Package index • sfnetworks

Construction & conversion

At the core of the package lies the sfnetwork class to represent spatial networks. There are several ways to construct instances of this class, and to convert them to other classes.

sfnetwork(): Create a sfnetwork

as_sfnetwork(): Convert a foreign object to a sfnetwork

create_from_spatial_lines(): Create a spatial network from linestring geometries

create_from_spatial_points(): Create a spatial network from point geometries

play_geometric(): Create random spatial networks

dodgr_to_sfnetwork() sfnetwork_to_dodgr(): Conversion between dodgr streetnets and sfnetworks

nb_to_sfnetwork() sfnetwork_to_nb(): Conversion between neighbor lists and sfnetworks

as.linnet.sfnetwork(): Convert a sfnetwork into a linnet

Analysis

The package enables an integrated analysis workflow that combines tidygraph and sf. On top of this, it extends the functionalities of those two packages with a set of analytical functions specific for spatial network analysis.

st_network_bbox(): Compute the bounding box of a spatial network

st_network_blend(): Blend spatial points into a spatial network

st_network_cost() st_network_distance(): Compute a cost matrix of a spatial network

st_network_faces(): Extract the faces of a spatial network

st_network_iso(): Compute isolines around nodes in a spatial network

st_network_join(): Join two spatial networks based on equality of node geometries

st_network_paths(): Find shortest paths between nodes in a spatial network

st_network_travel(): Find the optimal route through a set of nodes in a spatial network

st_project_on_network(): Project spatial points on a spatial network

centrality_straightness(): Compute spatial centrality measures

node_is_pseudo() node_is_dangling(): Query spatial node types

node_intersects() node_is_disjoint() node_touches() node_is_within() node_equals() node_is_covered_by() node_is_within_distance() node_is_nearest(): Query nodes with spatial predicates

node_X() node_Y() node_Z() node_M(): Query node coordinates

edge_azimuth() edge_circuity() edge_length() edge_displacement() edge_segment_count(): Query spatial edge measures

edge_intersects() edge_is_disjoint() edge_touches() edge_crosses() edge_is_within() edge_contains() edge_contains_properly() edge_overlaps() edge_equals() edge_covers() edge_is_covered_by() edge_is_within_distance() edge_is_nearest(): Query edges with spatial predicates

group_spatial_dbscan(): Group nodes based on spatial distance

to_spatial_contracted() to_spatial_directed() to_spatial_explicit() to_spatial_implicit() to_spatial_mixed() to_spatial_neighborhood() to_spatial_reversed() to_spatial_shortest_paths() to_spatial_simple() to_spatial_smooth() to_spatial_subdivision() to_spatial_subset() to_spatial_transformed() to_spatial_unique(): Morph spatial networks into a different structure

bind_spatial_nodes() bind_spatial_edges(): Add nodes or edges to a spatial network.

Utilities

The package exports different kind of utility and internal functions that are meant to make spatial network analysis smoother, and fitting both into tidyverse and non-tidyverse workflows.

Data extraction

These functions are about the extraction of data from a network.

node_data() edge_data(): Extract the node or edge data from a spatial network

node_ids() edge_ids(): Extract all node or edge indices from a spatial network

nearest_nodes() nearest_edges(): Extract the nearest nodes or edges to given spatial features

nearest_node_ids() nearest_edge_ids(): Extract the indices of nearest nodes or edges to given spatial features

n_nodes() n_edges(): Count the number of nodes or edges in a network

Edge geometries

These functions allow to modify edge geometries.

make_edges_directed(): Convert undirected edges into directed edges based on their geometries

make_edges_mixed(): Make some edges directed and some undirected

make_edges_explicit(): Construct edge geometries for spatially implicit networks

make_edges_implicit(): Drop edge geometries of spatially explicit networks

make_edges_follow_indices(): Match the direction of edge geometries to their specified incident nodes

make_edges_valid(): Match edge geometries to their incident node locations

Network cleaning

These functions are the internal workers behind the spatial morphers that are dedicted to network cleaning. They are exported to make it possible to do network cleaning outside of tidyverse workflows.

contract_nodes(): Contract groups of nodes in a spatial network

simplify_network(): Simplify a spatial network

smooth_pseudo_nodes(): Smooth pseudo nodes

subdivide_edges(): Subdivide edges at interior points

Internals

These are internal functions that are not meant to be called directly by users. They are exported such that developers aiming to extend the package can use them.

evaluate_weight_spec(): Specify edge weights in a spatial network

evaluate_node_query(): Query specific node indices from a spatial network

evaluate_edge_query(): Query specific edge indices from a spatial network

Other

These are other utility functions that do not fit in the categories above.

dual_weights(): Specify dual edge weights

is_sfnetwork() is.sfnetwork(): Check if an object is a sfnetwork

sf_attr(): Query sf attributes from the active element of a sfnetwork

st_duplicated(): Determine duplicated geometries

st_match(): Geometry matching

st_round(): Rounding of geometry coordinates

validate_network(): Validate the structure of a sfnetwork

wrap_igraph(): Run an igraph function on an sfnetwork object

Data

roxel: Road network of Münster Roxel

mozart: Point locations for places about W. A. Mozart in Salzburg, Austria