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Li Y, Li X, Wang W, Liao C, Guo R. Analysis of railway accessibility in Fujian Province and the influence of economic development on its spatial differentiation. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:11605-11621. [PMID: 38221558 DOI: 10.1007/s11356-023-31713-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 12/20/2023] [Indexed: 01/16/2024]
Abstract
Understanding railway accessibility supports railway regulation and development, but few studies consider different perspectives. We study the spatial distribution of accessibility indicators at the county (city) scale in Fujian Province by spatiotemporal syntax and weighted average travel time using railway timetable data. Export trade, rich commercial activities, and high-speed rail had a significant positive effect on objective accessibility. Fuzhou, Sanming, and Longyan were main transfer centers. The most accessible nodes based on weighted average travel time formed a "U"-shaped corridor along the coast. The county-wide average accessibility was 1.72 h. According to spatiotemporal syntax, local general public budget expenditure (0.758993) and export volume of goods-total retail sales of consumer goods (0.956257) had the most interactive impact, while according to weighted average accessibility, import volume of goods (0.618447) and per capita gross regional product-import volume of goods (0.878573) did. These results provide reference for transportation planning and regional development in Fujian Province.
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Affiliation(s)
- Yaxing Li
- Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China
- College of Design and Engineering, National University of Singapore, Singapore, 117566, Singapore
| | - Xiaoming Li
- Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China
- Shenzhen Key Laboratory of Spatial Smart Sensing and Services & MNR Technology Innovation Center of Territorial & Spatial Big Data & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen, 518060, China
- Shenzhen Key Laboratory of Digital Twin Technologies for Cities, Shenzhen, 518060, China
| | - Weixi Wang
- Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China
- Shenzhen Key Laboratory of Spatial Smart Sensing and Services & MNR Technology Innovation Center of Territorial & Spatial Big Data & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen, 518060, China
- Shenzhen Key Laboratory of Digital Twin Technologies for Cities, Shenzhen, 518060, China
| | - Chuangchang Liao
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430072, China
| | - Renzhong Guo
- Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China.
- Shenzhen Key Laboratory of Spatial Smart Sensing and Services & MNR Technology Innovation Center of Territorial & Spatial Big Data & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen, 518060, China.
- Shenzhen Key Laboratory of Digital Twin Technologies for Cities, Shenzhen, 518060, China.
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