Spatio-Temporal Comprehensive Measurements of Chinese Citizens’ Health Levels and Associated Influencing Factors

Health is the basis of a good life and a guarantee of a high quality of life. Furthermore, it is a symbol of social development and progress. How to further improve the health levels of citizens and reduce regional differences in citizens’ health status has become a research topic of great interest that is attracting attention globally. This study takes 31 provinces (municipalities and autonomous regions) of China as the research object. Through using GIS (Geographic Information System) technology, the entropy method, spatial autocorrelation, stepwise regression, and other quantitative analysis methods, measurement models and index systems are developed in order to perform an analysis of the spatio-temporal comprehensive measurements of Chinese citizens’ health levels. Furthermore, the associated influencing factors are analyzed. It has important theoretical and practical significance. The conclusions are as follows: (1) Between 2002 and 2018, the overall health levels of Chinese citizens have generally exhibited an upward trend. Moreover, for most provinces, the health levels of their citizens have improved dramatically, although some provinces, such as Tianjin and Henan, showed a fluctuating downward trend, suggesting that the health levels of citizens in these regions displayed a tendency to deteriorate. (2) The health levels of citizens from China’s various provinces showed clear spatial distribution characteristics of clustering, as well as an obvious spatial dependence and spatial heterogeneity. As time goes by, the degree of spatial clustering with regard to citizens’ health levels tends to weaken. The health levels of Chinese citizens have developed a certain temporal stability, the overall health status of Chinese citizens shows a spatial differentiation of a northeast–southwest distribution pattern. (3) The average years of education and urbanization rate have a significant positive effect on the improvement of citizens’ health levels. The increase of average years of education and urbanization rate can promote the per capita income, which certainly could help improve citizens’ health status. The Engel coefficient, urban–rural income ratio, and amount of wastewater discharge all pose a significant negative effect on the improvement of citizens’ health levels, these three factors have played important roles in hindering the improvements of citizen health.

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Coupling and Coordination Level of the Population, Land, Economy, Ecology and Society in the Process of Urbanization: Measurement and Spatial Differentiation

Sustainability ◽

10.3390/su13063171 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3171

Author(s):

Xuesong Sun ◽

Zaisheng Zhang

Keyword(s):

Spatial Differentiation ◽

Entropy Method ◽

Market Mechanism ◽

Hebei Province ◽

Sustainable Urban Development ◽

Practical Significance ◽

Coordinated Development ◽

Rapid Urbanization ◽

The Difference ◽

Coordination Degree

Coupled and coordinated development is key to improving the level of regional urbanization and sustainable urban development and has important practical significance for solving a series of problems that arise in the process of rapid urbanization. First, from the perspective of system coupling, the development mechanism of the urbanization internal subsystems was deconstructed into five dimensions: population, land, economy, ecology and society. Second, based on data from 2017, the coupling coordination degree of urbanization in 13 cities in the Beijing–Tianjin–Hebei region was measured using the entropy method and a coupling coordination model. Finally, the spatial differences in the levels of subsystem development, comprehensive development and coupling and coordination development of urbanization were analyzed using spatial analysis tools. The results indicate that there are significant differences in the development indices of urbanization subsystems in the Beijing–Tianjin–Hebei region, among which the economic and social development indices have the greatest differences, and the ecological development index has the smallest. The comprehensive urbanization index shows a core–periphery distribution pattern, in which Beijing and Tianjin have the highest values, the cities in middle-southern Hebei Province generally have lower values, and the cities in northern Hebei Province have the lowest values. The coupling coordination level of urbanization in the Beijing–Tianjin–Tangshan region and Shijiazhuang, the capital of Hebei Province, is high, and the difference is small. In contrast, in middle-southern and northern Hebei Province, the coupling coordination degree of urbanization is generally low, and the difference is large. Based on the current situation of urbanization in the Beijing–Tianjin–Hebei region, policy suggestions are proposed from the perspectives of strengthening the market mechanism of urbanization, adjusting the regional industrial structure and attaching importance to the coupled and coordinated development of urbanization.

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Space-Time Variation and Spatial Differentiation of COVID-19 Confirmed Cases in Hubei Province Based on Extended GWR

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9090536 ◽

2020 ◽

Vol 9 (9) ◽

pp. 536 ◽

Cited By ~ 1

Author(s):

Yanwen Liu ◽

Zongyi He ◽

Xia Zhou

Keyword(s):

Scale Effects ◽

Spatial Differentiation ◽

Hubei Province ◽

Practical Significance ◽

Population Migration ◽

Emigrants And Immigrants ◽

Related Factors ◽

Temporal Differentiation ◽

Spatio Temporal ◽

Ordered Data

Clarifying the regional transmission mechanism of COVID-19 has practical significance for effective protection. Taking 103 county-level regions of Hubei Province as an example, and taking the fastest-spreading stage of COVID-19, which lasted from 29 January 2020, to 29 February 2020, as the research period, we systematically analyzed the population migration, spatio-temporal variation pattern of COVID-19, with emphasis on the spatio-temporal differences and scale effects of related factors by using the daily sliding, time-ordered data analysis method, combined with extended geographically weighted regression (GWR). The results state that: Population migration plays a two-way role in COVID-19 variation. The emigrants’ and immigrants’ population of Wuhan city accounted for 3.70% and 73.05% of the total migrants’ population respectively; the restriction measures were not only effective in controlling the emigrants, but also effective in preventing immigrants. COVID-19 has significant spatial autocorrelation, and spatio-temporal differentiation has an effect on COVID-19. Different factors have different degrees of effect on COVID-19, and similar factors show different scale effects. Generally, the pattern of spatial differentiation is a transitional pattern of parallel bands from east to west, and also an epitaxial radiation pattern centered in the Wuhan 1 + 8 urban circle. This paper is helpful to understand the spatio-temporal evolution of COVID-19 in Hubei Province, so as to provide a reference for similar epidemic prevention.

