Catégories
Non classé

Coronavirus : spatially smoothed decease in France

Coronavirus decease in France

See also the animated map.

From the official data by Santé Publique France, we spatially smooth the decease (produced by SPF at the département scale) and normalize by a similarly smoothed population grid. For that we use the btb package.

# Carto décès COVID 19 France
# avec lissage


# sources -----------------------------------------------------------------

# https://www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/
fichier_covid <- "donnees/covid.csv"
url_donnees_covid <- "https://www.data.gouv.fr/fr/datasets/r/63352e38-d353-4b54-bfd1-f1b3ee1cabd7"

# https://www.insee.fr/fr/statistiques/2012713#tableau-TCRD_004_tab1_departements
fichier_pop <- "donnees/pop.xls"
url_donnees_pop <- "https://www.insee.fr/fr/statistiques/fichier/2012713/TCRD_004.xls"

# Adminexpress : à télécharger manuellement
# https://geoservices.ign.fr/documentation/diffusion/telechargement-donnees-libres.html#admin-express
#aex <- "donnees/1_DONNEES_LIVRAISON_2019-03-14/"
aex <- path_expand("~/Downloads/ADMIN-EXPRESS_2-2__SHP__FRA_2020-02-24/ADMIN-EXPRESS/1_DONNEES_LIVRAISON_2020-02-24")

# config ------------------------------------------------------------------
library(tidyverse)
library(httr)
library(fs)
library(sf)
library(readxl)
library(janitor)
library(glue)
library(tmap)
library(grid)
library(classInt)
# + btb, raster, fasterize, plyr

rayon <- 100000 # distance de lissage (m)
pixel <- 10000 # résolution grille (m)

force_download <- TRUE # retélécharger même si le fichier existe et a été téléchargé aujourd'hui ?

#' Kernel weighted smoothing with arbitrary bounding area
#'
#' @param df sf object (points)
#' @param field weight field in the df
#' @param bandwidth kernel bandwidth (map units)
#' @param resolution output grid resolution (map units)
#' @param zone sf study zone (polygon)
#' @param out_crs EPSG (should be an equal-area projection)
#'
#' @return a raster object
#' @import btb, raster, fasterize, dplyr, plyr, sf
lissage <- function(df, field, bandwidth, resolution, zone, out_crs = 3035) {
    if (st_crs(zone)$epsg != out_crs) {
      message("reprojecting data...")
      zone <- st_transform(zone, out_crs)
    }

    if (st_crs(df)$epsg != out_crs) {
      message("reprojecting study zone...")
      df <- st_transform(df, out_crs)
    }

    zone_bbox <- st_bbox(zone)

    # grid generation
    message("generating reference grid...")
    zone_xy <- zone %>%
      dplyr::select(geometry) %>%
      st_make_grid(
        cellsize = resolution,
        offset = c(
          plyr::round_any(zone_bbox[1] - bandwidth, resolution, f = floor),
          plyr::round_any(zone_bbox[2] - bandwidth, resolution, f = floor)
        ),
        what = "centers"
      ) %>%
      st_sf() %>%
      st_join(zone, join = st_intersects, left = FALSE) %>%
      st_coordinates() %>%
      as_tibble() %>%
      dplyr::select(x = X, y = Y)

    # kernel
    message("computing kernel...")
    kernel <- df %>%
      cbind(., st_coordinates(.)) %>%
      st_set_geometry(NULL) %>%
      dplyr::select(x = X, y = Y, field) %>%
      btb::kernelSmoothing(
        dfObservations = .,
        sEPSG = out_crs,
        iCellSize = resolution,
        iBandwidth = bandwidth,
        vQuantiles = NULL,
        dfCentroids = zone_xy
      )

    # rasterization
    message("\nrasterizing...")
    raster::raster(
      xmn = plyr::round_any(zone_bbox[1] - bandwidth, resolution, f = floor),
      ymn = plyr::round_any(zone_bbox[2] - bandwidth, resolution, f = floor),
      xmx = plyr::round_any(zone_bbox[3] + bandwidth, resolution, f = ceiling),
      ymx = plyr::round_any(zone_bbox[4] + bandwidth, resolution, f = ceiling),
      resolution = resolution
    ) %>%
      fasterize::fasterize(kernel, ., field = field)
  }


