Summary of Suspicious Vessels & Companies

Author

Group 1 - Arya Siahaan, Seng Jing Yi, Thet Myat Noe

Published

July 2, 2024

Objectives

In this article, we will summarise our findings on suspicious vessels and companies based on the vessels identified under “IUU Suspicion”.

Code

# Summary of codes to identify suspicious vessels based on IUU suspicion

pacman::p_load(jsonlite, tidyverse, tidyr, 
               knitr, lubridate, dplyr, 
               igraph, ggraph, ggdist, ggplot2, 
               SmartEDA, sf, tidygraph, reshape2, readr,
               DT, patchwork,plotly, RColorBrewer)

tx_qty <- read_csv("data/tx_qty.csv")
ping_activity <- read_csv("data/ping_activity.csv")
E_Hbrpt_v <- read_csv("data/hbrpt.csv")
N_vessel <- read_csv("data/N_vessel.csv")
#location_legend <- read_csv("data/location_legend.csv")
#vessel_movement <- read_rds("data/rds/vessel_movement_data.rds")
nearest_tx_date <- read_csv("data/mapped_records.csv")
time_summary_df <- read_csv("data/time-summary.csv")

## Determine vessels that have overstayed 
ping_activity$start_time <- ymd_hms(ping_activity$start_time)
ping_activity$month <- floor_date(ping_activity$start_time, "month")

# Convert the month column to Date format (first day of each month)
ping_activity$month <- as.Date(ping_activity$month)

# Calculate median dwell time per vessel_id per month for each area
agg_data <- ping_activity %>%
  group_by(area, vessel_id, month) %>%
  summarise(median_dwell = median(dwell, na.rm = TRUE)) %>%
  ungroup()

agg_data2 <- agg_data %>%
  group_by(area, month) %>%
  mutate(`90%_dwell_time` = quantile(median_dwell, 0.90, na.rm = TRUE)) %>%
  mutate(`Is_above_90%` = ifelse(median_dwell > `90%_dwell_time`, "Yes", "No")) %>%
  ungroup()

# count the number of times that vessel stayed >90% dwell time
summary_data <- agg_data2 %>%
  filter(area %in% c( "Ghoti Preserve", "Nemo Reef", "Don Limpet Preserve"), 
         `Is_above_90%` == "Yes") %>%
  group_by(vessel_id, area) %>%
  summarise(count_above_90 = n()) %>%
  filter(count_above_90 >=2) %>% ungroup()

summary_data_DT <- summary_data %>% 
  left_join(N_vessel %>% select("vessel_id", "vessel_name", "vessel_company", "vessel_type", "tonnage", "length_overall", "flag_country"),  
            by = c("vessel_id" = "vessel_id"))



## Codes to identify overfishing vessels 

vessel_cargo_map <- nearest_tx_date %>% 
  left_join(N_vessel, by = c("vessel_id" = "vessel_id")) %>% 
  group_by(cargo_id, tx_date, vessel_id, fish_species) %>%
  summarise(percent_fill = qty_tons/tonnage)

vessel_cargo_map <- vessel_cargo_map %>% mutate(tx_date = as.Date(tx_date))

vessel_cargo_map <- vessel_cargo_map %>%
  group_by(fish_species, month = floor_date(tx_date, "month")) %>%
  mutate(monthly_mean = mean(percent_fill, na.rm = TRUE),
         percentile_90 = quantile(percent_fill, 0.90, na.rm = TRUE)) %>%
  mutate(percent_fill_above_90 = ifelse(percent_fill > percentile_90, "Yes", "No")) %>%
  ungroup()

vessel_percent_fill_counts <- vessel_cargo_map %>%
  filter(percent_fill_above_90 == "Yes") %>%
  group_by(vessel_id) %>%
  summarize(count_above_90 = n()) %>%
  ungroup()

# Filter for vessel_id with count >= 5
filtered_vessel_counts <- vessel_percent_fill_counts %>%
  filter(count_above_90 >= 20)

