The Urban Transition Performance of Resource-Based Cities in Northeast China

Tan, Juntao; Zhang, Pingyu; Lo, Kevin; Jing, Li; Liu, Shiwei

doi:10.3390/su8101022

Cited by 66 publications

(34 citation statements)

References 29 publications

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“…GeoDetector is a spatial statistical method for detecting and analyzing spatial variation [40] and has been gradually applied to study regional economies, regional planning, land use, ecological environments, and so forth. [41][42][43][44]. Three detectors (factor, risk, and interaction detectors) were used in this study, excluding the ecological detector.…”

Section: Modeling Using Geodetectormentioning

confidence: 99%

Quantitative Analysis of the Determinants Influencing Urban Expansion: A Case Study in Beijing, China

Zheng

Zhang

2018

Sustainability

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A quantitative analysis of spatial influencing factors on urban sprawl can offer better support for urban planning and management. There are many concerns regarding the influence of each factor. However, a quantitative analysis to detect the interactions between factors is limited because of the complexity of the urban systems, especially the role of planning. Additionally, spatial heterogeneity is often overlooked. This study aims to improve and strengthen the knowledge in this field through a spatial statistical method known as GeoDetector. A new spatial quantification of urban expansion was presented in this study and the spatio-temporal characteristics and mechanism of urban growth in Beijing from 2010 to 2015 were also analyzed. The results show that urban expansion presents spatial heterogeneity with different grid cells, and the optimal scale was 4500 m. At this scale, urban expansion in Beijing linearly expands along the traffic trunk. In addition, urban expansion in Beijing is jointly influenced by socioeconomic, geographical, and policy factors. Population density has had the greatest influence on urban expansion from 2010 to 2015, and policy factors rank first. The impact of economic factors on urban growth is gradually weakening. It is important for urban geographical research to further plans and guide urban development.

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Section: Modeling Using Geodetectormentioning

confidence: 99%

Quantitative Analysis of the Determinants Influencing Urban Expansion: A Case Study in Beijing, China

Zheng

Zhang

2018

Sustainability

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“…It can detect the spatial stratified heterogeneity of dependent variables and reveal the driving force behind it with its great flexibility [18]. It has been widely used in various fields, such as natural and social sciences [19][20][21], environmental pollution [22,23] and disease risk detection [16,24], etc., but it is rarely used in the field of LE research.Therefore, we analyzed the spatial distribution characteristics of LE by descriptive methods and spatial autocorrelation analysis. Then we used the Geographical Detector technique to reveal the impact of social and environmental factors and their interactions on LE as well as their optimal range for the maximum LE level, providing reference for the research on LE, economic and educational development, utilization of medical resources, environmental protection as well as population management policy-making in China.…”

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confidence: 99%

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confidence: 99%

Spatial Characteristics of Life Expectancy and Geographical Detection of Its Influencing Factors in China

et al. 2020

IJERPH

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Life expectancy (LE) is a comprehensive and important index for measuring population health. Research on LE and its influencing factors is helpful for health improvement. Previous studies have neither considered the spatial stratified heterogeneity of LE nor explored the interactions between its influencing factors. Our study was based on the latest available LE and social and environmental factors data of 31 provinces in 2010 in China. Descriptive and spatial autocorrelation analyses were performed to explore the spatial characteristics of LE. Furthermore, the Geographical Detector (GeoDetector) technique was used to reveal the impact of social and environmental factors and their interactions on LE as well as their optimal range for the maximum LE level. The results show that there existed obvious spatial stratified heterogeneity of LE, and LE mainly presented two clustering types (high-high and low-low) with positive autocorrelation. The results of GeoDetector showed that the number of college students per 100,000 persons (NOCS) could mainly explained the spatial stratified heterogeneity of LE (Power of Determinant (PD) = 0.89, p < 0.001). With the discretization of social and environmental factors, we found that LE reached the highest level with birth rate, total dependency ratio, number of residents per household and water resource per capita at their minimum range; conversely, LE reached the highest level with consumption level, GDP per capita, number of college students per 100,000 persons, medical care expenditure and urbanization rate at their maximum range. In addition, the interaction of any two factors on LE was stronger than the effect of a single factor. Our study suggests that there existed obvious spatial stratified heterogeneity of LE in China, which could mainly be explained by NOCS. Int. J. Environ. Res. Public Health 2020, 17, 906 2 of 15 2010 [3]. Therefore, it is an urgent task to explore the spatial characteristics and influencing factors of LE in order to deal with the regional inequality of LE in China.There have been many studies concerning the various influencing factors of LE. These factors can mainly be divided into two categories: biological and social and environmental factors [4]. Biological factors involve the individual's genetic factors, living habits, etc. Differences in LE between men and women are mainly due to differences in gene expression intensity. In addition, a study also found that people who smoked, drank and exercised less were vulnerable to cancer, cardiovascular and cerebrovascular diseases, which caused a reduction of LE [5]. However, social and environmental factors were the most frequently considered and important influencing factors in LE research [6]. Many studies found that economy and demographic composition were key influencing factors of LE [7-10]. Studies have found that health care and services had a positive effect on disease prevention and treatment, so the LE level was often higher in areas with good medical conditions [11]. People with a higher educat...

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“…(2) Calculating vulnerability-resistance (VR) and adaptability-transformation (AT) (Tan et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

The Evolution of Regional Economic Resilience in the Old Industrial Bases in China: A Case Study of Liaoning Province, China

Zhang

et al. 2020

Chin. Geogr. Sci.

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The economic transformation of the old industrial bases is a key research topic among geographers in China. In this paper, we propose that the concept of regional economic resilience (RER) has unique theoretical value in analyzing the economic transformation of the old industrial bases. We constructed an analytical framework and an index system and applied the conceptual tools to study the evolution of RER in the old industrial base of Liaoning Province in China, which is currently subjected to not only sudden shocks but 'slow burn'-longer term processes of change that may nevertheless affect the regional economy. There are four main findings: first, the evolution of RER in Liaoning can be divided into four stages from 2000 to 2015. Liaoning is currently in its conservation-release period, and the next stage will be a release-reorganization period. Second, the RER of the majority of the studied cities is lower than the average value for Liaoning, and this is mainly attributed to the relatively weak vulnerability-resistance and adaptability-transformation capacity of these cities. Third, the RER levels of the 14 cities in Liaoning differ significantly. At the first level is Shenyang and Dalian, at the second level is Dandong and Yingkou, and the third level comprises the remaining cities. Fourth, regional economic resilience is mainly determined by vulnerability-resistance, which indirectly reflects Liaoning's lack of adaptability-transformation capacity, and the ability of the region to renew or create a new development path is weak.

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The Urban Transition Performance of Resource-Based Cities in Northeast China

Cited by 66 publications

References 29 publications

Quantitative Analysis of the Determinants Influencing Urban Expansion: A Case Study in Beijing, China

Quantitative Analysis of the Determinants Influencing Urban Expansion: A Case Study in Beijing, China

Spatial Characteristics of Life Expectancy and Geographical Detection of Its Influencing Factors in China

The Evolution of Regional Economic Resilience in the Old Industrial Bases in China: A Case Study of Liaoning Province, China

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