Analysis of Regional Logistics in Guangdong-Hong Kong-Macao Greater Bay Area Based on Grey Clustering

2020 ◽  
Author(s):  
Weiguo Li
Keyword(s):  
Hong Kong ◽  
Regional Logistics ◽  
Bay Area ◽  
Grey Clustering

scholarly journals SPATIAL CONNECTION CHARACTERISTICS AND DRIVING MECHANISM OF REGIONAL LOGISTICS IN GUANGDONG-HONG KONG-MACAO GREATER BAY AREA

2021 ◽  
Author(s):  
XIU-LI GAO ◽  
PEI-YI HU ◽  
FEI-RONG MENG
Keyword(s):  
Hong Kong ◽  
Industrial Structure ◽  
River Estuary ◽  
Telecommunications Industry ◽  
Pearl River Estuary ◽  
Driving Mechanism ◽  
Entropy Weight ◽  
Regional Logistics ◽  
Bay Area ◽  
Connection Pattern

The paper adopted the entropy weight TOPSIS method and the gravity model to explore the characteristics of logistics spatial connection of Guangdong-Hong Kong-Macao Greater Bay Area that includes nine prefecture-level cities and two special administrative regions, and analyzed the driving factors of the formation of logistics spatial connection pattern by geographical detector model. The results shows that: There are obvious imbalance in the comprehensive capacity of logistics in different regions, and the cities around the Pearl River estuary are generally strong in the logistics quality, like Guangzhou, Shenzhen and Hong Kong. The total amount of logistics links in different cities is significantly various. The logistics connection between cities are mainly weak, and the strong links are concentrated between Hong Kong and Shenzhen. In addition, Hong Kong, Guangzhou and Shenzhen are the three core nodes of the regional logistics network in Guangdong-Hong Kong-Macao Greater Bay Area. Industrial structure, economic scale, population scale, consumption level, the development of post and telecommunications industry are the main factors for the formation of the logistics spatial connection pattern. Moreover, these factors have a prominent driving effect on the cities with large amount of logistics links.

scholarly journals Patterns of the Network of Cross-Border University Research Collaboration in the Guangdong-Hong Kong-Macau Greater Bay Area

2020 ◽  
Vol 12 (17) ◽  
pp. 6846
Author(s):  
Jinyuan Ma ◽  
Fan Jiang ◽  
Liujian Gu ◽  
Xiang Zheng ◽  
Xiao Lin ◽  
...  
Keyword(s):  
Social Sciences ◽  
Hong Kong ◽  
Research Collaboration ◽  
Citation Impact ◽  
High Quality ◽  
High Quality Research ◽  
Cross Border ◽  
Bay Area ◽  
Research Areas ◽  
Industrial Distribution

This study analyzes the patterns of university co-authorship networks in the Guangdong-Hong Kong-Macau Greater Bay Area. It also examines the quality and subject distribution of co-authored articles within these networks. Social network analysis is used to outline the structure and evolution of the networks that have produced co-authored articles at universities in the Greater Bay Area from 2014 to 2018, at both regional and institutional levels. Field-weighted citation impact (FWCI) is used to analyze the quality and citation impact of co-authored articles in different subject fields. The findings of the study reveal that university co-authorship networks in the Greater Bay Area are still dispersed, and their disciplinary development is unbalanced. The study also finds that, while the research areas covered by high-quality co-authored articles fit the strategic needs of technological innovation and industrial distribution in the Greater Bay Area, high-quality research collaboration in the humanities and social sciences is insufficient.

scholarly journals Dynamic Changes of Local Climate Zones in the Guangdong–Hong Kong–Macao Greater Bay Area and Their Spatio-Temporal Impacts on the Surface Urban Heat Island Effect between 2005 and 2015

2021 ◽  
Vol 13 (11) ◽  
pp. 6374
Author(s):  
Yang Lu ◽  
Jiansi Yang ◽  
Song Ma
Keyword(s):  
Hong Kong ◽  
Land Surface ◽  
Morphological Changes ◽  
Mapping Method ◽  
Heat Radiation ◽  
Climate Zones ◽  
Local Climate ◽  
Bay Area ◽  
Local Climate Zones ◽  
Urban Heat

