,
Timothée Giraud [ctb] | Title: | Create Distance Cartograms |
|---|---|
| Description: | Creation of distance cartograms by deforming map layers to show the local deformations (computed using Waldo Tobler's bidimensional regression) between source points and image points. |
| Authors: | Matthieu Viry [cre, aut] ,
Timothée Giraud [ctb] |
| Maintainer: | Matthieu Viry <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.2.0 |
| Built: | 2025-03-05 10:28:50 UTC |
| Source: | https://github.com/riatelab/distanamo |
Computes the positions of the adjusted points by fitting image points to source points.
Note that points generated by the dc_generate_positions_from_durations function and by the dc_move_from_reference_point
do not need to be adjusted.
dc_adjust(source_points, image_points, adjustment_type = "euclidean")dc_adjust(source_points, image_points, adjustment_type = "euclidean")
source_points |
The source point layer, sf POINT object |
image_points |
The layer of point to be adjusted to fit |
adjustment_type |
The adjustment type to use, either "euclidean" or "affine" |
A list object with the transformation matrix, various metrics and the adjusted points
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read non adjusted image points image_pts_not_adj <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points-not-adjusted", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Adjust image points to source points adj_result <- dc_adjust( source_points = source_pts, image_points = image_pts_not_adj, "euclidean" ) # Use adjusted points to create the interpolation grid igrid <- dc_create( source_points = source_pts, image_points = adj_result$image_points, precision = 2, bbox = st_bbox(background_layer) ) # Deform the target layer background_deformed <- dc_interpolate( interpolation_grid = igrid, layer_to_deform = background_layer )library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read non adjusted image points image_pts_not_adj <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points-not-adjusted", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Adjust image points to source points adj_result <- dc_adjust( source_points = source_pts, image_points = image_pts_not_adj, "euclidean" ) # Use adjusted points to create the interpolation grid igrid <- dc_create( source_points = source_pts, image_points = adj_result$image_points, precision = 2, bbox = st_bbox(background_layer) ) # Deform the target layer background_deformed <- dc_interpolate( interpolation_grid = igrid, layer_to_deform = background_layer )
Takes a list of sf objects and compute the bounding box that covers them all.
dc_combine_bbox(list_layers)dc_combine_bbox(list_layers)
list_layers |
A list of sf objects |
An sf bounding box is returned.
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) bbox <- dc_combine_bbox(list(source_pts, background_layer)) bboxlibrary(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) bbox <- dc_combine_bbox(list(source_pts, background_layer)) bbox
Create concentric circles around the reference point, using the results of the
dc_move_from_reference_point.
dc_concentric_circles( positioning_result, steps = list(30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330, 360) )dc_concentric_circles( positioning_result, steps = list(30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330, 360) )
positioning_result |
The object returned by the |
steps |
The steps (in the unit of the durations used ine the
|
A sf LINESTRING object
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) durations_mat <- read.csv(system.file("csv/mat.csv", package = "distanamo"), row.names = 1) dur <- durations_mat["CAEN", ] source_pts$durations <- as.double(dur) ref_point <- subset(source_pts, source_pts$NOM_COM == "CAEN") other_points <- subset(source_pts, !source_pts$NOM_COM == "CAEN") # Generate position from durations between the reference point # and the other points positioning_result <- dc_move_from_reference_point( reference_point = ref_point, other_points = other_points, duration_col_name = "durations", factor = 1 ) # Create the interpolation grid igrid <- dc_create( source_points = positioning_result$source_points, image_points = positioning_result$image_points, precision = 2.0, bbox = st_bbox(background_layer) ) # Use the positioning result to create concentric circles # every 60-minutes circles <- dc_concentric_circles( positioning_result, c(60, 120, 180, 240, 300, 360, 420, 480, 540, 600, 660, 720) ) # Deform the background layer background_deformed <- dc_interpolate( interpolation_grid = igrid, layer_to_deform = background_layer )library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) durations_mat <- read.csv(system.file("csv/mat.csv", package = "distanamo"), row.names = 1) dur <- durations_mat["CAEN", ] source_pts$durations <- as.double(dur) ref_point <- subset(source_pts, source_pts$NOM_COM == "CAEN") other_points <- subset(source_pts, !source_pts$NOM_COM == "CAEN") # Generate position from durations between the reference point # and the other points positioning_result <- dc_move_from_reference_point( reference_point = ref_point, other_points = other_points, duration_col_name = "durations", factor = 1 ) # Create the interpolation grid igrid <- dc_create( source_points = positioning_result$source_points, image_points = positioning_result$image_points, precision = 2.0, bbox = st_bbox(background_layer) ) # Use the positioning result to create concentric circles # every 60-minutes circles <- dc_concentric_circles( positioning_result, c(60, 120, 180, 240, 300, 360, 420, 480, 540, 600, 660, 720) ) # Deform the background layer background_deformed <- dc_interpolate( interpolation_grid = igrid, layer_to_deform = background_layer )
Create a new interpolation grid which covers the source points and with a cell size deduced from the precision.
