To get started, we first tell R where the fishing_effort folder containing the downloaded datasets is located. These data can be used in conjunction with the “Daily Fishing Effort and at 10th Degree Resolution by MMSI” data to explore vessel and fleet characteristics and provide details (gear type, flag state, etc.) for the MMSI being analyzed. Since we will be using both datasets, let’s rename the fishing_effort folder to fishing_effort_byflag and then store both the fishing_effort_byflag and fishing_effort_byvessel folders within a directory called fishing_effort.ĭata containing details on the fishing vessels included in the fishing effort datasets are also available. The files generally increase in size over time due to the higher number of satellites receiving AIS data. The daily csvs are named according to the YYYY-MM-DD.csv format. zip file containing a folder named fishing_effort (or fishing_effort_byvessel) that itself contains the data readme.txt file and the daily_csvs folder. Upon downloading each dataset, users will receive a. Daily Fishing Effort and at 10th Degree Resolution by MMSI, 2012-2016.

Daily Fishing Effort and Vessel Presence at 100th Degree Resolution by Flag State and GearType, 2012-2016.Library(maps) # additional helpful mapping packagesĭaily fishing effort data are available for download in the following two formats:

# Load packages library(tidyverse) # for general data wrangling and plotting library(furrr) # for parallel operations on lists library(lubridate) # for working with dates library(sf) # for vector data library(raster) # for working with rasters If you do not already have these packages installed you can do so with the install.packages() function. There are also a few additional mapping packages that are helpful for plotting. The furrr package is useful for running operations in parallel, lubridate is great for working with dates, and the sf and raster packages are designed for working with vector and raster data, respectively, in R. The tidyverse package contains several helpful packages for data manipulation ( dplyr, tidyr, readr) and plotting ( ggplot2). We will use several R packages to work with the data.

The following code snippets provide examples for working with our downloadable fishing effort data in R in order to calculate, plot, and export various maps of fishing effort. R is a scientific programming language and a popular tool among our users for processing and visualizing Global Fishing Watch data.

Fishing planet map scale coordinates