Introduction

In this exercise we want to analyse the impact of COVID-19 on vessel densities in the Belgian Part of the North Sea and the Western Scheldt. There are different ways to calculate vessel densities, therefore we first start with some background information on two different types of vessel densities and where this data can be found. The data and code of this analysis are freely available, see the ‘Data availability’ and ‘Code availability’ sections at the end of this document. You can even reproduce the full analysis or modify parts of it, see the ‘reproducibility’ section.

This analysis is used in following policy information note (BIN) from Flanders Marine Institute: http://www.vliz.be/nl/catalogus?module=ref&refid=324473

Vessel densities

In this exercise we want to analyse vessel route densities in the Belgian part of the North Sea. The data for this exercise comes from EMODnet Human activities. EMODnet Human Activities has two types of vessel density data, one created by the Human Activities portal themselves, giving the vessel hours per square km per month by ship type. See here.

And one created by the European Maritime Safety Agency (EMSA), giving the number of routes per square km per month) by ship type. The advantage of the latter is that this provides recent information, montly data is available with only 2-3 weeks delay.For details, see here. We will use the EMSA dataset in the next sections.

All units are number of routes / km² / ship type

The focus of this exercise is the Belgian waters (the Belgian Exclusive Economic Zone, EEZ) and the Western Scheldt

We extract the Vessel density data, for example here for the Fishing map of January 2019: (units are number of routes / km² / ship type)

Belgian EEZ:

We extract all data from all the cells, and calculate the average for the whole EEZ.

The table below shows the average values for the whole Belgian EEZ, for the different fishing types. We only have monthly data, so each data represents one month, from January 2019 to April 2020. (units are: number of routes / km² / ship type)

A plot of the data:

Scheldt estuary:

The table below shows the average values for the Western Scheldt, for the different fishing types. We only have monthly data, so each data represents one month, from January 2019 to April 2020. (units are number of routes / km² / ship type)

A plot of the result:

2020 vs 2019

We create a average value for the periods

  • February - April 2019
  • February - April 2020

And plot both periods. For each vessel type, both periods are visualised on a single map. (units are number of routes / km² / ship type)

Cargo

Cargo ships Feb-April 2019 vs Feb-April 2020 (units are number of routes / km² / ship type)

Fishing

Fishing ships Feb-April 2019 vs Feb-April 2020 (units are number of routes / km² / ship type)

Passenger

Passenger ships Feb-April 2019 vs Feb-April 2020 (units are number of routes / km² / ship type)

Tanker

Tanker ships Feb-April 2019 vs Feb-April 2020 (units are number of routes / km² / ship type)

Other

Other ships Feb-April 2019 vs Feb-April 2020 (units are number of routes / km² / ship type)

All

All ships Feb-April 2019 vs Feb-April 2020 (units are number of routes / km² / ship type)

Anomaly maps

February - April 2019 compared to February - April 2020

Comparing the period February-April 2020 vs 2019: The different layers are the different boat types, click on the ‘layers’ button to visualise different layers. (units are the difference in number of routes / km² / ship type)

Month-by-month comparisons 2019 vs 2020

Comparing the month-by-month differences between 2020 vs 2019: You can select the different layers by clicking on the layer symbol.

Cargo

Anomaly of cargo ships per month (units are difference in number of routes / km² / ship type)

Fishing

Anomaly of fishing ships per month (units are difference in number of routes / km² / ship type)

Passenger

Anomaly of passenger ships per month (units are difference in number of routes / km² / ship type)

Tanker

Anomaly of tanker ships per month (units are difference in number of routes / km² / ship type)

Other

Anomaly of other ships per month (units are difference in number of routes / km² / ship type)

All

Anomaly of all ships per month (units are difference in number of routes / km² / ship type)

Additional analyses

Passengers excluding wind farm

There is quite some traffic to the windfarms that are classified as ‘Passenger’ traffic. As this might be confusing, we will exclude this from the analysis. The excluded area is visualised in blue on the map below.

Below is a table showing the average monthly passenger density (‘Passenger’ column) and the same analysis but excluding the traffic to the wind farm (‘Pass_no_windfarm’).

Fishing zones.

In this part we’ll look how much is being fished in the different zones within the Belgian EEZ:

  • from the coastline to 3 nautical miles (NM)
  • from 3 to 12 nautical miles (NM)
  • further than 12 nautical miles from the coastline

Data availability

  • Vessel densities The data from this exercise is freely available at the EMODnet Human activities portal. EMODnet stands for the European Marine Observation and Data Network and is a network of organisations that are collecting and standardizing European marine data, and making those data products freely available, supported by EU’s integrated marine policy. The vessel densities data used in this exercise are provided by the European Maritime Safety Agency (EMSA) to EMODnet human activities and are available here.

  • Maritime boundaries The maritime boundaries used in this exercise are from MarineRegions.org. MarineRegions.org maintains a standard, relational list of geographic names coupled with information and maps of the geographic location of these features. This improves access and clarity of the different geographic, marine names and allows an improved linking of these locations to databases. Marine Regions is developed by Flanders Marine Institute (VLIZ) as part of the Flemish contribution to LifeWatch, funded by Research Foundation - Flanders. The more information about the polygons used in this exercise:

Code availability

All the code needed to run this analysis is available here.

This code makes use of following R packages:

  • raster: for raster data
  • sf: for spatial data
  • mapview: for interactive maps
  • ggplot2: for plots
  • data.table: for manipulation dataframes/tables
  • DT: for visualisation of the data tables
  • mregions: for standardize marine regions from http://www.marineregions.org

Reproducibility

You can re-run the full analysis in an RStudio environment by clicking on the button below.

Binder

This will open an online RStudio environment. To run the analysis, open the ‘index.Rmd’ file. In this file, you can run separate code chunks or click on the ‘knit’ button to recreate the html file (this will take a while, and open a pop-up window). You can also edit the code and run your own analysis.

If you have any issues with running the analysis, please let us know by opening an issue.