Application of Social Network Analysis in the Economic Connection of Urban Agglomerations Based on Nighttime Lights Remote Sensing: A Case Study in the New Western Land-Sea Corridor, China

Zhang, Bin; Yin, Jifu; Jiang, Hongtao; Qiu, Yuanhong

doi:10.3390/ijgi11100522

Cited by 17 publications

(5 citation statements)

References 72 publications

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“…At present, the research objects focus on traditional administrative regions, ranging from an international perspective to countries, provinces, cities, and locations within the country [19][20][21][22] China's national-level urban agglomerations such as the Yangtze River Delta urban agglomeration and the Beijing-Tianjin-Hebei urban agglomeration have also attracted widespread attention from domestic and foreign scholars [23][24][25] . A small number of regional urban agglomeration studies measure the green development level of cities by calculating specific industries or factors, such as economic factors 26 , agricultural development 26 , and ecological governance 28 , making it difficult to fully reflect the urban agglomeration`s green development condition.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of Spatio-Temporal Evolution Trends and Driving Factors of Green Development in Harbin-Chang urban agglomeration

Tang

Yuan

Tian

2023

Preprint

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As China has entered a new stage of high-quality development, clarifying the mechanism and spatial characteristics of green development for urban agglomerations are critical to sustainable development. Based on the data of 11 major cities in the Harbin-Changchun urban agglomeration (HCUA) from 2010 to 2020, this study constructs an evaluation system of green development index (GDI) is composed of four dimensions, i.e., urban green construction (UGC), industrial green development (IGD), resource and environmental carrying capacity (RECC), and technological innovation support (TIS). Furthermore, using the entropy weight method to obtain the weights of evaluation indicators. And then, the comprehensive index calculation is used to evaluate the GDI. The driving factors of each level of GDI are determined by the Pearson correlation coefficient. The results infer some novel findings as follows: (1) The overall tendency of the GDI of the HCUA has gradually increased from 0.358 in 2010 to 0.379 in 2020 which is at the average level. The dimension of TIS shows the highest rate of contribution while IGD and RECC show a fluctuating trend over the time window. (2) The GDI in the HCUA exhibits a patchy clustering differentiation feature that spreads from the central area to the surrounding areas with a "high in the south and low in the north" pattern. Specifically, Changchun, Harbin, and Daqing form an "inverted triangle" structure in geographical location to drive the green development of neighboring areas. (3) The core cities of the HCUA, Changchun, and Harbin, show a much higher level than the other cities. Jilin and Daqing are at the average level, and besides, the rest of the cities of GDI are at the poor level with significant fluctuations in ranking. (4) There are different driving factors between each level of GDI. For cities with good and average levels should focus on protecting resources and the environment. Meanwhile, cities with poor level of GDI need to improve IGD to optimize the urban green structure. Thus, it is suggested to strengthen the flow of factors and implement differentiated strategies to promote coordinated development and spatial clustering.

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Section: Literature Reviewmentioning

confidence: 99%

Assessment of Spatio-Temporal Evolution Trends and Driving Factors of Green Development in Harbin-Chang urban agglomeration

Tang

Yuan

Tian

2023

Preprint

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“…The existing studies on regional economic connections usually use gravity models to measure the regional interaction effect, taking into account factors such as the number of non-agricultural populations, GDP, and the spatial distance between the target objects [33,34]. However, such indicators can only reflect the probability of generating economic connections between the two regions and cannot be used as a basis to reflect the actual economic relationship.…”

Section: • Economic Network Centrality Degreementioning

confidence: 99%

Analysis and Comparison of the Industrial Economic Resilience in the Taihu Lake Basin under the 2008 Financial Crisis and the 2018 Sino-US Trade War