The grid is then interpolated to match the image points. This then allows one to interpolate any point on the grid (enabling the deformation of geometries such as background layers) and to retrieve useful metrics about the deformation.
If the bbox provided does not cover all the source points, the grid will be extended to cover all the source points.
The precision controls the size of the grid cells (higher is more precise, for example 0.5 generally gives a coarse result, 2 a satisfactory result and 4 a particularly fine result). A precision of 2 is usually a good default value.
The number of iterations controls the number of iterations for the interpolation. It is generally 4 times the square root of the number of points (and this is the default value it the niter parameter is not provided.
Note that the number of source points must be equal to the number of image points, and either they must be supplied in the same order (as they are homologous points), or the name of a field containing an identifier must be supplied to enable them to be sorted in the same order.
dc_create(source_points, image_points, precision, bbox, niter, sort_by)dc_create(source_points, image_points, precision, bbox, niter, sort_by)
source_points |
The source point layer, sf POINT object |
image_points |
The image point layer, sf POINT object |
precision |
The precision of the grid to be created (a higher value means a higher precision - 0.5 gives usually a coarse result, 2 is good default, 4 is very detailed) |
bbox |
The bounding box of the grid to be created |
niter |
The number of iterations
(default is |
sort_by |
The field to sort the source and image points by |
An interpolation grid to be used to transform the layers
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read image points image_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Create the interpolation grid igrid <- dc_create( source_points = source_pts, image_points = image_pts, precision = 2, bbox = st_bbox(background_layer) ) # Plot various depictions of the interpolation grid plot(igrid) # Useful information about the interpolation grid summary(igrid) # Deform the target layer background_deformed <- dc_interpolate( interpolation_grid = igrid, layer_to_deform = background_layer ) plot(st_geometry(background_deformed))library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read image points image_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Create the interpolation grid igrid <- dc_create( source_points = source_pts, image_points = image_pts, precision = 2, bbox = st_bbox(background_layer) ) # Plot various depictions of the interpolation grid plot(igrid) # Useful information about the interpolation grid summary(igrid) # Deform the target layer background_deformed <- dc_interpolate( interpolation_grid = igrid, layer_to_deform = background_layer ) plot(st_geometry(background_deformed))
Generate positions from durations matrix, using PCoA to find the relative positions between points, then find the best fit between the source points and the image points using an affine or a Euclidean transformation.