# Summarising for table 
sus_overfish_cargo <- nearest_tx_date %>% 
  filter(vessel_id %in% filtered_vessel_counts$vessel_id) %>%
  group_by(vessel_id, fish_species) %>%
  summarize(total_qty_tons = sum(qty_tons, na.rm = TRUE),
            distinct_cargo_count = n_distinct(cargo_id)) %>%
  ungroup()
            
# identifying vessel diversion

vessel_trips <- time_summary_df %>%
  mutate(start_date = as.Date(start_date))

# Define the cutoff date for 14 May 2035
cutoff_date <- as.Date("2035-05-14")

trips_before_cutoff <- vessel_trips %>%
  filter(start_date <= cutoff_date)

# Filter trips that started in June 2035
trips_in_june <- vessel_trips %>%
  filter(start_date >= as.Date("2035-06-01") & start_date < as.Date("2035-07-01"))

# Filter trips that started after 14 May 2035
trips_after_cutoff <- vessel_trips %>%
  filter(start_date > cutoff_date)

# Function to get unique areas
get_unique_areas <- function(data) {
  data %>%
    pull(unique_areas) %>%
    strsplit(", ") %>%
    unlist() %>%
    unique()
}

# Get unique areas per vessel before the cutoff date
areas_before_cutoff <- trips_before_cutoff %>%
  group_by(vessel_id) %>%
  summarize(areas_before = list(get_unique_areas(cur_data())))

# Get unique areas per vessel after the cutoff date
areas_after_cutoff <- trips_after_cutoff %>%
  group_by(vessel_id) %>%
  summarize(areas_after = list(get_unique_areas(cur_data())))

# Get unique areas per vessel in June 2035
areas_in_june <- trips_in_june %>%
  group_by(vessel_id) %>%
  summarize(areas_june = list(get_unique_areas(cur_data())))

# Combine the data
combined_areas <- full_join(areas_before_cutoff, areas_after_cutoff, by = "vessel_id") %>%
  full_join(areas_in_june, by = "vessel_id")

# Determine new areas visited, old areas avoided, and old areas avoided in June for each vessel
results <- combined_areas %>%
  rowwise() %>%
  mutate(new_areas = if(length(setdiff(areas_after, areas_before)) > 0) 
                        paste(setdiff(areas_after, areas_before), collapse = ", ") else NA,
         old_areas_avoided = if(length(setdiff(areas_before, areas_after)) > 0) 
                                paste(setdiff(areas_before, areas_after), collapse = ", ") else NA,
         old_areas_avoided_june = if(length(setdiff(areas_before, areas_june)) > 0) 
                                    paste(setdiff(areas_before, areas_june), collapse = ", ") else NA,
         new_areas_count = ifelse(is.na(new_areas), 0, length(unlist(strsplit(new_areas, ", ")))),
         old_areas_avoided_count = ifelse(is.na(old_areas_avoided), 0, length(unlist(strsplit(old_areas_avoided, ", ")))),
         old_areas_avoided_june_count = ifelse(is.na(old_areas_avoided_june), 0, length(unlist(strsplit(old_areas_avoided_june, ", "))))
         ) %>%
  ungroup()

# Add new columns to identify vessels that avoided "Ghoti Preserve" in June but returned after the cutoff period
results <- results %>%
  mutate(avoided_ghoti_june = ifelse(grepl("Ghoti Preserve", old_areas_avoided_june), TRUE, FALSE),
         returned_to_ghoti_after_cutoff = ifelse(avoided_ghoti_june & grepl("Ghoti Preserve", areas_after), TRUE, FALSE))

# Select relevant columns for the final output
final_results <- results %>%
  select(vessel_id, new_areas, old_areas_avoided, old_areas_avoided_june, new_areas_count, old_areas_avoided_count, old_areas_avoided_june_count, avoided_ghoti_june, returned_to_ghoti_after_cutoff)


# combining the 3 datasets to identify suspicious vessels

overstay_vessel <- summary_data
diverted_vessel <- N_vessel %>% filter(vessel_id %in% final_results$vessel_id) %>% select(vessel_id, vessel_company)
overfish_vessel <- sus_overfish_cargo

overstay_vessel_processed <- overstay_vessel %>%
  mutate(count = 1) %>%
  pivot_wider(names_from = area, values_from = count, values_fill = list(count = 0)) %>%
  rename_at(vars(-vessel_id), ~paste0("overstay_", gsub(" ", "_", .)))

