Problems with spatial and graph operations on a Spatial Lines Network about sfnetworks

[luukvdmeer]

The "sfnetworks answer" to close this issue:

library(sfnetworks)
library(igraph)
library(tidygraph)
library(sf)
library(ggplot2)

net = as_sfnetwork(roxel, directed = FALSE)

Regarding ggplot, you will still need to extract nodes and edges as sf objects. However, this is fairly easy now since you can do that without activating first, and instead provide "nodes" or "edges" as argument to st_as_sf.

st_as_sf(net, "nodes")
#> Simple feature collection with 701 features and 0 fields
#> geometry type:  POINT
#> dimension:      XY
#> bbox:           xmin: 7.522622 ymin: 51.94151 xmax: 7.546705 ymax: 51.9612
#> CRS:            EPSG:4326
#> # A tibble: 701 x 1
#>               geometry
#>            <POINT [°]>
#>  1 (7.533722 51.95556)
#>  2 (7.533461 51.95576)
#>  3 (7.532442 51.95422)
#>  4  (7.53209 51.95328)
#>  5 (7.532709 51.95209)
#>  6 (7.532869 51.95257)
#>  7 (7.540063 51.94468)
#>  8  (7.53822 51.94546)
#>  9  (7.537673 51.9475)
#> 10 (7.537614 51.94562)
#> # … with 691 more rows

st_as_sf(net, "edges")
#> Simple feature collection with 851 features and 4 fields
#> geometry type:  LINESTRING
#> dimension:      XY
#> bbox:           xmin: 7.522594 ymin: 51.94151 xmax: 7.546705 ymax: 51.9612
#> CRS:            EPSG:4326
#> # A tibble: 851 x 5
#>     from    to name         type                                        geometry
#>    <int> <int> <fct>        <fct>                               <LINESTRING [°]>
#>  1     1     2 Havixbecker… reside…       (7.533722 51.95556, 7.533461 51.95576)
#>  2     3     4 Pienersallee second… (7.532442 51.95422, 7.53236 51.95377, 7.532…
#>  3     5     6 Schulte-Ber… reside… (7.532709 51.95209, 7.532823 51.95239, 7.53…
#>  4     7     8 <NA>         path    (7.540063 51.94468, 7.539696 51.94479, 7.53…
#>  5     9    10 Welsingheide reside…        (7.537673 51.9475, 7.537614 51.94562)
#>  6    11    12 <NA>         footway (7.543791 51.94733, 7.54369 51.94686, 7.543…
#>  7    13    14 <NA>         footway        (7.54012 51.94478, 7.539931 51.94514)
#>  8     8    10 <NA>         path    (7.53822 51.94546, 7.538131 51.94549, 7.538…
#>  9     7    15 <NA>         track   (7.540063 51.94468, 7.540338 51.94468, 7.54…
#> 10    16    17 <NA>         track   (7.5424 51.94599, 7.54205 51.94629, 7.54196…
#> # … with 841 more rows

Then plotting gets as easy as.

ggplot() +
  geom_sf(data = st_as_sf(net, "edges")) +
  geom_sf(data = st_as_sf(net, "nodes"))

The algorithms from igraph can be directly applied to the network.

head(igraph::edge_betweenness(net))
#> [1] 10406.362 12118.803  6837.847  4467.540 32124.052   674.000
igraph::is_connected(net)
#> [1] FALSE

Spatial filtering can be done with st_filter.

p1 = st_point(c(7.53173, 51.95662))
p2 = st_point(c(7.53173, 51.95190))
p3 = st_point(c(7.53778, 51.95190))
p4 = st_point(c(7.53778, 51.95662))

rect = st_multipoint(c(p1, p2, p3, p4)) %>% 
  st_cast('POLYGON') %>% 
  st_sfc(crs = 4326)

net_f = net %>%
  st_filter(rect, .pred = st_intersects)
#> although coordinates are longitude/latitude, st_intersects assumes that they are planar
#> although coordinates are longitude/latitude, st_intersects assumes that they are planar

par(mar = c(1, 1, 1, 1))
plot(net, col = "grey")
plot(rect, border = "Red", lwd = 2, add = TRUE)
plot(net_f, add = TRUE)

^{Created on 2020-06-16 by the reprex package (v0.3.0)}