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#' Blend geospatial points into a spatial network
#'
#' Blending a point into a network is the combined process of first snapping
#' the given point to its nearest point on its nearest edge in the network,
#' subsequently splitting that edge at the location of the snapped point, and
#' finally adding the snapped point as node to the network. If the location
#' of the snapped point is already a node in the network, the attributes of the
#' point (if any) will be joined to that node.
#'
#' @param x An object of class \code{\link{sfnetwork}}.
#'
#' @param y The spatial features to be blended, either as object of class
#' \code{\link[sf]{sf}} or \code{\link[sf]{sfc}}, with \code{POINT} geometries.
#'
#' @param tolerance The tolerance distance to be used. Only features that are
#' at least as close to the network as the tolerance distance will be blended.
#' Should be a non-negative number preferably given as an object of class
#' \code{\link[units]{units}}. Otherwise, it will be assumed that the unit is
#' meters. If set to \code{Inf} all features will be blended. Defaults to
#' \code{Inf}.
#'
#' @return The blended network as an object of class \code{\link{sfnetwork}}.
#'
#' @details There are two important details to be aware of. Firstly: when the
#' snap locations of multiple points are equal, only the first of these points
#' is blended into the network. By arranging \code{y} before blending you can
#' influence which (type of) point is given priority in such cases.
#' Secondly: when the snap location of a point intersects with multiple edges,
#' it is only blended into the first of these edges. You might want to run the
#' \code{\link{to_spatial_subdivision}} morpher after blending, such that
#' intersecting but unconnected edges get connected.
#'
#' @note Due to internal rounding of rational numbers, it may occur that the
#' intersection point between a line and a point is not evaluated as
#' actually intersecting that line by the designated algorithm. Instead, the
#' intersection point lies a tiny-bit away from the edge. Therefore, it is
#' recommended to set the tolerance to a very small number (for example 1e-5)
#' even if you only want to blend points that intersect the line.
#'
#' @examples
#' library(sf, quietly = TRUE)
#'
#' # Create a network and a set of points to blend.
#' n11 = st_point(c(0,0))
#' n12 = st_point(c(1,1))
#' e1 = st_sfc(st_linestring(c(n11, n12)), crs = 3857)
#'
#' n21 = n12
#' n22 = st_point(c(0,2))
#' e2 = st_sfc(st_linestring(c(n21, n22)), crs = 3857)
#'
#' n31 = n22
#' n32 = st_point(c(-1,1))
#' e3 = st_sfc(st_linestring(c(n31, n32)), crs = 3857)
#'
#' net = as_sfnetwork(c(e1,e2,e3))
#'
#' pts = net %>%
#' st_bbox() %>%
#' st_as_sfc() %>%
#' st_sample(10, type = "random") %>%
#' st_set_crs(3857) %>%
#' st_cast('POINT')
#'
#' # Blend points into the network.
#' # --> By default tolerance is set to Inf
#' # --> Meaning that all points get blended
#' b1 = st_network_blend(net, pts)
#' b1
#'
#' # Blend points with a tolerance.
#' tol = units::set_units(0.2, "m")
#' b2 = st_network_blend(net, pts, tolerance = tol)
#' b2
#'
#' ## Plot results.
#' # Initial network and points.
#' oldpar = par(no.readonly = TRUE)
#' par(mar = c(1,1,1,1), mfrow = c(1,3))
#' plot(net, cex = 2, main = "Network + set of points")
#' plot(pts, cex = 2, col = "red", pch = 20, add = TRUE)
#'
#' # Blend with no tolerance
#' plot(b1, cex = 2, main = "Blend with tolerance = Inf")
#' plot(pts, cex = 2, col = "red", pch = 20, add = TRUE)
#'
#' # Blend with tolerance.
