Paper
15 November 2023 An entropy method-based Newton model for port hinterland division: case study of ports in Northeast China
Jiaju Huang, Lingzhi Sang, Desheng Cao, Chengpeng Wan, Yanyi Chen
Author Affiliations +
Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 1281528 (2023) https://doi.org/10.1117/12.3010218
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China
Abstract
In recent years, ports and port groups have fiercely competed for the source of goods, leading some to blindly expand their scale. This has resulted in excess capacity, leading to a great waste of resources. Therefore, it is vitally important to define the scope of the port hinterland scientifically and reasonably, since this is the foundation for improving the current situation of harmful competition in the hinterland. Based on analysis of pertinent research, this paper identifies various parameters for characterising the quality of ports and employs the entropy weight approach and the Newton model to devise a model for the division of port hinterlands. The proposed model is then applied to the hinterlands surrounding ports in Northeast China. The findings indicate that the distance separating the ports and the hinterland has the most significant effect on the partitioning of the port hinterland in smaller-scale areas. Conversely, in larger-scale areas, the size of the port is the most influential factor. This study offers valuable guidance to port clusters, enabling them to position themselves effectively in the market and prevent the wasteful expenditure of economic resources on competition.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Jiaju Huang, Lingzhi Sang, Desheng Cao, Chengpeng Wan, and Yanyi Chen "An entropy method-based Newton model for port hinterland division: case study of ports in Northeast China", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 1281528 (15 November 2023); https://doi.org/10.1117/12.3010218
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KEYWORDS
Transportation

Analytical research

Data modeling

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