Difference between revisions of "Stock assessment and data management"

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(Seasonality and periodicity detection)
(Vessels Trajectories Interpolation)
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== Vessels Trajectories Interpolation ==
 
== Vessels Trajectories Interpolation ==
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An interpolation method relying on the implementation by the authoritative Study Group on VMS (SGVMS). The method uses two interpolation approached to simulate vessels points at a certain temporal resolution.
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The algorithm processes up to 10000 vessels trajectory points and interpolates the trajectories according to a user's defined temporal resolution. The estimation of trawling tracks use cubic Hermite spline interpolation of position registration data.
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The process is taken from the following reference work: Hintzen, N. T., Bastardie, F., Beare, D., Piet, G. J., Ulrich, C., Deporte, N., Egekvist, J., et al. 2012. VMStools: Open-source software for the processing, analysis and visualisation of fisheries logbook and VMS data. Fisheries Research, 115-116: 31-43. Hintzen, N. T., Piet, G. J., and Brunel, T. 2010.
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The input is a file in TACSAT format uploaded on the Statistical Manager. The output is another TACSAT file containing interpolated points.The underlying R code has been extracted from the SGVM VMSTools framework.
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Further details on using this tool are available [http://wiki.i-marine.eu/index.php/ICES_SGVMS here].
  
 
== Seasonality and periodicity detection ==
 
== Seasonality and periodicity detection ==
 
An algorithms applying signal processing to a time series of catch statistics. The process uniformly samples the series, then extracts hidden periodicities and signal properties. The sampling period is taken as the shortest time difference between two points. Finally, by using Caterpillar-SSA the algorithm forecasts the Time Series. The output shows the detected periodicity, the forecasted signal and the spectrogram.
 
An algorithms applying signal processing to a time series of catch statistics. The process uniformly samples the series, then extracts hidden periodicities and signal properties. The sampling period is taken as the shortest time difference between two points. Finally, by using Caterpillar-SSA the algorithm forecasts the Time Series. The output shows the detected periodicity, the forecasted signal and the spectrogram.
 
One experiment using this technique to predict fishing activity in the Indian Ocean is available [http://wiki.i-marine.eu/index.php/IOTC_Area_Predictive_analysis here].
 
One experiment using this technique to predict fishing activity in the Indian Ocean is available [http://wiki.i-marine.eu/index.php/IOTC_Area_Predictive_analysis here].

Revision as of 15:28, 2 February 2015

Overview

Stock Assessment

Length-Weight relation

Vessels Transmitted Information

Vessels Trajectories Interpolation

An interpolation method relying on the implementation by the authoritative Study Group on VMS (SGVMS). The method uses two interpolation approached to simulate vessels points at a certain temporal resolution. The algorithm processes up to 10000 vessels trajectory points and interpolates the trajectories according to a user's defined temporal resolution. The estimation of trawling tracks use cubic Hermite spline interpolation of position registration data.

The process is taken from the following reference work: Hintzen, N. T., Bastardie, F., Beare, D., Piet, G. J., Ulrich, C., Deporte, N., Egekvist, J., et al. 2012. VMStools: Open-source software for the processing, analysis and visualisation of fisheries logbook and VMS data. Fisheries Research, 115-116: 31-43. Hintzen, N. T., Piet, G. J., and Brunel, T. 2010. The input is a file in TACSAT format uploaded on the Statistical Manager. The output is another TACSAT file containing interpolated points.The underlying R code has been extracted from the SGVM VMSTools framework. Further details on using this tool are available here.

Seasonality and periodicity detection

An algorithms applying signal processing to a time series of catch statistics. The process uniformly samples the series, then extracts hidden periodicities and signal properties. The sampling period is taken as the shortest time difference between two points. Finally, by using Caterpillar-SSA the algorithm forecasts the Time Series. The output shows the detected periodicity, the forecasted signal and the spectrogram. One experiment using this technique to predict fishing activity in the Indian Ocean is available here.