Analisi della copertura del suolo in Italia dal 1990 al 2018 usando la CORINE Land Cover

Vediamo come è cambiata la copertura del suolo in Italia dal 1990 al 2018 usando i dati della CORINE Land Cover.
vector
geoprocessing
Data di Pubblicazione

26 agosto 2022

Qualche anno fa analizzai i dati del progetto CORINE Land Cover relativi alla Campania, focalizzandomi sui tre censimenti tra il 2000 ed il 2012. Oggi invece estenderò quell’analisi inserendo sia i dati del 1990 che gli ultimi disponibili attualmente, cioè quelli del 2018.

L’analisi questa volta la farò usando Python e la estenderò al resto dell’Italia verso la fine.

Prima di iniziare

Librerie

Per raggiungere l’obiettivo userò le librerie che seguono:

Codice
import pathlib
import geopandas as gpd
import pandas as pd
from functools import reduce
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

import warnings
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')

Fonti dati

Ho rielaborato i dati CORINE Land Cover dal 1990 al 2018 inserendoli in un unico GeoPackage che è possibile scaricare da qui. I dati sono stati processati preventivamente usando PostGIS in modo da associare ai poligoni della CLC anche quelli dei Comuni italiani al 2022 dell’ISTAT. E’ stato indispensabile questo passaggio preventivo perchè ho dovuto correggere gli errori topologici sui dati ISTAT e, al momento, trovo molto più comodo correggere gli errori topologici tramite SQL piuttosto che con Python.

Codice
main_folder = pathlib.Path('/home/max/Desktop/clc')
dataset = main_folder.joinpath('clcitalia.gpkg')

1. Lettura e normalizzazione dei dati

In questa fase ho proceduto ad estrarre un po’ di dati statistici che userò nei passaggi successivi. Sono perlopiu analisi mirate al calcolo delle superfici che ho espresso in ettari. Fatte queste analisi mi sono liberato dei dati geometrici perchè, non dovendoli più usare, mi alleggeriscono le analisi successive in termini di prestazioni hardware.

Codice

# CLC 1990
clc1990 = gpd.read_file(dataset, layer='clc1990')
clc1990.insert(loc=10, column='sup_com_ha', value=clc1990.sup_com_mq / 10000)
clc1990.insert(loc=11, column='surface_ha_1990', value=round(clc1990.geometry.area / 10000, 4))
clc1990.drop(columns={'sup_com_mq', 'geometry'}, inplace=True)

# CLC 2000
clc2000 = gpd.read_file(dataset, layer='clc2000')
clc2000.insert(loc=10, column='sup_com_ha', value=clc2000.sup_com_mq / 10000)
clc2000.insert(loc=11, column='surface_ha_2000', value=round(clc2000.geometry.area / 10000, 4))
clc2000.drop(columns={'sup_com_mq', 'geometry'}, inplace=True)

# CLC 2006
clc2006 = gpd.read_file(dataset, layer='clc2006')
clc2006.insert(loc=10, column='sup_com_ha', value=clc2006.sup_com_mq / 10000)
clc2006.insert(loc=11, column='surface_ha_2006', value=round(clc2006.geometry.area / 10000, 4))
clc2006.drop(columns={'sup_com_mq', 'geometry'}, inplace=True)

# CLC 2012
clc2012 = gpd.read_file(dataset, layer='clc2012')
clc2012.insert(loc=10, column='sup_com_ha', value=clc2012.sup_com_mq / 10000)
clc2012.insert(loc=11, column='surface_ha_2012', value=round(clc2012.geometry.area / 10000, 4))
clc2012.drop(columns={'sup_com_mq', 'geometry'}, inplace=True)

# CLC 2018
clc2018 = gpd.read_file(dataset, layer='clc2018')
clc2018.insert(loc=10, column='sup_com_ha', value=clc2018.sup_com_mq / 10000)
clc2018.insert(loc=11, column='surface_ha_2018', value=round(clc2018.geometry.area / 10000, 4))
clc2018.drop(columns={'sup_com_mq', 'geometry'}, inplace=True)

Test di visualizzazione

Codice
clc1990
code_90 label1 label2 label3 pro_com comune provincia regione sup_com_ha surface_ha_1990
0 211 Agricultural areas Arable land Non-irrigated arable land 101005.0 Castelsilano Crotone Calabria 4006.296249 1.1386
1 211 Agricultural areas Arable land Non-irrigated arable land 101005.0 Castelsilano Crotone Calabria 4006.296249 276.7171
2 223 Agricultural areas Permanent crops Olive groves 101005.0 Castelsilano Crotone Calabria 4006.296249 35.4001
3 223 Agricultural areas Permanent crops Olive groves 101005.0 Castelsilano Crotone Calabria 4006.296249 2.2285
4 241 Agricultural areas Heterogeneous agricultural areas Annual crops associated with permanent crops 101005.0 Castelsilano Crotone Calabria 4006.296249 1.3860
... ... ... ... ... ... ... ... ... ... ...
190238 321 Forest and semi natural areas Scrub and/or herbaceous vegetation associations Natural grasslands 81006.0 Castelvetrano Trapani Sicilia 20975.692026 34.8218
190239 323 Forest and semi natural areas Scrub and/or herbaceous vegetation associations Sclerophyllous vegetation 81006.0 Castelvetrano Trapani Sicilia 20975.692026 98.1617
190240 323 Forest and semi natural areas Scrub and/or herbaceous vegetation associations Sclerophyllous vegetation 81006.0 Castelvetrano Trapani Sicilia 20975.692026 264.2617
190241 323 Forest and semi natural areas Scrub and/or herbaceous vegetation associations Sclerophyllous vegetation 81006.0 Castelvetrano Trapani Sicilia 20975.692026 28.9076
190242 512 Water bodies Inland waters Water bodies 81006.0 Castelvetrano Trapani Sicilia 20975.692026 152.0754

190243 rows × 10 columns

Le colonne label* fanno riferimento alla nomenclatura delle tre classi della mappa CORINE Land Cover per ogni singolo anno. Per interpretare la nomenclatura vai qui. Come puoi vedere sono presenti le colonne relative al Comune, Provincia e Regione di apparteneza, l’estensione dell’area comunale(sup_com_ha) e quelle relative alle superfici della CLC che mi sono calcolato in precedenza.

2. Focus sulla Campania

Per appartenza territoriale partirò proprio dalla Campania, lo faccio anche per riprendere parte della analisi del mio vecchio articolo.

Codice
clc1990_campania = clc1990[clc1990['regione'] == 'Campania']
clc2000_campania = clc2000[clc2000['regione'] == 'Campania']
clc2006_campania = clc2006[clc2006['regione'] == 'Campania']
clc2012_campania = clc2012[clc2012['regione'] == 'Campania']
clc2018_campania = clc2018[clc2018['regione'] == 'Campania']

Test di visualizzazione

Codice
clc2000_campania
code_00 label1 label2 label3 pro_com comune provincia regione sup_com_ha surface_ha_2000
14 111 Artificial surfaces Urban fabric Continuous urban fabric 65134.0 Sapri Salerno Campania 1420.007490 82.5207
15 112 Artificial surfaces Urban fabric Discontinuous urban fabric 65134.0 Sapri Salerno Campania 1420.007490 36.5516
16 241 Agricultural areas Heterogeneous agricultural areas Annual crops associated with permanent crops 65134.0 Sapri Salerno Campania 1420.007490 17.7890
17 242 Agricultural areas Heterogeneous agricultural areas Complex cultivation patterns 65134.0 Sapri Salerno Campania 1420.007490 0.6866
18 242 Agricultural areas Heterogeneous agricultural areas Complex cultivation patterns 65134.0 Sapri Salerno Campania 1420.007490 56.9832
... ... ... ... ... ... ... ... ... ... ...
177542 323 Forest and semi natural areas Scrub and/or herbaceous vegetation associations Sclerophyllous vegetation 61072.0 Rocchetta e Croce Caserta Campania 1300.547184 206.0290
177543 324 Forest and semi natural areas Scrub and/or herbaceous vegetation associations Transitional woodland-shrub 61072.0 Rocchetta e Croce Caserta Campania 1300.547184 2.3093
177544 221 Agricultural areas Permanent crops Vineyards 61072.0 Rocchetta e Croce Caserta Campania 1300.547184 4.0201
177545 321 Forest and semi natural areas Scrub and/or herbaceous vegetation associations Natural grasslands 61072.0 Rocchetta e Croce Caserta Campania 1300.547184 1.8517
177546 222 Agricultural areas Permanent crops Fruit trees and berry plantations 61072.0 Rocchetta e Croce Caserta Campania 1300.547184 1.9998

11361 rows × 10 columns

2.1.1 Stato al 1990

Come primo step mi sono concentrato sui dati del 1990; ho pulito un po’ la tabella per rendere meglio comprensibile la sua lettura ed ho estratto valori per tutte e tre le classi. Quello che ho fatto per il 1990 l’ho ripetuto per i restanti anni.

