In atepoorthuis/containment-connectivity-urban-systems: Compendium Package for Containment and Connectivity in Dutch Urban Systems

library(tidyverse)
library(here)
library(sf)
library(rmapshaper)
library(patchwork)
library(tmap)
library(purrr)

Load results of community detection

We first load the results of the community detection procedure from the .tree files generated by Infomap for each of the three systems.

# we need the postcode data again so load that first
postcode <- st_read(here("analysis", "data", "raw_data", "postcode_polygons", "CBS_ESRI_PC4_2017.shp"), crs = 28992, stringsAsFactors = F) %>% 
  mutate(pc4 = as.integer(pc4)) %>% 
  ms_simplify(keep_shapes = T) %>%  # pc geometry is much more detailed than we need, simplify
  st_buffer(dist = 0)

isolates <- read_rds(here("analysis", "data", "derived_data", "isolate_postcodes.rds"))

dus <-
  read_csv(here("analysis", "data", "raw_data", "dus_pc_edges.csv"))
dus_com <- map(seq(0.10, 6, by = 0.10), function(i) {
  tesgcontainmentconnectivity::com_extract(here(
    "analysis",
    "data",
    "derived_data",
    "dus-communities",
    paste0("dus-", i, ".tree")
  ))
})
dus_stats <- tibble(
  markov = seq(0.10, 6, by = 0.10),
  members = map_int(dus_com, tesgcontainmentconnectivity::com_count),
  codeLength = map_chr(seq(0.10, 6, by = 0.10), function(i) {
    tesgcontainmentconnectivity::codeLength_extract(here(
      "analysis",
      "data",
      "derived_data",
      "dus-communities",
      paste0("dus-", i, ".tree")
    ))
  }),
  external = map_dbl(dus_com, function(com)
    tesgcontainmentconnectivity::com_external(com, totalFlow = dus, isolates = isolates)),
  externalMedian = map_dbl(dus_com, function(com)
    tesgcontainmentconnectivity::com_externalMedian(com, totalFlow = dus, isolates = isolates))
) %>%
  mutate(codeLength = as.numeric(codeLength))
dus_stats    

centralplace <-
  read_csv(here("analysis", "data", "raw_data", "centralplace_pc_edges.csv"))
cp_com <- map(seq(0.10, 7, by = 0.10), function(i) {
  tesgcontainmentconnectivity::com_extract(here(
    "analysis",
    "data",
    "derived_data",
    "cp-communities",
    paste0("cp-", i, ".tree")
  ))
})

cp_stats <- tibble(
  markov = seq(0.10, 7, by = 0.10),
  members = map_int(cp_com, tesgcontainmentconnectivity::com_count),
  codeLength = map_chr(seq(0.10, 7, by = 0.10), function(i) {
    tesgcontainmentconnectivity::codeLength_extract(here(
      "analysis",
      "data",
      "derived_data",
      "cp-communities",
      paste0("cp-", i, ".tree")
    ))
  }),
  external = map_dbl(cp_com, function(com)
    tesgcontainmentconnectivity::com_external(com, totalFlow = centralplace, isolates = isolates)),
  externalMedian = map_dbl(cp_com, function(com)
    tesgcontainmentconnectivity::com_externalMedian(com, totalFlow = centralplace, isolates = isolates))
) %>%
  mutate(codeLength = as.numeric(codeLength))
cp_stats                  

export <-
  read_csv(here("analysis", "data", "raw_data", "export_pc_edges.csv"))
ex_com <- map(seq(0.15, 7, by = 0.15), function(i) {
  tesgcontainmentconnectivity::com_extract(here(
    "analysis",
    "data",
    "derived_data",
    "ex-communities",
    paste0("ex-", i, ".tree")
  ))
})
ex_stats <- tibble(
  markov = seq(0.15, 7, by = 0.15),
  members = map_int(ex_com, tesgcontainmentconnectivity::com_count),
  codeLength = map_chr(seq(0.15, 7, by = 0.15), function(i) {
    tesgcontainmentconnectivity::codeLength_extract(here(
      "analysis",
      "data",
      "derived_data",
      "ex-communities",
      paste0("ex-", i, ".tree")
    ))
  }),
  external = map_dbl(ex_com, function(com)
    tesgcontainmentconnectivity::com_external(com, totalFlow = export, isolates = isolates)),
  externalMedian = map_dbl(ex_com, function(com)
    tesgcontainmentconnectivity::com_externalMedian(com, totalFlow = export, isolates = isolates))
)
ex_stats                  

