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

Wang, Rong; Feng, Yang; Wei, Yongliang; Tong, Xiaohua; Zhai, Shuting; Zhou, Yilun; Wu, Peiqi

doi:10.1111/tgis.12707

Cited by 11 publications

(3 citation statements)

References 66 publications

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“…Table 1 presents a set of auxiliary data that was used as the urbanization driving forces. In simulating urban sprawl, the probability of land‐use change is often determined by using factors that drive urban development, where proximity variables often represent factors of accessibility to markets (Wang et al., 2020). We normalized all driving forces between 0 and 1.…”

Section: Methodsmentioning

confidence: 99%

Modeling urban regions: Comparing random forest and support vector machines for cellular automata

Rienow

Mustafà

Krelaus

et al. 2021

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Cellular automaton (CA) are important tools that provide insight into urbanization dynamics and possible future patterns. The calibration process is the core theme of these models. This study compares the performance of two common machine‐learning classifiers, random forest (RF), and support vector machines (SVM), to calibrate CA. It focuses on the sensitivity analysis of the sample size and the number of input variables for each classifier. We applied the models to the Wallonia region (Belgium) as a case study to demonstrate the performance of each classifier. The results highlight that RF produces a land‐use pattern that simulates the observed pattern more precisely than SVM especially with a low sample size, which is important for study areas with low levels of land‐use change. Although zoning information notably enhances the accuracy of SVM‐based probability maps, zoning marginally influences the RF‐derived probability maps. In the case of the SVM, the CA model did not significantly improve due to the increased sample size. The performance of the 5,000 sample size was observed to be better than the 15,000 sample size. The RF‐driven CA had the best performance with a high sample, while zoning information was excluded.

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Section: Methodsmentioning

confidence: 99%

Modeling urban regions: Comparing random forest and support vector machines for cellular automata

Rienow

Mustafà

Krelaus

et al. 2021

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“…The YRD is a region dominated by impact plains [24,25], and studies have found that its geological structure is not sufficiently stable or geologically strong [10,26,27]. Since cities in the YRD are part of the same hydrogeologic unit and the groundwater aquifer systems are interconnected, surface deformation in one area can have a significant impact on neighboring areas.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Surface Deformation Monitoring Using SBAS-InSAR and Intelligent Prediction in Typical Cities of Yangtze River Delta

Wang,

Feng,

Tong

et al. 2023

Remote Sensing

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Large-scale short-term monitoring and prediction of surface deformation are of great significance for the prevention and control of geohazards in rapidly urbanizing developing cities. Most studies focus on individual cities, but it would be more meaningful to address large urban agglomerations and consider the relevance of the regions within them. In addition, the commonly used linear fitting prediction methods cannot accurately capture the dynamic mechanisms of deformation. In this study, we proposed an automatic PS extraction method (named PS-SBAS-InSAR) that improves SBAS-InSAR to extract surface deformation and an Informer-based short-term surface deformation prediction method for case studies in 16 typical cities of the Yangtze River Delta (YRD). The results show that PS-SBAS-InSAR successfully extracted accurate surface deformation sequences of the YRD. During the period from January 2019 to January 2021, the YRD experienced a slight deformation with an average deformation rate within [−4, 4] mm/year. Geographically neighboring cities may have associated deformation distributions and similar deformation trends, as indicated by average deformation rate maps and landscape metrics. Both types of deformation (i.e., subsidence/uplift) tend to occur simultaneously, with specific areas of subsidence/uplift occurring in close proximity to areas of concentrated deformation. The Informer model effectively captured the time-series variation in surface deformation, suggesting a slowdown of deformation over the next two months (February 2021–March 2021). Our work contributes to a better understanding of changes and trends in large-scale surface deformation and provides useful methods for monitoring and predicting surface deformation in coastal areas.

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“…In line with this, simulation techniques are used to determine and model the behavior of a complex and dynamic system (Batty, 2007; Benenson & Torrens, 2004). In this context, cellular automata (CA)‐based simulation methods are frequently used to model urban growth and LULC changes (Chaudhuri & Clarke, 2013; Clarke & Gaydos, 1998; Clewlow, 1989; Dennunzio, Formenti, & Kurka, 2012; Di Lena & Margara, 2008; Wang et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Using the T‐EFA method in a cellular automata‐based urban growth simulation's calibration step

Ayazli

Yakup

Bilen

2022

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Changes in land cover driven by urban sprawl increase the threat of urbanization of forests and agricultural lands. Therefore, monitoring urban sprawl by creating simulation models is frequently carried out to understand sustainable city management. Cellular automata‐based models are mostly preferred to reduce the damage led by urban sprawl, and the SLEUTH model is the most well known. Several methods have been developed for the SLEUTH model calibration step, such as optimum SLEUTH metrics and total exploratory factor analysis (T‐EFA), to improve the model accuracy. This study aims to create a high‐accuracy urban growth simulation model using low‐resolution data, investigate the T‐EFA method's success in the calibration step, and find the urban sprawl effects on land cover change. Istanbul was selected as our study area due to witnessing its tremendous urban sprawl since the 1950s. According to our results, the urban growth that occurred between 2000 and 2018 could be defined more closely to reality using the T‐EFA method, and Istanbul will continue to grow until 2040, with approximately 428.7 km2 of agricultural lands, 553.4 km2 of forests, and 0.1 km2 of wetlands being transformed to urban. In addition, the geologically risky areas under threat of urbanization will increase by 60% between 2018 and 2040.

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

Cited by 11 publications

References 66 publications

Modeling urban regions: Comparing random forest and support vector machines for cellular automata

Modeling urban regions: Comparing random forest and support vector machines for cellular automata

Large-Scale Surface Deformation Monitoring Using SBAS-InSAR and Intelligent Prediction in Typical Cities of Yangtze River Delta

Using the T‐EFA method in a cellular automata‐based urban growth simulation's calibration step

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