Config

library(readr)
library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
library(glue)
library(janitor)
library(sf)
library(fs)
library(cartogram)
library(ggrepel)

Data

Get the data from the Declarations of sales of plant protection products.

Select:

Achats
2022
Code postal de l’acheteur
Substance
France entière

and wait to get the download link by mail.

# get french départements from 
# https://static.data.gouv.fr/resources/admin-express-cog-simplifiee-2024-metropole-drom-saint-martin-saint-barthelemy/20240930-094021/adminexpress-cog-simpl-000-2024.gpkg
dep <- read_sf("~/data/adminexpress/adminexpress-cog-simpl-000-2024.gpkg",
               layer = "departement") |> 
  filter(insee_reg > "06") |> 
  st_transform("EPSG:2154")

unzip("BNVD_TRACABILITE_20241124_102856_ACHAT_.zip", exdir = "bnvd")
bnvd <- dir_ls("bnvd",
               regexp = "ACHAT_CP_SUBSTANCE_(?!INDETERMINEE).*\\.csv$", 
               perl = TRUE) |> 
  read_delim(delim = ";",
             na = c("-", "nc", ""), 
             col_types = cols(code_sandre_substance = col_character())) |> 
  filter(code_postal_acheteur < "97000") 
dir_delete("bnvd")

The {cartogram} package is used to generate the circles.

# Adding pesticides info to the polygons
bnvd_dep <- dep |> 
  left_join(bnvd |> 
              group_by(code_departement_acheteur) |> 
              summarise(substance_kg = sum(quantite_substance, na.rm = TRUE)),
            join_by(insee_dep == code_departement_acheteur)) |> 
  mutate(w = 1 - (substance_kg / max(substance_kg))) 

bnvd_dorling_dep <- bnvd_dep |> 
  cartogram_dorling(weight = "substance_kg",
                    m_weight = bnvd_dep$w)

Map

bnvd_dorling_dep |> 
  ggplot() +
  geom_sf(fill = "palevioletred2",
          color = "maroon4", 
          linewidth = 1) +
  geom_text_repel(aes(label = insee_dep, 
                      geometry = geom),
                  stat = "sf_coordinates", 
                  force = 0.001,
                  size = 3, 
                  color = "red4", 
                  bg.r = 0.1,
                  bg.color = "pink") +
  labs(title = "Pesticides bought in France",
       subtitle = "départements - 2022",
       caption = glue("https://r.iresmi.net/ - {Sys.Date()}
                      data: Substance weight - BNV-D (OFB)")) +
  theme_void() +
  theme(plot.caption = element_text(size = 6, color = "darkgrey"),
        text = element_text(color = "#ddd"),
        panel.background = element_blank(),
        plot.background = element_rect(fill = "grey10", color = NA),
        plot.margin = unit(c(5, 5, 5, 5), "mm"))

Map of pesticides quantities in France in 2022 — Figure 1: Pesticides by *département* in France – 2022

In this figure, we map more than just the pesticides bought: the area and the agriculture type in the département are the main drivers of the circles size. Vineyards, orchards and field crops (cereals, beets,…) are the most intensive users of pesticides.

References

Dorling, Daniel. 1996. “Area Cartograms: Their Use and Creation.” Concepts and Techniques in Modern Geography, no. 59. https://ora.ox.ac.uk/objects/uuid:5e9483f5-3e1c-4038-b10f-d09c5767c285.

