Spatio-Temporal Evolution Characteristics and Influencing Factors of Urban Service-Industry Land in China

Zhao, Sidong; Zhao, Kaixu; Yan, Yiran; Zhu, Kai; Guan, Chiming

doi:10.3390/land11010013

Cited by 35 publications

(24 citation statements)

References 78 publications

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“…Cold and hot spot analysis is mainly used to identify the spatial concentration of the estimated values in cold and hot spots in different local spaces. It is an analysis method of local autocorrelation characteristics [ 57 ]. The function formula of Getis-Ord

is as follows:

where:

is the statistic of the i -th grid, and the correlation degree between the i -th grid and the j -th grid is obtained through distance weight calculation;

represents a spatial adjacent weight matrix within the range of distance d ;

and

were mathematical expectation and variance of

.…”

Section: Methodsmentioning

confidence: 99%

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Spatiotemporal Changes of Cultivated Land System Health Based on PSR-VOR Model—A Case Study of the Two Lake Plains, China

Liu

2023

IJERPH

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Cultivated land resources are the material basis of sustainable agricultural development. Climate change, food security, land pollution, and other issues highlight the value of sustainable agricultural development, and the health of the cultivated land system has attracted much attention. By constructing “PSR-VOR” cultivated land system health evaluation framework under the 5 km grid scale and using GIS spatial analysis and mathematical statistics to comprehensively evaluate the health status of the cultivated land system in the two lake plains from 2000 to 2019. The major results have shown that: (1) Over the past 20 years, both the highest and average values of the health index of the cultivated land system have gone down, and the health status of the cultivated land system has changed and gotten worse over time. (2) The health status in the two lake plains has been generally good, mainly in Class I and Class II areas. However, the area of cultivated land with general and poor health status has increased rapidly. On the whole, the health level presents the characteristic of gradually decreasing from the northeast to the southwest and southeast. (3) During the study period, the global Moran’s I value of the cultivated land system health index in the two lake plains increased from 0.686 to 0.729, with significant spatial positive autocorrelation, and the spatial heterogeneity of the cultivated land system health index gradually increased. As shown by the spatial distribution characteristics of high in the north, low in the south, and decreasing from the middle to the outside, the distribution of the high-value cluster area and the low-value cluster area of the cultivated land system health index in the two lake plains has not changed significantly over the past 20 years. (4) The two lake plains are divided into five areas: a moderate optimization area, a collaborative optimization area, a potential improvement area, a key improvement area, and a priority improvement area. The urgency of regulating the health status from the moderate optimization area to the priority improvement area has gradually increased, and the differentiated utilization and management of cultivated land resources need to be carried out according to local conditions.

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is as follows:

where:

is the statistic of the i -th grid, and the correlation degree between the i -th grid and the j -th grid is obtained through distance weight calculation;

represents a spatial adjacent weight matrix within the range of distance d ;

and

were mathematical expectation and variance of

.…”

Section: Methodsmentioning

confidence: 99%

Section: Cold and Hot Spot Analysismentioning

confidence: 99%

Spatiotemporal Changes of Cultivated Land System Health Based on PSR-VOR Model—A Case Study of the Two Lake Plains, China

Liu

2023

IJERPH

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“…Additionally, lagging economic development will affect the motivation of cities to control COVID-19, as well as the scale of investment in medical facilities and health resources [ 49 ]. In addition, the industrialization has a huge impact on its own development and that of the world for China as the “factory of the world”, and the surge in industrial orders in China after the outbreak of COVID-19 has put forward new requirements and challenges regarding the prevention and control of the epidemic for a large number of factories that have resumed work and production [ 50 ]. Therefore, Gross Domestic Product (GDP), Revenue, Expenditure, Per-GDP, and Number of Industrial Enterprises are adopted to represent the influence of the quantity and quality of urban economic development on the spread and distribution of the COVID-19 epidemic.…”

Section: Methodsmentioning

confidence: 99%

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

Zhang

Zhao

et al. 2022

TropicalMed

Self Cite

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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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“…The BCG method is mainly applied to business management and economics. Based on the interaction of two factors of "sales growth" and "market share", the product or market is classified into four types: stars, questions, cows, and dogs [113]. In this paper, we draw on its basic principles for evaluating urban urbanization development policy zones and classify cities under study into four categories of H-H, H-L, L-H, and L-L depending on the relative dependent variable share and the average growth, to provide a basis for decision making in differentiated policy formulation.…”

Section: Graphical Representationmentioning

confidence: 99%

Spatio-Temporal Evolution and Driving Mechanism of Urbanization in Small Cities: Case Study from Guangxi

Zhao

Wang

et al. 2022

Land

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Urbanization has an abundant connotation in dimensions such as population, economy, land, and society and is an important sign to measure regional economic development and social progress. The use of Night Light Data from remote sensing satellites as a proxy variable can significantly improve the accuracy and comprehensiveness of the measurement of urbanization development dynamics. Based on the Night Light Data and statistical data from 2015 to 2019, this paper quantitatively analyzes the spatio-temporal evolution pattern of urbanization in Guangxi and its driving mechanism using exploratory time-space data analysis, GeoDetector and Matrix: Boston Consulting Group, providing an important basis for sustainable urban development planning and scientific decision-making by the government. The findings show that (1) there is a high level of spatial heterogeneity and spatial autocorrelation of urbanization in Guangxi, with the Gini index of urban night light index and urban night light expansion vitality index always greater than 0.5, the global Moran’s I greater than 0.17, the spatial differentiation converging but the spatial correlation increasing. (2) The spatial pattern of urbanization in Guangxi has long been solidified, but there is a differentiation in urban development trend, with the coexistence of urban expansion and shrinkage, requiring differentiated policy design for urban governance. (3) The development and evolution of urbanization in Guangxi present a complex intertwined dynamic mechanism of action, with interaction effects of bifactor enhancement and non-linear enhancement among factors. It should be noted that the influence of factors varies greatly, with the added value of the tertiary industry, gross domestic product, total retail sales of social consumer goods having the strongest direct effect on the urban night light index, while the added value of secondary industry, per capita GDP, gross domestic product having the strongest direct effect on the urban night light expansion vitality index. All of them are key factors, followed by some significant influence factors such as government revenue, population urbanization rate, per government revenue, population urbanization rate, per capita disposable income of urban and rural residents that should not be ignored, and the rest that play indirect roles mainly by interaction.

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Spatio-Temporal Evolution Characteristics and Influencing Factors of Urban Service-Industry Land in China

Cited by 35 publications

References 78 publications

Spatiotemporal Changes of Cultivated Land System Health Based on PSR-VOR Model—A Case Study of the Two Lake Plains, China

Spatiotemporal Changes of Cultivated Land System Health Based on PSR-VOR Model—A Case Study of the Two Lake Plains, China

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

Spatio-Temporal Evolution and Driving Mechanism of Urbanization in Small Cities: Case Study from Guangxi

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