dc_generate_positions_from_durations( durations, source_points, adjustment_type = "euclidean" )dc_generate_positions_from_durations( durations, source_points, adjustment_type = "euclidean" )
durations |
The durations matrix |
source_points |
The source points, an sf POINT object |
adjustment_type |
The adjustment type to use, either "euclidean" or "affine" |
A list object with the source points and the image points, ready to be used with the dc_create function
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Read durations matrix durations_mat <- read.csv(system.file("csv/mat.csv", package = "distanamo"), row.names = 1) # Generate the positions from the whole duration matrix and # adjust the result to the source points pos_result <- dc_generate_positions_from_durations( durations = durations_mat, source_points = source_pts, adjustment_type = "euclidean" ) # Display and plot useful information about the positioning step plot(pos_result) summary(pos_result) # Use the result of the positioning to create the interpolation grid igrid <- dc_create( source_points = pos_result$source_points, image_points = pos_result$image_points, precision = 2.0, bbox = st_bbox(background_layer) ) # Deform the background layer background_deformed <- dc_interpolate( interpolation_grid = igrid, layer_to_deform = background_layer )library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Read durations matrix durations_mat <- read.csv(system.file("csv/mat.csv", package = "distanamo"), row.names = 1) # Generate the positions from the whole duration matrix and # adjust the result to the source points pos_result <- dc_generate_positions_from_durations( durations = durations_mat, source_points = source_pts, adjustment_type = "euclidean" ) # Display and plot useful information about the positioning step plot(pos_result) summary(pos_result) # Use the result of the positioning to create the interpolation grid igrid <- dc_create( source_points = pos_result$source_points, image_points = pos_result$image_points, precision = 2.0, bbox = st_bbox(background_layer) ) # Deform the background layer background_deformed <- dc_interpolate( interpolation_grid = igrid, layer_to_deform = background_layer )
Interpolate a sf layer using the interpolation grid.
dc_interpolate(interpolation_grid, layer_to_deform)dc_interpolate(interpolation_grid, layer_to_deform)
interpolation_grid |
The interpolation grid |
layer_to_deform |
The sf layer to interpolate |
The sf layer deformed by the interpolation grid
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read image points image_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Create the interpolation grid igrid <- dc_create( source_points = source_pts, image_points = image_pts, precision = 2, bbox = st_bbox(background_layer) ) # Plot various depictions of the interpolation grid plot(igrid) # Useful information about the interpolation grid summary(igrid) # Deform the target layer background_deformed <- dc_interpolate( interpolation_grid = igrid, layer_to_deform = background_layer ) plot(st_geometry(background_deformed))library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read image points image_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Create the interpolation grid igrid <- dc_create( source_points = source_pts, image_points = image_pts, precision = 2, bbox = st_bbox(background_layer) ) # Plot various depictions of the interpolation grid plot(igrid) # Useful information about the interpolation grid summary(igrid) # Deform the target layer background_deformed <- dc_interpolate( interpolation_grid = igrid, layer_to_deform = background_layer ) plot(st_geometry(background_deformed))
Interpolate a list of sf layers using the interpolation grid.
dc_interpolate_parallel(interpolation_grid, layers_to_deform)dc_interpolate_parallel(interpolation_grid, layers_to_deform)
interpolation_grid |
The interpolation grid |
layers_to_deform |
A list of sf layers to interpolate |
The sf layers deformed by the interpolation grid
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read non adjusted image points image_pts_not_adj <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points-not-adjusted", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Adjust image points to source points adj_result <- dc_adjust( source_points = source_pts, image_points = image_pts_not_adj, "euclidean" ) # Use adjusted points to create the interpolation grid igrid <- dc_create( source_points = source_pts, image_points = adj_result$image_points, precision = 2, bbox = st_bbox(background_layer) ) # Deform the target layer background_deformed <- dc_interpolate_parallel( interpolation_grid = igrid, layers_to_deform = list(background_layer = background_layer, source_pts = source_pts) )library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read non adjusted image points image_pts_not_adj <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points-not-adjusted", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Adjust image points to source points adj_result <- dc_adjust( source_points = source_pts, image_points = image_pts_not_adj, "euclidean" ) # Use adjusted points to create the interpolation grid igrid <- dc_create( source_points = source_pts, image_points = adj_result$image_points, precision = 2, bbox = st_bbox(background_layer) ) # Deform the target layer background_deformed <- dc_interpolate_parallel( interpolation_grid = igrid, layers_to_deform = list(background_layer = background_layer, source_pts = source_pts) )
Move points from a reference point using durations between the reference point and all the other points.
