Create a spflow_network-class()
spflow_network(
id_net,
node_neighborhood = NULL,
node_data = NULL,
node_key_column,
node_coord_columns,
derive_coordinates = missing(node_coord_columns),
prefer_lonlat = TRUE,
normalize_byrow = FALSE
)A character that serves as an identifier for the set of nodes
A matrix that describes the neighborhood of the nodes
A data.frame that contains all information describing the nodes
A character indicating the column containing the identifiers for the nodes
A character indicating the columns that represent the coordinates of the
nodes. For example c("LON", "LAT").
A logical indicating whether there should be an attempt to infer the coordinates from the node_data.
A logical indicating whether the coordinates should be transformed to longitude and latitude.
A logical, if TRUE the neighborhood will be row-normalized, otherwise
it is scaled to have a spectral radius of one.
An S4 class of type spflow_network-class()
spflow_network(
"germany",
spdep::nb2mat(spdep::poly2nb(germany_grid)),
as.data.frame(germany_grid),
"ID_STATE")
#> Spatial network nodes with id: germany
#> --------------------------------------------------
#> Number of nodes: 16
#> Average number of links per node: 5.125
#> Density of the neighborhood matrix: 32.03% (non-zero connections)
#>
#> Data on nodes:
#> ID_STATE X COORD_X COORD_Y
#> SH SH 10 1 5
#> HH HH 15 1 4
#> MV MV 20 2 4
#> NW NW 7 0 3
#> HB HB 20 1 3
#> BB BB 25 2 3
#> BE BE 15 3 3
#> RP RP 10 0 2
#> NI NI 30 1 2
#> ST ST 20 2 2
#> SN SN 15 3 2
#> SL SL 10 0 1
#> HE HE 15 1 1
#> TH TH 10 2 1
#> BW BW 7 1 0
#> BY BY 7 2 0
#>