The roroph package provides standardized & machine-readable data for the Philippine Nautical Highway as of the 2024-2026 operational state. This guide shows how to map the 108 bidirectional links to visualize regional connectivity within the Philippines.This is complete with GADM-standardized naming, geospatial coordinates, and metrics such as distance, travel time, and vessel frequency, supporting other analyses other than mere visualization.

Setup

We will use ggplot2 for plotting and sf for spatial handling.

library(roroph)       
library(rnaturalearth) 
library(sf)
library(ggrepel)
library(dplyr)
library(ggspatial)
library(ggplot2)

# Load the dataset
data("roro_routes")

Mapping the Core Philippine Maritime Network

We can visualize the RoRo links as segments connecting provincial capitals. We’ll color-code them by their MARINA Highway classification (Western, Central, Eastern, or Missionary).


# 1. Required Libraries
library(roroph)
library(rnaturalearth)
library(sf)
library(ggrepel)
library(dplyr)
library(ggspatial)

# 2. Data Preparation
ph_land <- ne_countries(scale = "medium", country = "Philippines", returnclass = "sf")

roro_viz <- roro_routes %>%
  mutate(
    highway_type = case_when(
      grepl("WNH", marina_code) ~ "Western",
      grepl("CNH", marina_code) ~ "Central",
      grepl("ENH", marina_code) ~ "Eastern",
      grepl("MR", marina_code)  ~ "Missionary",
      TRUE ~ "Other"
    ),
    highway_type = factor(highway_type, levels = c("Western", "Central", "Eastern", "Missionary"))
  )

hubs_to_label <- roro_viz %>%
  group_by(from_prov) %>%
  summarise(
    total_freq = sum(freq_daily, na.rm = TRUE),
    lon = first(from_lon),
    lat = first(from_lat),
    .groups = "drop"
  ) %>%
  arrange(desc(total_freq)) %>%
  slice(unique(c(1:15, which(from_prov %in% c("Batanes", "Tawi-Tawi"))))) %>%
  mutate(
    lon = case_when(
      from_prov == "Cebu"      ~ 123.75, 
      from_prov == "Bohol"     ~ 124.20,
      from_prov == "Samal"     ~ 125.75, 
      from_prov == "Tawi-Tawi" ~ 119.95,
      TRUE ~ lon
    ),
    lat = case_when(
      from_prov == "Bohol"     ~ 9.85,
      from_prov == "Samal"     ~ 7.00,
      from_prov == "Tawi-Tawi" ~ 5.05,
      TRUE ~ lat
    )
  )

# 3. High-Fidelity Execution (Google Maps/Explorer Vibe)
ggplot() +
  # LAYER 1: The Routes (Placed FIRST so they are 'behind' the land)
  geom_segment(data = roro_viz %>% arrange(freq_daily), 
               aes(x = from_lon, y = from_lat, xend = to_lon, yend = to_lat, 
                   color = highway_type, linewidth = freq_daily), 
               alpha = 0.7, lineend = "round") + 

  # LAYER 2: The Landmass (Placed SECOND to act as a 'Shadow Mask')
  geom_sf(data = ph_land, fill = "#F8F9FA", color = "#DADCE0", linewidth = 0.3) +
  
  # LAYER 3: The Hub Markers (Destination Pins)
  geom_point(data = hubs_to_label, aes(x = lon, y = lat), 
             color = "white", fill = "#1A73E8", size = 1.2, shape = 21, stroke = 1) +
  
  # LAYER 4: Strategic Labels
  geom_text_repel(data = hubs_to_label, 
                  aes(x = lon, y = lat, label = from_prov),
                  size = 3, color = "#3C4043", fontface = "bold",
                  box.padding = 0.4, point.padding = 0.3,
                  segment.color = '#70757A', segment.alpha = 0.8) +

  # Google Palette Scales
  scale_linewidth_continuous(range = c(0.6, 4), name = "Trips per Day") +
  scale_color_manual(values = c(
    "Western"    = "#1A73E8", # Google Blue
    "Central"    = "#34A853", # Google Green
    "Eastern"    = "#EA4335", # Google Red
    "Missionary" = "#70757A"  # Google Grey
  ), name = "Highway System") +
  
  # Theme: Modern Explorer
  theme_minimal() +
  theme(
    # Entire background is now the Ocean Blue
    plot.background = element_rect(fill = "#AADAFF", color = NA),
    panel.background = element_rect(fill = "#AADAFF", color = NA),
    panel.grid = element_blank(),
    
    # Typography
    plot.title = element_text(color = "#202124", face = "bold", size = 18, hjust = 0.5),
    plot.subtitle = element_text(color = "#444b52", size = 10, hjust = 0.5),
    plot.caption = element_text(color = "#444b52", size = 8, hjust = 0.95),
    
    # Floating Legend Card
    legend.position = c(0.15, 0.85),
    legend.background = element_rect(fill = "white", color = "#DADCE0", linewidth = 0.5),
    legend.title = element_text(size = 8, face = "bold", color = "#202124"),
    legend.text = element_text(size = 7, color = "#3C4043"),
    
    axis.text = element_blank(),
    axis.title = element_blank()
  ) +
  labs(
    title = "Philippine Maritime Connectivity",
    subtitle = "RoRo Routes from Batanes to Tawi-Tawi",
    caption = "Data: MARINA/PPA (2026) | Package: roroph"
  ) +
  coord_sf(xlim = c(116.5, 127.5), ylim = c(4.5, 21.5), expand = FALSE)

Network Interpretation and Validation

The network is interpreted through several logistical lenses:

The Nautical Spine: The map accurately recreates the Strong Republic Nautical Highway (SRNH) corridors. The heavy weighting of the Western Nautical Highway (Blue) from Batangas through the Mindoro-Panay-Negros corridor reflects its status as the primary logistics artery for consumer goods.

