Modeling urban growth using spatially heterogeneous cellular automata models: Comparison of spatial lag, spatial error and GWR

Gao, Chen; Feng, Yongjiu; Tong, Xiaohua; Lei, Zhenkun; Chen, Shurui; Zhai, Shuting

doi:10.1016/j.compenvurbsys.2020.101459

Cited by 72 publications

(31 citation statements)

References 68 publications

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“…We used the land-use datasets interpreted from Landsat TM/ETM (National Aeronautics and Space Administration, Washington, DC, USA) and Landsat OLI (National Aeronautics and Space Administration, Washington, DC, USA) images for 1990, 1995, 2000, 2005, 2010, and 2015. The land-use datasets used in the research had already been supervised classified and widely used in published research [39][40][41]. The satellite images are classified using the supervised maximum likelihood classification method in ENVI 5.2 (https://www.l3harrisgeospatial.com/docs/whatsnew_envi52.html) (accessed on 24 February 2021) to generate land-use patterns and the overall accuracies of the classified land use data are above 97.3% [41].…”

Section: Datasetsmentioning

confidence: 99%

Spatiotemporal Dynamics of Urban Green Space Influenced by Rapid Urbanization and Land Use Policies in Shanghai

Song

Zhang

Feng

et al. 2021

Forests

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Urbanization has led to the continuous expansion of built-up areas and the ever-growing urban population, threatening the quantity and quality of urban green space (UGS). Exploring the spatiotemporal variations of UGS is substantially conducive to the formulation of land-use policies to protect the ecosystems. As one of the largest megacities all around the world, Shanghai has experienced rapid urbanization in the past three decades. Insights into how UGS changes in response to urbanization and greening policies are essential for guiding sustainable urban development. This paper employed integrated approaches to characterize the changing patterns and intensities of green space in Shanghai, China from 1990 to 2015. The spatiotemporal dynamics of the UGS pattern were derived through four main methods: green space ratio, dynamic change degree (DCD), transition matrix and landscape metrics. The results showed that Shanghai’s green space decreased from 84.8% in 1990 to 61.9% in 2015 while the built-up areas increased from 15.0% to 36.5%. Among the green space sub-types, farmland was largely encroached and fragmented by urban sprawl, especially in the Outer Ring Expressway and Suburban Ring Expressway belts of the city. About 1522 km2 of the green space has transferred into built-up areas, followed by farmland, waterbody, forest, and grassland in descending order. The 2000–2010 period witnessed the strong urban expansion and dramatic changes in UGS, but then the change around 2015 turned down and stable. The landscape pattern metrics showed that the entire green space in Shanghai was growingly fragmented and isolated during the past 25 years. Combined with the green space-related planning and policies issued in 1990–2015, the results revealed that both rapid urbanization and greening policies accounted for the spatiotemporal dynamics of UGS. Based on the results, some implicants to new urban planning and policies of Shanghai were highlighted.

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Section: Datasetsmentioning

confidence: 99%

Spatiotemporal Dynamics of Urban Green Space Influenced by Rapid Urbanization and Land Use Policies in Shanghai

Song

Zhang

Feng

et al. 2021

Forests

Self Cite

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“…Random effect model or fixed effect model are selected for each model by Hausman test. If the Hausman statistic is less than 0, it can accept the original hypothesis of random effect (H 0 : Individual effects are independent of regression variables) [ 23 ]. In order to further verify the results of model selection, GPM, SAR, SEM, SAC and SDM models are estimated and compared.…”

Section: Results and Interpretationmentioning

confidence: 99%

“…After 2008, the carbon emissions have increased steadily. Our results reveal agricultural carbon emissions may have an increasing trend in the future [ 23 ].…”

Section: Methodology and Datamentioning

confidence: 99%

The influencing factors and spillover effects of interprovincial agricultural carbon emissions in China