#' within = st_is_within_distance(pts, st_geometry(net, "edges"), tol)
#' pts_within = pts[lengths(within) > 0]
#' plot(b2, cex = 2, main = "Blend with tolerance = 0.2 m")
#' plot(pts, cex = 2, col = "grey", pch = 20, add = TRUE)
#' plot(pts_within, cex = 2, col = "red", pch = 20, add = TRUE)
#' par(oldpar)
#'
#' @export
st_network_blend = function(x, y, tolerance = Inf) {
UseMethod("st_network_blend")
}
#' @export
st_network_blend.sfnetwork = function(x, y, tolerance = Inf) {
require_explicit_edges(x, hard = TRUE)
stopifnot(has_single_geom_type(y, "POINT"))
stopifnot(have_equal_crs(x, y))
stopifnot(as.numeric(tolerance) >= 0)
if (will_assume_planar(x)) raise_assume_planar("st_network_blend")
blend_(x, y, tolerance)
}
#' @importFrom dplyr bind_rows full_join
#' @importFrom igraph is_directed vcount
#' @importFrom sf st_as_sf st_cast st_crs st_crs<- st_distance st_equals
#' st_geometry st_geometry<- st_intersects st_is_within_distance
#' st_nearest_feature st_nearest_points st_precision st_precision<-
#' @importFrom sfheaders sfc_linestring sfc_to_df
#' @importFrom units set_units
blend_ = function(x, y, tolerance) {
# Extract the following:
# --> The node data of x and its geometries.
# --> The edge data of x and its geometries.
# --> The geometries of the features to be blended.
nodes = nodes_as_sf(x)
edges = edges_as_sf(x)
N = st_geometry(nodes)
E = st_geometry(edges)
Y = st_geometry(y)
# For later use:
# --> Check wheter x is directed.
# --> Count the number of nodes in x.
# --> Retrieve the name of the geometry column of the nodes in x.
directed = is_directed(x)
ncount = vcount(x)
geom_colname = attr(nodes, "sf_column")
## ===========================
# STEP I: PARSE THE TOLERANCE
# If tolerance is not a units object:
# --> Convert into units object assuming a units of meters.
# --> Unless tolerance is infinite.
## ===========================
if (! (is.infinite(tolerance) || inherits(tolerance, "units"))) {
tolerance = set_units(tolerance, "m")
}
## ================================
# STEP II: DEFINE SPATIAL RELATIONS
# Relate each feature in y to the edges of x by checking if:
# --> The feature in y is located *on* an edge in x.
# --> The feature in y is located *close* to an edge in x.
# With *on* being defined as:
# --> Intersecting with the edge.
# With *close* being defined as:
# --> Within the tolerance distance from an edge.
# --> But not intersecting with that edge.
## ================================
# Find indices of features in y that are located:
# --> *on* an edge in x.
intersects = suppressMessages(st_intersects(Y, E))
is_on = lengths(intersects) > 0
# Find indices of features in y that are located:
# --> *close* to an edge in x.
# We define a feature yi being *close* to an edge xj when:
# --> yi is located within a given tolerance distance from xj.
# --> yi is not located on xj.
if (as.numeric(tolerance) == 0 | all(is_on)) {
# If tolerance is 0.
# --> By definition no feature is *close*.
# If all features are already *on* an edge.
# --> By definition no feature is *close*.
is_close = rep(FALSE, length(is_on))
} else if (is.infinite(tolerance)) {
# If tolerance was set to infinite:
# --> That implies there is no upper bound for what to define as *close*.
# --> Hence, all features that are not *on* are *close*.
is_close = !is_on
} else {
# If a non-infinite tolerance was set:
# --> Features are *close* if within tolerance distance from an edge.
# --> But not *on* an edge.
is_within = st_is_within_distance(Y[!is_on], E, tolerance)
is_close = !is_on
is_close[is_close] = lengths(is_within) > 0
}
## =======================
# STEP III: SNAP FEATURES
# We need to "project" the features in y onto the edges of the network.
# This is also called "snapping".
# The geometries of the *on* features in y do not have to be changed.
# Since they already are located on an edge geometry.
# The geometries of the *close* features in y should be replaced by:
# --> Their nearest point on their nearest edge.
## =======================
if (any(is_close)) {
# Find the nearest edge to each close feature.
A = suppressMessages(st_nearest_feature(Y[is_close], E))
# Find the nearest point on the nearest edge to each close feature.
# st_nearest_points returns a straight line between two features.
# Hence, the endpoint of that line is the location we are looking for.
B = suppressMessages(st_nearest_points(Y[is_close], E[A], pairwise = TRUE))
B = linestring_boundary_points(B)
B = B[seq(2, length(B), 2)]
# Replace the geometries of the *close* features.
Y[is_close] = B
}
## ========================
# STEP IV: SUBSET FEATURES
# Subset the features in y by removing those that:
# --> Are neither *on* nor *close* to an edge in x.
# --> Are duplicated.
## ========================
# Keep only features that are *on* or *close*.
Y = Y[is_on | is_close]
# Return x when there are no features left to be blended.
if (length(Y) == 0) {
warning(
"No points were blended. Increase the tolerance distance?",
call. = FALSE
)
return (x)
} else {
if (will_assume_constant(x)) raise_assume_constant("st_network_blend")