# set 'chosen' urban system based on theoretical cut-off
dus_i <- which.min(dus_stats$external > 0.05)
cp_i <- which.min(cp_stats$external > 0.01)
ex_i <- which.min(ex_stats$external > 0.06)

Merge isolates postcodes with their nearest neighbor

We calculate a distance matrix between all postcodes and merge isolates with their nearest neighbor based on this matrix. This is so the resulting maps are not 'cluttered' with isolated postcodes.

filter_out <- postcode %>% 
  left_join(ex_com[[ex_i]], by=c("pc4" = "name")) %>% 
  rename("exp" = "community") %>% 
  left_join(cp_com[[cp_i]], by=c("pc4" = "name")) %>% 
  rename("cp" = "community") %>% 
  left_join(dus_com[[dus_i]], by=c("pc4" = "name")) %>% 
  rename("dus" = "community") %>% 
  as_tibble() %>%
  filter((is.na(exp) | is.na(cp) | is.na(dus))) %>% 
  st_as_sf()

filter_in <- postcode %>% 
  left_join(ex_com[[ex_i]], by=c("pc4" = "name")) %>% 
  rename("exp" = "community") %>% 
  left_join(cp_com[[cp_i]], by=c("pc4" = "name")) %>% 
  rename("cp" = "community") %>% 
  left_join(dus_com[[dus_i]], by=c("pc4" = "name")) %>% 
  rename("dus" = "community") %>% 
  as_tibble() %>%
  filter(!(is.na(exp) | is.na(cp) | is.na(dus))) %>% 
  st_as_sf()

dist <- st_distance(filter_in, filter_out)
filter_replace <- filter_out %>% 
  mutate(index = row_number()) %>% 
  rowwise() %>% 
  mutate(closest = which.min(dist[,index])) %>% 
  rowwise() %>% 
  mutate(merge_with = filter_in[closest,]$pc4) %>% 
  select(pc4, merge_with)

postcode_with_isolates_merged <- postcode %>%
  left_join(filter_replace, by = c("pc4" = "pc4")) %>% 
  mutate(merge_with=coalesce(merge_with,pc4)) %>% 
  mutate(pc4 = merge_with) %>% 
  select(pc4) %>% 
  group_by(pc4) %>% 
  summarise(count = n()) %>% 
  ms_simplify(keep = 0.9, keep_shapes = T) %>% 
  st_buffer(dist = 0)

Figure 2

p1 <- dus_stats %>% 
  ggplot() + geom_line(aes(x = markov, y = members)) + xlab("Markov Time") + ylab("Number of Regions")

p2 <- dus_stats %>% 
  ggplot() + geom_line(aes(x = markov, y = external)) + xlab("Markov Time") + ylab("% of interactions to/from outside region") +
  annotate("text", x = 3.1, y = 0.05, hjust = 0, vjust = 0.4, label = "nodalization cut-off point", fontface = "italic") + 
  annotate("segment", x = 1.6, xend = 3, y = 0.05, yend = 0.05, color = "darkgray") +
  annotate("point", x = dus_stats$markov[5], y = dus_stats$external[5]) +
  annotate("point", x = dus_stats$markov[9], y = dus_stats$external[9]) +
  annotate("point", x = dus_stats$markov[13], y = dus_stats$external[13]) +
  annotate("point", x = dus_stats$markov[25], y = dus_stats$external[25])

p1 + p2 + plot_layout(ncol = 1)
# ggsave("Figure 2a.pdf", height = 6)

map1 <- tesgcontainmentconnectivity::comm_merge(dus_com[[5]], postcode_with_isolates_merged, dus_i) %>% 
  ms_simplify(keep = 0.9, keep_shapes = T) %>%  # clean up some weird lines after merging
  tm_shape() + tm_borders(lwd = 0.5, col = "black") + tm_layout(frame = F) + tm_layout(title = "Markov Time 0.5", title.size = 0.9)

map2 <- tesgcontainmentconnectivity::comm_merge(dus_com[[9]], postcode_with_isolates_merged, dus_i) %>% 
  ms_simplify(keep = 0.9, keep_shapes = T) %>%  # clean up some weird lines after merging
  tm_shape() + tm_borders(lwd = 0.5, col = "black") + tm_layout(frame = F) + tm_layout(title = "Markov Time 0.9", title.size = 0.9)