Livello 1

Codice
campania_clc1990_label1 = pd.DataFrame(clc1990_campania.groupby('label1')['surface_ha_1990'].sum())
campania_clc1990_label1.reset_index(inplace=True)
campania_clc1990_label1.rename(columns={'label1': 'CORINE Land Cover - level 1'}, inplace=True)
campania_clc1990_label1
CORINE Land Cover - level 1 surface_ha_1990
0 Agricultural areas 761041.0303
1 Artificial surfaces 80649.9550
2 Forest and semi natural areas 522870.7142
3 Water bodies 1593.6569
4 Wetlands 608.8997

Livello 2

Codice
campania_clc1990_label2 = pd.DataFrame(clc1990_campania.groupby('label2')['surface_ha_1990'].sum())
campania_clc1990_label2.reset_index(inplace=True)
campania_clc1990_label2.rename(columns={'label2': 'CORINE Land Cover - level 2'}, inplace=True)
campania_clc1990_label2
CORINE Land Cover - level 2 surface_ha_1990
0 Arable land 330066.4318
1 Artificial, non-agricultural vegetated areas 1046.0670
2 Forests 383826.2982
3 Heterogeneous agricultural areas 297503.9919
4 Industrial, commercial and transport units 8083.1341
5 Inland waters 1567.8002
6 Inland wetlands 608.8997
7 Marine waters 25.8567
8 Mine, dump and construction sites 1631.6247
9 Open spaces with little or no vegetation 8966.3806
10 Pastures 15620.1214
11 Permanent crops 117850.4852
12 Scrub and/or herbaceous vegetation associations 130078.0354
13 Urban fabric 69889.1292

Livello 3

Codice
campania_clc1990_label3 = pd.DataFrame(clc1990_campania.groupby('label3')['surface_ha_1990'].sum())
campania_clc1990_label3.reset_index(inplace=True)
campania_clc1990_label3.rename(columns={'label3': 'CORINE Land Cover - level 3'}, inplace=True)
campania_clc1990_label3
CORINE Land Cover - level 3 surface_ha_1990
0 Agro-forestry areas 420.5049
1 Airports 383.8998
2 Annual crops associated with permanent crops 42881.8119
3 Bare rocks 1122.7257
4 Beaches, dunes, sands 2282.0112
5 Broad-leaved forest 367521.2801
6 Coastal lagoons 25.8567
7 Complex cultivation patterns 165047.1097
8 Coniferous forest 7457.8770
9 Construction sites 25.4060
10 Continuous urban fabric 32608.0637
11 Discontinuous urban fabric 37281.0655
12 Dump sites 207.8811
13 Fruit trees and berry plantations 56044.2433
14 Green urban areas 731.9919
15 Industrial or commercial units 6590.0765
16 Inland marshes 608.8997
17 Land principally occupied by agriculture, with... 89154.5654
18 Mineral extraction sites 1398.3376
19 Mixed forest 8847.1411
20 Moors and heathland 16.7825
21 Natural grasslands 57150.7848
22 Non-irrigated arable land 304750.4005
23 Olive groves 58951.7577
24 Pastures 15620.1214
25 Permanently irrigated land 25316.0313
26 Port areas 458.6026
27 Road and rail networks and associated land 650.5552
28 Sclerophyllous vegetation 19255.7695
29 Sparsely vegetated areas 5561.6437
30 Sport and leisure facilities 314.0751
31 Transitional woodland-shrub 53654.6986
32 Vineyards 2854.4842
33 Water bodies 1095.5920
34 Water courses 472.2082

2.1.2 Stato al 2000

Codice
campania_clc2000_label1 = pd.DataFrame(clc2000_campania.groupby('label1')['surface_ha_2000'].sum())
campania_clc2000_label1.reset_index(inplace=True)
campania_clc2000_label1.rename(columns={'label1': 'CORINE Land Cover - level 1'}, inplace=True)

campania_clc2000_label2 = pd.DataFrame(clc2000_campania.groupby('label2')['surface_ha_2000'].sum())
campania_clc2000_label2.reset_index(inplace=True)
campania_clc2000_label2.rename(columns={'label2': 'CORINE Land Cover - level 2'}, inplace=True)

campania_clc2000_label3 = pd.DataFrame(clc2000_campania.groupby('label3')['surface_ha_2000'].sum())
campania_clc2000_label3.reset_index(inplace=True)
campania_clc2000_label3.rename(columns={'label3': 'CORINE Land Cover - level 3'}, inplace=True)

2.1.3 Stato al 2006

Codice
campania_clc2006_label1 = pd.DataFrame(clc2006_campania.groupby('label1')['surface_ha_2006'].sum())
campania_clc2006_label1.reset_index(inplace=True)
campania_clc2006_label1.rename(columns={'label1': 'CORINE Land Cover - level 1'}, inplace=True)

campania_clc2006_label2 = pd.DataFrame(clc2006_campania.groupby('label2')['surface_ha_2006'].sum())
campania_clc2006_label2.reset_index(inplace=True)
campania_clc2006_label2.rename(columns={'label2': 'CORINE Land Cover - level 2'}, inplace=True)

campania_clc2006_label3 = pd.DataFrame(clc2006_campania.groupby('label3')['surface_ha_2006'].sum())
campania_clc2006_label3.reset_index(inplace=True)
campania_clc2006_label3.rename(columns={'label3': 'CORINE Land Cover - level 3'}, inplace=True)

2.1.4 Stato al 2012

Codice
campania_clc2012_label1 = pd.DataFrame(clc2012_campania.groupby('label1')['surface_ha_2012'].sum())
campania_clc2012_label1.reset_index(inplace=True)
campania_clc2012_label1.rename(columns={'label1': 'CORINE Land Cover - level 1'}, inplace=True)

campania_clc2012_label2 = pd.DataFrame(clc2012_campania.groupby('label2')['surface_ha_2012'].sum())
campania_clc2012_label2.reset_index(inplace=True)
campania_clc2012_label2.rename(columns={'label2': 'CORINE Land Cover - level 2'}, inplace=True)

campania_clc2012_label3 = pd.DataFrame(clc2012_campania.groupby('label3')['surface_ha_2012'].sum())
campania_clc2012_label3.reset_index(inplace=True)
campania_clc2012_label3.rename(columns={'label3': 'CORINE Land Cover - level 3'}, inplace=True)

2.1.5 Stato al 2018

Codice
campania_clc2018_label1 = pd.DataFrame(clc2018_campania.groupby('label1')['surface_ha_2018'].sum())
campania_clc2018_label1.reset_index(inplace=True)
campania_clc2018_label1.rename(columns={'label1': 'CORINE Land Cover - level 1'}, inplace=True)

campania_clc2018_label2 = pd.DataFrame(clc2018_campania.groupby('label2')['surface_ha_2018'].sum())
campania_clc2018_label2.reset_index(inplace=True)
campania_clc2018_label2.rename(columns={'label2': 'CORINE Land Cover - level 2'}, inplace=True)

campania_clc2018_label3 = pd.DataFrame(clc2018_campania.groupby('label3')['surface_ha_2018'].sum())
campania_clc2018_label3.reset_index(inplace=True)
campania_clc2018_label3.rename(columns={'label3': 'CORINE Land Cover - level 3'}, inplace=True)

2.2 Grafici

E’ il momento di approfondire questi dati! Ho estratto grafici per tutte e tre le classi ed ho cercato di dare una interpretazione del perchè di alcuni fenomeni.

2.2.1 Grafici per il livello 1

Codice
lvl1 = [campania_clc1990_label1, campania_clc2000_label1, campania_clc2006_label1, campania_clc2012_label1, campania_clc2018_label1]
lvl1_df = reduce(lambda  left,right: pd.merge(left,right,on=['CORINE Land Cover - level 1'], how='outer'), lvl1)
lvl1_df.fillna(0, inplace=True)
lvl1_df.insert(loc=6, column='Surf.Diff. | 2018-1990', value=(lvl1_df.surface_ha_2018 - lvl1_df.surface_ha_1990))
lvl1_df.set_index('CORINE Land Cover - level 1', inplace=True)
lvl1_df.rename(columns={'surface_ha_1990': '1990', 'surface_ha_2000': '2000', 'surface_ha_2006': '2006', 'surface_ha_2012': '2012', 'surface_ha_2018': '2018'}, inplace=True)
lvl1_df.sort_index(ascending=True, inplace=True)
lvl1_df
1990 2000 2006 2012 2018 Surf.Diff. | 2018-1990
CORINE Land Cover - level 1
Agricultural areas 761041.0303 753145.2349 755438.9748 752611.5454 752077.3861 -8963.6442
Artificial surfaces 80649.9550 89935.2388 97747.4059 101771.7643 102304.6984 21654.7434
Forest and semi natural areas 522870.7142 521005.1536 510731.5181 509096.9289 508050.0206 -14820.6936
Water bodies 1593.6569 2195.8983 2463.1760 2948.2571 2948.2571 1354.6002
Wetlands 608.8997 482.9084 386.6832 339.2620 339.2620 -269.6377
Codice
fig1 = plt.figure(figsize=(20, 10), dpi=100)
sub_fig = GridSpec(nrows=1, ncols=1, figure=fig1, hspace=0.35)
ax = fig1.add_subplot(sub_fig[0, 0])
lvl1_df.plot.bar(ax=ax, rot=0)

ax.yaxis.set_label_text('ha')
ax.set_xlabel('')
ax.grid(color='lightgrey', linestyle='dashdot')

plt.legend(loc='best')
<matplotlib.legend.Legend at 0x7fe2cfea3760>

E’ visibile abbastanza chiaramente un netto aumento delle superfici artificiali con un conseguente arretramento di quelle forestate e di quelle agricole. Le aree forestate tra il 1990 ed il 2018 hanno subito un arretramento quasi doppio rispetto a quelle agricole e le superfici artificiali sono quelle che hanno maggiormente “beneficiato” di questo arretramento complessivo delle due classi di CLC. Si potrebbe dire che l’urbanizzato ha eroso foreste ed aree agricole in quasi 30 anni.