}
# Remove duplicated features in y.
# These features will have the same blending location.
# Only one point can be blended per location.
is_duplicated = st_duplicated(Y)
Y = Y[!is_duplicated]
## ==========================================
# STEP V: INCLUDE FEATURES IN EDGE GEOMETRIES
# The snapped features in y should be included in the edge geometries.
# Only then we can start to split the edges.
# There are two options:
# --> The feature already matches an interior or endpoint of an edge.
# --> The feature does not match any interior or endpoint of an edge.
# In the first case we need to map the feature to the edge point.
# In the second case we also need to include a new point in the edge.
## ==========================================
# Decompose the edge geometries into their points.
# Map each of these points to the index of its "parent edge".
edge_pts = st_cast(E, "POINT")
pts_idxs = rep(seq_along(E), lengths(E) / 2)
# Define for each snapped feature in y which edge point it equals.
# If it equals more than one edge point, only the first match is taken.
# Since blending only blends a feature into a single edge.
matches = do.call("c", lapply(st_equals(Y, edge_pts), `[`, 1))
# Define which snapped features in y:
# --> Are actually equal to an edge point.
# --> Are not equal to any edge point.
real_matches = which(!is.na(matches))
na_matches = which(is.na(matches))
# Convert the edge points object into a dataframe.
# As additional information, we will store for each edge point:
# --> The index of its edge.
# --> The index of the snapped feature in y that equals it, if any.
# --> The row index.
edge_pts = data.frame(
geom = edge_pts,
edge_id = pts_idxs,
feat_id = NA,
row_id = seq_along(edge_pts)
)
# Add the indices of the snapped features in y that equal an edge point.
if (length(real_matches) > 0) {
edge_pts$feat_id[matches[real_matches]] = real_matches
}
# Include the locations of the other snapped features as an edge point.
if (length(na_matches) > 0) {
# First we need to define where to include the feature geometries.
# For that we need to subdivide the edge geometries into their segments.
# A segment is the part of an edge between two edge points.
# Hence: decompose the edge geometries into their segments.
edge_sgs = linestring_segments(E)
# Map each of these segments to the index of its "parent edge".
sgs_idxs = rep(seq_along(E), lengths(E) / 2 - 1)
# Define for each segment its position within its "parent edge".
# Hence, the first segment within an edge gets a 1, etc.
sgs_psns = do.call("c", lapply(rle(sgs_idxs)$lengths, seq_len))
# Now we find for each feature its nearest segment.
# Then we know exactly where to include the feature geometry.
nearest = suppressMessages(st_nearest_feature(Y[na_matches], edge_sgs))
# Include the features by looping over the identified nearest segments.
# If only a single feature needs to be included in that segment:
# --> Add that feature at the right position in the edge points table.
# If multiple features need to be included in a single segment:
# --> Order these features by distance to the startpoint of the segment.
# --> Add them at the right position in the edge points table.
include = function(i) {
# Retrieve the following with respect to the current segment:
# --> The index of the edge of which the segment is part.
# --> The index of the edge point at the start of the segment.
# --> The indices of the features for which this segment is nearest.
edge_id = sgs_idxs[i]
src_id = which(pts_idxs == edge_id)[sgs_psns[i]]
feat_idxs = na_matches[which(nearest == i)]
# If there are multiple features for which this segment is nearest:
# --> Order them by distance to the startpoint of the segment.
n = length(feat_idxs)
if (n > 1) {
feats = Y[feat_idxs]
point = edge_pts$geom[src_id]
dists = st_distance(point, feats)
feat_idxs = feat_idxs[order(dists)]
}
# Define where to insert the features in the edge points table.
# This is directly after the startpoint of the segment.
# The row indices of the features should be a value between:
# --> The row index of the startpoint of the segment.
# --> The row index of the endpoint of the segment.
# Recall that the latter is the startpoint index plus 1.
# Hence, for the features to be inserted we need:
# --> A value between 0 and 1 added to the segment startpoint index.
# If there are multiple features, their order should be preserved.
stepsize = 1 / (n + 1)
values = seq(stepsize, stepsize * n, stepsize)
row_idxs = values + src_id
# Return in the same format as the edge points table.
data.frame(
geom = Y[feat_idxs],
edge_id = rep(edge_id, n),
feat_id = feat_idxs,
row_id = row_idxs
)
}
new_pts = do.call("rbind", lapply(unique(nearest), include))
edge_pts = bind_rows(edge_pts, new_pts)
edge_pts = edge_pts[order(edge_pts$row_id), ]
