Spatiotemporal evolution and spatial relevance of urban resilience: Evidence from cities of China

Shi, Tao; Qiao, Yurong; Zhou, Qian

doi:10.1111/grow.12554

Cited by 42 publications

(31 citation statements)

References 33 publications

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“…However, though under a coordinated development plan, this region’s urban–rural and intra-region gaps are still significant, manifested in the developed cities of Beijing and Tianjin and backward rural areas in Hebei ( Figure 2 ). As for resilience in this region, according to resilience evaluation in previous research, the BTHUA is high in resilience compared with other areas of China, while within this region, the resilience ranking is Beijing, Tianjin, and Hebei, from high to low [ 42 , 43 , 44 ]. In the latest urban master plan (2016–2035), Beijing is dedicated to improving resilience to raise ecological quality, achieving sustainable use of water resources, and strengthening disaster prevention and mitigation capabilities.…”

Section: Methodsmentioning

confidence: 97%

Urbanization Impact on Regional Sustainable Development: Through the Lens of Urban-Rural Resilience

Shi

Zhu

et al. 2022

IJERPH

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The urban–rural system is an economically, socially, and environmentally interlinked space, which requires the integration of industry, space, and population. To achieve sustainable and coordinated development between urban and rural systems, dynamic land use change within the urban–rural system and the ecological and social consequences need to be clarified. This study uses system resilience to evaluate such an impact and explores the impact of land use change, especially land conversion induced by urbanization on regional development through the lens of urban–rural resilience. The empirical case is based on the Beijing-Tianjin-Hebei Urban Agglomeration (BTHUA) in China from 2000 to 2020 when there was rapid urbanization in this region. The results show that along with urbanization in the BTHUA, urban–rural resilience is high in urban core areas and low in peripheral areas. From the urban core to the rural outskirts, there is a general trend that comprehensive resilience decreases with decreased social resilience and increased ecological resilience in this region. Specifically, at the city level, comprehensive resilience decreases sharply from the urban center to its 3–5 km buffer zone and then remains relatively stable in the rural regions. A similar trend goes for social resilience at the city level, while ecological resilience increases sharply from the urban center to its 1–3 km buffer zone, and then remains relatively stable in the rural regions in this region, except for cities in the west and south of Hebei. This study contributes to the conceptualization and measurement of urban–rural resilience in the urban–rural system with empirical findings revealing the impact of rapid urbanization on urban–rural resilience over the last twenty years in the BTHUA in China. In addition, the spatial heterogeneity results could be used for policy reference to make targeted resilience strategies in the study region.

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Section: Methodsmentioning

confidence: 97%

Urbanization Impact on Regional Sustainable Development: Through the Lens of Urban-Rural Resilience

Shi

Zhu

et al. 2022

IJERPH

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“…Furthermore, with the purpose of enhancing adaptability, the gravity model is corrected. The corrected gravity model refers to related literature [ 21 ]. With the correction, the gravity model is:

…”

Section: Index System Construction and Research Methodsmentioning

confidence: 99%

“…With the development of the YRB, the flow of production factor resources has become more frequent, and a cross-regional spatial correlation network has gradually formed. Some scholars used the social network analysis method to analyze the spatial network characteristics of urban resilience [ 20 , 21 ]. As a consequence, it is important for the interpretation of the ecological protection and economic development of the YRB from the perspective of spatial networks.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Evolution and Spatial Network Analysis of the Urban Ecological Carrying Capacity in the Yellow River Basin

