC, a cellular automata (CA) model was utilized in combination with an advanced patch-generating land use simulation (PLUS) model. This integrated approach allowed for multiple land use predictions based on different scenarios. Under the baseline scenario (BLS), it was projected that the area of urban expansion in Zhejiang Province would be approximately 4501.62 km2. However, under the scenario of cultivated land and ecological protection, i.e., CLPS and EPS, urban growth was observed to be 538.64 km2 and 1776.16 km2, respectively. These findings indicate that the extent of built-up area development in Zhejiang Province is significantly reduced when the CLPS and EPS are implemented in comparison to the BLS. Therefore, policy interventions are crucial to protect agricultural land and conserve ecological areas. This research provides the scientific data needed for proper planning and serves as reference data for other regions with similar rapid urbanization.

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“…Numerous CA models such as CLUE-S, IDRISI, FLUS, LUSD, and PLUS divide the cell-based model of land-use change into two modules, one involving macro-scale land-use demand and one involving micro-scale changes in land use [18][19][20][21][22]. Multiple models or techniques were integrated into the constrained cellular-based land-use model and now serve as the basis for discerning land demand [23][24][25]. When overall land use is static in a particular area, growth in one type of land use must be accompanied by a decrease in another type (or types) of land use.…”

Section: Introductionmentioning

confidence: 99%

Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways

2023

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Accurately estimating land-use demand is essential for urban models to predict the evolution of urban spatial morphology. Due to the uncertainties inherent in socioeconomic development, the accurate forecasting of urban land-use demand remains a daunting challenge. The present study proposes a modeling framework to determine the scaling relationship between the population and urban area and simulates the spatiotemporal dynamics of land use and land cover (LULC). An allometric scaling (AS) law and a Markov (MK) chain are used to predict variations in LULC. Random forest (RF) and cellular automata (CA) serve to calibrate the transition rules of change in LULC and realize its micro-spatial allocation (MKCARF-AS). Furthermore, this research uses several shared socioeconomic pathways (SSPs) as scenario storylines. The MKCARF-AS model is used to predict changes in LULC under various SSP scenarios in Jinjiang City, China, from 2020 to 2065. The results show that the figure of merit (FoM) and the urban FoM of the MKCARF-AS model improve by 3.72% and 4.06%, respectively, compared with the MKCAANN model during the 2005–2010 simulation period. For a 6.28% discrepancy between the predicted urban land-use demand and the actual urban land-use demand over the period 2005–2010, the urban FoM degrades by 21.42%. The growth of the permanent urban population and urban area in Jinjiang City follows an allometric scaling law with an exponent of 0.933 for the period 2005–2020, and the relative residual and R2 are 0.0076 and 0.9994, respectively. From 2020 to 2065, the urban land demand estimated by the Markov model is 19.4% greater than the urban area predicted under scenario SSP5. At the township scale, the different SSP scenarios produce significantly different spatial distributions of urban expansion rates. By coupling random forest and allometric scaling, the MKCARF-AS model substantially improves the simulation of urban land use.

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Simulating urban expansion using cellular automata model with spatiotemporally explicit representation of urban demand

Cited by 22 publications

References 49 publications

Spatial Diffusion Waves of Human Activities: Evidence from Harmonized Nighttime Light Data during 1992–2018 in 234 Cities of China

Spatial Diffusion Waves of Human Activities: Evidence from Harmonized Nighttime Light Data during 1992–2018 in 234 Cities of China

Spatiotemporal Analysis and Prediction of Urban Land Use/Land Cover Changes Using a Cellular Automata and Novel Patch-Generating Land Use Simulation Model: A Study of Zhejiang Province, China

Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways

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