Evaluating future railway-induced urban growth of twelve cities using multiple SLEUTH models with open-source geospatial inputs

Varquez, Alvin C. G.; Dong, Sifan; Hanaoka, Shinya; Kanda, Manabu

doi:10.1016/j.scs.2023.104442

Cited by 12 publications

(1 citation statement)

References 54 publications

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“…Previous studies have mainly used models such as SLEUTH [41] and FLUS [42] to simulate urban growth. These models are encapsulated and easy to use [43].…”

Section: Urban Growth Simulationmentioning

confidence: 99%

Quota and Space Allocations of New Urban Land Supported by Urban Growth Simulations: A Case Study of Guangzhou City, China

Zhu

Liu

et al. 2023

Land

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Previous allocations of new urban land were ineffective because they lacked synergy between quota and space, challenging the government planning authority. This study proposes a new and more reasonable urban land allocation method to guide the smart growth of cities. We used a logistic regression model and multisource data to explore the laws of urban growth and employed a cellular automata (CA) model to simulate this under inertial and constrained scenarios. In addition, the disparities between both scenarios concerning allocation were analyzed. We realized the synergy of quota and space allocations of new urban land through urban growth simulation. Further, the allocation of new urban land was more consistent with the development strategy of Guangzhou under a constrained scenario. The allocation of space was more regular and concentrated under a constrained scenario, which aligns with the requirements of the Government Land Space Planning. Additionally, in the constrained scenario, the bottom lines of cultivated land protection, ecological service, and geological safety were better controlled. This study compensated for the shortcomings of the disjoined quota and space allocations of new urban land and proved that a constrained scenario can more effectively promote reasonable urban growth.

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“…Previous studies have mainly used models such as SLEUTH [41] and FLUS [42] to simulate urban growth. These models are encapsulated and easy to use [43].…”

Section: Urban Growth Simulationmentioning

confidence: 99%

Quota and Space Allocations of New Urban Land Supported by Urban Growth Simulations: A Case Study of Guangzhou City, China

Zhu

Liu

et al. 2023

Land

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Geospatial Analysis of Urban Growth

Bhailume,

Sonawane

2024

Advances in Geographical and Environmental Sciences

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Deep learning-based modeling of land use/land cover changes impact on land surface temperature in Greater Amman Municipality, Jordan (1980–2030)

Alkaraki,

Hazaymeh,

Al-Tarawneh

et al. 2024

GeoJournal

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Modeling the impacts of Land Use/Land Cover changes (LULCC) on Land Surface Temperature (LST) is crucial in understanding and managing urban heat islands, climate change, energy consumption, human health, and ecosystem dynamics. This study aimed to model past, present, and future LULCC on Land Surface Temperatures in the Greater Amman Municipality (GAM) in Jordan between 1980 and 2030. A set of maps for land cover, LST, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and topography was integrated into the Cellular Automata-Artificial Neural Network (CA-ANN) and the Long-Short-Term Model (LSTM) models to predict the LULC and LST for 2030. The results showed an expansion of urban areas in GAM from 54.13 km2 (6.6%) in 1980 to 374.1 km2 (45.3%) in 2023. However, agricultural areas decreased from 152.13 km2 (18.5%) in 1980 to 140.38 km2 (17%) in 2023, while barren lands decreased from 54.44 km2 (6.6%) in 1980 to 34.71 km2 (4.22%) in 2023. Forested areas declined from 4.58 km2 (0.56%) in 1980 to 4.35 km2 (0.53%) in 2023. Rangelands/ sparsely vegetated areas declined from 557 km2 (67.7%) in 1980 to 270.71 km2 (32.9%) in 2023. The results of modeling LST showed an increase in average LST for all land cover types, with the most significant increases evident within urban areas and Rangelands/Sparsely vegetated areas. The slightest increase in LST was within forested areas as the average LST increased from 28.42 °C in 1980 to 34.16 °C in 2023. The forecasts for the future showed a continuous increase in LST values in all land cover types. These findings highlight the impact of land surface dynamics and their impact on increasing land surface temperature, which urges the adoption of more sustainable planning policies for more livable and thermally comfortable cities.

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Evaluating future railway-induced urban growth of twelve cities using multiple SLEUTH models with open-source geospatial inputs

Cited by 12 publications

References 54 publications

Quota and Space Allocations of New Urban Land Supported by Urban Growth Simulations: A Case Study of Guangzhou City, China

Quota and Space Allocations of New Urban Land Supported by Urban Growth Simulations: A Case Study of Guangzhou City, China

Geospatial Analysis of Urban Growth

Deep learning-based modeling of land use/land cover changes impact on land surface temperature in Greater Amman Municipality, Jordan (1980–2030)

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