Analysis of Spatial–Temporal Characteristics of Industrial Land Supply Scale in Relation to Industrial Structure in China

Dai, Peichao; Sheng, Ruxu; Miao, Zhongzhen; Chen, Zanxu; Zhou, Yuan

doi:10.3390/land10111272

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…The impact of the IoT sector on the optimization of the high-tech industry structure will be obvious in different places and at different times. [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

“…High- and new-tech industries and high-tech industries are interchangeable, and it usually refers to a technology-intensive bureau formed from upstream to downstream of a certain technology. Also, emerging industries with short life cycles are defined differently in Western countries [ 30 ]. High- and new-tech industry generally refers to emerging industry groups with high technology intensity, fast update speed, and high added value.…”

Section: Optimization Of the Structure Of High-tech Industriesmentioning

confidence: 99%

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The Influence of the Development of the Internet of Things Industry on the Optimization of the High- and New-Tech Industry Structure under the Wireless Mobile Network

Liu

Zhengchao

Jiang

et al. 2022

Computational Intelligence and Neuroscience

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At present, the pressure on China’s economic development is increasing day by day due to the profound changes in the internal and external environment. The global economic pattern is undergoing significant changes in terms of the external environment. Adjusting and optimizing the industrial structure will aid in achieving the goal of facilitating transformation through steady growth in the short term. Meanwhile, it will also accelerate sustainable economic development. In this study, the relevant theories of industrial structure optimization are described based on the impact of wireless mobile networks and the Internet of things (IoT) industry. Based on the gray correlation degree, the high- and new-tech industries under the development of the IoT industry are analyzed and the impact of optimization of the high- and new-tech industry structure is investigated. The results show that the development of the IoT industry has driven the development of the high- and new-tech industry. The gray correlation between the development of the IoT industry and the high- and new-tech industry obtained is 0.64, indicating a strong correlation. The average output share of the electronic computer and office equipment manufacturing industries is 47.09%. The average output ratio of the industrial structure optimization of the electronics and communication manufacturing industry is 42.55%. Moreover, the proportion of the output of medical manufacturing and medical equipment and instrument manufacturing industrial structure optimization is small, 15.63% and 10.54%, respectively. The results have significant value in the research on the impact of the development of the IoT industry on the high- and new-tech industry under the wireless mobile network and the effect of its industrial structure optimization.

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“…The impact of the IoT sector on the optimization of the high-tech industry structure will be obvious in different places and at different times. [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

Section: Optimization Of the Structure Of High-tech Industriesmentioning

confidence: 99%

The Influence of the Development of the Internet of Things Industry on the Optimization of the High- and New-Tech Industry Structure under the Wireless Mobile Network

Liu

Zhengchao

Jiang

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The data will have a more pronounced directionality when there is a larger difference (larger flattening) between the semi-major axis and the semi minor-axis. For the semi minor-axis of the standard deviation ellipse, its shorter length indicates a more concentrated geographical distribution of study objects and a stronger influence of agglomeration and centripetal forces on the spatial pattern [ 37 , 38 ]. This paper provides a visual analysis of the spatial pattern of geographic distribution of COVID-19 patients and deaths based on the cluster analysis and the standard deviation ellipse method of ARCGIS, thus presenting the spatial heterogeneity features of epidemic risk.…”

Section: Methodsmentioning

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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