Spatial Inequality in China’s Housing Market and the Driving Mechanism

Zhao, Sidong; Zhao, Kaixu; Zhang, Ping

doi:10.3390/land10080841

Cited by 34 publications

(19 citation statements)

References 116 publications

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“…The asymptomatic infections found account for a very low percentage of all infections at present, which is quite common for all cities, so their impact on the analysis results is negligible or tolerable. The coefficients of variation of patients are greater than 7, while the coefficient of variation of death is close to 10, all much greater than 0.36, reflecting high disparities [ 55 , 56 ]. The Gini index of patients is about 0.91 and that of death is up to 0.95, all much greater than 0.6 (according to the United Nations Development Programme, a value greater than 0.6 indicates a disparity), indicating a very uneven spatial distribution of COVID-19.…”

Section: Resultsmentioning

confidence: 99%

The Geographical Distribution and Influencing Factors of COVID-19 in China

Zhang

Zhao

et al. 2022

TropicalMed

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The study of the spatial differentiation of COVID-19 in cities and its driving mechanism is helpful to reveal the spatial distribution pattern, transmission mechanism and diffusion model, and evolution mechanism of the epidemic and can lay the foundation for constructing the spatial dynamics model of the epidemic and provide theoretical basis for the policy design, spatial planning and implementation of epidemic prevention and control and social governance. Geodetector(Origin version,Beijing,PR China) is a great tool for analysis of spatial differentiation and its influencing factors, and it provides decision support for differentiated policy design and its implementation in executing the city-specific policies. Using factor detection and interaction analysis of Geodetector, 15 indicators of economic, social, ecological, and environmental dimensions were integrated, and 143 cities were selected for the empirical research in China. The research shows that, first of all, risks of both infection and death show positive spatial autocorrelation, but the geographical distribution of local spatial autocorrelation differs significantly between the two. Secondly, the inequalities in urban economic, social, and residential environments interact with COVID-19 spatial heterogeneity, with stronger explanatory power especially when multidimensional inequalities are superimposed. Thirdly, the spatial distribution and spread of COVID-19 are highly spatially heterogeneous and correlated due to the complex influence of multiple factors, with factors such as Area of Urban Construction Land, GDP, Industrial Smoke and Dust Emission, and Expenditure having the strongest influence, the factors such as Area of Green, Number of Hospital Beds and Parks, and Industrial NOx Emissions having unignorable influence, while the factors such as Number of Free Parks and Industrial Enterprises, Per-GDP, and Population Density play an indirect role mainly by means of interaction. Fourthly, the factor interaction effect from the infected person’s perspective mainly shows a nonlinear enhancement effect, that is, the joint influence of the two factors is greater than the sum of their direct influences; but from the perspective of the dead, it mainly shows a two-factor enhancement effect, that is, the joint influence of the two factors is greater than the maximum of their direct influences but less than their sum. Fifthly, some suggestions are put forward from the perspectives of building a healthy, resilient, safe, and smart city, providing valuable reference and decision basis for city governments to carry out differentiated policy design.

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Section: Resultsmentioning

confidence: 99%

The Geographical Distribution and Influencing Factors of COVID-19 in China

Zhang

Zhao

et al. 2022

TropicalMed

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“…Based on the GI and the CR values and the findings of available studies, this paper classifies spatial heterogeneity into three grades and concentration into six grades. According to the research protocols of scholars such as Zhao [100], Miyamoto [101], Bain [102], and Li [103] and the classification criteria of the United Nations Development Program, a classification criterion for spatial differentiation and concentration are proposed in this paper (Table 1). Created by the Boston Consulting Group in the 1970s, BCG is a method for analyzing and optimizing existing business portfolios that is mainly applied in the fields of business management and economics.…”

Section: Spatial Heterogeneity and Agglomeration Methods: CV Gi Cr Hhimentioning

confidence: 99%

A Dynamic Performance and Differentiation Management Policy for Urban Construction Land Use Change in Gansu, China