Wang

Liu

et al. 2023

Land

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Since China acceded to the WTO, the industrial economy of urban areas has experienced a prosperous phase. However, disturbed by the global financial crisis and reverse globalization since 2008, the past crude development path has been unsustainable. Therefore, it is urgent and necessary to improve industrial resilience to avoid falling into a declining trap. This study integrates multi-source spatiotemporal information such as enterprise big data and panel data using the methods of GIS spatial analysis, complex network analysis, and multi-indicator comprehensive evaluation to evaluate the industrial economic resilience of Taihu Lake Basin (TLB). Resistance indicators such as resistance sensitivity, industrial land area, and regional economic connections are used to evaluate the resistance ability of the industrial economy in the TLB during the 2008 financial crisis and the 2018 Sino-US trade conflict. Resistance sensitivity and independent innovation ability are introduced to assess the recovery ability after two rounds of shocks, and comprehensive economic resilience is evaluated based on the entropy weighting method. The results show that in the face of the two economic shocks, the industrial economy in the TLB is increasingly vulnerable to external economic shocks and has a significantly stronger ability to adapt to economic shocks. Under successive shocks, the industrial economy of the TLB continues to transition to a new path of innovation, which contributes to higher value-added and more efficient use of industrial land. Shanghai and Suzhou, which not only have shown strong economic resilience of their own but are also centers of independent innovation in the TLB, badly need to further reduce their reliance on low-end manufacturing in the future. Among the other cities, Huzhou and Zhenjiang show the highest level of resilience, while Changzhou, Wuxi, and Jiaxing are at the middle level, and Hangzhou is evaluated as the city with the lowest industrial economic resilience. Changzhou and Wuxi need to further increase the technical complexity of their industrial products, while Jiaxing, Huzhou, and Zhenjiang are supposed to strengthen their economic connections with Shanghai, Suzhou, and Hangzhou to expand the industrial scale further. Although Hangzhou shows the lowest comprehensive resilience, it still has a catalytic role to play in the development of industrial land and the upgrading and transformation of manufacturing in Jiaxing and Huzhou.

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“…Nighttime light remote sensing has found extensive use in the dynamic monitoring of cities at various regions and scales. Analyzing long-term time series nighttime light imagery, it has been employed to study the spatiotemporal pattern evolution of regions and city clusters [13][14] , in addition to large-scale studies investigating the dynamics of cities in China, Japan, India, and globally [15][16][17][18][19][20] . The findings of these studies indicate the feasibility of using nighttime light remote sensing data in examining the spatiotemporal characteristics and evolution of urban lighting.…”

Section: Introductionmentioning

confidence: 99%

Changes of nighttime lights in the Guanzhong region of China during the COVID-19 pandemic

Liu,

Chen,

Zhang

et al. 2024

Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023)

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The severe impact of the COVID-19 pandemic on socio-economic development has prompted an exploration of its effects on urban activities. Using December 2021 as a case study during the pandemic in the Guanzhong region of China, this paper conducts a multiscale analysis of the area using NPP-VIIRS monthly composite data. The results indicate that during the pandemic, more than 80% of areas in Xi'an, Weinan, and Baoji showed a downward trend in nighttime lights, with Xi'an being the most severely affected but also recovering the fastest, while Tongchuan was the least affected and had the slowest recovery. The spatiotemporal evolution pattern in the Guanzhong region exhibited a northwest-to-southeast directionality, with changes in the standard deviation ellipse area primarily driven by changes in urban lighting in central cities. This study further verified that the Remote sensing of nighttime lights effectively reflects changes in urban lighting and indicated the significance in assessing the impact of policy implementations during major emergencies.

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Application of Social Network Analysis in the Economic Connection of Urban Agglomerations Based on Nighttime Lights Remote Sensing: A Case Study in the New Western Land-Sea Corridor, China

Cited by 17 publications

References 72 publications

Assessment of Spatio-Temporal Evolution Trends and Driving Factors of Green Development in Harbin-Chang urban agglomeration

Assessment of Spatio-Temporal Evolution Trends and Driving Factors of Green Development in Harbin-Chang urban agglomeration

Analysis and Comparison of the Industrial Economic Resilience in the Taihu Lake Basin under the 2008 Financial Crisis and the 2018 Sino-US Trade War

Changes of nighttime lights in the Guanzhong region of China during the COVID-19 pandemic

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