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Create a Simulated Historical Map Dataset for Demonstration

create_demo_data.Rd

Generates a simulated dataset representing a distorted historical map and a corresponding set of homologous points (GCPs), saving them as files.

Usage

create_demo_data(
  type = "complex",
  noise_sd = 0.5,
  n_points = 15,
  output_dir = tempdir(),
  seed = 42,
  grid_limits = c(0, 100, 0, 100),
  helmert_params = list(s = 1.005, angle_deg = 1, tx = 2, ty = -3),
  poly_params = list(cE1 = 2e-05, cE2 = -8e-04, cN1 = 2e-04, cN2 = 0.0015),
  gauss_params = list(A = 4, Ec = 50, Nc = 0, sigma2 = 20)
)

Arguments

type

A character string specifying the distortion type. One of "helmert", "nonlinear", or "complex". Defaults to "complex".

noise_sd

A numeric value for the standard deviation of the Gaussian noise added to the distorted coordinates. Defaults to 0.5.

n_points

An integer specifying the number of points along each axis of the initial grid. The total number of homologous points will be n_points^2. Defaults to 15.

output_dir

A character string specifying the directory where the demo files will be saved. Defaults to a temporary directory (tempdir()).

seed

An integer for setting the random seed for reproducibility. Defaults to 42.

grid_limits

A numeric vector of the form c(xmin, xmax, ymin, ymax) defining the extent of the "true" grid. Defaults to c(0, 100, 0, 100).

helmert_params

A list of parameters for the Helmert transformation: s (scale), angle_deg (rotation in degrees), tx (translation in x), and ty (translation in y). Defaults to list(s = 1.005, angle_deg = 1, tx = 2, ty = -3).

poly_params

A list of coefficients (cE1, cE2, cN1, cN2) for the polynomial warp. Defaults to list(cE1 = 0.00002, cE2 = -0.0008, cN1 = 0.0002, cN2 = 0.0015).

gauss_params

A list of parameters for the Gaussian warp: A (amplitude), Ec, Nc (center coordinates), and sigma2 (variance). Defaults to list(A = 4, Ec = 50, Nc = 0, sigma2 = 20).

Value

A list containing the full paths to the generated files:

shp_path

The path to the 'demo_map.shp' shapefile.

gcp_path

The path to the 'demo_gcps.csv' file.

Details

This function implements the simulation framework described in Vantas and Mirkopoulou, 2025. It first creates a regular grid of points representing the "true" geography. It then applies one of three distortion types from the paper:

  • "helmert": A simple global transformation involving scale, rotation, and translation.

  • "nonlinear": A Helmert transformation followed by a smooth polynomial warp, simulating material stretching.

  • "complex" (Default): Combines the Helmert and nonlinear warp with a localized Gaussian deformation, simulating a complex mix of global, regional, and local errors.

Finally, random noise is added to the distorted coordinates. The function outputs a shapefile representing the distorted grid (as a set of grid lines) and a CSV file of homologous points ready for use with read_correction_data().

Examples

if (FALSE) { # \dontrun{
# --- 1. Generate the demonstration data with default complex distortion ---
demo_files <- create_demo_data(type = "complex", noise_sd = 0.5)

# --- Generate data with only Helmert distortion and custom parameters ---
custom_helmert_params <- list(s = 1.0, angle_deg = 5, tx = 10, ty = 15)
helmert_files <- create_demo_data(
  type = "helmert",
  helmert_params = custom_helmert_params,
  n_points = 10
 )

# --- 2. Use the generated files in the new workflow ---
# Read the GCPs and map separately, providing the CRS
gcp_data <- read_gcps(gcp_path = demo_files$gcp_path, crs = 3857)
map_to_correct <- read_map(shp_path = demo_files$shp_path, crs = 3857)

# --- 3. Train a model ---
rf_model <- train_pai_model(gcp_data, method = "rf")

# --- 4. Apply the trained model to the map ---
corrected_map <- apply_pai_model(pai_model = rf_model, map = map_to_correct)

# --- 5. Visualize the results ---
plot_correction_surface(rf_model, gcp_data)
} # }