}
## =============================
# STEP V: SPLIT EDGE GEOMETRIES
# New nodes should be added for snapped features of y whenever:
# --> There is not an existing node yet at that location.
# The edges should be splitted at the locations of these new nodes.
## =============================
# First, we define where to split the edges. This is at edge points that:
# --> Are equal to a snapped feature in y.
# --> Are *not* already an edge boundary.
is_startpoint = !duplicated(edge_pts$edge_id)
is_endpoint = !duplicated(edge_pts$edge_id, fromLast = TRUE)
is_boundary = is_startpoint | is_endpoint
is_split = !is.na(edge_pts$feat_id) & !is_boundary
# Create a repetition vector:
# --> This defines for each edge point if it should be duplicated.
# --> A value of '1' means 'store once', i.e. don't duplicate.
# --> A value of '2' means 'store twice', i.e. duplicate.
# --> Split points will be part of two new edges and should be duplicated.
reps = rep(1L, nrow(edge_pts))
reps[is_split] = 2L
# Extract a coordinate data frame from the edge points.
# Apply the repitition vector to this data frame.
# This gives us the coordinates of the new edge points.
edge_coords = sfc_to_df(edge_pts$geom)
edge_coords = edge_coords[names(edge_coords) %in% c("x", "y", "z", "m")]
new_edge_coords = data.frame(lapply(edge_coords, rep, reps))
# Apply the repetition vector also to the edge indices of the edge points.
# This gives us the *original* edge index of the new edge points.
orig_edge_idxs = rep(edge_pts$edge_id, reps)
# Update these original edge indices according to the splits.
# Remember that edges are splitted at each split point.
# That is: a new edge originates from each split point.
# Hence, to get the new edge indices:
# --> Increment each original edge index by 1 at each split point.
incs = integer(nrow(new_edge_coords)) # By default don't increment.
incs[which(is_split) + seq_len(sum(is_split))] = 1L # Add 1 after each split.
new_edge_idxs = orig_edge_idxs + cumsum(incs)
new_edge_coords$edge_id = new_edge_idxs
# Build the new edge geometries.
new_edge_geoms = sfc_linestring(new_edge_coords, linestring_id = "edge_id")
st_crs(new_edge_geoms) = st_crs(edges)
st_precision(new_edge_geoms) = st_precision(edges)
new_edge_coords$edge_id = NULL
## ================================
# STEP VI: RESTORE EDGE ATTRIBUTES
# We now have the geometries of the new edges.
# However, the original edge attributes got lost.
# We will restore them by:
# --> Adding back the attributes to edges that were not split.
# --> Duplicating original attributes within splitted edges.
# Beware that from and to columns will remain unchanged at this stage.
# We will update them later.
## ================================
# First, we find which *original* edge belongs to which *new* edge:
# --> Use the lists of edge indices mapped to the new edge points.
# --> There we already mapped each new edge point to its original edge.
# --> First define which new edge points are startpoints of new edges.
# --> Then retrieve the original edge index from these new startpoints.
# --> This gives us a single original edge index for each new edge.
is_new_startpoint = !duplicated(new_edge_idxs)
orig_edge_idxs = orig_edge_idxs[is_new_startpoint]
# Duplicate original edge data whenever needed.
new_edges = edges[orig_edge_idxs, ]
# Set the new edge geometries as geometries of these new edges.
st_geometry(new_edges) = new_edge_geoms
## =================================================
# STEP VII: UPDATE FROM AND TO INDICES OF NEW EDGES
# Now we have:
# --> Constructed new edge geometries.
# --> Duplicated edge attributes wherever needed.
# Still left to do is updating the from and to indices of the new edges.
# They should match with the indices of the new nodes in the network.
# The new nodes are a combination of:
# --> Already existing nodes.
# --> New nodes that are going to be added at split points.
## =================================================
# Map each of the original edge points to the index of an original node.
# Edge points that do no equal an original node get assigned NA.
edge_pts$node_id = rep(NA, nrow(edge_pts))
if (directed) {
edge_pts[is_boundary, ]$node_id = edge_boundary_node_indices(x)
} else {
edge_pts[is_boundary, ]$node_id = edge_boundary_point_indices(x)
