metro_anal.R

library(tidyverse)
library(sf)
library(sfnetworks)
library(tidygraph)
library(dbscan)
stations <- st_read("METRO_STAT.shp")
lines <- st_read("METRO_LINE.shp")
plot(stations$geometry)
clus <- st_coordinates(stations) %>% 
  dbscan(eps = 300,minPts = 1) %>% 
  `$`(cluster) 
lines %>% 
  st_cast("LINESTRING") %>% 
  as_sfnetwork(directed = F) %>% 
  convert(to_spatial_subdivision) %>% 
  st_network_blend(stations) %>% 
  {
    clus <- (.) %>% 
      st_coordinates() %>% 
      dbscan(eps = 300,minPts = 1) %>% 
      `$`(cluster) 
    (.) %>% mutate(clus = clus)
  } %>% 
  convert(to_spatial_contracted,clus,simplify = T) %>% 
  mutate(bet = centrality_betweenness(),
         close = centrality_closeness(),
         degree = centrality_degree(),
         louvain = group_louvain(edge_length()^(-1))) %>% 
  select(-.tidygraph_node_index) %>% 
  {
    eds <- (.) %E>% st_as_sf()
    nds <- (.) %N>% st_as_sf() #%>% 
      # top_n(10,close)
    ggplot() +
      geom_sf(data = eds) +
      geom_sf(data = nds,mapping = aes(color = as.factor(louvain)))+
      scale_color_manual(values = randomcoloR::distinctColorPalette(nds$louvain %>% unique() %>% length()))
    # mapview::mapview(eds$geometry) + mapview::mapview(nds,zcol = "bet")
    # eds %>% st_drop_geometry()
  }