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Flow

Michael
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Flow – CC-BY-NC by Sebastian Bender

Day 17 of 30DayMapChallenge: « Flow » (previously).

Mapping the commuters to Lyon in France. Data comes from INSEE and is part of the national census.

< section id="setup" class="level2">

Setup

library(tidyverse)
library(sf)
library(glue)
library(janitor)
< section id="data" class="level2">

Data

Paris, Lyon and Marseille are subdivided in this dataset (by arrondissement); we filter out Lyon origins and keep only Lyon destinations and we aggregate the arrondissements for the 3 cities.

# Home-work commute in France 2019, by commune
# https://www.insee.fr/fr/information/2383337
# https://www.insee.fr/fr/statistiques/6454112
hwc_file <- "base-csv-flux-mobilite-domicile-lieu-travail-2019.zip"

if (!file.exists(hwc_file)) {
  download.file(paste0("https://www.insee.fr/fr/statistiques/fichier/6454112/",
                       hwc_file), 
                hwc_file)
}

hwc <- read_delim(hwc_file,
                  delim = ";",
                  locale = locale(decimal_mark = ".")) |> 
  clean_names() |> 
  filter(str_detect(dclt, "6938[1-9]"),
         !str_detect(codgeo, "6938[1-9]")) |> 
  mutate(across(c(codgeo, dclt), 
                ~ case_when(between(.x, "13201", "13216") ~ "13055",
                            between(.x, "75101", "75120") ~ "75056",
                            between(.x, "69381", "69389") ~ "69123",
                            .default = .x))) |> 
  group_by(codgeo, dclt) |> 
  summarise(nbflux_c19_actocc15p = sum(nbflux_c19_actocc15p),
            .groups = "drop")

# France communes and régions (polygons)
# See https://r.iresmi.net/posts/2021/simplifying_polygons_layers/ for the data
c("commune", "region") |> 
  set_names() |> 
  map(\(x) read_sf("~/data/adminexpress/adminexpress_cog_simpl_000_2022.gpkg",
                   layer = x) |> 
        filter(insee_reg > "06") |> 
        st_transform("EPSG:2154")) |> 
  list2env(envir = .GlobalEnv)
< section id="build-flow-coordinates" class="level2">

Build flow coordinates

# get coordinates for origin points
com_orig <- commune |> 
  st_point_on_surface() |> 
  mutate(x = st_coordinates(geom)[, 1],
         y = st_coordinates(geom)[, 2]) |> 
  select(insee_com, x, y)

# we only need one destination point: Lyon
com_dest <- com_orig |> 
  filter(insee_com == "69123")

# Add origine and destination coords to the commute table
flow <- hwc |> 
  left_join(com_orig, 
            join_by(codgeo == insee_com)) |> 
  left_join(com_dest, 
            join_by(dclt == insee_com), 
            suffix = c("_orig", "_dest"))
< section id="map" class="level2">

Map

ggplot(region) +
  geom_sf(color = "grey70",
          fill="grey95") +
  geom_curve(data = flow,
             aes(x = x_orig, y = y_orig, xend = x_dest, yend = y_dest,
                 linewidth = nbflux_c19_actocc15p, 
                 alpha = nbflux_c19_actocc15p),
             color = "dodgerblue3",
             curvature = 0.2) +
  scale_linewidth_continuous(labels = scales::label_number(big.mark = " "),
                             trans = "log10",
                             breaks = c(10, 100, 1000, 10000),
                             range = c(0.05, 3)) +
  scale_alpha_continuous(labels = scales::label_number(big.mark = " "),
                         trans = "log10",
                         breaks = c(10, 100, 1000, 10000),
                         range = c(0.05, .4)) +
  labs(title = "Working flow to Lyon",
       subtitle = "2019",
       linewidth = "workers",
       alpha = "workers",
       caption = glue("https://www.iresmi.net {Sys.Date()}
                      Base map from IGN Admin Express 2022
                      Data: INSEE 2019")) +
  theme_void() +
  theme(text = element_text(family = "Courier"),
        plot.margin = margin(0, .3, 0.1, .3, "cm"),
        plot.background = element_rect(color = NA, fill = "white"),
        plot.caption = element_text(size = 6))
Figure 1: Working in Lyon

The 2-hours commute from Paris by TGV seems popular…

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