Difference between revisions of "Stock assessment and data management"
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Further details on using this tool are available [http://wiki.i-marine.eu/index.php/ICES_SGVMS here]. | Further details on using this tool are available [http://wiki.i-marine.eu/index.php/ICES_SGVMS here]. | ||
− | * Name of the algorithm on [https://i-marine.d4science.org/group/biodiversitylab/processing-tools StatMan]: Sgvm Interpolation | + | * Name of the algorithm on [https://i-marine.d4science.org/group/biodiversitylab/processing-tools StatMan]: '''Sgvm Interpolation''' |
== Seasonality and periodicity detection == | == Seasonality and periodicity detection == |
Revision as of 15:30, 2 February 2015
Contents
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.
- Name of the algorithm on StatMan: Sgvm Interpolation
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.
- Name of the algorithm on StatMan: Time Series Analysis