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Spatial Analysis with Tivy

library(Tivy)

Spatial Analysis

Tivy provides tools for spatial analysis of fisheries data, including distance calculations and coordinate processing.

Distance to Coast

Calculate distances from fishing locations to the coastline:

# Basic distance calculation
distances <- coast_distance(
  lon = your_data$longitude,
  lat = your_data$latitude,
  distance_type = "haversine",
  unit = "nm"
)

# With custom coastline data
distances <- coast_distance(
  lon = your_data$longitude,
  lat = your_data$latitude,
  coastline = custom_coastline,
  unit = "km"
)

Coordinate Conversion

Convert coordinates between formats:

# Convert DMS to decimal degrees
decimal_coords <- dms_to_decimal(
  coordinates = c("15°30'S", "75°45'W"),
  hemisphere = "S",
  correct_errors = TRUE
)

# Handle coordinate errors automatically
problematic_coords <- c("15°70'S", "invalid")  # Invalid minutes
corrected <- dms_to_decimal(
  coordinates = problematic_coords,
  correct_errors = TRUE
)

Land vs Sea Classification

Determine if coordinates are on land or at sea:

# Classify points
classification <- land_points(
  x_point = your_data$longitude,
  y_point = your_data$latitude,
  parallel = TRUE,
  cores = 2
)

Built-in Coastline Data

Use the included Peru coastline:

# Load Peru coastline
data("peru_coastline")

# Use in calculations
distances <- coast_distance(
  lon = c(-77.0, -76.5),
  lat = c(-12.0, -11.5),
  coastline = peru_coastline
)

Distance Categories

Create distance categories for analysis:

# Add distance categories to your data
enhanced_data <- add_variables(
  data = your_data,
  distance_type = "haversine",
  unit = "nm"
)

# The function adds:
# - dc: distance to coast
# - dc_cat: distance categories (05-15 nm, 15-30 nm, etc.)

Parallel Processing

For large datasets, use parallel processing:

# Enable parallel processing
distances <- coast_distance(
  lon = large_dataset$longitude,
  lat = large_dataset$latitude,
  parallel = TRUE,
  cores = 4
)

Fishing Zone Analysis

Create and analyze fishing zones:

# Plot fishing zones (if you have zone data)
plot_fishing_zones(
  data = zone_data,
  coastline = peru_coastline,
  type = "static",
  title = "Fishing Zones"
)

Tips for Spatial Analysis

  1. Coordinate validation: Always check coordinate ranges for your region
  2. Distance units: Choose appropriate units (nautical miles for marine data)
  3. Performance: Use parallel processing for large datasets
  4. Error correction: Enable coordinate error correction when data quality is uncertain

For detailed parameter descriptions, see the function documentation.

References

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.