Package {AISanalyze}

Title:	Processing and Analyzing AIS Vessel Tracking Data
Version:	3.1.2
Description:	Processes Automatic Identification System (AIS) vessel tracking data, including travel estimation, trajectory correction, interpolation, extraction, and summarising vessel information. The package is designed to facilitate reproducible analyses of maritime traffic in ecological, environmental, and marine spatial planning applications. For more details see https://remip48.github.io/AISanalyze/.
License:	MIT + file LICENSE
Depends:	R(≥ 4.2.0)
Imports:	dplyr, sf, purrr, stats, stringr, doParallel, parallel, foreach, assertthat, data.table, magrittr
Suggests:	knitr, units, lubridate, rmarkdown, testthat
URL:	https://github.com/remip48/AISanalyze, https://remip48.github.io/AISanalyze/
BugReports:	https://github.com/remip48/AISanalyze/issues
Encoding:	UTF-8
Config/testthat/edition:	3
LazyData:	true
Config/roxygen2/version:	8.0.0
VignetteBuilder:	knitr
RoxygenNote:	7.3.1
NeedsCompilation:	no
Packaged:	2026-08-20 21:25:59 UTC; 234028
Author:	Rémi Pigeault [aut, cre]
Maintainer:	Rémi Pigeault <remi.pigeault@tiho-hannover.de>
Repository:	CRAN
Date/Publication:	2026-08-26 19:40:01 UTC

AISanalyze

Description

Tools for analysing, cleaning, interpolating and extracting Automatic Identification System (AIS) vessel data.

Details

Main functions:

Travel metrics

• AIStravel()

• AIScorrect_speed()

Vessel characteristics

• AISinfos()

• AISidentify_stations_aircraft()

Interpolation

• AISinterpolate()

Extraction

• AISextract()

Author(s)

Maintainer: Rémi Pigeault remi.pigeault@tiho-hannover.de (ORCID)

Authors:

• Rémi Pigeault remi.pigeault@tiho-hannover.de (ORCID)

See Also

Useful links:

• https://github.com/remip48/AISanalyze

• https://remip48.github.io/AISanalyze/

• Report bugs at https://github.com/remip48/AISanalyze/issues

Correct GPS errors in AIS tracks

Description

Detects and corrects GPS errors and delayed AIS messages that generate unrealistic vessel speeds, travelled distances, and travel times. Note: For consecutive GPS errors, only the first point is removed to avoid overcorrection.

Usage

AIScorrect_speed(
  ais_data,
  crs_meters = 3035,
  threshold_speed_to_correct = 100,
  threshold_speed_to_correct_function = NULL,
  nb_cores = 1,
  outfile = tempfile()
)

Arguments

Details

When threshold_speed_to_correct_function is set to NULL, a vessel-specific speed threshold is estimated from the observed vessel speed_kmh. The threshold is calculated as:

T = 15 + \operatorname{median}(v_{>1}) + 5 \times \operatorname{SD}(v_{1 < v < Q_{0.75}})

where v_{>1} represents vessel speeds greater than 1 km/h, and v_{1 < v < Q_{0.75}} represents vessel speeds between 1 km/h and the 75th percentile. The equation is designed to capture the typical travelling speed of the vessel while accounting for variation in its observed travelling speeds.

Value

The input data with corrected travel metrics and the following columns:

• speed_kmh_corrected: Whether the speed was corrected.

• time_travelled: Travel time (s).

• distance_travelled: Travelled distance (m).

• speed_kmh: Vessel speed (km/h).

Examples

data("ais")

# use only a sample for the example:
ais <- ais[ais$mmsi %in% ais$mmsi[1:5], ]

# Define the Unix time (seconds since 1970-01-01)
ais$timestamp <- as.numeric(lubridate::ymd_hms(ais$datetime))

# calculate the travelled distance, time, and speed:
ais <- AIStravel(ais_data = ais)

# Correct speed:
out <- AIScorrect_speed(ais_data = ais,
                        crs_meters = 3035)

Extract AIS positions around target locations and times

Description

Returns either (depending on return_all_vessel_locations):

• the position of each vessel at the time closest to the target timestamps.