dc_move_from_reference_point( reference_point, other_points, duration_col_name, factor )dc_move_from_reference_point( reference_point, other_points, duration_col_name, factor )
reference_point |
The point from which the other points will be moved, an sf POINT object |
other_points |
The other points to move, an sf POINT object |
duration_col_name |
The name of the column containing the durations in the other_points sf object |
factor |
The factor of displacement (default: 1) |
A list object with the source points and the image points, ready to be used with the dc_create function
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) durations_mat <- read.csv(system.file("csv/mat.csv", package = "distanamo"), row.names = 1) dur <- durations_mat["CAEN", ] source_pts$durations <- as.double(dur) ref_point <- subset(source_pts, source_pts$NOM_COM == "CAEN") other_points <- subset(source_pts, !source_pts$NOM_COM == "CAEN") # Generate position from durations between the reference point # and the other points positioning_result <- dc_move_from_reference_point( reference_point = ref_point, other_points = other_points, duration_col_name = "durations", factor = 1 ) # Display and plot useful information about the positioning step plot(positioning_result) summary(positioning_result) # Create the interpolation grid igrid <- dc_create( source_points = positioning_result$source_points, image_points = positioning_result$image_points, precision = 2.0, bbox = st_bbox(background_layer) ) # Deform the background layer background_deformed <- dc_interpolate( interpolation_grid = igrid, layer_to_deform = background_layer )library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) durations_mat <- read.csv(system.file("csv/mat.csv", package = "distanamo"), row.names = 1) dur <- durations_mat["CAEN", ] source_pts$durations <- as.double(dur) ref_point <- subset(source_pts, source_pts$NOM_COM == "CAEN") other_points <- subset(source_pts, !source_pts$NOM_COM == "CAEN") # Generate position from durations between the reference point # and the other points positioning_result <- dc_move_from_reference_point( reference_point = ref_point, other_points = other_points, duration_col_name = "durations", factor = 1 ) # Display and plot useful information about the positioning step plot(positioning_result) summary(positioning_result) # Create the interpolation grid igrid <- dc_create( source_points = positioning_result$source_points, image_points = positioning_result$image_points, precision = 2.0, bbox = st_bbox(background_layer) ) # Deform the background layer background_deformed <- dc_interpolate( interpolation_grid = igrid, layer_to_deform = background_layer )
This package enables the creation of what is often defined as a distance cartogram. Distance cartograms are a type of cartogram that deforms the layers of a map according to the distances between a set of source points and a set of image points. This is done by extending (by interpolation) to the layer(s) of the study area (territorial divisions, network...) the local displacement between the source coordinates and the image coordinates, derived from the distances between each pair of homologous points (source / image points).
Maintainer: Matthieu Viry [email protected] (ORCID)
Other contributors:
Timothée Giraud [email protected] (ORCID) [contributor]
Useful links:
Report bugs at https://github.com/riatelab/distanamo/issues/
Plot the result of dc_adjust
## S3 method for class 'adjustment_result' plot(x, ...)## S3 method for class 'adjustment_result' plot(x, ...)
x |
object of class adjustment_result |
... |
further specifications, see plot for details |
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read non adjusted image points image_pts_not_adj <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points-not-adjusted", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Adjust image points to source points adj_result <- dc_adjust( source_points = source_pts, image_points = image_pts_not_adj, "euclidean" ) # Plot result of the adjustment step plot(adj_result) # Summary statistics of the adjustment step summary(adj_result)library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read non adjusted image points image_pts_not_adj <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points-not-adjusted", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Adjust image points to source points adj_result <- dc_adjust( source_points = source_pts, image_points = image_pts_not_adj, "euclidean" ) # Plot result of the adjustment step plot(adj_result) # Summary statistics of the adjustment step summary(adj_result)
Plot the interpolation grid, resulting from dc_create
## S3 method for class 'interpolation_grid' plot( x, which = 1:4, ask = interactive(), caption = list("Source grid", "Interpolated grid", "Image to interpolated points", "Deformation strength"), ... )## S3 method for class 'interpolation_grid' plot( x, which = 1:4, ask = interactive(), caption = list("Source grid", "Interpolated grid", "Image to interpolated points", "Deformation strength"), ... )
x |
object of class interpolation_grid |
which |
which plot to display, a subset of 1:4 (the default) |
ask |
logical; if TRUE, the user is asked before each plot |
caption |
captions to appear above the plots; character vector or list of valid graphics annotations |
... |
further specifications, see plot for details |
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read image points image_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Create the interpolation grid igrid <- dc_create( source_points = source_pts, image_points = image_pts, precision = 2, bbox = st_bbox(background_layer) ) # Plot various depictions of the interpolation grid plot(igrid) # Useful information about the interpolation grid summary(igrid)library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read image points image_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Create the interpolation grid igrid <- dc_create( source_points = source_pts, image_points = image_pts, precision = 2, bbox = st_bbox(background_layer) ) # Plot various depictions of the interpolation grid plot(igrid) # Useful information about the interpolation grid summary(igrid)
Plot the result of dc_generate_positions_from_durations
## S3 method for class 'multipolar_displacement_result' plot(x, ...)## S3 method for class 'multipolar_displacement_result' plot(x, ...)