# téléchargement--------------------------------------------------------------
if (!file_exists(fichier_covid) |
    file_info(fichier_covid)$modification_time < Sys.Date() |
    force_download) {
  GET(url_donnees_covid,
      progress(),
      write_disk(fichier_covid, overwrite = TRUE))
}

if (!file_exists(fichier_pop)) {
  GET(url_donnees_pop,
      progress(),
      write_disk(fichier_pop))
}


# données -----------------------------------------------------------------

covid <- read_csv2(fichier_covid)

# adminexpress prétéléchargé
dep <- read_sf(path(aex, "ADE_2-2_SHP_LAMB93_FR/DEPARTEMENT.shp")) %>%
  clean_names() %>%
  st_set_crs(2154)

dep_971 <- read_sf(path(aex, "ADE_2-2_SHP_RGAF09UTM20_D971/DEPARTEMENT.shp")) %>%
  clean_names() %>%
  st_set_crs(5490)

dep_972 <- read_sf(path(aex, "ADE_2-2_SHP_RGAF09UTM20_D972/DEPARTEMENT.shp")) %>%
  clean_names() %>%
  st_set_crs(5490)

dep_973 <- read_sf(path(aex, "ADE_2-2_SHP_UTM22RGFG95_D973/DEPARTEMENT.shp")) %>%
  clean_names() %>%
  st_set_crs(2972)

dep_974 <- read_sf(path(aex, "ADE_2-2_SHP_RGR92UTM40S_D974/DEPARTEMENT.shp")) %>%
  clean_names() %>%
  st_set_crs(2975)

dep_976 <- read_sf(path(aex, "ADE_2-2_SHP_RGM04UTM38S_D976/DEPARTEMENT.shp")) %>%
  clean_names() %>%
  st_set_crs(4471)

pop <- read_xls(fichier_pop, skip = 2) %>%
  clean_names()


# prétraitement -----------------------------------------------------------

# contour métropole
fr <- dep %>%
  st_union() %>%
  st_sf()

# jointures des données
creer_df <- function(territoire) {
  territoire %>%
    left_join(pop, by = c("insee_dep" = "x1")) %>%
    left_join(
      covid %>%
        filter(jour == max(jour),
               sexe == 0) %>%
        group_by(dep) %>%
        summarise(deces = sum(dc, na.rm = TRUE),
                  reanim = sum(rea, na.rm = TRUE),
                  hospit = sum(hosp, na.rm = TRUE)),
      by = c("insee_dep" = "dep")) %>%
    st_point_on_surface()
}

covid_geo_pop     <- creer_df(dep)
covid_geo_pop_971 <- creer_df(dep_971)
covid_geo_pop_972 <- creer_df(dep_972)
covid_geo_pop_973 <- creer_df(dep_973)
covid_geo_pop_974 <- creer_df(dep_974)
covid_geo_pop_976 <- creer_df(dep_976)

# lissage -----------------------------------------------------------------
# génération des grilles mortalité, hospitalisation et réanimation et population
# et création des grilles pour 100000 habitants

# décès métropole et DOM
d <- covid_geo_pop %>%
  lissage("deces", rayon, pixel, fr)

d_971 <- covid_geo_pop_971 %>%
  lissage("deces", rayon, pixel, dep_971, 5490)

d_972 <- covid_geo_pop_972 %>%
  lissage("deces", rayon, pixel, dep_972, 5490)

d_973 <- covid_geo_pop_973 %>%
  lissage("deces", rayon, pixel, dep_973, 2972)

d_974 <- covid_geo_pop_974 %>%
  lissage("deces", rayon, pixel, dep_974, 2975)

d_976 <- covid_geo_pop_976 %>%
  lissage("deces", rayon, pixel, dep_976, 4471)

# population métropole et DOM
p <- covid_geo_pop %>%
  lissage("x2020_p", rayon, pixel, fr)

p_971 <- covid_geo_pop_971 %>%
  lissage("x2020_p", rayon, pixel, dep_971, 5490)

p_972 <- covid_geo_pop_972 %>%
  lissage("x2020_p", rayon, pixel, dep_972, 5490)

p_973 <- covid_geo_pop_973 %>%
  lissage("x2020_p", rayon, pixel, dep_973, 2972)

p_974 <- covid_geo_pop_974 %>%
  lissage("x2020_p", rayon, pixel, dep_974, 2975)

p_976 <- covid_geo_pop_976 %>%
  lissage("x2020_p", rayon, pixel, dep_976, 4471)