2.2.2 Grafici per il livello 2

Codice
lvl2 = [campania_clc1990_label2, campania_clc2000_label2, campania_clc2006_label2, campania_clc2012_label2, campania_clc2018_label2]
lvl2_df = reduce(lambda  left,right: pd.merge(left,right,on=['CORINE Land Cover - level 2'], how='outer'), lvl2)
lvl2_df.fillna(0, inplace=True)
lvl2_df.insert(loc=6, column='Surf.Diff. | 2000-1990', value=(lvl2_df.surface_ha_2000 - lvl2_df.surface_ha_1990))
lvl2_df.insert(loc=7, column='Surf.Diff. | 2006-2000', value=(lvl2_df.surface_ha_2006 - lvl2_df.surface_ha_2000))
lvl2_df.insert(loc=8, column='Surf.Diff. | 2012-2006', value=(lvl2_df.surface_ha_2012 - lvl2_df.surface_ha_2006))
lvl2_df.insert(loc=9, column='Surf.Diff. | 2018-2012', value=(lvl2_df.surface_ha_2018 - lvl2_df.surface_ha_2012))
lvl2_df.insert(loc=9, column='Surf.Diff. | 2018-1990', value=(lvl2_df.surface_ha_2018 - lvl2_df.surface_ha_1990))
lvl2_df.set_index('CORINE Land Cover - level 2', inplace=True)
lvl2_df = lvl2_df[['Surf.Diff. | 2000-1990', 'Surf.Diff. | 2006-2000', 'Surf.Diff. | 2012-2006', 'Surf.Diff. | 2018-2012', 'Surf.Diff. | 2018-1990']]
lvl2_df.sort_index(ascending=True, inplace=True)
lvl2_df
Surf.Diff. | 2000-1990 Surf.Diff. | 2006-2000 Surf.Diff. | 2012-2006 Surf.Diff. | 2018-2012 Surf.Diff. | 2018-1990
CORINE Land Cover - level 2
Arable land -6226.9986 -15007.4805 1362.7374 26.9417 -19844.8000
Artificial, non-agricultural vegetated areas 330.5367 -4.4113 58.5588 396.9458 781.6300
Forests 2963.0601 -4293.0050 1859.3801 -1391.9151 -862.4799
Heterogeneous agricultural areas -947.6309 4546.7408 9331.1283 -448.2578 12481.9804
Industrial, commercial and transport units 1550.4505 3173.2827 2094.7855 154.4961 6973.0148
Inland waters 602.2416 267.2776 398.3284 0.0000 1267.8476
Inland wetlands -125.9913 -96.2252 -73.2778 0.0000 -295.4943
Marine waters -0.0002 0.0001 86.7527 0.0000 86.7526
Maritime wetlands 0.0000 0.0000 25.8566 0.0000 25.8566
Mine, dump and construction sites -31.1788 474.9460 112.8079 -23.5198 533.0553
Open spaces with little or no vegetation 1019.9233 -287.3449 20220.0532 3005.5295 23958.1611
Pastures -1351.7336 -788.2046 -997.1098 -110.4579 -3247.5059
Permanent crops 630.5677 13542.6842 -12524.1853 -2.3853 1646.6813
Scrub and/or herbaceous vegetation associations -5848.5440 -5693.2856 -23714.0225 -2660.5227 -37916.3748
Urban fabric 7435.4754 4168.3497 1758.2062 5.0120 13367.0433
Codice
fig2 = plt.figure(figsize=(20, 10), dpi=100)
sub_fig = GridSpec(nrows=1, ncols=1, figure=fig2, hspace=0.35)
ax = fig2.add_subplot(sub_fig[0, 0])
lvl2_df.plot.bar(ax=ax, rot=0)

ax.yaxis.set_label_text('ha')
ax.set_xlabel('')
ax.grid(color='lightgrey', linestyle='dashdot')

plt.xticks(rotation = 90)
plt.legend(loc='best')
<matplotlib.legend.Legend at 0x7fe2eb2bc4f0>

Scendendo di un livello, quindi andando ad approfondire di più il dato passando al secondo livello CLC, possiamo verificare i trend negativi a quali specifiche superfici fanno riferimento.

Le aree arbustive e con alberi radi hanno avuto la peggio, a seguire i terreni arabili, pascoli e foreste. Netto è invece l’aumento delle aree aperte, classe in cui nel terzo livello CLC ci sono le aree percorse dal fuoco(da tenere in mente per richiami successivi!). Seguono, nell’incremento, le aree urbanizzate, le aree per colture eterogenee e quelle per lo sviluppo di siti industriali e commerciali.

2.2.3 Grafici per il livello 3

Codice
lvl3 = [campania_clc1990_label3, campania_clc2000_label3, campania_clc2006_label3, campania_clc2012_label3, campania_clc2018_label3]
lvl3_df = reduce(lambda  left,right: pd.merge(left,right,on=['CORINE Land Cover - level 3'], how='outer'), lvl3)
lvl3_df.fillna(0, inplace=True)
lvl3_df.insert(loc=6, column='Surf.Diff. | 2000-1990', value=(lvl3_df.surface_ha_2000 - lvl3_df.surface_ha_1990))
lvl3_df.insert(loc=7, column='Surf.Diff. | 2006-2000', value=(lvl3_df.surface_ha_2006 - lvl3_df.surface_ha_2000))
lvl3_df.insert(loc=8, column='Surf.Diff. | 2012-2006', value=(lvl3_df.surface_ha_2012 - lvl3_df.surface_ha_2006))
lvl3_df.insert(loc=9, column='Surf.Diff. | 2018-2012', value=(lvl3_df.surface_ha_2018 - lvl3_df.surface_ha_2012))
lvl3_df.insert(loc=10, column='Surf.Diff. | 2018-1990', value=(lvl3_df.surface_ha_2018 - lvl3_df.surface_ha_1990))
lvl3_df.set_index('CORINE Land Cover - level 3', inplace=True)

lvl3_df.sort_index(ascending=True, inplace=True)
lvl3_df
surface_ha_1990 surface_ha_2000 surface_ha_2006 surface_ha_2012 surface_ha_2018 Surf.Diff. | 2000-1990 Surf.Diff. | 2006-2000 Surf.Diff. | 2012-2006 Surf.Diff. | 2018-2012 Surf.Diff. | 2018-1990
CORINE Land Cover - level 3
Agro-forestry areas 420.5049 420.5224 140.9079 79.0823 79.0823 0.0175 -279.6145 -61.8256 0.000000e+00 -341.4226
Airports 383.8998 556.4307 556.4308 556.4308 556.4308 172.5309 0.0001 0.0000 0.000000e+00 172.5310
Annual crops associated with permanent crops 42881.8119 44016.3987 23501.2894 24459.1737 24062.2280 1134.5868 -20515.1093 957.8843 -3.969457e+02 -18819.5839
Bare rocks 1122.7257 1006.1738 979.8852 1274.5252 1274.5252 -116.5519 -26.2886 294.6400 0.000000e+00 151.7995
Beaches, dunes, sands 2282.0112 1852.6891 1835.3037 1904.4059 1904.4059 -429.3221 -17.3854 69.1022 0.000000e+00 -377.6053
Broad-leaved forest 367521.2801 369187.8047 365453.4552 367034.3194 366436.4566 1666.5246 -3734.3495 1580.8642 -5.978628e+02 -1084.8235
Burnt areas 0.0000 354.7607 438.4148 770.8451 3802.9844 354.7607 83.6541 332.4303 3.032139e+03 3802.9844
Coastal lagoons 25.8567 25.8565 25.8566 112.6093 112.6093 -0.0002 0.0001 86.7527 0.000000e+00 86.7526
Complex cultivation patterns 165047.1097 162777.2069 181971.5611 185634.2569 185582.9448 -2269.9028 19194.3542 3662.6958 -5.131210e+01 20535.8351
Coniferous forest 7457.8770 8154.5708 7620.7143 8876.9029 8373.6888 696.6938 -533.8565 1256.1886 -5.032141e+02 915.8118
Construction sites 25.4060 70.0172 246.1018 58.2792 58.2792 44.6112 176.0846 -187.8226 0.000000e+00 32.8732
Continuous urban fabric 32608.0637 33436.5752 29309.2673 33833.4142 33833.4142 828.5115 -4127.3079 4524.1469 0.000000e+00 1225.3505
Discontinuous urban fabric 37281.0655 43888.0294 52183.6870 49417.7463 49422.7583 6606.9639 8295.6576 -2765.9407 5.012000e+00 12141.6928
Dump sites 207.8811 27.2633 174.0802 438.6634 406.9530 -180.6178 146.8169 264.5832 -3.171040e+01 199.0719
Fruit trees and berry plantations 56044.2433 54999.5524 61954.0631 56585.3863 56585.3142 -1044.6909 6954.5107 -5368.6768 -7.210000e-02 541.0709
Green urban areas 731.9919 758.4238 805.2857 780.1128 1177.0586 26.4319 46.8619 -25.1729 3.969458e+02 445.0667
Industrial or commercial units 6590.0765 8168.7442 11005.1236 12643.3548 12797.8509 1578.6677 2836.3794 1638.2312 1.544961e+02 6207.7744
Inland marshes 608.8997 482.9084 386.6832 313.4054 313.4054 -125.9913 -96.2252 -73.2778 0.000000e+00 -295.4943
Land principally occupied by agriculture, with significant areas of natural vegetation 89154.5654 89342.2330 95489.3434 100261.7172 100261.7172 187.6676 6147.1104 4772.3738 0.000000e+00 11107.1518
Mineral extraction sites 1398.3376 1503.1654 1655.2099 1691.2572 1699.4478 104.8278 152.0445 36.0473 8.190600e+00 301.1102
Mixed forest 8847.1411 9446.9828 9422.1838 8444.5111 8153.6729 599.8417 -24.7990 -977.6727 -2.908382e+02 -693.4682
Moors and heathland 16.7825 0.0000 0.0000 0.0000 0.0000 -16.7825 0.0000 0.0000 0.000000e+00 -16.7825
Natural grasslands 57150.7848 53354.5398 48427.4297 30587.1997 29539.0655 -3796.2450 -4927.1101 -17840.2300 -1.048134e+03 -27611.7193
Non-irrigated arable land 304750.4005 298328.2037 283148.7731 284508.0453 284503.2765 -6422.1968 -15179.4306 1359.2722 -4.768800e+00 -20247.1240
Olive groves 58951.7577 60805.0013 66147.7874 61604.3214 61632.6846 1853.2436 5342.7861 -4543.4660 2.836320e+01 2680.9269
Pastures 15620.1214 14268.3878 13480.1832 12483.0734 12372.6155 -1351.7336 -788.2046 -997.1098 -1.104579e+02 -3247.5059
Permanently irrigated land 25316.0313 25511.2295 25683.1796 25686.6448 25718.3553 195.1982 171.9501 3.4652 3.171050e+01 402.3240
Port areas 458.6026 469.9879 481.6963 459.2550 459.2550 11.3853 11.7084 -22.4413 0.000000e+00 0.6524
Road and rail networks and associated land 650.5552 438.4218 763.6166 1242.6122 1242.6122 -212.1334 325.1948 478.9956 0.000000e+00 592.0570
Salt marshes 0.0000 0.0000 0.0000 25.8566 25.8566 0.0000 0.0000 25.8566 0.000000e+00 25.8566
Sclerophyllous vegetation 19255.7695 18537.4944 17072.9128 15623.7660 15530.6877 -718.2751 -1464.5816 -1449.1468 -9.307830e+01 -3725.0818
Sparsely vegetated areas 5561.6437 6772.6803 6445.3553 25969.2360 25942.6262 1211.0366 -327.3250 19523.8807 -2.660980e+01 20380.9825
Sport and leisure facilities 314.0751 618.1799 566.9067 650.6384 650.6384 304.1048 -51.2732 83.7317 0.000000e+00 336.5633
Transitional woodland-shrub 53654.6986 52337.4572 53035.8633 48611.2176 47091.9074 -1317.2414 698.4061 -4424.6457 -1.519310e+03 -6562.7912
Vineyards 2854.4842 2676.4992 3921.8866 1309.8441 1279.1677 -177.9850 1245.3874 -2612.0425 -3.067640e+01 -1575.3165
Water bodies 1095.5920 1373.7744 1687.8687 1981.1362 1981.1362 278.1824 314.0943 293.2675 2.273737e-13 885.5442
Water courses 472.2082 796.2674 749.4507 854.5116 854.5116 324.0592 -46.8167 105.0609 0.000000e+00 382.3034
Codice
fig3 = plt.figure(figsize=(20, 10), dpi=100)
sub_fig = GridSpec(nrows=1, ncols=1, figure=fig3, hspace=0.35)
ax = fig3.add_subplot(sub_fig[0, 0])
lvl3_df['Surf.Diff. | 2018-1990'].plot.bar(ax=ax, rot=0)

