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, 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.