---
title: "AISanalyze: User guide"
author: "Rémi Pigeault"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{AISanalyze: User guide}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---
  
```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(AISanalyze)
library(dplyr)
library(lubridate)
```

# 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

```{r}
data("ais")
data("point_to_extract")
```

Convert timestamps to Unix time.

```{r}
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

```{r}
ais <- AIStravel(ais_data = ais)
```

Three variables are added:
  
- `distance_travelled`
- `time_travelled`
- `speed_kmh`

# Identify stations and aircraft

```{r}
ais <- AISidentify_stations_aircraft(ais_data = ais)
```

Two logical variables are added:
  
- `station`
- `high_speed`

# Correct GPS errors

```{r}
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.

```{r eval=FALSE}
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.

```{r eval=FALSE}
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:

```{r eval=FALSE}
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`).

```{r eval=FALSE}
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):

```{r eval=FALSE}
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`: 

```{r eval=FALSE}
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

```{r eval=FALSE}
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:
  
  ```text
AIS data
│
▼
AIStravel()
│
▼
AISidentify_stations_aircraft() (optional)
│
▼
AIScorrect_speed() (optional)
│
▼
AISinterpolate()   (optional)
│
▼
AISextract()
│
▼
AISinfos()         (optional)
```
