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Generation EPT 2023 "one-shot"

Compiler un EPT (établissements publics territoriaux)/des EPT

Les établissements publics territoriaux (EPT) sont parmis les "EPCI sans fiscalité propre".

Source des données

Fichiers résultats

"One-shot" pour un EPT en bash, avec mapshaper, jq et wget installé

list_insee=$(echo "94017
94015
94018
94033
94042
94058
94046
94052
94067
94068
94069
94079
94080
")

for i in $list_insee;
  do wget -O commune-${i}.geojson "https://geo.api.gouv.fr/communes/$i?format=geojson&geometry=contour";
done;

jq --slurp '{"type": "FeatureCollection","features": .}' commune-*.geojson >| communes-all.geojson

mapshaper communes-all.geojson -dissolve -o format=geojson geojson-type=FeatureCollection ept-200057941.geojson

Version généralisée à tous les EPT

Utilise Python et les bibliothèques associées Pandas et Geopandas

import urllib
import geopandas as gpd
import pandas as pd

insee_remote_url = "https://www.insee.fr/fr/statistiques/fichier/2510634/ept_au_01-01-2023.xlsx"
insee_filename = insee_remote_url.split('/')[-1]
urllib.request.urlretrieve(insee_remote_url, insee_filename)

from openpyxl.styles.colors import WHITE, RGB
__old_rgb_set__ = RGB.__set__

def __rgb_set_fixed__(self, instance, value):
    try:
        __old_rgb_set__(self, instance, value)
    except ValueError as e:
        if e.args[0] == 'Colors must be aRGB hex values':
            __old_rgb_set__(self, instance, WHITE)  # Change color here

RGB.__set__ = __rgb_set_fixed__

xl = pd.ExcelFile(insee_filename)
xl.sheet_names  # see all sheet names
df_composition_communale = xl.parse("Composition_communale", header=5)
df_composition_communale_short = df_composition_communale[['CODGEO', 'EPT', 'LIBEPT']]
df_composition_communale_short['CODGEO']= df_composition_communale_short['CODGEO'].astype(str)

gdf_communes = gpd.read_file("http://etalab-datasets.geo.data.gouv.fr/contours-administratifs/2023/geojson/communes-5m.geojson")

df_merged = communes.merge(df_composition_communale_short, left_on="code", right_on="CODGEO")
df_merged_short = df_merged[['geometry', 'CODGEO', 'EPT', 'LIBEPT']]
ept_zones = df_merged_short.dissolve(by=['EPT', 'LIBEPT'])
ept_zones.to_file("ept_2023.geojson", driver="GeoJSON")
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