ax.yaxis.set_label_text('ha')
ax.set_xlabel('')
ax.grid(color='lightgrey', linestyle='dashdot')

plt.xticks(rotation = 90)
plt.legend(loc='best')
<matplotlib.legend.Legend at 0x7fe2df35bd90>

Passando al livello tre della CLC, ho voluto graficare la sola differenza tra 1990 e 2018 per motivi di leggibilità del grafico. In termini di aumento di copertura del suolo c’è l’avanzamento, quasi identico, delle piccole aree destinate a colture annuali e delle aree con vegetazione frammentata. A seguire ci sono le aree urbane discontinue e quelle occupate da orti, vigneti e alberi da frutto.

Una riflessione che mi sento di fare è che l’avanzamento del tessuto urbano discontinuo ha frazionato il territorio agricolo, generando aree a piccole colture agricole.

Significativo anche l’aumento di estensione delle aree percorse dal fuoco.

I decrementi sono la cartina di tornasole degli incrementi che ho descritto poco fa e che potrebbero confermare la mia riflessione sul frazionamento del territorio naturale ed agricolo, portato avanti dall’avanzamento delle classi legati ai suoli artificiali.

2.3 Focus sui Comuni

L’analisi che segue tenta di rispondere alla domanda: “quali sono i Comuni campani in cui i fenomeni esposti poco fa hanno avuto un maggior impatto?”

Mi concentrerò sul terzo livello della CLC, in particolare sulle aree artificiali, aree agricole e foreste ma anche sulle discariche, le aree percorse dal fuoco ed il verde urbano.

Visto che dal 2000 in poi l’accuratezza geometrica del dato CLC è aumentata considerevolmente, le analisi di dettaglio che seguono si concentreranno sulla differenza tra 2000 e 2018.

Codice

# Dati 1990
focus_1990 = clc1990_campania[['pro_com', 'comune', 'provincia', 'sup_com_ha', 'label3', 'surface_ha_1990']]
focus_1990.set_index('label3', inplace=True)
focus_1990_group = pd.DataFrame(focus_1990.groupby(['pro_com', 'comune', 'sup_com_ha', 'label3'])['surface_ha_1990'].sum())
focus_1990_group.reset_index(inplace=True)

# Dati 2000
focus_2000 = clc2000_campania[['pro_com', 'comune', 'provincia', 'sup_com_ha', 'label3', 'surface_ha_2000']]
focus_2000.set_index('label3', inplace=True)
focus_2000_group = pd.DataFrame(focus_2000.groupby(['pro_com', 'comune', 'sup_com_ha', 'label3'])['surface_ha_2000'].sum())
focus_2000_group.reset_index(inplace=True)

# Dati 2006
focus_2006 = clc2006_campania[['pro_com', 'comune', 'provincia', 'sup_com_ha', 'label3', 'surface_ha_2006']]
focus_2006.set_index('label3', inplace=True)
focus_2006_group = pd.DataFrame(focus_2006.groupby(['pro_com', 'comune', 'sup_com_ha', 'label3'])['surface_ha_2006'].sum())
focus_2006_group.reset_index(inplace=True)

# Dati 2012
focus_2012 = clc2012_campania[['pro_com', 'comune', 'provincia', 'sup_com_ha', 'label3', 'surface_ha_2012']]
focus_2012.set_index('label3', inplace=True)
focus_2012_group = pd.DataFrame(focus_2012.groupby(['pro_com', 'comune', 'sup_com_ha', 'label3'])['surface_ha_2012'].sum())
focus_2012_group.reset_index(inplace=True)

# Dati 2018
focus_2018 = clc2018_campania[['pro_com', 'comune', 'provincia', 'sup_com_ha', 'label3', 'surface_ha_2018']]
focus_2018.set_index('label3', inplace=True)
focus_2018_group = pd.DataFrame(focus_2018.groupby(['pro_com', 'comune', 'sup_com_ha', 'label3'])['surface_ha_2018'].sum())
focus_2018_group.reset_index(inplace=True)
Codice
focus_1990_group
pro_com comune sup_com_ha label3 surface_ha_1990
0 61001.0 Ailano 1605.560469 Broad-leaved forest 472.5747
1 61001.0 Ailano 1605.560469 Complex cultivation patterns 93.2780
2 61001.0 Ailano 1605.560469 Continuous urban fabric 31.0972
3 61001.0 Ailano 1605.560469 Land principally occupied by agriculture, with... 188.9317
4 61001.0 Ailano 1605.560469 Non-irrigated arable land 752.7540
... ... ... ... ... ...
4543 65158.0 Bellizzi 802.200746 Continuous urban fabric 93.9079
4544 65158.0 Bellizzi 802.200746 Discontinuous urban fabric 40.9309
4545 65158.0 Bellizzi 802.200746 Fruit trees and berry plantations 24.3220
4546 65158.0 Bellizzi 802.200746 Non-irrigated arable land 426.7024
4547 65158.0 Bellizzi 802.200746 Vineyards 33.8787

4548 rows × 5 columns

2.3.1 Aree artificiali

I gruppi 1.1 ed 1.2 della CLC

Codice

# Dati 1990
artificial_surface_1990 = focus_1990_group[focus_1990_group['label3'].isin(['Continuous urban fabric', 'Discontinuous urban fabric', 'Industrial or commercial units', 'Road and rail networks and associated land', 'Port areas', 'Airports'])]
artificial_surface_1990_group = pd.DataFrame(artificial_surface_1990.groupby(['pro_com'])['surface_ha_1990'].sum())
artificial_surface_1990_group.reset_index(inplace=True)

# Dati 2000
artificial_surface_2000 = focus_2000_group[focus_2000_group['label3'].isin(['Continuous urban fabric', 'Discontinuous urban fabric', 'Industrial or commercial units', 'Road and rail networks and associated land', 'Port areas', 'Airports'])]
artificial_surface_2000_group = pd.DataFrame(artificial_surface_2000.groupby(['pro_com'])['surface_ha_2000'].sum())
artificial_surface_2000_group.reset_index(inplace=True)

# Dati 2006
artificial_surface_2006 = focus_2006_group[focus_2006_group['label3'].isin(['Continuous urban fabric', 'Discontinuous urban fabric', 'Industrial or commercial units', 'Road and rail networks and associated land', 'Port areas', 'Airports'])]
artificial_surface_2006_group = pd.DataFrame(artificial_surface_2006.groupby(['pro_com'])['surface_ha_2006'].sum())
artificial_surface_2006_group.reset_index(inplace=True)

# Dati 2012
artificial_surface_2012 = focus_2012_group[focus_2012_group['label3'].isin(['Continuous urban fabric', 'Discontinuous urban fabric', 'Industrial or commercial units', 'Road and rail networks and associated land', 'Port areas', 'Airports'])]
artificial_surface_2012_group = pd.DataFrame(artificial_surface_2012.groupby(['pro_com'])['surface_ha_2012'].sum())
artificial_surface_2012_group.reset_index(inplace=True)

# Dati 2018
artificial_surface_2018 = focus_2018_group[focus_2018_group['label3'].isin(['Continuous urban fabric', 'Discontinuous urban fabric', 'Industrial or commercial units', 'Road and rail networks and associated land', 'Port areas', 'Airports'])]
artificial_surface_2018_group = pd.DataFrame(artificial_surface_2018.groupby(['pro_com', 'comune', 'sup_com_ha'])['surface_ha_2018'].sum())
artificial_surface_2018_group.reset_index(inplace=True)

