missing data for 2 municipalities

[83221n4ndr34]

I noticed that compared to the istat dataset (https://www.istat.it/it/archivio/222527) the data is missing for 2 municipalities.
From wikipedia: one municipality changed code on 15/05/2023 (https://it.wikipedia.org/wiki/Moransengo-Tonengo) while the other was established on 01/01/2023 mergering 3 municipalities (https://it.wikipedia.org/wiki/Bardello_con_Malgesso_e_Bregano).

Here is a quick R code for verification. (The thing is easily noticed also graphically because using municipalities maps have holes)

#### installation of the required packages if they are not already present and loading

libraries <- c("dplyr", "sf")
for(library in libraries){
  if(!library %in% installed.packages()){
    install.packages(library)
  }
  library(library, character.only = TRUE)
}


### download of the 2 data sets

## download from github

# https://github.com/openpolis/geojson-italy
# GitHub repository url
url_regions <- "https://github.com/openpolis/geojson-italy/raw/master/geojson/limits_IT_regions.geojson"
url_provinces <- "https://github.com/openpolis/geojson-italy/raw/master/geojson/limits_IT_provinces.geojson"
url_municipalities <- "https://github.com/openpolis/geojson-italy/raw/master/geojson/limits_IT_municipalities.geojson"
# reading GeoJSON files
regions_github <- st_read(url_regions)
provinces_github <- st_read(url_provinces)
municipalities_github <- st_read(url_municipalities)

## download from istat website

# https://www.istat.it/it/archivio/222527 -> description: https://www.istat.it/it/files//2018/10/Descrizione-dei-dati-geografici-2020-03-19.pdf
# download file "Administrative boundaries 2023 (zip)" from the link
# setting the correct working directory
setwd("...")
# import of the extracted files based on the administrative category
# with transformation of data from CRS WGS 84 / UTM zone 32N to CRS WGS84 (EPSG:4326)
municipalities <- st_transform(st_read("Limiti01012023/Com01012023/Com01012023_WGS84.shp"), crs_wgs84)
provinces <- st_transform(st_read("Limiti01012023/ProvCM01012023/ProvCM01012023_WGS84.shp"), crs_wgs84)
regions <- st_transform(st_read("Limiti01012023/Reg01012023/Reg01012023_WGS84.shp"), crs_wgs84)


### verification that 2 municipalities are missing

## conversion of spatial data into a normal dataframe
municipalities_df <- as.data.frame(municipalities)
municipalities_github_df <- as.data.frame(municipalities_github)
provinces_df <- as.data.frame(provinces)
provinces_github_df <- as.data.frame(provinces_github)
regions_df <- as.data.frame(regions)
regions_github_df <- as.data.frame(regions_github)

## verification using istat codes

# change of column names to make the comparison easier
municipalities_github_df <- rename(municipalities_github_df, PRO_COM_T = com_istat_code)
provinces_github_df <- rename(provinces_github_df, COD_PROV = prov_istat_code_num)
regions_github_df <- rename(regions_github_df, COD_REG  = reg_istat_code_num)

# anti_join to select the elements of the left df that are not present in the right df
differences_municipalities <- anti_join(municipalities_df, municipalities_github_df, by = "PRO_COM_T")
differences_provinces <- anti_join(provinces_df, provinces_github_df, by = "COD_PROV")
differences_regions <- anti_join(regions_df, regions_github_df, by = "COD_REG")

# print the number of differences found
print(paste("Number of missing municipalities:", nrow(differences_municipalities))) # -> : 2
print(paste("Number of missing provinces:", nrow(differences_provinces))) # -> : 0
print(paste("Number of missing regions:", nrow(differences_regions))) # -> : 0

# print the names of the missing municipalities
print(differences_municipalities$COMUNE)
# -> 
# [1] "Bardello with Malgesso and Bregano"
# [2] "Moransengo-Tonengo"

[sghignone]

oh...I was indeed investigating the hole in my Piedmont map!