Urban Network Spatial Connection and Structure in China Based on Railway Passenger Flow Big Data
China’s transportation industry has made great achievements in the past 40 years of reform and opening up. At the same time, it has gradually accumulated a series of problems. These problems have led to closer and more complex social and economic connection within and between regions of different sc...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/11/2/225 |
| _version_ | 1797478794370482176 |
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| author | Minmin Li Wenhua Guo Renzhong Guo Biao He Zhichao Li Xiaoming Li Wenchao Liu Yong Fan |
| author_facet | Minmin Li Wenhua Guo Renzhong Guo Biao He Zhichao Li Xiaoming Li Wenchao Liu Yong Fan |
| author_sort | Minmin Li |
| collection | DOAJ |
| description | China’s transportation industry has made great achievements in the past 40 years of reform and opening up. At the same time, it has gradually accumulated a series of problems. These problems have led to closer and more complex social and economic connection within and between regions of different scales. The existing research only carries out the characteristic analysis of urban network spatial connection and pattern from a single perspective such as “flow space” theory, spatial interaction model and accessibility method, and fails to accurately describe the complex socio-economic relations between regions. Based on the big data of railway passenger flow, this study selected weighted average travel time, railway network density, and the economic connection model to express the urban network spatial connection and structure of China in 2016 from the perspectives of time, space, and interaction. In 2016, the accessibility, connectivity, and total urban external economic connection of the railway network showed a trend of declining from the east to the west. The top 50 cities ranked by interurban economic connection were all located in the central and eastern regions and showed “diamond shape” distribution characteristics. The four diamond-shaped pairs were Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and Chengyu urban agglomerations. This shape was basically in line with the T-shaped space that has existed for a long time in China’s regional development. The accessibility, connectivity, and total external economic connection of national-level urban agglomerations were greater than those of regional-level urban agglomerations, and far greater than those of local-level urban agglomerations. The results showed that there was a mismatch between the layout of the railway network and the population. It will still be necessary to focus on strengthening the construction of transportation infrastructure in urban agglomerations and densely populated areas in the future. This study enriches the “flow space” theory, more fully describes urban network spatial connection and structure in China by considering the three perspectives of time, space, and interaction, and can provides reasonable suggestions for the development of national comprehensive three-dimensional transportation network planning, regional spatial structure optimization, and sustainable development. |
| first_indexed | 2024-03-09T21:36:43Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-09T21:36:43Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-d5434628cc394813ace7a3c9e65a6a612023-11-23T20:42:53ZengMDPI AGLand2073-445X2022-02-0111222510.3390/land11020225Urban Network Spatial Connection and Structure in China Based on Railway Passenger Flow Big DataMinmin Li0Wenhua Guo1Renzhong Guo2Biao He3Zhichao Li4Xiaoming Li5Wenchao Liu6Yong Fan7Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518060, ChinaInformation Center of Ministry of Natural Resources of the People’s Republic of China, Beijing 100036, ChinaKey Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518060, ChinaKey Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518060, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518060, ChinaInformation Center of Ministry of Natural Resources of the People’s Republic of China, Beijing 100036, ChinaKey Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518060, ChinaChina’s transportation industry has made great achievements in the past 40 years of reform and opening up. At the same time, it has gradually accumulated a series of problems. These problems have led to closer and more complex social and economic connection within and between regions of different scales. The existing research only carries out the characteristic analysis of urban network spatial connection and pattern from a single perspective such as “flow space” theory, spatial interaction model and accessibility method, and fails to accurately describe the complex socio-economic relations between regions. Based on the big data of railway passenger flow, this study selected weighted average travel time, railway network density, and the economic connection model to express the urban network spatial connection and structure of China in 2016 from the perspectives of time, space, and interaction. In 2016, the accessibility, connectivity, and total urban external economic connection of the railway network showed a trend of declining from the east to the west. The top 50 cities ranked by interurban economic connection were all located in the central and eastern regions and showed “diamond shape” distribution characteristics. The four diamond-shaped pairs were Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and Chengyu urban agglomerations. This shape was basically in line with the T-shaped space that has existed for a long time in China’s regional development. The accessibility, connectivity, and total external economic connection of national-level urban agglomerations were greater than those of regional-level urban agglomerations, and far greater than those of local-level urban agglomerations. The results showed that there was a mismatch between the layout of the railway network and the population. It will still be necessary to focus on strengthening the construction of transportation infrastructure in urban agglomerations and densely populated areas in the future. This study enriches the “flow space” theory, more fully describes urban network spatial connection and structure in China by considering the three perspectives of time, space, and interaction, and can provides reasonable suggestions for the development of national comprehensive three-dimensional transportation network planning, regional spatial structure optimization, and sustainable development.https://www.mdpi.com/2073-445X/11/2/225big data of railway passenger flowweighted average travel timerailway network densityeconomic connection modelspatial interaction |
| spellingShingle | Minmin Li Wenhua Guo Renzhong Guo Biao He Zhichao Li Xiaoming Li Wenchao Liu Yong Fan Urban Network Spatial Connection and Structure in China Based on Railway Passenger Flow Big Data Land big data of railway passenger flow weighted average travel time railway network density economic connection model spatial interaction |
| title | Urban Network Spatial Connection and Structure in China Based on Railway Passenger Flow Big Data |
| title_full | Urban Network Spatial Connection and Structure in China Based on Railway Passenger Flow Big Data |
| title_fullStr | Urban Network Spatial Connection and Structure in China Based on Railway Passenger Flow Big Data |
| title_full_unstemmed | Urban Network Spatial Connection and Structure in China Based on Railway Passenger Flow Big Data |
| title_short | Urban Network Spatial Connection and Structure in China Based on Railway Passenger Flow Big Data |
| title_sort | urban network spatial connection and structure in china based on railway passenger flow big data |
| topic | big data of railway passenger flow weighted average travel time railway network density economic connection model spatial interaction |
| url | https://www.mdpi.com/2073-445X/11/2/225 |
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