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Influencing factors to Academic Achievement according to Demographic and Socio-economic Characteristics, Health Behaviors and Health Status in Korean Adolescents

The Journal of Korean Society for School & Community Health Education ◽

10.35133/kssche.20191231.01 ◽

2019 ◽

Vol 20 (3) ◽

pp. 1-13

Author(s):

Byoung-Jun Yoon ◽

Keyword(s):

Academic Achievement ◽

Health Status ◽

Health Behaviors ◽

Influencing Factors ◽

Korean Adolescents

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Spatial Distribution and Land Use of Traditional Villages in Southwest China

Sustainability ◽

10.3390/su13116326 ◽

2021 ◽

Vol 13 (11) ◽

pp. 6326

Author(s):

Xiye Zheng ◽

Jiahui Wu ◽

Hongbing Deng

Keyword(s):

Cultural Heritage ◽

Influencing Factors ◽

Landscape Pattern ◽

Southwest China ◽

Spatial Clustering ◽

Interaction Mechanism ◽

Cultivated Land ◽

River Population ◽

Local Conditions ◽

Countermeasures And Suggestions

Traditional villages are the historical and cultural heritage of people around the world. With the increases in urbanization and industrialization, the continuation of traditional villages and the inheritance of historical and cultural heritage are facing risk. Therefore, to grasp the spatial characteristics of them and the human–nature interaction mechanism in Southwest China, we analyzed the distribution pattern of traditional villages using the ArcGIS software. Then, we further analyzed the spatial clustering characteristics, influencing factors and landscape pattern, and put forward relevant protection countermeasures and suggestions. The results revealed that traditional villages in Southwest China were clustered, being mainly distributed in areas with relatively low elevation, gentle slopes, low relative positions, nearby water sources, and convenient transportation. They can be divided into four categories due to obvious differences in influencing factors such as elevation, slope, relative position, distance to the nearest river, population density, etc. The landscape pattern of traditional villages differed among the different clusters, being mainly composed of forests, shrubs, and cultivated land. With the increase in the buffer radius, the landscape pattern of them changed significantly. The results of this study reflect that traditional villages and the natural environment are interdependent, so the protection of traditional villages should carry out measures according to local conditions.

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Spatio-temporal evolution characteristics and influencing factors of carbon emission reduction potential in China

Environmental Science and Pollution Research ◽

10.1007/s11356-021-14913-3 ◽

2021 ◽

Author(s):

Zhangwen Li ◽

Caijiang Zhang ◽

Yu Zhou

Keyword(s):

Influencing Factors ◽

Emission Reduction ◽

Carbon Emission ◽

Reduction Potential ◽

Temporal Evolution ◽

Carbon Emission Reduction ◽

Evolution Characteristics ◽

Emission Reduction Potential ◽

Spatio Temporal

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Research on the Evolution of Population Distribution and Influencing Factors in Xi’an During the COVID-19 Epidemic Control Period: Based on a Perspective of Multi-source Spatio-Temporal Big Data

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/769/2/022066 ◽

2021 ◽

Vol 769 (2) ◽

pp. 022066

Author(s):

Kaixu Zhao ◽

Shuaibing Zhang ◽

Enlong Li ◽

Xiaolan Chen ◽

Fengqi Wu

Keyword(s):

Big Data ◽

Influencing Factors ◽

Population Distribution ◽

Control Period ◽

Epidemic Control ◽

Spatio Temporal

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Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data

Remote Sensing ◽

10.3390/rs13061150 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1150

Author(s):

Yang Zhong ◽

Aiwen Lin ◽

Chiwei Xiao ◽

Zhigao Zhou

Keyword(s):

Influencing Factors ◽

Yangtze River ◽

Electrical Power ◽

Random Forest Algorithm ◽

Temporal Dynamic ◽

Urban Agglomerations ◽

Evolution Characteristics ◽

Spatio Temporal ◽

Electrical Power Consumption ◽

Gwr Model

In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China’s Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze River(UAMRYR), and Chengdu-Chongqing urban agglomeration(CCUA) as the research objects. In addition, the coefficient of variation (CV), kernel density analysis, cold hot spot analysis, trend analysis, standard deviation ellipse and Moran’s I Index were used to analyze the Spatio-temporal Dynamic Evolution Characteristics of EPC in the three urban agglomerations of the YREB. In addition, we also use geographically weighted regression (GWR) model and random forest algorithm to analyze the influencing factors of EPC in the three major urban agglomerations in YREB. The results of this study show that from 1992 to 2013, the CV of the EPC in the three urban agglomerations of YREB has been declining at the overall level. At the same time, the highest EPC value is in YRDUA, followed by UAMRYR and CCUA. In addition, with the increase of time, the high-value areas of EPC hot spots are basically distributed in YRDUA. The standard deviation ellipses of the EPC of the three urban agglomerations of YREB clearly show the characteristics of “east-west” spatial distribution. With the increase of time, the correlations and the agglomeration of the EPC in the three urban agglomerations of the YREB were both become more and more obvious. In terms of influencing factor analysis, by using GWR model, we found that the five influencing factors we selected basically have a positive impact on the EPC of the YREB. By using the Random forest algorithm, we found that the three main influencing factors of EPC in the three major urban agglomerations in the YREB are the proportion of secondary industry in GDP, Per capita disposable income of urban residents, and Urbanization rate.

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