• or all vessel positions within a specified time window.

Usage

AISextract(
  ais_data,
  data,
  crs_meters = 3035,
  return_all_vessel_locations = TRUE,
  search_into_radius_m = 50000,
  search_shape = "circle",
  interval_time_before = 5 * 60,
  interval_time_after = 5 * 60,
  nb_cores = 1,
  outfile = tempfile()
)

Arguments

Value

data joined with matching AIS positions. Rows are duplicated when several vessel positions match a target location and time. If no vessel is found, AIS columns (including mmsi) are filled with NA. The output also includes distance_vessel_to_location_m, the distance (m) between the target location and vessel positions.

Examples

data("ais")
data("point_to_extract")

# use only a sample for the example:
ais <- ais[20000:30000, ]

# Define the Unix time (seconds since 1970-01-01)
point_to_extract$timestamp <- as.numeric(lubridate::ymd_hm(point_to_extract$datetime))
ais$timestamp <- as.numeric(lubridate::ymd_hms(ais$datetime))

# calculate the travelled distance, time, speed, and interpolate AIS data:
ais <- ais |>
  AIStravel()

# Extract all vessel positions within the target time interval and radius:
out <- AISextract(ais_data = ais,
                  data = point_to_extract,
                  crs_meters = 3035,
                  return_all_vessel_locations = TRUE, # set FALSE to only
                  # extract the vessel position closest in time to the
                  # target timestamps.
                  search_into_radius_m = 50000,
                  interval_time_before = 5 * 60,
                  interval_time_after = 5 * 60)

Identify AIS base stations and high-speed craft

Description

Stations and aircraft are identified from speed, distance and time only. Other criteria (e.g. MMSIs with fewer than 9 digits) are not considered.

Usage

AISidentify_stations_aircraft(ais_data, crs_meters = 3035)

Arguments

Value

The input AIS data with the following additional columns:

• station: Whether the MMSI is classified as a base station.

• high_speed: Whether the MMSI is classified as a high-speed craft.

• n_point_mmsi_initial_data: Number of valid AIS positions for the MMSI.

• id_mmsi_point_initial: Sequential identifier of each AIS position.

Examples

data("ais")

# Define the Unix time (seconds since 1970-01-01)
ais$timestamp <- as.numeric(lubridate::ymd_hms(ais$datetime))

# calculate the travelled distance, time, and speed:
ais <- AIStravel(ais_data = ais)

# Identify stations and aircrafts:
out <- AISidentify_stations_aircraft(ais_data = ais)

Estimate vessel characteristics

Description

Estimates the most likely vessel characteristics for each mmsi from AIS messages, including ship type, length, width, draught, IMO number, and vessel name. Estimates are based on the most frequent values, giving greater weight to records with more complete information. Warnings are printed if any value of length, draught, width, or IMO cannot be converted to numeric (and is therefore set to NA) or any value of ship type or name cannot be converted to character (set to NA).

Usage

AISinfos(
  ais_data,
  threshold_length = 475,
  threshold_draught = 30,
  threshold_width = 75,
  weight_complete_data = 10
)

Arguments

Value

A list containing:

• estimated_values: Estimated vessel characteristics for each mmsi.

• summary: Summary statistics for each mmsi, including:

  • Number of AIS positions.

  • Number of non-missing values for length, shiptype, width, draught, imo, and name.

  • All valid values observed for each characteristic.

  • The most likely value for each characteristic.

Examples

data("ais")

out <- AISinfos(ais_data = ais)

Interpolate AIS positions

Description

Interpolates vessel positions either: (depending on type_interpolation)

• to ensure time intervals do not exceed a specified maximum (⁠= maximum_gap_seconds⁠).

• at user-defined timestamps (⁠= exact_timestamp⁠). Interpolation can optionally be restricted to specific regions to reduce computation time with locations_of_interest and radius arguments.