# Aggregate to ensure one row per vessel_id
overstay_vessel_agg <- overstay_vessel_processed %>%
  group_by(vessel_id) %>%
  summarise(across(starts_with("overstay_"), ~sum(. != 0)))

# Process the overfish_vessel dataframe
overfish_vessel_processed <- overfish_vessel %>%
  mutate(count = 1) %>%
  pivot_wider(names_from = fish_species, values_from = count, values_fill = list(count = 0)) %>%
  rename_at(vars(-vessel_id), ~paste0("overfish_", gsub(" ", "_", .)))

# Aggregate to ensure one row per vessel_id
overfish_vessel_agg <- overfish_vessel_processed %>%
  group_by(vessel_id) %>%
  summarise(across(starts_with("overfish_"), ~sum(. != 0)))

# Process the diverted_vessel dataframe
diverted_vessel_processed <- diverted_vessel %>%
  mutate(diverted = 1) %>%
  select(vessel_id, diverted)

# Merge the dataframes
combined_df <- overstay_vessel_agg %>%
  full_join(overfish_vessel_agg, by = "vessel_id") %>%
  full_join(diverted_vessel_processed, by = "vessel_id") %>%
  mutate(across(starts_with("overstay_"), ~replace_na(.x, 0)),
         across(starts_with("overfish_"), ~replace_na(.x, 0)),
         diverted = replace_na(diverted, 0)) %>%
  select(-c("overstay_count_above_90", "overfish_total_qty_tons", "overfish_distinct_cargo_count"))

# Remove specified columns
combined_df <- combined_df %>%
  rowwise() %>%
  mutate(count_of_overstay_areas = sum(c_across(2:4)),
         count_of_overfish_species = sum(c_across(5:12)),
         suspicion_count = count_of_overstay_areas + count_of_overfish_species + diverted)

# Remove specified columns
likely_suspicious <- combined_df %>% 
  filter(suspicion_count >= 2) %>% 
  arrange(desc(suspicion_count))
  
sus_vessel <- likely_suspicious %>% left_join(N_vessel %>% select(vessel_id, vessel_name, vessel_company, flag_country, vessel_type, tonnage, length_overall), by = c("vessel_id"))

#write_csv(sus_vessel, "data/sus_vessel.csv")

Summarising the suspicious vessels, we observed a total of 34 vessels.

Out of the 34 vessels, 3 are tour vessels (Mr. Ray, Clown Fish and Himark Royal) and 2 vessels (Webigail and Louie the II) under “Others” which may be used for recreational use with unknown company from Oceans. Hence overstaying in preserves might be deemed reasonable.

This leaves us with 29 suspicious fishing vessels, 4 of which are non-Oceanus.

Redfin Pickerel Raider from Solis PLC of Solterrrix for both overstaying in Ghoti and overfishing multiple fish species.
Bigeye Tuna Buccaneer from Calabrand for overfishing multiple fish species.
Crappie Capturer from Kondarica & Muskellunge Master from Osterivaro for overstaying in Ghoti Preserve

We further observed 4 notable companies with more than 1 vessels identified as suspicious which prompts further investigation. This includes:

Company of “Robinson, Mcmillan and Watson” for their vessels of White Sucker Wrangler and Fish Flinger.

Company of “Faulkner, Shaffer and Moyer” for their vessels of Tide Tamer and Coho Salmon Capturer.

Company of “Smith, Collins and Johnson” for their vessels of Cutthroat Trouth Catcher and Sea Serpent

Company of “Mcpherson-Wright” for their vessels of “Brill Bandit” and “Sockeye Salmon Seeker”.

All of these vessels has a disappearance from Ghoti Reserve, or drastic drop in visit for Sea Serpent to Ghoti Preserve in the month of June 2035.