x |
object of class multipolar_displacement_result |
... |
further specifications, see plot for details |
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Read durations matrix durations_mat <- read.csv(system.file("csv/mat.csv", package = "distanamo"), row.names = 1) # Generate the positions from the whole duration matrix and # adjust the result to the source points pos_result <- dc_generate_positions_from_durations( durations = durations_mat, source_points = source_pts, adjustment_type = "euclidean" ) # Plot result of the positioning step plot(pos_result) # Summary statistics of the positioning step summary(pos_result)library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Read durations matrix durations_mat <- read.csv(system.file("csv/mat.csv", package = "distanamo"), row.names = 1) # Generate the positions from the whole duration matrix and # adjust the result to the source points pos_result <- dc_generate_positions_from_durations( durations = durations_mat, source_points = source_pts, adjustment_type = "euclidean" ) # Plot result of the positioning step plot(pos_result) # Summary statistics of the positioning step summary(pos_result)
Plot the result of dc_move_from_reference_point
## S3 method for class 'unipolar_displacement_result' plot(x, ...)## S3 method for class 'unipolar_displacement_result' plot(x, ...)
x |
object of class unipolar_displacement_result |
... |
further specifications, see plot for details |
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) durations_mat <- read.csv(system.file("csv/mat.csv", package = "distanamo"), row.names = 1) dur <- durations_mat["CAEN", ] source_pts$durations <- as.double(dur) ref_point <- subset(source_pts, source_pts$NOM_COM == "CAEN") other_points <- subset(source_pts, !source_pts$NOM_COM == "CAEN") # Generate position from durations between the reference point # and the other points positioning_result <- dc_move_from_reference_point( reference_point = ref_point, other_points = other_points, duration_col_name = "durations", factor = 1 ) # Plot result of the positioning step plot(positioning_result) # Summary statistics of the positioning step summary(positioning_result)library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) durations_mat <- read.csv(system.file("csv/mat.csv", package = "distanamo"), row.names = 1) dur <- durations_mat["CAEN", ] source_pts$durations <- as.double(dur) ref_point <- subset(source_pts, source_pts$NOM_COM == "CAEN") other_points <- subset(source_pts, !source_pts$NOM_COM == "CAEN") # Generate position from durations between the reference point # and the other points positioning_result <- dc_move_from_reference_point( reference_point = ref_point, other_points = other_points, duration_col_name = "durations", factor = 1 ) # Plot result of the positioning step plot(positioning_result) # Summary statistics of the positioning step summary(positioning_result)
Compute summary statistics for adjustment_result
## S3 method for class 'adjustment_result' summary(object, ...)## S3 method for class 'adjustment_result' summary(object, ...)
