The Characteristics and Modes of Urban Network Evolution in the Yangtze River Delta in China from 1990 to 2017

Zhang, Yizhen; Song, Rui; Zhang, Kun; Wang, Tao

doi:10.1109/access.2020.3048948

Cited by 7 publications

(2 citation statements)

References 45 publications

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“…The robustness of urban networks is often accessed by the level of structural redundancy and modularity. Key indicators for assessing network redundancy include network density, average number of independent paths, and standard network structure entropy (Hou & Sun, 2022;Zhang & Huang, 2022), while the modularity coefficient is often utilized to characterize the network's modularity level (Zhang et al, 2021). Furthermore, some researchers have applied hierarchy and transmission indicators, commonly employed to characterize RFUN, to describe ROUN.…”

Section: Static Structural Indicators Of Urban Network Resiliencementioning

confidence: 99%

Defining urban network resilience: a review

Jie,

Wentao,

Yuting

et al. 2024

FURP

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As concerns over the security of intercity connections intensify, the concept of urban network resilience has increasingly captured the attention of researchers in related fields. This study aimed to clarify the varying interpretations of urban network resilience among scholars and to identify the advancements and potential gaps in existing literature. The results indicated that differences in perceptions of urban networks have led scholars to define urban network resilience from two distinct perspectives. This divergence influenced the focal points of research, as well as the methodologies, structural measurement indicators, and optimization strategies employed in these studies. We argued for the need to further explore the concept of urban network resilience by considering the nuances of different urban networks, refining methodologies for the identification, description, and measurement of resilience, and recognizing the interconnections among various types and scales of urban networks. This review can provide scholars and policymakers with comprehensive insights into urban resilience, thereby assisting them in making more informed and effective decisions.

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Section: Static Structural Indicators Of Urban Network Resiliencementioning

confidence: 99%

Defining urban network resilience: a review

Jie,

Wentao,

Yuting

et al. 2024

FURP

View full text Add to dashboard Cite

show abstract

“…A country's or city's current development level is a crucial determinant of competition for potential development from a growth standpoint. In today's global economy, a city's growth capacity is primarily defined by its ability to draw creative elements such as finance, knowledge, and professional knowledge, rather than conventional regional advantages such as property, energy, productivity, and other material wealth assets [29,30].…”

Section: Introductionmentioning

confidence: 99%

Digital and Geographical Feature Detection by Machine Learning Techniques Using Google Earth Engine for CPEC Traffic Management

Mazhar

Sun

Alsallakh

et al. 2022

Wireless Communications and Mobile Computing

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The center of human settlements is in the cities, which must have high-quality habitats for their inhabitants. Many megachallenges of urbanization, population development, global advancement, environmental destruction, traffic management, and climate change must be addressed. This study is aimed at understanding how to maintain balanced land development in rapidly urbanizing towns to solve this challenge and mobility issues. Climate and weather forecasts, land cover, environmental indices, nonoptical and optical wavelengths, water history, and air quality are only some of the datasets available on Google Earth Engine, a publicly usable data repository. Machine learning techniques, i.e., random forest (RF), support vector machine (SVM), and classification and regression tree (CART), are used to monitor spatial-temporal change regarding water, vegetation, and urbanization for Pakistan from 2013 to 2021 using Landsat 8. The detection of urban land suitability concerning multiple metrics such as ecological response variables, environmental tension, socio-economic development potential, and natural resource potential is also found. Dataset features were classified as bands in the Google Earth Engine. Moreover, for 2020 and 2021, classification results showing the change in water, vegetation, and urbanization are also represented concerning China Pakistan Economic Corridor (CPEC) highway and the railway track to monitor and control traffic and its management.

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Research on the impact of financial agglomeration on the coordinated development of urban ecological green: based on the empirical comparison of four metropolitan areas in the Yangtze River Delta of China

Chen

Yang

2022

Environ Sci Pollut Res

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Based on the panel data of a total of 25 prefecture-level cities in the four major metropolitan areas in China's Yangtze River Delta region from 2003 to 2019, the Dubin model of three types of spatial weight matrix is used to compare and explore the impact effect and mechanism of nancial agglomeration on the coordinated development of ecological green. The results show that, rst, the positive local and neighborhood effects of nancial agglomeration on the coordinated development of ecological green are mainly re ected in the Hefei, Hangzhou and Shanghai metropolitan areas, and the intensity of the effect decreases sequentially. Information transfer and technology correlation are important mechanisms to improve the local effect of nancial agglomeration, and the neighborhood effect varies from metropolitan area to a metropolitan area. Second, in addition to the adverse impact of the concentration of nancial personnel on the integration of ecological green, the agglomeration of securities, banking and insurance industries can release the "green driving potential energy", and the intensity of contribution gradually weakens. Third, the radiation effect of Shanghai's nancial agglomeration is the strongest, Nanjing has a "U" shaped impact, Hangzhou has an inverted "U" shaped impact, and Hefei does not play a role. HighlightsExplore the impact of nancial agglomeration on the coordinated development of ecological greenery in the four major metropolitan areas of the Yangtze River Delta. Financial agglomeration can have a positive local and neighborhood impact on the coordinated development of ecological green, and this varies by metropolitan area. Information transfer and technical correlation are important intermediate mechanisms. Different agglomerations of nancial institutions and personnel have different "green potential energy" effects.Shanghai has the greatest radiation effect, Nanjing has a "U" type effect, Hangzhou has an inverted "U" type effect, and Hefei has no effect.

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The Characteristics and Modes of Urban Network Evolution in the Yangtze River Delta in China from 1990 to 2017

Cited by 7 publications

References 45 publications

Defining urban network resilience: a review

Defining urban network resilience: a review

Digital and Geographical Feature Detection by Machine Learning Techniques Using Google Earth Engine for CPEC Traffic Management

Research on the impact of financial agglomeration on the coordinated development of urban ecological green: based on the empirical comparison of four metropolitan areas in the Yangtze River Delta of China

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