Commit 11329361 authored by Dumoulin Nicolas's avatar Dumoulin Nicolas
Browse files

target surface of each culture given by the PAT are now stored in file

parent 0d9692c0
cultgeopat;2016;2050
Céréales;39.1578162680355;42.4510757826923
Prairies;83.9947787550856;76.3917619047619
Oléagineux;4.02651841988345;4.36515758215069
Fourrages;26.7457940864208;15.4649021294656
Fruits et légumes;0.35475779373955496;3.97664751692735
Cultures industrielles;2.31418094174966;2.52551689693261
Protéagineux;0.704985252201107;8.59885297148308
......@@ -5,7 +5,8 @@ class Proximite:
'''
Function calculating the proximity indice.
The purpose of this indice is to reflect the proximity between productions site and consommation site.
We are calculating it with a comparison of the area of patches cultivating fruits and vegetables and the population of living area (bassin de vie) in the PAT's territory
We are calculating it with a comparison of the area of patches cultivating fruits and vegetables
and the population of living area (bassin de vie) in the PAT's territory
A living area with a low population will require a lower cultivating area to get a good value.
'''
......@@ -31,6 +32,11 @@ class Proximite:
if __name__ == '__main__':
import geopandas as gpd
import pandas as pd
patches = gpd.GeoDataFrame.from_file("../output/PAT_patches/PAT_patches.shp", encoding='utf-8')
prox = Proximite(patches, 20165939.605135553)
patches = patches[patches['cultgeopat']!='Non Considérée']
target = pd.read_csv('../resources/targetPAT.csv', sep=';',index_col=0)
targetRatio = (target['2050']-target['2016'])/target['2016']
targetPAT = patches.groupby('cultgeopat')['SURF_PARC'].sum()*(1+targetRatio)
prox = Proximite(patches, targetPAT['Fruits et légumes'])
print(prox.compute_indicator(patches, True))
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