  Vessel Monitoring System (VMS) data provide information about speed and
position of fishing vessels. This opens the door to methods of estimating and
mapping fishing effort with a high level of detail. To addess this task, we
propose a new method belonging to the class of hidden Markov models (HMM) that
accounts for autocorrelation in time along the fishing events and offers a good
trade-off between model complexity and computational efficiency. We carry out
an objective comparison between this method and two competing approaches on a
set of VMS data from Denmark for which the true activity is known from on-board
sensors. The DMKMG approach proposed outperformed the competitors approach with
6% and 15% more accurate estimates in the vessel-by-vessel and trip-by-trip
case, respectively. In addition, these better performances are not paid in
terms of computation time. We also showcase our method on an extensive dataset
from Sweden. A quick (real-time) data processing has the potential to change
how fisheries can be better harmonized to other utilisation of the seas and
fill the gap between the local scales at which fishing pressure and stock
depletion occurs with the large temporal and spatial scales of traditional
fisheries assessment and management. The computer code developped in this work
is made publicly available as an R package from
http://www2.imm.dtu.dk/~gigu/HMM-VMS