# grilles pour 100000 hab
d100k <- d * 100000 / p
d100k_971 <- d_971 * 100000 / p_971
d100k_972 <- d_972 * 100000 / p_972
d100k_973 <- d_973 * 100000 / p_973
d100k_974 <- d_974 * 100000 / p_974
d100k_976 <- d_976 * 100000 / p_976

# réanimation et hospitalisation métropole uniquement
r <- covid_geo_pop %>%
  lissage("reanim", rayon, pixel, fr)
r100k <- r * 100000 / p

h <- covid_geo_pop %>%
  lissage("hospit", rayon, pixel, fr)
h100k <- h * 100000 / p


# carto -------------------------------------------------------------------

# décès métropole et DOM

# classification à réutiliser pour les 6 cartes
set.seed(1234)
classes <- classIntervals(raster::values(d100k), n = 5, style = "kmeans", dataPrecision = 0)$brks

# métro et DOM
(carte_d <- tm_layout(title = paste("COVID-19 - France - cumul au", max(covid$jour)),
                     legend.position = c("left", "bottom"),
                     frame = FALSE) +
  tm_shape(d100k) +
  tm_raster(title = glue("décès à l'hôpital
                         pour 100 000 hab."),
            style = "fixed",
            breaks = classes,
            palette = "viridis",
            legend.format = list(text.separator = "à moins de",
                                 digits = 0),
            legend.reverse = TRUE) +
  tm_shape(dep) +
  tm_borders() +
  tm_credits(glue("http://r.iresmi.net/
                  lissage noyau bisquare {rayon / 1000} km sur grille {pixel / 1000} km
                  classif. kmeans
                  projections LAEA Europe (métropole) et locales (DOM)
                  données départementales Santé publique France,
                  INSEE RP 2020, IGN Adminexpress 2020"),
             size = .5,
             position = c(.5, .025))
)

tm_971 <- tm_shape(d100k_971, ext = 0.7) +
  tm_raster(style = "fixed",
            breaks = classes,
            palette = "viridis",
            legend.show = FALSE) +
  tm_shape(dep_971) +
  tm_borders() +
  tm_layout(frame = FALSE,
            bg.color = NA)

tm_972 <- tm_shape(d100k_972, ext = 0.7) +
  tm_raster(style = "fixed",
            breaks = classes,
            palette = "viridis",
            legend.show = FALSE) +
  tm_shape(dep_972) +
  tm_borders() +
  tm_layout(frame = FALSE,
            bg.color = NA)

tm_973 <- tm_shape(d100k_973) +
  tm_raster(style = "fixed",
            breaks = classes,
            palette = "viridis",
            legend.show = FALSE) +
  tm_shape(dep_973) +
  tm_borders() +
  tm_layout(frame = FALSE,
            bg.color = NA)

tm_974 <- tm_shape(d100k_974, ext = 0.75) +
  tm_raster(style = "fixed",
            breaks = classes,
            palette = "viridis",
            legend.show = FALSE) +
  tm_shape(dep_974) +
  tm_borders()+
  tm_layout(frame = FALSE,
            bg.color = NA)

tm_976 <- tm_shape(d100k_976, ext = 0.6) +
  tm_raster(style = "fixed",
            breaks = classes,
            palette = "viridis",
            legend.show = FALSE) +
  tm_shape(dep_976) +
  tm_borders()+
  tm_layout(frame = FALSE,
            bg.color = NA)

# assemblage
fichier_carto <- glue("resultats/covid_fr_{max(covid$jour)}.png")

tmap_save(carte_d, fichier_carto, width = 900, height = 900, scale = .4,
          insets_tm = list(tm_971, tm_972, tm_973, tm_974, tm_976),
          insets_vp = list(viewport(x = .1, y = .65, width = .15, height = .15),
                           viewport(x = .1, y = .58, width = .15, height = .15),
                           viewport(x = .15, y = .4, width = .35, height = .45),
                           viewport(x = .9, y = .4, width = .15, height = .15),
                           viewport(x = .9, y = .5, width = .15, height = .15)))