map3 <- tesgcontainmentconnectivity::comm_merge(dus_com[[13]], postcode_with_isolates_merged, dus_i) %>% 
  ms_simplify(keep = 0.9, keep_shapes = T) %>%  # clean up some weird lines after merging
  tm_shape() + tm_borders(lwd = 0.5, col = "black") + tm_layout(frame = F) + tm_layout(title = "Markov Time 1.3", title.size = 0.9)

map4 <- tesgcontainmentconnectivity::comm_merge(dus_com[[25]], postcode_with_isolates_merged, dus_i) %>% 
  ms_simplify(keep = 0.9, keep_shapes = T) %>%  # clean up some weird lines after merging
  tm_shape() + tm_borders(lwd = 0.5, col = "black") + tm_layout(frame = F) + tm_layout(title = "Markov Time 2.5", title.size = 0.9)

tmap_arrange(list(map1, map2, map3, map4), ncol = 2)
# tmap_save(tmap_arrange(list(map1, map2, map3, map4), ncol = 2), filename = "Figure2b.pdf", height = 6)

Figure 3

We need a background layer to indicate towns/built-up areas. We use the TOP10NL built-up place layer ('plaats'). Easy-to-use extracts used to be provided by Jan-Willem van Aalst at Imergis but as of 1 November 2019, they are no longer available. Instead, download TOP10NL data directly from PDOK. TOP10NL data is made available by the Dutch Kadaster as part of the Basisregistratie Topografie (BRT) under a CC BY 4.0 license.

towns <- st_read(here("analysis", "data", "raw_data", "top10_buildup", "Top10NL-Plaats_kern.shp")) %>% 
  select(naamnl) %>% 
  ms_simplify(keep = 0.09) %>% 
  st_buffer(dist = 0)
towns_plot <- tm_shape(towns) + tm_fill(col = "#2F4F4F", alpha = 0.5)

# the edges between communities in the DUS are based on commuting flows
dus_flow <- read_csv(here("analysis", "data", "raw_data", "export_pc_edges.csv"))
dus_flow <- tesgcontainmentconnectivity::merge_flow(dus_flow, dus_com[[dus_i]], isolates)

dus_final <- tesgcontainmentconnectivity::com_statsPerCom(dus_com[[dus_i]], dus, isolates) %>% 
  filter(internal > 1000)
dus_final_sf <- tesgcontainmentconnectivity::comm_merge(dus_com[[dus_i]], postcode_with_isolates_merged, dus_i) %>% 
  left_join(dus_final, by = c("community" = "com")) %>% 
  ms_simplify(keep = 0.9, keep_shapes = T) # clean up some weird lines after merging

dus_centroid <- st_centroid(tesgcontainmentconnectivity::comm_merge(dus_com[[dus_i]], postcode_with_isolates_merged, dus_i)) %>%
  select(community) %>%
  left_join(., dus_final, by = c("community" = "com"))

dus_lines <- dus_flow %>%
  left_join(dus_centroid, by = c("source" = "community")) %>%
  left_join(dus_centroid, by = c("sink" = "community")) %>%
  drop_na() %>% 
  filter(weight > 100) %>% # filter very small interactions
  rowwise() %>%
  mutate(geometry = st_combine(c(geometry.x, geometry.y)) %>% st_cast("LINESTRING")) %>% select(-geometry.x, -geometry.y) %>%
  st_as_sf(crs = 28992)

dus_outline <- tm_shape(dus_final_sf) + tm_borders(lwd = 0.9, col = "#383838")

dus_fill <-
  tm_shape(dus_final_sf) + tm_fill(
    col = "outgoingRel",
    style = "jenks",
    palette = "YlGn",
    showNA = F,
    legend.is.portrait = F,
    title = "% Outgoing Interaction"
  ) +
  tm_legend(
    legend.position = c("left", "bottom"),
    legend.width = 0.55,
    legend.format = list(
      text.separator = "to",
      fun = function(x)
        paste0(formatC(
          x * 100, digits = 1, format = "f"
        ), "%")
    )
  )

dus_fill_no_legend <- tm_shape(dus_final_sf) +
  tm_fill(
    col = "outgoingRel",
    palette = "YlGn",
    showNA = F,
    n = 5,
    style = "jenks",
    legend.is.portrait = F,
    title = "Incoming Interactions",
    legend.show = F
  ) +
  tm_legend(legend.position = c("left", "bottom"),
            legend.width = 0.55)