# Dati aggregati
focus_artificial = [artificial_surface_2018_group, artificial_surface_2012_group, artificial_surface_2006_group, artificial_surface_2000_group, artificial_surface_1990_group]
focus_artificial_df = reduce(lambda  left,right: pd.merge(left,right,on=['pro_com'], how='outer'), focus_artificial)
focus_artificial_df = focus_artificial_df[focus_artificial_df['sup_com_ha'] > 0]
focus_artificial_df.fillna(0, inplace=True)
focus_artificial_df.insert(loc=3, column='perc_1990', value=round(focus_artificial_df.surface_ha_1990 / focus_artificial_df.sup_com_ha, 4) * 100)
focus_artificial_df.insert(loc=4, column='perc_2000', value=round(focus_artificial_df.surface_ha_2000 / focus_artificial_df.sup_com_ha, 4) * 100)
focus_artificial_df.insert(loc=5, column='perc_2006', value=round(focus_artificial_df.surface_ha_2006 / focus_artificial_df.sup_com_ha, 4) * 100)
focus_artificial_df.insert(loc=6, column='perc_2012', value=round(focus_artificial_df.surface_ha_2012 / focus_artificial_df.sup_com_ha, 4) * 100)
focus_artificial_df.insert(loc=7, column='perc_2018', value=round(focus_artificial_df.surface_ha_2018 / focus_artificial_df.sup_com_ha, 4) * 100)
focus_artificial_df.insert(loc=8, column='Perc.Diff. | 2018-2000', value=(focus_artificial_df.perc_2018 - focus_artificial_df.perc_2000))
focus_artificial_df.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=True, inplace=True)
focus_artificial_df
pro_com comune sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
201 63036.0 Grumo Nevano 287.688759 47.45 64.93 64.93 51.31 51.31 -13.62 147.6065 147.6065 186.8019 186.8019 136.5066
232 63067.0 San Giorgio a Cremano 415.383936 60.96 76.57 68.51 68.51 68.51 -8.06 284.5767 284.5767 284.5768 318.0615 253.2174
197 63032.0 Frattamaggiore 538.816178 58.54 71.14 72.46 63.97 63.97 -7.17 344.6705 344.6705 390.4469 383.2980 315.4185
476 65121.0 San Mango Piemonte 602.370147 7.81 7.81 3.14 3.14 3.14 -4.67 18.8890 18.8890 18.8890 47.0198 47.0222
137 62046.0 Pago Veiano 2374.895020 1.83 5.24 2.13 2.13 2.13 -3.11 50.6037 50.6037 50.6037 124.5097 43.5550
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
228 63063.0 Quarto 1415.896056 24.40 24.95 26.90 39.26 39.26 14.31 555.9065 555.9065 380.8373 353.2075 345.4612
254 63089.0 Volla 620.630205 45.68 49.86 49.94 64.34 64.34 14.48 399.3008 399.3008 309.9351 309.4482 283.5034
212 63047.0 Monte di Procida 370.428037 31.25 31.25 31.25 48.28 48.28 17.03 178.8412 178.8412 115.7459 115.7458 115.7439
190 63025.0 Castello di Cisterna 392.135970 21.89 46.04 68.31 68.31 68.31 22.27 267.8514 267.8514 267.8514 180.5292 85.8311
380 65011.0 Atrani 12.061182 0.00 0.00 52.01 52.01 52.01 52.01 6.2730 6.2730 6.2730 0.0000 0.0000

507 rows × 14 columns

Codice
# Superfici in arretramento
reduction_artificial_area = focus_artificial_df.iloc[0:10]
reduction_artificial_area
pro_com comune sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
201 63036.0 Grumo Nevano 287.688759 47.45 64.93 64.93 51.31 51.31 -13.62 147.6065 147.6065 186.8019 186.8019 136.5066
232 63067.0 San Giorgio a Cremano 415.383936 60.96 76.57 68.51 68.51 68.51 -8.06 284.5767 284.5767 284.5768 318.0615 253.2174
197 63032.0 Frattamaggiore 538.816178 58.54 71.14 72.46 63.97 63.97 -7.17 344.6705 344.6705 390.4469 383.2980 315.4185
476 65121.0 San Mango Piemonte 602.370147 7.81 7.81 3.14 3.14 3.14 -4.67 18.8890 18.8890 18.8890 47.0198 47.0222
137 62046.0 Pago Veiano 2374.895020 1.83 5.24 2.13 2.13 2.13 -3.11 50.6037 50.6037 50.6037 124.5097 43.5550
485 65132.0 San Valentino Torio 915.632363 15.51 15.56 13.27 13.27 13.27 -2.29 121.4587 121.4587 121.4588 142.4372 141.9949
4 61005.0 Aversa 885.176217 59.33 67.87 67.96 66.24 66.24 -1.63 586.3294 586.3294 601.5394 600.7846 525.1697
170 63005.0 Arzano 473.103075 83.22 86.97 87.40 85.42 85.42 -1.55 404.1348 404.1348 413.4904 411.4785 393.7116
474 65119.0 San Giovanni a Piro 3790.148485 2.12 4.21 2.72 2.72 2.72 -1.49 103.0783 103.0783 103.0783 159.6782 80.3555
57 61067.0 Recale 321.954739 32.81 32.81 31.54 31.54 31.54 -1.27 101.5467 101.5467 101.5467 105.6199 105.6193
Codice
# Superfici in avanzamento
increase_artificial_area = focus_artificial_df.iloc[-10:]
increase_artificial_area.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=False, inplace=True)
increase_artificial_area
pro_com comune sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
380 65011.0 Atrani 12.061182 0.00 0.00 52.01 52.01 52.01 52.01 6.2730 6.2730 6.2730 0.0000 0.0000
190 63025.0 Castello di Cisterna 392.135970 21.89 46.04 68.31 68.31 68.31 22.27 267.8514 267.8514 267.8514 180.5292 85.8311
212 63047.0 Monte di Procida 370.428037 31.25 31.25 31.25 48.28 48.28 17.03 178.8412 178.8412 115.7459 115.7458 115.7439
254 63089.0 Volla 620.630205 45.68 49.86 49.94 64.34 64.34 14.48 399.3008 399.3008 309.9351 309.4482 283.5034
228 63063.0 Quarto 1415.896056 24.40 24.95 26.90 39.26 39.26 14.31 555.9065 555.9065 380.8373 353.2075 345.4612
82 61094.0 Trentola Ducenta 665.513019 30.18 30.19 31.44 44.05 44.05 13.86 293.1760 293.1760 209.2403 200.8859 200.8848
15 61016.0 Carinaro 631.593670 28.55 34.93 44.25 47.41 47.41 12.48 299.4077 299.4077 279.4878 220.6269 180.3185
80 61092.0 Teverola 669.906081 31.59 37.22 50.70 48.78 48.78 11.56 326.7535 326.7535 339.6324 249.3457 211.6063
188 63023.0 Casoria 1213.840286 57.66 72.14 72.14 83.48 83.48 11.34 1013.2593 1013.2593 875.7210 875.7214 699.8511
123 62032.0 Forchia 545.454120 0.00 0.00 0.00 11.00 11.00 11.00 59.9761 59.9761 0.0000 0.0000 0.0000
Codice
artificial_df = pd.concat([reduction_artificial_area, increase_artificial_area])
artificial_df.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=True, inplace=True)
artificial_df = artificial_df[['comune', 'Perc.Diff. | 2018-2000']]
artificial_df['Perc.Diff. | 2018-2000'] = artificial_df['Perc.Diff. | 2018-2000']
artificial_df.set_index('comune', inplace=True)

fig_artificial = plt.figure(figsize=(20, 10), dpi=100)
sub_fig_artificial = GridSpec(nrows=1, ncols=1, figure=fig_artificial, hspace=0.35)
ax_artificial = fig_artificial.add_subplot(sub_fig_artificial[0, 0])
artificial_df.plot.bar(ax=ax_artificial, rot=0)

ax_artificial.yaxis.set_label_text('%')
ax_artificial.set_xlabel('')
ax_artificial.grid(color='lightgrey', linestyle='dashdot')

plt.xticks(rotation = 90)
plt.legend(loc='best')
<matplotlib.legend.Legend at 0x7fe2d646e0d0>

Onestamente non so come interpretare l’arretramento delle aree artificiali, andrebbe fatta una ulteriore analisi per valutare se sono Comuni che si sono spopolati in quasi trenta anni.

L’aumento è abbastanza facile da indagare per me che sono del posto. Napoli ha una popolazione di quasi un milione di abitanti che triplica nei giorni feriali, per non parlare poi dei turisti. E’ un’area molto congestionata con possibilità di nuova edilizia prossime allo zero; risulta normale quindi che Comuni più prossimi al Capoluogo di Regione stiano accogliendo “l’esubero” di popolazione. Faccio riferimento a Castello di Cisterna, Volla e Quarto.

Il caso di Atrani è particolare perchè è il Comune più piccolo d’Italia ed è posto in una zona abbastanza impervia della costiera amalfitana; è probabile che nel 2000 non si sia data molta importanza all’area.

2.3.2 Aree forestate

Il gruppo 3.1 della CLC

Codice

# Dati 1990
forest_surface_1990 = focus_1990_group[focus_1990_group['label3'].isin(['Broad-leaved forest', 'Coniferous forest', 'Mixed forest'])]
forest_surface_1990_group = pd.DataFrame(forest_surface_1990.groupby(['pro_com'])['surface_ha_1990'].sum())
forest_surface_1990_group.reset_index(inplace=True)

# Dati 2000
forest_surface_2000 = focus_2000_group[focus_2000_group['label3'].isin(['Broad-leaved forest', 'Coniferous forest', 'Mixed forest'])]
forest_surface_2000_group = pd.DataFrame(forest_surface_2000.groupby(['pro_com'])['surface_ha_2000'].sum())
forest_surface_2000_group.reset_index(inplace=True)

# Dati 2006
forest_surface_2006 = focus_2006_group[focus_2006_group['label3'].isin(['Broad-leaved forest', 'Coniferous forest', 'Mixed forest'])]
forest_surface_2006_group = pd.DataFrame(forest_surface_2006.groupby(['pro_com'])['surface_ha_2006'].sum())
forest_surface_2006_group.reset_index(inplace=True)

# Dati 2012
forest_surface_2012 = focus_2012_group[focus_2012_group['label3'].isin(['Broad-leaved forest', 'Coniferous forest', 'Mixed forest'])]
forest_surface_2012_group = pd.DataFrame(forest_surface_2012.groupby(['pro_com'])['surface_ha_2012'].sum())
forest_surface_2012_group.reset_index(inplace=True)

# Dati 2018
forest_surface_2018 = focus_2018_group[focus_2018_group['label3'].isin(['Broad-leaved forest', 'Coniferous forest', 'Mixed forest'])]
forest_surface_2018_group = pd.DataFrame(forest_surface_2018.groupby(['pro_com', 'comune', 'sup_com_ha'])['surface_ha_2018'].sum())
forest_surface_2018_group.reset_index(inplace=True)