Usage

AISinterpolate(
  ais_data,
  type_interpolation,
  maximum_gap_seconds,
  exact_timestamp = list(timestamp_to_interpolate, locations_of_interest, radius),
  crs_meters = 3035,
  nb_cores = 1,
  outfile = tempfile()
)

Arguments

Value

The interpolated AIS data with an additional column:

• interpolated: Whether the position was interpolated.

Examples

data("ais")
data("point_to_extract")

# use only a sample for the example:
ais <- ais[20000:30000, ]

# Define the Unix time (seconds since 1970-01-01)
point_to_extract$timestamp <- as.numeric(lubridate::ymd_hm(point_to_extract$datetime))
ais$timestamp <- as.numeric(lubridate::ymd_hms(ais$datetime))

# calculate the travelled distance, time, and speed:
ais <- AIStravel(ais_data = ais)

# Interpolate all AIS signals further than > 120 seconds:
out <- AISinterpolate(ais_data = ais,
                      type_interpolation = "maximum_gap_seconds",
                      maximum_gap_seconds = 120, ## Alternatively, you can
                      ## interpolate at target timestamps with:
                      # exact_timestamp = list(
                      #      timestamp_to_interpolate = point_to_extract$timestamp,
                      #      locations_of_interest = data.frame(lon = point_to_extract$lon,
                      #                                         lat = point_to_extract$lat),
                      #      radius = 200000),
                      crs_meters = 3035)

Calculate vessel travel metrics

Description

Calculates the distance travelled (m), travel time (s), and speed (km/h) between consecutive AIS positions for each vessel (mmsi).

Usage

AIStravel(ais_data, crs_meters = 3035, nb_cores = 1, outfile = tempfile())

Arguments

Value

The input AIS data with the following additional columns:

• time_travelled: Travel time (s) since the previous AIS position.

• distance_travelled: Distance travelled (m) since the previous AIS position.

• speed_kmh: Vessel speed (km/h).

• X, Y: coordinates in crs_meters.

Examples

data("ais")

# Define the Unix time (seconds since 1970-01-01)
ais$timestamp <- as.numeric(lubridate::ymd_hms(ais$datetime))

# calculate the travelled distance, time, and speed:
out <- AIStravel(ais_data = ais)

Example AIS dataset

Description

A subset of Automatic Identification System (AIS) messages collected in the North Sea on 1 November 2022. The dataset contains vessel positions together with static and dynamic AIS information, including vessel identity, navigational status, speed, heading, dimensions, draught and destination.

Format

A data frame with AIS messages for multiple vessels. The main variables include:

datetime

Date and time of the AIS message (UTC).

mmsi

Anonymized Maritime Mobile Service Identity (MMSI) of the vessel.

lon

Longitude (decimal degrees, WGS84).

lat

Latitude (decimal degrees, WGS84).

navigational_status

Reported navigational status.

SOG

Speed over ground (knots).

Heading

True heading (degrees).

imo

Reported International Maritime Organization (IMO) number.

shiptype

Reported vessel type.

length

Reported vessel length (m).

name

Anonymized vessel name.

width

Reported vessel width (m).

draught

Reported vessel draught (m).

Details

The dataset is intended for demonstrating the main functions of AISanalyze, including travel distance estimation, GPS error correction, vessel characteristic estimation, interpolation and vessel extraction. Original MMSI identifiers and vessel names have been replaced with anonymized values to protect vessel confidentiality.

Source

Example subset extracted from AIS observations.

Example extraction locations

Description

Example locations and timestamps used to demonstrate AISextract() and interpolation at exact timestamps with AISinterpolate(). Each row defines a target location and time for which nearby vessel positions can be extracted or interpolated.

Format

A data frame with the following variables:

point

Unique identifier of the target location.

lon, lat

Longitude and latitude (WGS84).

datetime

Target date and time (UTC).

See Also

AISextract(), AISinterpolate()