Can maritime big data be applied to shipping industry analysis? Focussing on commodities and vessel sizes of dry bulk carriers
Enriched navigational information provided by an automatic identification system (AIS) could improve the estimation accuracy of trade patterns analysis by using different data sources. This paper estimates the global trade flow pattern of dry bulk cargo by commodity, namely iron ore, coal, grains, f...
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| Veröffentlicht in: | Maritime economics & logistics 2021-06, Vol.23 (2), p.211-236 |
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| container_title | Maritime economics & logistics |
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| creator | Kanamoto, Kei Murong, Liwen Nakashima, Minato Shibasaki, Ryuichi |
| description | Enriched navigational information provided by an automatic identification system (AIS) could improve the estimation accuracy of trade patterns analysis by using different data sources. This paper estimates the global trade flow pattern of dry bulk cargo by commodity, namely iron ore, coal, grains, fertilisers, and iron and steel. We use AIS data and the information on commodities handled in ports, estimated by using a two-tiered Geohash geocoding. Estimation results are accurate at country level except for iron and steel. The results are used to quantify the impact of the previously identified variables on vessel size selection by regression analysis and a multinomial logit model. Finally, our model is used to forecast the future shipping demand by vessel type and commodity. |
| doi_str_mv | 10.1057/s41278-020-00171-6 |
| format | Article |
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| subjects | Bulk carriers Business and Management Commodities Economic forecasting Fertilizers Flow distribution Flow pattern International trade Iron Iron ores Logistics Logit models Operations Management Original Article Regression analysis Shipping Shipping industry Steel |
| title | Can maritime big data be applied to shipping industry analysis? Focussing on commodities and vessel sizes of dry bulk carriers |
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