# Dati aggregati
focus_forest = [forest_surface_2018_group, forest_surface_2012_group, forest_surface_2006_group, forest_surface_2000_group, forest_surface_1990_group]
focus_forest_df = reduce(lambda  left,right: pd.merge(left,right,on=['pro_com'], how='outer'), focus_forest)
focus_forest_df = focus_forest_df[focus_forest_df['sup_com_ha'] > 0]
focus_forest_df.fillna(0, inplace=True)
focus_forest_df.insert(loc=3, column='perc_1990', value=round(focus_forest_df.surface_ha_1990 / focus_forest_df.sup_com_ha, 4) * 100)
focus_forest_df.insert(loc=4, column='perc_2000', value=round(focus_forest_df.surface_ha_2000 / focus_forest_df.sup_com_ha, 4) * 100)
focus_forest_df.insert(loc=5, column='perc_2006', value=round(focus_forest_df.surface_ha_2006 / focus_forest_df.sup_com_ha, 4) * 100)
focus_forest_df.insert(loc=6, column='perc_2012', value=round(focus_forest_df.surface_ha_2012 / focus_forest_df.sup_com_ha, 4) * 100)
focus_forest_df.insert(loc=7, column='perc_2018', value=round(focus_forest_df.surface_ha_2018 / focus_forest_df.sup_com_ha, 4) * 100)
focus_forest_df.insert(loc=8, column='Perc.Diff. | 2018-2000', value=(focus_forest_df.perc_2018 - focus_forest_df.perc_2000))
focus_forest_df.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=True, inplace=True)
focus_forest_df
pro_com comune sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
139 62073.0 Solopaca 3112.664003 43.63 43.63 43.46 11.69 11.69 -31.94 363.9901 363.9901 1352.7210 1358.0359 1358.0275
143 62077.0 Vitulano 3599.050416 50.86 50.81 50.81 26.82 26.82 -23.99 965.1276 965.1276 1828.5273 1828.5272 1830.3353
410 65115.0 Salento 2378.507005 51.73 50.93 37.94 37.93 37.93 -13.00 902.2741 902.2741 902.4736 1211.3768 1230.4272
179 63082.0 Terzigno 2346.480829 20.68 20.68 20.68 20.68 8.01 -12.67 187.8420 485.2334 485.2333 485.2338 485.2356
147 63007.0 Barano d'Ischia 1096.089426 49.21 49.21 49.21 38.59 38.59 -10.62 422.9915 422.9915 539.3662 539.3660 539.3685
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
439 65147.0 Torchiara 846.167482 20.76 20.76 20.86 29.14 29.14 8.38 246.5860 246.5860 176.5072 175.6504 175.6495
436 65144.0 Stella Cilento 1451.726154 39.51 43.25 43.25 52.64 52.64 9.39 764.2043 764.2043 627.8681 627.8690 573.5525
393 65098.0 Pollica 2816.525386 11.44 17.14 17.15 27.01 27.01 9.87 760.6785 760.6785 482.9555 482.8660 322.2088
359 65064.0 Lustra 1524.153297 34.01 34.02 34.02 44.61 44.61 10.59 679.9189 679.9189 518.4959 518.4971 518.4368
408 65113.0 Sacco 2365.702940 42.11 42.02 42.02 63.01 63.01 20.99 1490.6391 1490.6391 994.0414 994.0408 996.2456

450 rows × 14 columns

Codice
# Superfici in arretramento
reduction_forest_area = focus_forest_df.iloc[0:10]
reduction_forest_area
pro_com comune sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
139 62073.0 Solopaca 3112.664003 43.63 43.63 43.46 11.69 11.69 -31.94 363.9901 363.9901 1352.7210 1358.0359 1358.0275
143 62077.0 Vitulano 3599.050416 50.86 50.81 50.81 26.82 26.82 -23.99 965.1276 965.1276 1828.5273 1828.5272 1830.3353
410 65115.0 Salento 2378.507005 51.73 50.93 37.94 37.93 37.93 -13.00 902.2741 902.2741 902.4736 1211.3768 1230.4272
179 63082.0 Terzigno 2346.480829 20.68 20.68 20.68 20.68 8.01 -12.67 187.8420 485.2334 485.2333 485.2338 485.2356
147 63007.0 Barano d'Ischia 1096.089426 49.21 49.21 49.21 38.59 38.59 -10.62 422.9915 422.9915 539.3662 539.3660 539.3685
304 65006.0 Amalfi 570.014402 48.51 48.51 38.86 38.86 38.86 -9.65 221.4838 221.4838 221.4838 276.5111 276.5144
170 63064.0 Ercolano 1989.359843 21.47 21.26 21.26 17.39 11.75 -9.51 233.6730 345.9226 422.9398 422.9397 427.1795
155 63035.0 Gragnano 1464.244074 53.35 53.35 52.42 44.07 44.07 -9.28 645.3063 645.3063 767.6083 781.1773 781.1751
159 63041.0 Marano di Napoli 1565.253641 13.09 13.09 13.09 3.98 3.98 -9.11 62.3734 62.3734 204.9146 204.9145 204.9150
183 63091.0 Trecase 621.312671 8.86 8.86 8.86 8.86 0.08 -8.78 0.4777 55.0368 55.0368 55.0368 55.0371
Codice
# Superfici in avanzamento
increase_forest_area = focus_forest_df.iloc[-10:]
increase_forest_area.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=False, inplace=True)
increase_forest_area
pro_com comune sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
408 65113.0 Sacco 2365.702940 42.11 42.02 42.02 63.01 63.01 20.99 1490.6391 1490.6391 994.0414 994.0408 996.2456
359 65064.0 Lustra 1524.153297 34.01 34.02 34.02 44.61 44.61 10.59 679.9189 679.9189 518.4959 518.4971 518.4368
393 65098.0 Pollica 2816.525386 11.44 17.14 17.15 27.01 27.01 9.87 760.6785 760.6785 482.9555 482.8660 322.2088
436 65144.0 Stella Cilento 1451.726154 39.51 43.25 43.25 52.64 52.64 9.39 764.2043 764.2043 627.8681 627.8690 573.5525
439 65147.0 Torchiara 846.167482 20.76 20.76 20.86 29.14 29.14 8.38 246.5860 246.5860 176.5072 175.6504 175.6495
405 65110.0 Romagnano al Monte 966.995480 18.35 18.35 18.35 26.64 26.64 8.29 257.5984 257.5984 177.4251 177.4254 177.4249
151 63022.0 Casola di Napoli 259.318212 34.28 34.28 42.15 42.15 42.15 7.87 109.3078 109.3078 109.3078 88.8917 88.8916
102 62036.0 Ginestra degli Schiavoni 1478.706162 22.20 22.20 22.20 29.90 29.90 7.70 442.1457 442.1457 328.2494 328.2500 328.2562
229 64045.0 Luogosano 606.809422 10.43 12.63 12.63 18.42 18.42 5.79 111.7932 111.7932 76.6287 76.6287 63.2980
355 65060.0 Laureana Cilento 1374.420460 25.62 25.64 25.64 30.10 31.15 5.51 428.1047 413.7489 352.3810 352.3819 352.1644
Codice
forest_df = pd.concat([reduction_forest_area, increase_forest_area])
forest_df.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=True, inplace=True)
forest_df = forest_df[['comune', 'Perc.Diff. | 2018-2000']]
forest_df['Perc.Diff. | 2018-2000'] = forest_df['Perc.Diff. | 2018-2000']
forest_df.set_index('comune', inplace=True)

fig_forest = plt.figure(figsize=(20, 10), dpi=100)
sub_fig_forest = GridSpec(nrows=1, ncols=1, figure=fig_forest, hspace=0.35)
ax_forest = fig_forest.add_subplot(sub_fig_forest[0, 0])
forest_df.plot.bar(ax=ax_forest, rot=0)

ax_forest.yaxis.set_label_text('%')
ax_forest.set_xlabel('')
ax_forest.grid(color='lightgrey', linestyle='dashdot')

plt.xticks(rotation = 90)
plt.legend(loc='best')
<matplotlib.legend.Legend at 0x7fe2d401af40>

E’ significativo per me che nella top 10 del decremento di aree forestate ci siano Terzigno, Ercolano e Trecase. Lo è perchè sono tra i Comuni ricadenti nell’area del Parco Nazionale del Vesuvio che hanno subito danni al patrimonio boschivo a causa dell’incendio che ci fu nel 2017.

Le aree in cui i boschi avanzano sono perlopiu aree rurali, a vocazione agricola, che pian piano si stanno spopolando.

2.3.3 Aree agricole

Il gruppo 2.1 della CLC

Codice

# Dati 1990
arable_lands_surface_1990 = focus_1990_group[focus_1990_group['label3'].isin(['Non-irrigated arable land', 'Permanently irrigated land'])]
arable_lands_surface_1990_group = pd.DataFrame(arable_lands_surface_1990.groupby(['pro_com'])['surface_ha_1990'].sum())
arable_lands_surface_1990_group.reset_index(inplace=True)

# Dati 2000
arable_lands_surface_2000 = focus_2000_group[focus_2000_group['label3'].isin(['Non-irrigated arable land', 'Permanently irrigated land'])]
arable_lands_surface_2000_group = pd.DataFrame(arable_lands_surface_2000.groupby(['pro_com'])['surface_ha_2000'].sum())
arable_lands_surface_2000_group.reset_index(inplace=True)

# Dati 2006
arable_lands_surface_2006 = focus_2006_group[focus_2006_group['label3'].isin(['Non-irrigated arable land', 'Permanently irrigated land'])]
arable_lands_surface_2006_group = pd.DataFrame(arable_lands_surface_2006.groupby(['pro_com'])['surface_ha_2006'].sum())
arable_lands_surface_2006_group.reset_index(inplace=True)

# Dati 2012
arable_lands_surface_2012 = focus_2012_group[focus_2012_group['label3'].isin(['Non-irrigated arable land', 'Permanently irrigated land'])]
arable_lands_surface_2012_group = pd.DataFrame(arable_lands_surface_2012.groupby(['pro_com'])['surface_ha_2012'].sum())
arable_lands_surface_2012_group.reset_index(inplace=True)

# Dati 2018
arable_lands_surface_2018 = focus_2018_group[focus_2018_group['label3'].isin(['Non-irrigated arable land', 'Permanently irrigated land'])]
arable_lands_surface_2018_group = pd.DataFrame(arable_lands_surface_2018.groupby(['pro_com', 'comune', 'sup_com_ha'])['surface_ha_2018'].sum())
arable_lands_surface_2018_group.reset_index(inplace=True)

