Delineation of Urban Growth Boundaries Using a Patch-Based Cellular Automata Model under Multiple Spatial and Socio-Economic Scenarios

Yang, Jianxin; Gong, Jian; Tang, Wenwu; Shen, Yang; Liu, Chunyan; Gao, Jing

doi:10.3390/su11216159

Cited by 24 publications

(13 citation statements)

References 70 publications

(90 reference statements)

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“…The presented scenario simulation illustrates the urban land pattern in 2030 if the historical development trend is maintained. In order to maintain sustainable development, some unfavorable development needs to be identified and controlled by planning tools (Yang et al, 2019). Land-use changes in urban environments are stochastically and complex processes influenced by government decisions and urban planning.…”

Section: Future Simulationmentioning

confidence: 99%

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Integrating a deep forest algorithm with vector‐based cellular automata for urban land change simulation

Zhuang

Yan

et al. 2022

Transactions in GIS

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With the capacity to represent irregular geographical entities precisely, vector‐based cellular automata (VCA) have been extensively employed in urban land change simulation at the land parcel level. However, while more driving factors are considered, modeling the complicated nonlinear relationship between land parcel attributes and multiple land‐use changes is increasingly difficult. Moreover, in VCA, the driving factors are unstructured and cannot be directly modeled by traditional deep learning methods, which can only be applied to structured data processing. In order to address these problems, a new VCA model DF‐VCA that adopts the deep forest (DF) algorithm for mining CA transition rules was proposed to simulate urban land‐use change at the parcel level. The DF algorithm is a new deep learning method that can directly mine high‐level features from unstructured data and obtain accurate land‐use transition rules. The proposed DF‐VCA model was applied to simulate the urban land change in Shenzhen, China. Compared with several traditional VCA models, the DF‐VCA model achieved the best simulation performance at parcel‐level (Figure of Merit = 39.88%), pattern‐level (similarity = 96.47%), and community‐level (correlation coefficient = 0.9269). The results show that the DF‐VCA model with strong representative learning ability could simulate urban land change at fine scales precisely. Furthermore, the proposed DF‐VCA model was applied in Shenzhen's future land change simulations to guide sustainable urban development.

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Section: Future Simulationmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Integrating a deep forest algorithm with vector‐based cellular automata for urban land change simulation

Zhuang

Yan

et al. 2022

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“…According to He et al [50], China's Ministry of Housing and the Ministry of Land and Resource intend to expand this planning approach to cover more than 600 cities [50]. A more recent publication even mentions extending this to over 3000 of the country's towns and cities [51]. However, their use is also the subject of controversy.…”

Section: Urban Growth Boundariesmentioning

confidence: 99%

Automatic Delineation of Urban Growth Boundaries Based on Topographic Data Using Germany as a Case Study

Harig

Hecht

Burghardt

et al. 2021

IJGI

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Urban Growth Boundary (UGB) is a growth management policy that designates specific areas where growth should be concentrated in order to avoid urban sprawl. The objective of such a boundary is to protect agricultural land, open spaces and the natural environment, as well as to use existing infrastructure and public services more efficiently. Due to the inherent heterogeneity and complexity of settlements, UGBs in Germany are currently created manually by experts. Therefore, every dataset is linked to a specific area, investigation period and dedicated use. Clearly, up-to-date, homogeneous, meaningful and cost-efficient delineations created automatically are needed to avoid this reliance on manually or semi-automatically generated delineations. Here, we present an aggregative method to produce UGBs using building footprints and generally available topographic data as inputs. It was applied to study areas in Frankfurt/Main, the Hanover region and rural Brandenburg while taking full account of Germany’s planning and legal framework for spatial development. Our method is able to compensate for most of the weaknesses of available UGB data and to significantly raise the accuracy of UGBs in Germany. Therefore, it represents a valuable tool for generating basic data for future studies. Application elsewhere is also conceivable by regionalising the employed parameters.

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“…3. [41] This model is one of the additive models which calculates its estimations by joining results from a grouping of base models. All the more officially, we can compose this class of models, as shown in Eq.…”

Section: Random Forest (Rf) Regressionmentioning

confidence: 99%

Real-Time Analysis of COVID-19 Pandemic on Most Populated Countries Worldwide

Gupta¹,

Jain²,

Gupta³

et al. 2020

Computer Modeling in Engineering &Amp; Sciences

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The spread of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has already taken on pandemic extents, influencing even more than 200 nations in a couple of months. Although, regulation measures in China have decreased new cases by over 98%, this decrease is not the situation everywhere, and most of the countries still have been affected by it. The objective of this research work is to make a comparative analysis of the top 5 most populated countries namely United States, India, China, Pakistan and Indonesia, from 1st January 2020 to 31st July 2020. This research work also targets to predict an increase in the number of deaths and total infected cases in these five countries. In our research, the performance of the proposed framework is determined by using three Machine Learning (ML) regression algorithms namely Linear Regression (LR), Support Vector Regression (SVR), and Random Forest (RF) Regression. The proposed model is also validated upon the infected and death cases of further dates. The performance of these three algorithms is compared using the Root Mean Square Error (RMSE) metrics. Random Forest algorithm shows best performance as compared to other proposed algorithms, with the lowest RMSE value in the prediction of total infected and total deaths cases for all the top five most populated countries.

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Delineation of Urban Growth Boundaries Using a Patch-Based Cellular Automata Model under Multiple Spatial and Socio-Economic Scenarios

Cited by 24 publications

References 70 publications

Integrating a deep forest algorithm with vector‐based cellular automata for urban land change simulation

Integrating a deep forest algorithm with vector‐based cellular automata for urban land change simulation

Automatic Delineation of Urban Growth Boundaries Based on Topographic Data Using Germany as a Case Study

Real-Time Analysis of COVID-19 Pandemic on Most Populated Countries Worldwide

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