dus_line_plot <-
  tm_shape(dus_lines) + tm_lines(
    col = "#B02430",
    lwd = "weight",
    alpha = 0.9,
    scale = 8,
    n = 5,
    legend.lwd.is.portrait = F,
    title.lwd = "Estimation of daily commuting trips"
  ) +
  tm_legend(
    legend.position = c("left", "bottom"),
    legend.width = 0.55,
    legend.format = list(
      fun = function(x)
        formatC(x, digits = 0, format = "f")
    )
  ) + tm_shape(dus_centroid %>% top_n(50, internal)) + tm_bubbles(col = "#B02430",
                                                                  size = 0.6,
                                                                  border.lwd = 0) + tm_text(text = "community", col = "white", size = 0.6)

fig3a <- dus_fill + towns_plot + dus_outline + tm_layout(frame = F)
fig3b <- dus_fill_no_legend + towns_plot + dus_outline + dus_line_plot + tm_layout(frame = F)

tmap_arrange(list(fig3b, fig3a), ncol = 2)

#tmap_save(tmap_arrange(list(fig3b, fig3a), ncol = 2), filename = "Figure3.pdf", width = 7.5, height = 4.5)

Figure 4

# the edges between communities in the CP system are based on shopping flows
cp_flow <- read_csv(here("analysis", "data", "raw_data", "centralplace_pc_edges.csv"))
cp_flow <- tesgcontainmentconnectivity::merge_flow(cp_flow, cp_com[[cp_i]], isolates)

cp_final <- tesgcontainmentconnectivity::com_statsPerCom(cp_com[[cp_i]], centralplace, isolates) %>%
  filter(internal > 1000) %>% 
  mutate(balance = incoming - outgoing) %>% 
  mutate(incomeRel = incoming / internal)
cp_final_sf <- tesgcontainmentconnectivity::comm_merge(cp_com[[cp_i]], postcode_with_isolates_merged, cp_i) %>% 
  left_join(cp_final, by = c("community" = "com")) %>% 
  ms_simplify(keep = 0.9, keep_shapes = T) # clean up some weird lines after merging


cp_centroid <- st_centroid(tesgcontainmentconnectivity::comm_merge(cp_com[[cp_i]], postcode_with_isolates_merged, cp_i)) %>% select(community) %>% left_join(., cp_final, by = c("community" = "com"))

cp_lines <- cp_flow %>%
  left_join(cp_centroid, by = c("source" = "community")) %>%
  left_join(cp_centroid, by = c("sink" = "community")) %>%
  rowwise() %>%
  mutate(geometry = st_combine(c(geometry.x, geometry.y)) %>% st_cast("LINESTRING")) %>% select(-geometry.x, -geometry.y) %>%
  st_as_sf(crs = 28992)

cp_fill <-
  tm_shape(cp_final_sf) + tm_fill(
    col = "incoming",
    palette = "GnBu",
    showNA = F,
    n = 5,
    style = "jenks",
    legend.is.portrait = F,
    title = "Incoming Interactions"
  ) +
  tm_legend(legend.position = c("left", "bottom"),
            legend.width = 0.55)
cp_outline <-
  tm_shape(cp_final_sf) + tm_borders(lwd = 0.9, col = "#383838")

cp_fill_no_legend <-
  tm_shape(cp_final_sf) + tm_fill(
    col = "incoming",
    palette = "GnBu",
    showNA = F,
    n = 5,
    style = "jenks",
    legend.is.portrait = F,
    title = "Incoming Interactions",
    legend.show = F
  ) +
  tm_legend(legend.position = c("left", "bottom"),
            legend.width = 0.55)
cp_line_plot <-
  tm_shape(cp_lines) + tm_lines(
    col = "#B02430",
    lwd = "weight",
    alpha = 0.9,
    scale = 7,
    n = 5,
    legend.lwd.is.portrait = F,
    title.lwd = "Estimation of daily shopping trips"
  ) +
  tm_legend(
    legend.position = c("left", "bottom"),
    legend.width = 0.55,
    legend.format = list(
      fun = function(x)
        formatC(x, digits = 0, format = "f")
    )
  ) + tm_shape(cp_centroid %>% top_n(50, internal)) + tm_bubbles(col = "#B02430",
                                                                 size = 0.6,
                                                                 border.lwd = 0) + tm_text(text = "community", col = "white", size = 0.6)

fig4a <- cp_fill + towns_plot + cp_outline + tm_layout(frame = F)
fig4b <- cp_fill_no_legend + towns_plot + cp_outline + cp_line_plot + tm_layout(frame = F)

tmap_arrange(list(fig4b, fig4a), ncol = 2)
# tmap_save(tmap_arrange(list(fig4b, fig4a), ncol = 2), filename = "Figure4.pdf", width = 7.5, height = 4.5)