# Dati aggregati
focus_arable_lands = [arable_lands_surface_2018_group, arable_lands_surface_2012_group, arable_lands_surface_2006_group, arable_lands_surface_2000_group, arable_lands_surface_1990_group]
focus_arable_lands_df = reduce(lambda  left,right: pd.merge(left,right,on=['pro_com'], how='outer'), focus_arable_lands)
focus_arable_lands_df = focus_arable_lands_df[focus_arable_lands_df['sup_com_ha'] > 0]
focus_arable_lands_df.fillna(0, inplace=True)
focus_arable_lands_df.insert(loc=3, column='perc_1990', value=round(focus_arable_lands_df.surface_ha_1990 / focus_arable_lands_df.sup_com_ha, 4) * 100)
focus_arable_lands_df.insert(loc=4, column='perc_2000', value=round(focus_arable_lands_df.surface_ha_2000 / focus_arable_lands_df.sup_com_ha, 4) * 100)
focus_arable_lands_df.insert(loc=5, column='perc_2006', value=round(focus_arable_lands_df.surface_ha_2006 / focus_arable_lands_df.sup_com_ha, 4) * 100)
focus_arable_lands_df.insert(loc=6, column='perc_2012', value=round(focus_arable_lands_df.surface_ha_2012 / focus_arable_lands_df.sup_com_ha, 4) * 100)
focus_arable_lands_df.insert(loc=7, column='perc_2018', value=round(focus_arable_lands_df.surface_ha_2018 / focus_arable_lands_df.sup_com_ha, 4) * 100)
focus_arable_lands_df.insert(loc=8, column='Perc.Diff. | 2018-2000', value=(focus_arable_lands_df.perc_2018 - focus_arable_lands_df.perc_2000))
focus_arable_lands_df.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=True, inplace=True)
focus_arable_lands_df
pro_com comune sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
238 64072.0 Pietradefusi 924.149874 77.90 77.90 20.16 20.16 20.16 -57.74 186.3344 186.3344 186.3345 719.9375 719.9308
229 64056.0 Montefusco 823.823558 59.13 59.13 2.01 2.01 2.01 -57.12 16.5238 16.5238 16.5238 487.1327 487.1269
250 64093.0 Santa Paolina 843.429641 29.95 29.95 0.25 0.25 0.25 -29.70 2.0832 2.0832 2.0832 252.5909 252.5864
189 63089.0 Volla 620.630205 33.87 29.74 29.74 1.05 1.05 -28.69 6.5071 6.5071 184.6057 184.6056 210.2015
260 64110.0 Torre Le Nocelle 1004.158412 43.42 43.42 17.52 17.52 17.52 -25.90 175.8839 175.8839 175.8840 436.0116 436.0185
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
130 62049.0 Paupisi 683.272709 0.00 0.00 0.00 23.31 23.31 23.31 159.2668 159.2668 0.0000 0.0000 0.0000
13 61016.0 Carinaro 631.593670 13.06 9.15 9.15 41.13 41.13 31.98 259.7728 259.7728 57.8113 57.8112 82.5094
17 61020.0 Casaluce 955.842829 0.70 0.70 0.70 60.53 60.53 59.83 578.6141 578.6141 6.7225 6.7226 6.7222
32 61037.0 Frignano 985.795577 0.00 0.00 0.00 69.03 69.03 69.03 680.5149 680.5149 0.0000 0.0000 0.0000
84 61098.0 Villa di Briano 854.692612 3.91 3.91 3.91 74.03 74.03 70.12 632.7436 632.7436 33.4422 33.4424 33.4453

363 rows × 14 columns

Codice

# Superfici in arretramento
reduction_arable_lands_area = focus_arable_lands_df.iloc[0:10]
reduction_arable_lands_area
pro_com comune sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
238 64072.0 Pietradefusi 924.149874 77.90 77.90 20.16 20.16 20.16 -57.74 186.3344 186.3344 186.3345 719.9375 719.9308
229 64056.0 Montefusco 823.823558 59.13 59.13 2.01 2.01 2.01 -57.12 16.5238 16.5238 16.5238 487.1327 487.1269
250 64093.0 Santa Paolina 843.429641 29.95 29.95 0.25 0.25 0.25 -29.70 2.0832 2.0832 2.0832 252.5909 252.5864
189 63089.0 Volla 620.630205 33.87 29.74 29.74 1.05 1.05 -28.69 6.5071 6.5071 184.6057 184.6056 210.2015
260 64110.0 Torre Le Nocelle 1004.158412 43.42 43.42 17.52 17.52 17.52 -25.90 175.8839 175.8839 175.8840 436.0116 436.0185
190 63090.0 Santa Maria la Carità 397.883761 24.44 24.44 0.29 0.29 0.29 -24.15 1.1544 1.1544 1.1544 97.2554 97.2552
76 61088.0 Sessa Aurunca 16218.704457 25.28 25.35 2.23 2.56 2.56 -22.79 415.3546 415.3546 362.1214 4112.0036 4099.7632
166 63025.0 Castello di Cisterna 392.135970 44.25 44.25 21.85 21.85 21.85 -22.40 85.6953 85.6953 85.6953 173.5036 173.5048
232 64059.0 Montemiletto 2163.480616 34.90 33.95 14.70 14.70 14.70 -19.25 318.1006 318.1006 318.1007 734.5263 755.1347
163 63017.0 Casalnuovo di Napoli 783.538578 44.85 41.66 41.66 27.45 27.45 -14.21 215.1126 215.1126 326.3900 326.3901 351.4509
Codice

# Superfici in avanzamento
increase_arable_lands_area = focus_arable_lands_df.iloc[-10:]
increase_arable_lands_area.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=False, inplace=True)
increase_arable_lands_area
pro_com comune sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
84 61098.0 Villa di Briano 854.692612 3.91 3.91 3.91 74.03 74.03 70.12 632.7436 632.7436 33.4422 33.4424 33.4453
32 61037.0 Frignano 985.795577 0.00 0.00 0.00 69.03 69.03 69.03 680.5149 680.5149 0.0000 0.0000 0.0000
17 61020.0 Casaluce 955.842829 0.70 0.70 0.70 60.53 60.53 59.83 578.6141 578.6141 6.7225 6.7226 6.7222
13 61016.0 Carinaro 631.593670 13.06 9.15 9.15 41.13 41.13 31.98 259.7728 259.7728 57.8113 57.8112 82.5094
130 62049.0 Paupisi 683.272709 0.00 0.00 0.00 23.31 23.31 23.31 159.2668 159.2668 0.0000 0.0000 0.0000
143 62062.0 San Lorenzo Maggiore 1629.550368 0.93 0.93 0.93 18.00 18.00 17.07 293.3587 293.3587 15.1138 15.1136 15.1112
134 62053.0 Ponte 1791.529818 18.98 18.98 18.11 35.86 35.86 16.88 642.4110 642.4110 324.5012 339.9875 339.9830
217 64037.0 Greci 3026.329172 33.15 34.35 50.04 50.02 50.02 15.67 1513.7004 1513.7004 1514.3325 1039.4665 1003.1417
36 61043.0 Gricignano di Aversa 997.757144 27.33 19.84 19.70 35.43 35.43 15.59 353.4710 353.4710 196.5586 197.9250 272.6607
63 61074.0 San Cipriano d'Aversa 619.009752 44.14 44.14 43.49 56.38 56.38 12.24 348.9748 348.9748 269.2010 273.2419 273.2415
Codice
arable_lands_df = pd.concat([reduction_arable_lands_area, increase_arable_lands_area])
arable_lands_df.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=True, inplace=True)
arable_lands_df = arable_lands_df[['comune', 'Perc.Diff. | 2018-2000']]
arable_lands_df['Perc.Diff. | 2018-2000'] = arable_lands_df['Perc.Diff. | 2018-2000']
arable_lands_df.set_index('comune', inplace=True)

fig_arable_lands = plt.figure(figsize=(20, 10), dpi=100)
sub_fig_arable_lands = GridSpec(nrows=1, ncols=1, figure=fig_arable_lands, hspace=0.35)
ax_arable_lands = fig_arable_lands.add_subplot(sub_fig_arable_lands[0, 0])
arable_lands_df.plot.bar(ax=ax_arable_lands, rot=0)

ax_arable_lands.yaxis.set_label_text('%')
ax_arable_lands.set_xlabel('')
ax_arable_lands.grid(color='lightgrey', linestyle='dashdot')

plt.xticks(rotation = 90)
plt.legend(loc='best')
<matplotlib.legend.Legend at 0x7fe2d5ed0fd0>

Tra i Comuni con maggior aumento della aree articifiali abbiamo visto che c’è Volla che è tra i Comuni con maggior decremento delle aree agricole. Poco sopra ho fatto una riflessione sul perchè della redistribuzione del livello 1 della CLC. Due indizi non fanno una prova, ma tre?