The Matnog-Allen Bottleneck: The thick orange segment of the Eastern Nautical Highway (ENH) between Sorsogon and Northern Samar represents the highest-frequency “water bridge” in the country. This link is the critical pivot point for the Pan-Philippine Highway (AH26), where maritime frequency directly dictates national land-trucking efficiency.

Cebu as the Central Hub: The “hub-and-spoke” topology of the Central Nautical Highway (Green) highlights Cebu’s role as the maritime heart of the Visayas, facilitating distributed connectivity rather than a linear transit spine.

Missionary Route Expansion: The presence of low-frequency Missionary Routes (Grey) extending to Batanes, Palawan, and Tawi-Tawi captures the MARINA 2026 initiatives designed to bridge the “connectivity gap” in geographically isolated and disadvantaged areas (GIDA).

Transport Analysis: Capacity vs. Distance

The roroph package allows us to move beyond visualization and into Transport Modeling. By utilizing vessel frequency and nautical distance, we can identify how capacity is allocated across the archipelago’s diverse geography.


# --- Step 1: Initialize the Capacity vs. Distance Analysis ---
# We map passenger capacity against nautical distance to identify regional archetypes
ggplot(roro_routes, aes(x = dist_nm, y = pax_cap, color = from_region)) +
  
  # Step 2: Plot the Data Points
  # We use jitter to separate overlapping points, revealing 'clusters' of standard vessel sizes
  geom_jitter(size = 2.2, alpha = 0.5, width = 1.5, height = 5) +
  
  # Step 3: Apply the "Maritime Reality" Curve
  # LOESS smoothing identifies non-linear patterns that a simple straight line would miss
  geom_smooth(method = "loess", se = TRUE, alpha = 0.1, linewidth = 1) +
  
  # Step 4: Regional Color Coding
  # Defining a consistent palette to distinguish Luzon, Visayas, and Mindanao logistics
  scale_color_manual(values = c("Luzon" = "#e74c3c", "Visayas" = "#3498db", "Mindanao" = "#2ecc71"), 
                     name = "Origin Region") +
  
  # Step 5: Branding and Documentation
  # Setting clear titles and integrating the dev's signature in the caption
  labs(
    title = "REGIONAL MARITIME CAPACITY SCALING",
    subtitle = "Vessel Capacity (Pax) vs. Nautical Distance across the Philippine Archipelago",
    x = "Route Distance (Nautical Miles)",
    y = "Passenger Capacity per Trip",
    caption = "Source: roroph | dev: NJ Talingting | 2026 Simulation Framework"
  ) +
  
  # Step 6: Professional Science Styling
  # Using theme_minimal for a clean, publication-ready aesthetic
  theme_minimal(base_size = 11) +
  theme(
    plot.title = element_text(face = "bold", size = 14, color = "#2c3e50"),
    plot.subtitle = element_text(size = 10, color = "#7f8c8d", margin = margin(b = 15)),
    
    # Refining the caption to act as a definitive 'Verdict' space
    plot.caption = element_text(size = 8.5, color = "#34495e", hjust = 0, lineheight = 1.2, margin = margin(t = 20)),
    
    panel.grid.minor = element_blank(),
    legend.position = "bottom",
    legend.title = element_text(face = "bold"),
    axis.title = element_text(face = "bold", size = 9)
  )
#> `geom_smooth()` using formula = 'y ~ x'

Interpretation

This analysis reveals that Philippine maritime capacity does not follow a linear physics model. Instead, it is a reflection of Regional Logistics Archetypes:

The Visayan “Short-Haul” Hub: High density of large-capacity vessels over short distances, reflecting the intense connectivity of the Central Spine RoRo Route.

Mindanao’s Frontier Connectivity: Longer distances with specialized vessel scaling, showing a reliance on strategic port hubs like Davao and Cagayan de Oro.

Luzon’s Hub-and-Spoke: A clear stratification of capacity that centers on the Batangas-Mindoro corridor.

Conclusion

The roroph package serves as a foundational tool for understanding the spatial architecture of the Philippine maritime network. By providing standardized GADM-compliant provincial links alongside critical transport metrics, it enables researchers to:

Identify logistical bottlenecks within the National Nautical Highway.
Model regional accessibility for economic development studies.
Simulate disaster-response scenarios by identifying alternative inter-island routes.

As the first R-native dataset for this domain, roroph bridges the gap between raw administrative records and spatial analysis for the Philippines.

Disclaimer

The maritime industry in the Philippines is highly dynamic. While the routes, distances, and MARINA codes in this package are based on official 2024–2026 administrative reports from the Maritime Industry Authority (MARINA) and the Philippine Ports Authority (PPA), users should note:

Operational Status: Routes may be temporarily suspended due to weather (e.g., tropical cyclones), maintenance, or regulatory grounding of specific fleets.

Variable Metrics: Values for freq_daily (frequency) and pax_cap (capacity) are representative averages. Actual daily throughput fluctuates based on seasonal demand (e.g., Holy Week, Christmas) and private operator schedules.

Navigational Use: This dataset is for statistical and spatial modeling purposes only. It is NOT intended for actual marine navigation. Always consult official Notices to Mariners (NOTAMs) and PPA port advisories for real-time travel planning.

User Responsibility: Users are encouraged to verify critical data points against the latest MARINA sectoral releases when using this package for policy-making, logistics planning, among other academic and commercial usage.