Local climate zones (LCZs) emphasize the influence of representative geometric properties and surface cover characteristics on the local climate. In this paper, we propose a multi-temporal LCZ mapping method, which was used to obtain LCZ maps for 2005 and 2015 in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), and we analyze the effects of LCZ changes in the GBA on land surface temperature (LST) changes. The results reveal that: (1) The accuracy of the LCZ mapping of the GBA for 2005 and 2015 is 85.03% and 85.28%, respectively. (2) The built type category showing the largest increase in area from 2005 to 2015 is LCZ8 (large low-rise), with a 1.01% increase. The changes of the LCZs also vary among the cities due to the different factors, such as the economic development level and local policies. (3) The area showing a warming trend is larger than the area showing a cooling trend in all the cities in the GBA study area. The main reasons for the warming are the increase of built types, the enhancement of human activities, and the heat radiation from surrounding high-temperature areas. (4) The spatial morphology changes of the built type categories are positively correlated with the LST changes, and the morphological changes of the LCZ4 (open high-rise) and LCZ5 (open midrise) built types exert the most significant influence. These findings will provide important insights for urban heat mitigation via rational landscape design in urban planning management.

scholarly journals Spatial Pattern Analysis of the Ecosystem Services in the Guangdong-Hong Kong-Macao Greater Bay Area Using Sentinel-1 and Sentinel-2 Imagery Based on Deep Learning Method

2021 ◽  
Vol 13 (13) ◽  
pp. 7044
Author(s):  
Dawei Wen ◽  
Song Ma ◽  
Anlu Zhang ◽  
Xinli Ke
Keyword(s):  
Hong Kong ◽  
Deep Learning ◽  
Ecosystem Services ◽  
Land Cover ◽  
Spatial Pattern ◽  
Sustainable Urban Development ◽  
Spatial Pattern Analysis ◽  
Bay Area ◽  
Urban Rural ◽  
Sentinel 2

Assessment of ecosystem services supply, demand, and budgets can help to achieve sustainable urban development. The Guangdong-Hong Kong-Macao Greater Bay Area, as one of the most developed megacities in China, sets up a goal of high-quality development while fostering ecosystem services. Therefore, assessing the ecosystem services in this study area is very important to guide further development. However, the spatial pattern of ecosystem services, especially at local scales, is not well understood. Using the available 2017 land cover product, Sentinel-1 SAR and Sentinel-2 optical images, a deep learning land cover mapping framework integrating deep change vector analysis and the ResUnet model was proposed. Based on the produced 10 m land cover map for the year 2020, recent spatial patterns of the ecosystem services at different scales (i.e., the GBA, 11 cities, urban–rural gradient, and pixel) were analyzed. The results showed that: (1) Forest was the primary land cover in Guangzhou, Huizhou, Shenzhen, Zhuhai, Jiangmen, Zhaoqing, and Hong Kong, and an impervious surface was the main land cover in the other four cities. (2) Although ecosystem services in the GBA were sufficient to meet their demand, there was undersupply for all the three general services in Macao and for the provision services in Zhongshan, Dongguan, Shenzhen, and Foshan. (3) Along the urban–rural gradient in the GBA, supply and demand capacity showed an increasing and decreasing trend, respectively. As for the city-level analysis, Huizhou and Zhuhai showed a fluctuation pattern while Jiangmen, Zhaoqing, and Hong Kong presented a decreasing pattern along the gradient. (4) Inclusion of neighborhood landscape led to increased demand scores in a small proportion of impervious areas and oversupply for a very large percent of bare land.

Impacts of urbanization on extreme precipitation in the Guangdong-Hong Kong-Macau Greater Bay Area

Urban Climate ◽  
2021 ◽  
Vol 38 ◽  
pp. 100904
Author(s):  
Yanni Li ◽  
Weiwen Wang ◽  
Ming Chang ◽  
Xuemei Wang
Keyword(s):  
Hong Kong ◽  
Extreme Precipitation ◽  
Bay Area
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