Figure 5

# the edges between communities in the export base system are based on business trips
# because these are relatively infrequent between postcodes, the filtered (>5 trips) aggregated postcode OD matrix would suppress too many connections
# instead we share the aggregated community matrix, which has been constructed using the `tesgcontainmentconnectivity::merge_flow` function used for both the DUS and CP data above
ex_flow <- read_csv(here("analysis", "data", "raw_data", "export_community_edges.csv"), col_types = list("c", "c", "d","d"))

ex_final <- tesgcontainmentconnectivity::com_statsPerCom(ex_com[[ex_i]], export, isolates) %>% 
  filter(internal > 1000)
ex_final_sf <- tesgcontainmentconnectivity::comm_merge(ex_com[[ex_i]], postcode_with_isolates_merged, ex_i) %>% 
  left_join(ex_final, by = c("community" = "com")) %>% 
  ms_simplify(keep = 0.9, keep_shapes = T) # clean up some weird lines after merging



ex_centroid <- st_centroid(tesgcontainmentconnectivity::comm_merge(ex_com[[ex_i]], postcode_with_isolates_merged, ex_i)) %>% select(community)

ex_lines <- ex_flow %>%
  left_join(ex_centroid, by = c("source" = "community")) %>%
  left_join(ex_centroid, by = c("sink" = "community")) %>%
  rowwise() %>%
  mutate(geometry = st_combine(c(geometry.x, geometry.y)) %>% st_cast("LINESTRING")) %>% select(-geometry.x, -geometry.y) %>%
  st_as_sf(crs = 28992)

ex_fill <-
  tm_shape(ex_final_sf) + tm_fill(
    col = "outgoingRel",
    style = "jenks",
    palette = "RdPu",
    showNA = F,
    legend.is.portrait = F,
    title = "% Outgoing Interaction"
  ) +
  tm_legend(
    legend.position = c("left", "bottom"),
    legend.width = 0.55,
    legend.format = list(
      text.separator = "to",
      fun = function(x)
        paste0(formatC(
          x * 100, digits = 1, format = "f"
        ), "%")
    )
  )

ex_outline <-
  tm_shape(ex_final_sf) + tm_borders(lwd = 0.9, col = "#383838")

ex_fill_no_legend <-
  tm_shape(ex_final_sf) + tm_fill(
    col = "outgoingRel",
    style = "jenks",
    palette = "RdPu",
    showNA = F,
    legend.is.portrait = F,
    title = "% Outgoing Interaction",
    legend.show = F
  )
ex_line_plot <-
  tm_shape(ex_lines) + tm_lines(
    col = "#B02430",
    lwd = "weight",
    alpha = 0.9,
    scale = 7,
    n = 5,
    legend.lwd.is.portrait = F,
    title.lwd = "Estimation of daily business trips"
  ) +
  tm_legend(
    legend.position = c("left", "bottom"),
    legend.width = 0.55,
    legend.format = list(
      fun = function(x)
        formatC(x, digits = 0, format = "f")
    )
  ) + tm_shape(ex_centroid) + tm_bubbles(col = "#B02430",
                                         size = 0.6,
                                         border.lwd = 0) + tm_text(text = "community", col = "white", size = 0.6)

fig5a <- ex_fill + towns_plot + ex_outline + tm_layout(frame = F)
fig5b <- ex_fill_no_legend + towns_plot + ex_outline + ex_line_plot + tm_layout(frame = F)

tmap_arrange(list(fig5b, fig5a), ncol = 2)
# tmap_save(tmap_arrange(list(fig5b, fig5a), ncol = 2), filename = "Figure5.pdf", width = 7.5, height = 4.5)

Figure 7

postcode_all_systems <- postcode_with_isolates_merged %>% 
  left_join(ex_com[[ex_i]], by=c("pc4" = "name")) %>% 
  rename("exp" = "community") %>% 
  left_join(cp_com[[cp_i]], by=c("pc4" = "name")) %>% 
  rename("cp" = "community") %>% 
  left_join(dus_com[[dus_i]], by=c("pc4" = "name")) %>% 
  rename("dus" = "community") %>% 
  as_tibble() %>%
  filter(!(is.na(exp) | is.na(cp) | is.na(dus)))

match_ex <- postcode_all_systems %>%
  group_by(pc4) %>%
  nest() %>%
  mutate(ex = purrr::map(data, function(x) x$exp == postcode_all_systems$exp)) %>%
  select(pc4, ex)

