Measurement and Prediction of Urban Land Traffic Accessibility and Economic Contact Based on GIS: A Case Study of Land Transportation in Shandong Province, China

Int J Environ Res Public Health. 2022 Nov 11;19(22):14867. doi: 10.3390/ijerph192214867.


As the basic support of regional economic and social development, land transportation is one of the important engines to promote regional development, and its construction and improvement will have an important impact on the regional economic pattern. Based on the road network of Shandong Province, China, in 2020, according to the Medium and Long-term Development Plan of Comprehensive Transportation Network of Shandong Province (2018-2035), this paper uses the GIS network analysis method, weighted average travel time, modified gravity model and other methods to study the land traffic accessibility and economic relation intensity of prefecture-level cities in Shandong Province, China, in 2020 and 2035. The results show that the distribution of land traffic accessibility in Shandong Province, China, shows a certain regional main road pointing characteristic in 2020, and the urban accessibility level gradually decreases along the Beijing-Shanghai high-speed railway and Jinan-Qingdao high-speed railway to the periphery. In 2035, the land traffic accessibility of Shandong Province, China, will be more spatially distributed as "concentric circles". From 2020 to 2035, the urban land traffic accessibility and the balance of economic contact in Shandong Province, China, will be improved significantly. The research results can provide a theoretical reference for optimizing the traffic network pattern and promoting urban economic contact in Shandong Province, China.

Keywords: GIS; economic contact; land traffic accessibility; spatial pattern.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Beijing
  • China
  • Cities
  • Geographic Information Systems*
  • Transportation*

Grant support

This research was funded by the National Natural Science Foundation of China, grant numbers 71874123 and 71974122, the Natural Science Foundation of Shandong Province, grant number ZR2022QG029, and the China Postdoctoral Science Foundation 2022M712047.