}
# Update this vector of original node indices by:
# --> Adding a new, unique node index to each of the split points.
# --> Applying the repetition vector to map them to the new edge points.
new_node_idxs = edge_pts$node_id
added_node_idxs = c((ncount + 1):(ncount + sum(is_split)))
new_node_idxs[is_split] = added_node_idxs
new_node_idxs = rep(new_node_idxs, reps)
# Drop NA values from this vector of new node indices.
# Recall that NA values belong to edge points that do not equal a node.
# After dropping them we are left with an index vector of the form:
# --> [source node edge 1, target node edge 1, source node edge 2, ...]
new_node_idxs = new_node_idxs[!is.na(new_node_idxs)]
# Define for each of the indices if it belongs to a source node.
is_source = rep(c(TRUE, FALSE), length(new_node_idxs) / 2)
# Update the from and to columns of the new edges accordingly.
new_edges$from = new_node_idxs[is_source]
new_edges$to = new_node_idxs[!is_source]
## ==================================================
# STEP VIII: JOIN THE NODES WITH THE BLENDED FEATURES
# The blended features of y are either:
# --> Matched to an already existing node.
# --> A new node in the network.
# In the first case:
# --> Their attributes (if any) should be joined with the existing nodes.
# In the second case:
# --> They should be binded to the already existing nodes.
## ==================================================
# When a snapped feature in y matched a original node of x:
# --> Get the index of both the feature and the node.
is_match = is_boundary & !is.na(edge_pts$feat_id)
matched_node_idxs = edge_pts$node_id[is_match]
matched_feat_idxs = edge_pts$feat_id[is_match]
# When a snapped feature in y is a new node of x:
# --> Get the index of that feature.
is_new = is_split
new_feat_idxs = edge_pts$feat_id[is_new]
# Join the orignal node data and the blended features.
# Different scenarios require a different approach.
if (is.sf(y) && ncol(y) > 1) {
# Scenario I: the features in y have attributes.
# This requires:
# --> A full join between the original node data and the features.
# First, subset y to keep only those features that were blended.
y = y[is_on | is_close, ]
y = y[!is_duplicated, ]
# Add an index column matching the features in y to their new node index.
y$.sfnetwork_index = NA_integer_
y[matched_feat_idxs, ]$.sfnetwork_index = matched_node_idxs
y[new_feat_idxs, ]$.sfnetwork_index = added_node_idxs
# Add an index column matching the orginal nodes to their new node index.
nodes$.sfnetwork_index = seq_len(ncount)
# Remove the geometry columns.
# Since the full join is an attribute join.
# We will re-add geometries later on.
st_geometry(y) = NULL
st_geometry(nodes) = NULL
# Perform a full join between the attributes of the nodes and features.
# Base the join on the created index column.
# Remove that index column afterwards.
new_nodes = full_join(nodes, y, by = ".sfnetwork_index")
new_nodes = new_nodes[order(new_nodes$.sfnetwork_index), ]
new_nodes$.sfnetwork_index = NULL
# Add the new node geometries.
new_node_geoms = c(N, Y[new_feat_idxs])
new_nodes[geom_colname] = list(new_node_geoms)
new_nodes = st_as_sf(new_nodes, sf_column_name = geom_colname)
} else if (ncol(nodes) > 1) {
# Scenario II: the features in y don't have attributes but the nodes do.
# This requires:
# --> The geometries of the new nodes binded to the original nodes.
# --> The attribute values of these new nodes being filled with NA.
# First, we select only those blended features that became a new node.
y_new = st_as_sf(Y[new_feat_idxs])
# Align the name of the geometry columns.
names(y_new)[1] = geom_colname
st_geometry(y_new) = geom_colname
# Bind the new nodes with original nodes.
# The dplyr::bind_rows function will take care of the NA filling.
new_nodes = bind_rows(nodes, y_new)
} else {
# Scenario III: neither the features in y nor the nodes have attributes.
# This requires:
# --> The geometries of the new nodes binded to the original nodes.
# First, we select only those blended features that became a new node.
y_new = Y[new_feat_idxs]
# Bind these geometries to the original node geometries.
new_nodes = st_as_sf(c(N, y_new))
# Set the geometry column name equal to the one in the original network.
names(new_nodes)[1] = geom_colname
st_geometry(new_nodes) = geom_colname
}
## ============================
# STEP IX: RECREATE THE NETWORK
# Use the new nodes data and the new edges data to create the new network.
## ============================
x_new = sfnetwork_(new_nodes, new_edges, directed = directed)
x_new %preserve_network_attrs% x
}
```

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