Introduction

AISanalyze provides a workflow to analyse Automatic Identification System (AIS) data, including:

• estimating vessel travel distance, time and speed;
• correcting GPS errors and delays;
• identifying AIS stations and aircraft;
• interpolating vessel positions;
• extracting vessels around target locations;
• estimating vessel characteristics.

This vignette illustrates a typical workflow.

Example data

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

Convert timestamps to Unix time.

ais$timestamp <- as.numeric(lubridate::ymd_hms(ais$datetime))
point_to_extract$timestamp <- as.numeric(lubridate::ymd_hm(point_to_extract$datetime))

Estimate travelled distance and speed

ais <- AIStravel(ais_data = ais)

Three variables are added:

• distance_travelled
• time_travelled
• speed_kmh

Identify stations and aircraft

ais <- AISidentify_stations_aircraft(ais_data = ais)

Two logical variables are added:

• station
• high_speed

Correct GPS errors

ais <- AIScorrect_speed(ais_data = ais)

This step corrects unrealistic speeds caused by GPS errors or transmission delays.

Interpolate vessel positions

Interpolates AIS data to ensure that consecutive vessel positions are no more than 60 seconds apart.

ais_interpolated_60sec <- AISinterpolate(
  ais_data = ais,
  type_interpolation = "maximum_time_interval",
  maximum_gap_seconds = 60
)

Alternatively, vessel positions can be interpolated at exact timestamps. Target locations and a search radius (m) can be specified to limit interpolation to a specific area and reduce computation time.

ais_interpolated_exact_timestamps <- AISinterpolate(
  ais_data = ais,
  type_interpolation = "exact_timestamp",
  exact_timestamp = list(
    timestamp_to_interpolate = point_to_extract$timestamp,
    locations_of_interest = point_to_extract[c("lon", "lat")],
    radius = 200000
  )
)

The datetime column in the interpolated datasets can then be updated:

ais_interpolated_60sec$datetime <- lubridate::as_datetime(ais_interpolated_60sec$timestamp)

ais_interpolated_exact_timestamps$datetime <- lubridate::as_datetime(ais_interpolated_exact_timestamps$timestamp)

Extract nearby vessels

Extract all vessel positions within 50 km and ±5 minutes of the target locations and timestamps (point_to_extract).

AISextract(
  ais_data = ais_interpolated_60sec,
  data = point_to_extract,
  return_all_vessel_locations = TRUE,
  search_into_radius_m = 50000,
  interval_time_before = 300,
  interval_time_after = 300
)

Alternatively, set return_all_vessel_locations = FALSE to return only one vessel position per timestamp (the closest in time to the target timestamps):

AISextract(
  ais_data = ais_interpolated_exact_timestamps,
  data = point_to_extract,
  return_all_vessel_locations = FALSE,
  search_into_radius_m = 50000,
  interval_time_before = 300,
  interval_time_after = 300
)

Furthermore, you can extract vessel positions over a square grid (instead of a circular radius) by setting search_shape = "square" and passing the cell centroids to data:

AISextract(
  ais_data = ais_interpolated_exact_timestamps,
  data = point_to_extract,
  return_all_vessel_locations = FALSE, # or TRUE
  search_into_radius_m = 50000,
  search_shape = "square",
  interval_time_before = 300,
  interval_time_after = 300
)

Estimate vessel characteristics

infos <- AISinfos(ais)

summary_values <- infos$summary
estimated_values <- infos$estimated_values

This function estimates the most likely vessel characteristics for each MMSI, including ship type, dimensions, draught, IMO number, and name. summary_values summarises all values found in the AIS data, whereas estimated_values contains the estimated characteristic for each vessel.

Workflow summary

The recommended workflow is:

AIS data
│
▼
AIStravel()
│
▼
AISidentify_stations_aircraft() (optional)
│
▼
AIScorrect_speed() (optional)
│
▼
AISinterpolate()   (optional)
│
▼
AISextract()
│
▼
AISinfos()         (optional)