Analysis of cargo ship density and typical spatial characteristics based on automatic identification system data

Guangze Li; Yuan Zhou; Bo Ai; Ping Wang

doi:10.1117/12.3019467

23 January 2024 Analysis of cargo ship density and typical spatial characteristics based on automatic identification system data

Guangze Li, Yuan Zhou, Bo Ai, Ping Wang

Author Affiliations +

Proceedings Volume 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023); 129781Q (2024) https://doi.org/10.1117/12.3019467
Event: 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), 2023, wuhan, China

Abstract

As a major maritime and trade province, the strategic position of marine transportation in Guangdong Province is particularly important. This study uses AIS, waterway, port, sea ownership, and other data, combined with GIS and Python technology to realize track drawing and regional density statistical analysis, extract the dense route of cargo ship routes along the coast of Guangdong and divide the 14 coastal sea areas into dense, relatively dense and non-dense sea areas according to the density of cargo ship routes. Explore the number and density characteristics of cargo routes in different sea areas, and provide a reference for government departments to optimize port resources and plan trade routes. The results show that there are significant differences in the density of cargo routes in the coastal waters of Guangdong Province. The dense routes are mainly distributed in the Pearl River Delta, and there are a small number of dense routes in the remaining sea areas. The density of cargo ships in the sea area of coastal cities shows a decreasing trend with the Pearl River Delta as the center to the two sides of the sea area.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guangze Li, Yuan Zhou, Bo Ai, and Ping Wang "Analysis of cargo ship density and typical spatial characteristics based on automatic identification system data", Proc. SPIE 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 129781Q (23 January 2024); https://doi.org/10.1117/12.3019467

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