Zhang

Zhao

et al. 2022

Land

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Making efforts to promote rationalized urban construction land change, distribution, allocation, and its performance is the core task of territory spatial planning and a complex issue that the government must face and solve. Based on the Boston Consulting Group matrix, a decoupling model, and a GIS tool, this paper constructs a new tool that integrates “dynamic analysis + performance evaluation + policy design” for urban construction land. We reached the following findings from an empirical study of Gansu, China: (1) Urban construction land shows diversified changes, where expansion is dominant and shrink cannot be ignored. (2) Most cities are in the non-ideal state of LH (Low-High) and LL (Low-Low), with a small number in the state of HH (High-High) and HL (High-Low). (3) Urban construction land change and population growth, economic development, and income increase are in a discordant relationship, mostly in strong negative decoupling and expansive negative decoupling. (4) The spatial heterogeneity of urban construction land change and its performance are at a high level, and they show a slow upward trend. Additionally, the cold and the hot spots show obvious spatial clustering characteristics, and the spatial pattern of different indexes is different to some extent. (5) It is suggested that in territory spatial planning Gansu should divide the space into four policy areas—incremental, inventory, a reduction development policy area, and a transformation leading policy area—to implement differentiated management policies and to form a new spatial governance system of “control by zoning and management by class.” The change of urban construction land, characterized by dynamics and complexity, is a direct mapping of the urban growth process. The new tools constructed in this paper will help to reveal the laws of urban development and to improve the accuracy of territory spatial planning in the new era. They are of great theoretical significance and practical value for promoting high-quality and sustainable urban development.

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“…The geodetector model is a statistical method to effectively detect the spatial heterogeneity of geographic phenomena and reveal their driving factors, which are used to detect the degrees of influence of multiple factors under different spatial units and their inter-relationships. This model has been now widely used in land use, regional economy, ecological environment, and other fields to explore the driving forces of various social phenomena and their interactions [ 53 , 54 , 55 ]. In this study, two modules, factor detection and interaction detection, were used to study the driving factors of green space change and their interactions.…”

Section: Methodsmentioning

confidence: 99%

Study on Spatial-Temporal Change of Urban Green Space in Yangtze River Economic Belt and Its Driving Mechanism

Chen

Zhu

2021

IJERPH

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Urban green space plays an important role in beautifying the environment, improving the quality of life of residents, and promoting sustainable urban development. Rapid urbanization has led to great changes in the spatial structure and layout of urban green space. It is urgent to put forward the sustainable development strategy of green space through the research on the change of urban green space. Based on the geographical spatial differences of urban green space and integrating the factors of economy, society, industry, land use, and the environment, we constructed a research framework of “space-supply-demand” integration of urban green space by GI and geodetector methods, and we conducted an empirical study on the spatial–temporal changes of urban green space and its driving mechanism in prefecture-level cities along the Yangtze River Economic Belt in China. First, the urban green space along the Yangtze River Economic Belt is concentrated in spatial distribution, while uneven development appears in urban greening among the zones. Second, the influence of different factors on urban green space change varies greatly and can be divided into three types: key factors, important factors, and auxiliary factors. The driving mechanism of the spatial distribution of urban green space supply and demand is quite different, but urban population and commercial service facilities land are their key influence factors, having a comprehensive influence on the spatial–temporal changes of urban green space. Third, the factors are classified into three categories of high, medium, and low levels according to the mean of interacting forces; in particular, the factors of per capita GDP, utility land, industrial smoke (dust) emissions, and other factors have a very strong interactive effect with other factors. Fourth, according to the spatial distribution characteristics of urban green space and its driving mechanism, this paper puts forward planning and policy suggestions, providing reference for other areas to deal with the green space change.

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Spatial Inequality in China’s Housing Market and the Driving Mechanism

Cited by 34 publications

References 116 publications

The Geographical Distribution and Influencing Factors of COVID-19 in China

The Geographical Distribution and Influencing Factors of COVID-19 in China

A Dynamic Performance and Differentiation Management Policy for Urban Construction Land Use Change in Gansu, China

Study on Spatial-Temporal Change of Urban Green Space in Yangtze River Economic Belt and Its Driving Mechanism

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