Cao

Peng

2020

PLoS ONE

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Agricultural carbon emissions have become the constraints of agricultural low-carbon and circular economy development in China. China’s agricultural production faces severe pressures and challenges in agricultural carbon reduction. In this paper, we take observation for the 31 provinces in china from 1997 to 2017, to explore the influencing factors and spatial spillover effects of agricultural by estimating spatial panel data models. The results show that China’s agricultural carbon emissions will continue to increase in the future, because the growth of per capita gross domestic product (GDP) is the main driving force to accelerate the growth of agricultural carbon emissions, but the agricultural input factors will help to reduce the growth of carbon emissions. Moreover, it is proved that economic factors and agricultural input factors have direct effects and spatial spillover effects on agricultural carbon emissions except for agricultural environmental factors. In the short term, strengthening environmental protection may bring some pressure to the economic development of some places, but to achieve high-quality development, we must fundamentally solve the problem of environmental pollution. The conclusion provides important enlightenment and scientific basis for formulating effective policies to curb the growth of CO 2 emissions in China.

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“…The previously mentioned study mainly simulates the time series of the number of infections, immunizations, deaths, cures and time nodes of pandemic influenza based on the classical infectious disease infection model [17], [18]. In addition to the traditional SIR/SEIR infectious disease model, some analytical methods from the Geographic Information System (GIS) and Social Network Analysis (SNA) have been introduced to the derivation of infectious diseases [19]- [23]. Powerful geospatial data collection, management, processing, analysis and display capabilities of Geographic Informa-tion System are increasingly employed by scholars for early warning research on infectious disease surveillance based on a combination of the strength of prevention and control measures, support capacity, support resources and severity of outbreaks in different regions [24], [25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Due to the rapid development of urbanization and tourism and the changing ecological environment, as well as the fragile public health system, epidemics are becoming more frequent, more complex and more difficult to prevent and control [1]. Novel infectious diseases, such as Ebola hemorrhagic fever, Middle East respiratory syndrome, and Coronavirus Disease 2019 (COVID- 19), are constantly emerging. Some of these diseases are characterized as zoonosis and/or coexist with other epidemics, which greatly increase their infectiousness and pathogenicity.…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment of COVID-19 Based on Multisource Data From a Geographical Viewpoint

Zhang

Yang

et al. 2020

IEEE Access

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In June 4, 2020, Corona Virus Disease 2019(COVID-19) cases in Wuhan were cleared, and the epidemic situation was basically controlled. Such public safety infectious disease includes influences great pressure on the national economy. At present, some countries and regions in the world are still in epidemic situation, and there is an urgent need to judge the infection situation and travel risk in the region. In a relatively fine scale down to perceive the surrounding situation, and then rational zoning decisions to promote the resumption of production and work. In this study, indicators for the evaluation of COVID-19 epidemic were constructed using multi-sourced data. A computational evaluation of 736 fine-grained grids was performed using the GeoDetector model and the decision tree model. The study found that the risk level in older neighborhoods was much higher than in newer neighborhoods; the population density was the most important determinant of infection; the number of urban people slumped to 37% of that in usual times according to Tencent data after the "city closure"; The model this paper used portrays the major factor in defining low-risk areas and high-risk areas, and offers suggestions and assessment from a geographical perspective to fight COVID-19, thus presenting great practical value. INDEX TERMS COVID-19, geographical detectors, decision tree, risk map, spatial grid.

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Modeling urban growth using spatially heterogeneous cellular automata models: Comparison of spatial lag, spatial error and GWR

Cited by 72 publications

References 68 publications

Spatiotemporal Dynamics of Urban Green Space Influenced by Rapid Urbanization and Land Use Policies in Shanghai

Spatiotemporal Dynamics of Urban Green Space Influenced by Rapid Urbanization and Land Use Policies in Shanghai

The influencing factors and spillover effects of interprovincial agricultural carbon emissions in China

Risk Assessment of COVID-19 Based on Multisource Data From a Geographical Viewpoint

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