match_cp <- postcode_all_systems %>%
  group_by(pc4) %>%
  nest() %>%
  mutate(cp = purrr::map(data, function(x) x$cp == postcode_all_systems$cp)) %>%
  select(pc4, cp)

match_dus <- postcode_all_systems %>%
  group_by(pc4) %>%
  nest() %>%
  mutate(dus = purrr::map(data, function(x) x$dus == postcode_all_systems$dus)) %>%
  select(pc4, dus)

match_all <- left_join(match_ex, match_cp) %>%
  left_join(match_dus)

t_ex <- match_all %>% 
  rowwise() %>% 
  mutate(n11 = sum(unlist(cp) & unlist(ex))) %>% 
  mutate(n00 = sum(!unlist(cp) & !unlist(ex))) %>% 
  mutate(n10 = sum(unlist(cp) & !unlist(ex))) %>% 
  mutate(n01 = sum(!unlist(cp) & unlist(ex))) %>% 
  mutate(wallace1_ex = n11 / (n11 + n10)) %>% 
  mutate(wallace2_ex = n11 / (n11 + n01)) %>% 
  mutate(jaccard_ex = n11 / (n11 + n01 + n10)) %>% 
  select(-ex, -cp, -dus)

t_ex %>%
  ungroup() %>% 
  summarise(n11 = sum(n11), n10 = sum(n10), n01 = sum(n01)) %>% 
  mutate(wallace1_ex = n11 / (n11 + n10)) %>% 
  mutate(wallace2_ex = n11 / (n11 + n01)) %>% 
  mutate(jaccard_ex = n11 / (n11 + n01 + n10))

t_cp <- match_all %>% 
  rowwise() %>% 
  mutate(n11 = sum(unlist(dus) & unlist(cp))) %>% 
  mutate(n00 = sum(!unlist(dus) & !unlist(cp))) %>% 
  mutate(n10 = sum(unlist(dus) & !unlist(cp))) %>% 
  mutate(n01 = sum(!unlist(dus) & unlist(cp))) %>% 
  mutate(wallace1_cp = n11 / (n11 + n10)) %>% 
  mutate(wallace2_cp = n11 / (n11 + n01)) %>% 
  mutate(jaccard_cp = n11 / (n11 + n01 + n10)) %>% 
  select(-ex, -cp, -dus)

t_cp %>%
  ungroup() %>% 
  summarise(n11 = sum(n11), n10 = sum(n10), n01 = sum(n01)) %>% 
  mutate(wallace1_ex = n11 / (n11 + n10)) %>% 
  mutate(wallace2_ex = n11 / (n11 + n01)) %>% 
  mutate(jaccard_ex = n11 / (n11 + n01 + n10))

pc_wallace <- left_join(t_ex, t_cp, by = c("pc4" = "pc4")) %>%
  select(-starts_with('n')) %>% 
  mutate(mean_wall = mean(c(wallace1_ex, wallace1_cp))) %>% 
  left_join(postcode_with_isolates_merged) %>%
  st_as_sf()


ex_outline_thick <- tm_shape(ex_final_sf) + tm_borders(lwd = 1, col = "black")

wallace_fill <-
  tm_shape(pc_wallace) + tm_fill(
    col = "mean_wall" ,
    palette = "-BuPu",
    style = "jenks",
    legend.is.portrait = F,
    title = "Mean Wallace I Index"
  ) +
  tm_legend(legend.position = c("left", "bottom"),
            legend.width = 0.55)

fig7a <- wallace_fill + towns_plot + ex_outline_thick + tm_layout(frame = F)

wallace_binary <-
  tm_shape(pc_wallace) + tm_fill(
    col = "mean_wall",
    palette = c("#810f7c", "#edf8fb"),
    breaks = c(0, 0.95, 1),
    legend.is.portrait = F,
    title = "Mean Wallace I Index"
  ) +
  tm_legend(legend.position = c("left", "bottom"),
            legend.width = 0.55)

fig7b <- wallace_binary + towns_plot + ex_outline_thick + tm_layout(frame = F)

tmap_arrange(list(fig7a, fig7b), ncol = 2)
# tmap_save(tmap_arrange(list(fig7a, fig7b), ncol = 2), filename = "Figure7.pdf", width = 7.5, height = 4.5)

atepoorthuis/containment-connectivity-urban-systems documentation built on Nov. 25, 2019, 11:30 a.m.