Liu

Yang

Zhou

et al. 2021

IJERPH

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Based on the panel data of 82 cities in the Yellow River Basin (YRB) during 2008–2017, this paper calculated the urban ecological carrying capacity (UECC) index by means of the entropy method, drew a spatiotemporal evolution map using ArcGIS10.3 software, used a spatial cold–hot spot model to explore the spatial characteristics of the UECC index, and used the revised gravity model to construct the spatial network of the UECC. In addition, through social network analysis, we obtained the spatial network correlation characteristics of the UECC of 82 cities in the YRB. The study found the following: (1) The UECC index of the cities in the YRB increased steadily, and showed strong non-stationarity in space. The cold and hot spot patterns both changed greatly. Overall, the changes of the hot and cold spots were very significant. (2) The spatial correlation and linkage effects of the UECC in the YRB were not significant. The central cities with higher point centrality and closeness centrality showed the same spatial distribution, and most of them are located in the midstream and downstream of the YRB. The central cities in the midstream and downstream of the YRB had high betweenness centrality, and stood in the center of the association network. (3) The four plates in the spatial correlation network of the UECC in the YRB all showed their advantages and functions. The first plate was the net spillover plate, which was principally allocated in the upstream and midstream of the YRB. The second plate was the broker plate, which was principally located in the midstream and downstream of the YRB, and a few cities in the upper reaches. The third plate was the net inflow plate, which was distributed sporadically in the upstream and downstream of the YRB. The fourth plate was the broker plate, which was scattered in upstream, midstream, and downstream of the YRB. Therefore, it is necessary to shorten the gap of and promote the improvement of the UECC in the YRB.

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“…Hot spot analysis can identify significant hot or cold spot areas to reflect the spatial distribution characteristics of COVID‐19 cases (Shi et al., 2021; Wang, Liang, et al, 2019; Zhang, Zhang, et al, 2021). The model is as follows:

G_{i}^{*} = {false\sum}_{j}^{n} W_{italicij} X_{italicij} / {false\sum}_{j}^{n} X_{j}

Z_{i} = \frac{G_{i}^{*} ‐ E (G_{i}^{*})}{\sqrt{V a r ()(, G_{i}^{*})}}

where

G_{i}^{*}

is the hot spot index of city i ; W ij is the spatial weight matrix, which is represented by the geographical distance weight matrix in this paper (see model 8 for specific construction methods); X j refers to the number of COVID‐19 cases in city j ; E(

G_{i}^{*}

) and Var(

G_{i}^{*}

) are the mathematical expected value and variance of

G_{i}^{*}

, respectively.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal spread characteristics and influencing factors of COVID‐19 cases: Based on big data of population migration in China

et al. 2021

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As a public health emergency, the COVID-19 pandemic has attracted widespread attention from scholars worldwide. Combining social network models, GIS analysis and spatial econometric models, we explored the characteristics of the Wuhan population outflow network and factors affecting the number of COVID-19 cases. The results show that the Wuhan population outflow network has strong temporal and spatial heterogeneity. Cities in Hubei Province, central cities such as Beijing and Shanghai, and cities rich in tourism resources were the main destinations of Wuhan's population inflow. The distribution of COVID-19 cases not only showed a strong spatial autocorrelation but also a hierarchical diffusion effect. The benchmark regression results showed that the population outflow from Wuhan determines the number of COVID-19 cases in other cities. Temperature was negatively correlated with the number of COVID-19 cases, while the PM2.5 concentration failed the significance test.Thus, the lower is the temperature, the greater are the survival and spread of the virus facilitated. Furthermore, cities with a higher population density and more employees in the middle reaches of the Yangtze River are more vulnerable to COVID-19. Finally, by replacing the weight matrix and setting instrumental variables, we proved the robustness of the above main conclusions.

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Spatiotemporal evolution and spatial relevance of urban resilience: Evidence from cities of China

Cited by 42 publications

References 33 publications

Urbanization Impact on Regional Sustainable Development: Through the Lens of Urban-Rural Resilience

Urbanization Impact on Regional Sustainable Development: Through the Lens of Urban-Rural Resilience

Spatiotemporal Evolution and Spatial Network Analysis of the Urban Ecological Carrying Capacity in the Yellow River Basin

Spatiotemporal spread characteristics and influencing factors of COVID‐19 cases: Based on big data of population migration in China

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