A complete step-by-step workflow is available in the User guide.
📖 Full documentation, tutorials and function reference: https://remip48.github.io/AISanalyze/
AISanalyze is an R package providing a fast and reproducible workflow for preprocessing Automatic Identification System (AIS) vessel tracking data for environmental and ecological research. It streamlines common preprocessing tasks through a small set of user-friendly functions, including vessel trajectory reconstruction, GPS correction, interpolation, and the extraction of vessel positions around target locations or time periods.
The package emphasizes computational efficiency and reproducibility, allowing large AIS datasets to be prepared for downstream analyses in seconds to minutes. Its main functionalities include:
- estimating vessel travel distance, time, and speed;
- correcting GPS errors and delays;
- identifying AIS base stations and aircraft;
- interpolating vessel positions;
- extracting vessel positions around target locations and times;
- retrieving vessel characteristics (ship type, length, width, draught, IMO number, and vessel name).
# install.packages("remotes")
remotes::install_github("remip48/AISanalyze")| Function | Description |
|---|---|
AIStravel() |
Estimate travelled distance, time and speed |
AISidentify_stations_aircraft() |
Identify AIS stations and aircraft |
AIScorrect_speed() |
Correct GPS errors and delays |
AISinterpolate() |
Interpolate vessel positions |
AISextract() |
Extract vessels around target locations |
AISinfos() |
Estimate vessel characteristics |
library(AISanalyze)
data("ais")
data("point_to_extract")
## define the Unix time (seconds since 1970-01-01)
ais$timestamp <- as.numeric(lubridate::ymd_hms(ais$datetime)))
point_to_extract$timestamp <- as.numeric(lubridate::ymd_hm(point_to_extract$datetime)))
results <- ais |>
AIStravel(nb_cores = 4) |> # estimate travelled speed, distance, time
AISidentify_stations_aircraft() |>
dplyr::filter(!station & !high_speed) |> # remove stations and aircrafts
AIScorrect_speed(nb_cores = 4) |> # correct speed
AISinterpolate(., # interpolate AIS data
type_interpolation = "maximum_gap_seconds",
maximum_gap_seconds = 60,
nb_cores = 4) |>
AISextract(data = point_to_extract, # extract around your points
search_into_radius_m = 10000,
nb_cores = 4)
# done!Total execution time to complete the example workflow with 100 points to extract and 4 CPU cores:
| AIS dataset size | 100,000 points | 1,000,000 points | 2,500,000 points |
|---|---|---|---|
| Time | 14 sec | 68 sec | 146 sec |
If you use AISanalyze, please cite:
Pigeault R., Ruser A., Ramírez-Martínez N.C., Geelhoed S.C.V., Haelters J., Nachtsheim D.A., Schaffeld T., Sveegaard S., Siebert U., Gilles A. (2024). Maritime traffic alters distribution of the harbour porpoise in the North Sea. Marine Pollution Bulletin. 208: 116925. DOI: 10.1016/j.marpolbul.2024.116925
citation("AISanalyze")This project is licensed under the MIT License. See the LICENSE file
for details.
Contributions are welcome! Whether you would like to report a bug, suggest a new feature, or contribute code or documentation, please read our CONTRIBUTING.md guide to get started.
Please use the GitHub issue tracker to report bugs, request features, or ask questions. For other enquiries, you may also contact the package author directly.