object |
object of class adjustment_result |
... |
further specifications, see summary for details |
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read non adjusted image points image_pts_not_adj <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points-not-adjusted", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Adjust image points to source points adj_result <- dc_adjust( source_points = source_pts, image_points = image_pts_not_adj, "euclidean" ) # Plot result of the adjustment step plot(adj_result) # Summary statistics of the adjustment step summary(adj_result)library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read non adjusted image points image_pts_not_adj <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points-not-adjusted", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Adjust image points to source points adj_result <- dc_adjust( source_points = source_pts, image_points = image_pts_not_adj, "euclidean" ) # Plot result of the adjustment step plot(adj_result) # Summary statistics of the adjustment step summary(adj_result)
Compute summary statistics for interpolation_grid
## S3 method for class 'interpolation_grid' summary(object, ...)## S3 method for class 'interpolation_grid' summary(object, ...)
object |
object of class interpolation_grid |
... |
further specifications, see summary for details |
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read image points image_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Create the interpolation grid igrid <- dc_create( source_points = source_pts, image_points = image_pts, precision = 2, bbox = st_bbox(background_layer) ) # Plot various depictions of the interpolation grid plot(igrid) # Useful information about the interpolation grid summary(igrid)library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read image points image_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "image-points", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Create the interpolation grid igrid <- dc_create( source_points = source_pts, image_points = image_pts, precision = 2, bbox = st_bbox(background_layer) ) # Plot various depictions of the interpolation grid plot(igrid) # Useful information about the interpolation grid summary(igrid)
Compute summary statistics for multipolar_displacement_result
## S3 method for class 'multipolar_displacement_result' summary(object, ...)## S3 method for class 'multipolar_displacement_result' summary(object, ...)
object |
object of class multipolar_displacement_result |
... |
further specifications, see summary for details |
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Read durations matrix durations_mat <- read.csv(system.file("csv/mat.csv", package = "distanamo"), row.names = 1) # Generate the positions from the whole duration matrix and # adjust the result to the source points pos_result <- dc_generate_positions_from_durations( durations = durations_mat, source_points = source_pts, adjustment_type = "euclidean" ) # Plot result of the positioning step plot(pos_result) # Summary statistics of the positioning step summary(pos_result)library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) # Read durations matrix durations_mat <- read.csv(system.file("csv/mat.csv", package = "distanamo"), row.names = 1) # Generate the positions from the whole duration matrix and # adjust the result to the source points pos_result <- dc_generate_positions_from_durations( durations = durations_mat, source_points = source_pts, adjustment_type = "euclidean" ) # Plot result of the positioning step plot(pos_result) # Summary statistics of the positioning step summary(pos_result)
Compute summary statistics for unipolar_displacement_result
## S3 method for class 'unipolar_displacement_result' summary(object, ...)## S3 method for class 'unipolar_displacement_result' summary(object, ...)
object |
object of class unipolar_displacement_result |
... |
further specifications, see summary for details |
library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) durations_mat <- read.csv(system.file("csv/mat.csv", package = "distanamo"), row.names = 1) dur <- durations_mat["CAEN", ] source_pts$durations <- as.double(dur) ref_point <- subset(source_pts, source_pts$NOM_COM == "CAEN") other_points <- subset(source_pts, !source_pts$NOM_COM == "CAEN") # Generate position from durations between the reference point # and the other points positioning_result <- dc_move_from_reference_point( reference_point = ref_point, other_points = other_points, duration_col_name = "durations", factor = 1 ) # Plot result of the positioning step plot(positioning_result) # Summary statistics of the positioning step summary(positioning_result)library(sf) # Read source points source_pts <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "prefecture", quiet = TRUE ) # Read the background layer to deform background_layer <- st_read( dsn = system.file("gpkg/data-prefecture.gpkg", package = "distanamo"), layer = "departement", quiet = TRUE ) durations_mat <- read.csv(system.file("csv/mat.csv", package = "distanamo"), row.names = 1) dur <- durations_mat["CAEN", ] source_pts$durations <- as.double(dur) ref_point <- subset(source_pts, source_pts$NOM_COM == "CAEN") other_points <- subset(source_pts, !source_pts$NOM_COM == "CAEN") # Generate position from durations between the reference point # and the other points positioning_result <- dc_move_from_reference_point( reference_point = ref_point, other_points = other_points, duration_col_name = "durations", factor = 1 ) # Plot result of the positioning step plot(positioning_result) # Summary statistics of the positioning step summary(positioning_result)