--- title: "Pesticides" description: "Day 24 of 30DayMapChallenge. Circles" author: "Michaël" date: "2024-11-24T10:00:00" date-modified: last-modified categories: - R - spatial - datavisualization - 30DayMapChallenge draft: false freeze: true editor_options: chunk_output_type: console chunk_output_type: console execute: eval: true --- ```{r} #| label: preview #| echo: false #| fig-cap: "Tractor fertilizes field pesticide and insecticide -- CC-BY by Aqua Mechanical" #| fig-alt: "A photo of Holi festivities in India" #| fig-link: https://www.flickr.com/photos/aquamech-utah/24443679794/ #| class-output: "preview-image" #| out-width: "100%" knitr::include_graphics("images/24443679794_b3c15e77bd_c.jpg") ``` Day 24 of [30DayMapChallenge](https://30daymapchallenge.com/): « Only circular shapes » ([previously](/#category=30DayMapChallenge)). We'll map the pesticides bought in France as a [Dorling cartogram](https://en.wikipedia.org/wiki/Cartogram#Diagrammatic_(Dorling)_cartograms) [@DorlingAreacartogramstheir1996]. # Config ```{r} #| label: setup library(readr) library(dplyr) library(tidyr) library(stringr) library(ggplot2) library(glue) library(janitor) library(sf) library(fs) library(cartogram) library(ggrepel) ``` # Data Get the data from the [Declarations of sales of plant protection products](https://ventes-produits-phytopharmaceutiques.eaufrance.fr/search). Select: - Achats - 2022 - Code postal de l'acheteur - Substance - France entière and wait to get the download link by mail. ```{r} #| label: data # get french départements from # https://static.data.gouv.fr/resources/admin-express-cog-simplifiee-2024-metropole-drom-saint-martin-saint-barthelemy/20240930-094021/adminexpress-cog-simpl-000-2024.gpkg dep <- read_sf("~/data/adminexpress/adminexpress-cog-simpl-000-2024.gpkg", layer = "departement") |> filter(insee_reg > "06") |> st_transform("EPSG:2154") unzip("BNVD_TRACABILITE_20241124_102856_ACHAT_.zip", exdir = "bnvd") bnvd <- dir_ls("bnvd", regexp = "ACHAT_CP_SUBSTANCE_(?!INDETERMINEE).*\\.csv$", perl = TRUE) |> read_delim(delim = ";", na = c("-", "nc", ""), col_types = cols(code_sandre_substance = col_character())) |> filter(code_postal_acheteur < "97000") dir_delete("bnvd") ``` The [{cartogram}](https://github.com/sjewo/cartogram) package is used to generate the circles. ```{r} #| label: processing # Adding pesticides info to the polygons bnvd_dep <- dep |> left_join(bnvd |> group_by(code_departement_acheteur) |> summarise(substance_kg = sum(quantite_substance, na.rm = TRUE)), join_by(insee_dep == code_departement_acheteur)) |> mutate(w = 1 - (substance_kg / max(substance_kg))) bnvd_dorling_dep <- bnvd_dep |> cartogram_dorling(weight = "substance_kg", m_weight = bnvd_dep$w) ``` # Map ```{r} #| label: fig-map #| fig-cap: "Pesticides by *département* in France -- 2022" #| fig-alt: "Map of pesticides quantities in France in 2022" #| dev: "ragg_png" #| out-width: "100%" #| fig-height: 8 #| dev.args: #| bg: "transparent" bnvd_dorling_dep |> ggplot() + geom_sf(fill = "palevioletred2", color = "maroon4", linewidth = 1) + geom_text_repel(aes(label = insee_dep, geometry = geom), stat = "sf_coordinates", force = 0.001, size = 3, color = "red4", bg.r = 0.1, bg.color = "pink") + labs(title = "Pesticides bought in France", subtitle = "départements - 2022", caption = glue("https://r.iresmi.net/ - {Sys.Date()} data: Substance weight - BNV-D (OFB)")) + theme_void() + theme(plot.caption = element_text(size = 6, color = "darkgrey"), text = element_text(color = "#ddd"), panel.background = element_blank(), plot.background = element_rect(fill = "grey10", color = NA), plot.margin = unit(c(5, 5, 5, 5), "mm")) ``` In this figure, we map more than just the pesticides bought: the area and the agriculture type in the *département* are the main drivers of the circles size. Vineyards, orchards and field crops (cereals, beets,...) are the most intensive users of pesticides.