2.3.4 Aree bruciate

Codice

# Dati 1990
burnt_surface_1990 = focus_1990_group[focus_1990_group['label3'].isin(['Burnt areas'])]
burnt_surface_1990_group = pd.DataFrame(burnt_surface_1990.groupby(['pro_com'])['surface_ha_1990'].sum())
burnt_surface_1990_group.reset_index(inplace=True)

# Dati 2000
burnt_surface_2000 = focus_2000_group[focus_2000_group['label3'].isin(['Burnt areas'])]
burnt_surface_2000_group = pd.DataFrame(burnt_surface_2000.groupby(['pro_com'])['surface_ha_2000'].sum())
burnt_surface_2000_group.reset_index(inplace=True)

# Dati 2006
burnt_surface_2006 = focus_2006_group[focus_2006_group['label3'].isin(['Burnt areas'])]
burnt_surface_2006_group = pd.DataFrame(burnt_surface_2006.groupby(['pro_com'])['surface_ha_2006'].sum())
burnt_surface_2006_group.reset_index(inplace=True)

# Dati 2012
burnt_surface_2012 = focus_2012_group[focus_2012_group['label3'].isin(['Burnt areas'])]
burnt_surface_2012_group = pd.DataFrame(burnt_surface_2012.groupby(['pro_com'])['surface_ha_2012'].sum())
burnt_surface_2012_group.reset_index(inplace=True)

# Dati 2018
burnt_surface_2018 = focus_2018_group[focus_2018_group['label3'].isin(['Burnt areas'])]
burnt_surface_2018_group = pd.DataFrame(burnt_surface_2018.groupby(['pro_com', 'comune', 'sup_com_ha'])['surface_ha_2018'].sum())
burnt_surface_2018_group.reset_index(inplace=True)

# Dati aggregati
focus_arable_lands = [burnt_surface_2018_group, burnt_surface_2012_group, burnt_surface_2006_group, burnt_surface_2000_group, burnt_surface_1990_group]
focus_burnt_df = reduce(lambda  left,right: pd.merge(left,right,on=['pro_com'], how='outer'), focus_arable_lands)
focus_burnt_df = focus_burnt_df[focus_burnt_df['sup_com_ha'] > 0]
focus_burnt_df.fillna(0, inplace=True)
focus_burnt_df.insert(loc=3, column='perc_1990', value=round(focus_burnt_df.surface_ha_1990 / focus_burnt_df.sup_com_ha, 4) * 100)
focus_burnt_df.insert(loc=4, column='perc_2000', value=round(focus_burnt_df.surface_ha_2000 / focus_burnt_df.sup_com_ha, 4) * 100)
focus_burnt_df.insert(loc=5, column='perc_2006', value=round(focus_burnt_df.surface_ha_2006 / focus_burnt_df.sup_com_ha, 4) * 100)
focus_burnt_df.insert(loc=6, column='perc_2012', value=round(focus_burnt_df.surface_ha_2012 / focus_burnt_df.sup_com_ha, 4) * 100)
focus_burnt_df.insert(loc=7, column='perc_2018', value=round(focus_burnt_df.surface_ha_2018 / focus_burnt_df.sup_com_ha, 4) * 100)
focus_burnt_df.insert(loc=8, column='Perc.Diff. | 2018-2000', value=(focus_burnt_df.perc_2018 - focus_burnt_df.perc_2000))
focus_burnt_df.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=False, inplace=True)
focus_burnt_df
pro_com comune sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
20 63082.0 Terzigno 2346.480829 0.0 0.00 0.00 0.00 18.50 18.50 434.1155 0.0000 0.0000 0.0000 0.0
16 63064.0 Ercolano 1989.359843 0.0 0.00 0.00 8.49 17.54 17.54 348.9169 168.9727 0.0000 0.0000 0.0
10 63009.0 Boscotrecase 753.047593 0.0 0.00 0.00 0.00 16.95 16.95 127.6552 0.0000 0.0000 0.0000 0.0
22 63091.0 Trecase 621.312671 0.0 0.00 0.00 0.00 13.75 13.75 85.4539 0.0000 0.0000 0.0000 0.0
13 63051.0 Ottaviano 2002.268341 0.0 0.00 0.00 0.88 12.06 12.06 241.4028 17.6023 0.0000 0.0000 0.0
50 65142.0 Siano 856.918877 0.0 0.00 0.00 0.00 10.24 10.24 87.7539 0.0000 0.0000 0.0000 0.0
24 64076.0 Quadrelle 693.114011 0.0 0.00 0.00 0.00 9.65 9.65 66.9010 0.0000 0.0000 0.0000 0.0
21 63084.0 Torre del Greco 3066.110669 0.0 0.00 0.00 1.58 9.19 9.19 281.8574 48.5022 0.0000 0.0000 0.0
11 63041.0 Marano di Napoli 1565.253641 0.0 0.00 0.00 9.11 9.11 9.11 142.5412 142.5412 0.0000 0.0000 0.0
45 65108.0 Roccapiemonte 531.325342 0.0 3.85 0.00 0.00 10.68 6.83 56.7487 0.0000 0.0000 20.4508 0.0
41 65067.0 Mercato San Severino 3033.314668 0.0 3.72 0.00 0.00 9.55 5.83 289.6134 0.0000 0.0000 112.7573 0.0
36 65047.0 Corbara 673.181063 0.0 0.00 0.00 0.00 5.43 5.43 36.5565 0.0000 0.0000 0.0000 0.0
27 64100.0 Sirignano 618.669347 0.0 0.00 0.00 0.00 5.42 5.42 33.5478 0.0000 0.0000 0.0000 0.0
33 65020.0 Calvanico 1490.919524 0.0 0.00 0.00 0.00 4.50 4.50 67.0813 0.0000 0.0000 0.0000 0.0
1 61024.0 Castel di Sasso 2032.307571 0.0 0.00 0.00 0.00 3.73 3.73 75.8659 0.0000 0.0000 0.0000 0.0
29 64121.0 Montoro 4014.094054 0.0 0.10 0.00 0.00 3.73 3.63 149.7874 0.0000 0.0000 4.0627 0.0
30 65013.0 Baronissi 1792.982916 0.0 0.00 0.00 0.00 3.61 3.61 64.6775 0.0000 0.0000 0.0000 0.0
17 63065.0 Roccarainola 2833.151640 0.0 0.00 0.00 0.00 3.55 3.55 100.4408 0.0000 0.0000 0.0000 0.0
9 62028.0 Durazzano 1290.490693 0.0 0.00 0.00 3.36 3.36 3.36 43.3596 43.3596 0.0000 0.0000 0.0
8 62009.0 Bonea 1146.261236 0.0 0.00 0.00 0.00 3.09 3.09 35.4456 0.0000 0.0000 0.0000 0.0
44 65079.0 Nocera Superiore 1466.245166 0.0 0.00 0.00 0.00 3.00 3.00 44.0193 0.0000 0.0000 0.0000 0.0
48 65131.0 Santomenna 891.925366 0.0 0.00 0.00 2.94 2.94 2.94 26.1979 26.1980 0.0000 0.0000 0.0
4 61051.0 Mignano Monte Lungo 5309.523303 0.0 0.00 0.00 0.00 2.72 2.72 144.4552 0.0000 0.0000 0.0000 0.0
51 65151.0 Tramonti 2482.596555 0.0 0.00 0.00 2.59 2.59 2.59 64.2754 64.2754 0.0000 0.0000 0.0
14 63060.0 Pozzuoli 4343.389600 0.0 0.00 0.00 2.38 2.38 2.38 103.1784 103.1784 0.0000 0.0000 0.0
49 65135.0 Sarno 4000.061759 0.0 0.00 0.00 0.00 2.15 2.15 85.9694 0.0000 0.0000 0.0000 0.0
40 65060.0 Laureana Cilento 1374.420460 0.0 0.00 0.00 0.00 1.98 1.98 27.2732 0.0000 0.0000 0.0000 0.0
6 61075.0 San Felice a Cancello 2717.900285 0.0 0.00 0.00 0.00 1.98 1.98 53.7304 0.0000 0.0000 0.0000 0.0
25 64077.0 Quindici 2390.824174 0.0 0.00 0.00 0.00 1.90 1.90 45.4393 0.0000 0.0000 0.0000 0.0
42 65069.0 Moio della Civitella 1718.872945 0.0 0.00 0.00 0.00 1.58 1.58 27.1520 0.0000 0.0000 0.0000 0.0
23 63092.0 Massa di Somma 304.205206 0.0 0.00 0.00 0.00 1.51 1.51 4.6046 0.0000 0.0000 0.0000 0.0
46 65124.0 San Mauro la Bruca 1905.442534 0.0 0.00 0.00 0.00 1.34 1.34 25.6184 0.0000 0.0000 0.0000 0.0
28 64101.0 Solofra 2221.354782 0.0 0.00 0.00 0.00 1.30 1.30 28.9118 0.0000 0.0000 0.0000 0.0
43 65078.0 Nocera Inferiore 2094.467250 0.0 0.00 0.00 0.00 1.24 1.24 26.0176 0.0000 0.0000 0.0000 0.0
15 63063.0 Quarto 1415.896056 0.0 0.00 0.00 1.17 1.17 1.17 16.5525 16.5525 0.0000 0.0000 0.0
18 63068.0 San Giuseppe Vesuviano 1417.126283 0.0 0.00 0.00 0.00 1.04 1.04 14.7355 0.0000 0.0000 0.0000 0.0
32 65019.0 Caggiano 3542.966709 0.0 0.00 0.00 0.93 0.93 0.93 32.9540 32.9540 0.0000 0.0000 0.0
34 65034.0 Castel San Giorgio 1358.506777 0.0 0.00 0.00 0.00 0.75 0.75 10.1730 0.0000 0.0000 0.0000 0.0
0 61022.0 Caserta 5406.845910 0.0 0.00 0.00 0.67 0.67 0.67 36.0326 36.0326 0.0000 0.0000 0.0
12 63049.0 Napoli 11894.407425 0.0 0.00 0.00 0.53 0.53 0.53 63.0803 63.0803 0.0000 0.0000 0.0
5 61052.0 Mondragone 5572.569052 0.0 0.00 2.74 0.00 0.52 0.52 29.0151 0.0000 152.8844 0.0000 0.0
39 65055.0 Giffoni Sei Casali 3507.503961 0.0 0.00 0.00 0.00 0.22 0.22 7.7114 0.0000 0.0000 0.0000 0.0
31 65016.0 Bracigliano 1440.868176 0.0 0.26 0.00 0.00 0.47 0.21 6.7033 0.0000 0.0000 3.7261 0.0
19 63079.0 Somma Vesuviana 3066.640742 0.0 0.00 0.00 0.11 0.11 0.11 3.3491 3.3491 0.0000 0.0000 0.0
3 61045.0 Liberi 1758.913730 0.0 0.00 0.00 0.00 0.10 0.10 1.7792 0.0000 0.0000 0.0000 0.0
2 61026.0 Castel Morrone 2534.392208 0.0 0.00 0.00 0.10 0.10 0.10 2.6154 2.6154 0.0000 0.0000 0.0
26 64099.0 Serino 5249.939474 0.0 0.00 0.00 0.00 0.04 0.04 1.9971 0.0000 0.0000 0.0000 0.0
38 65054.0 Futani 1485.058368 0.0 0.00 0.00 0.00 0.02 0.02 0.3298 0.0000 0.0000 0.0000 0.0
7 61079.0 San Pietro Infine 1371.735131 0.0 0.00 0.00 0.00 0.01 0.01 0.1720 0.0000 0.0000 0.0000 0.0
47 65130.0 Sant'Egidio del Monte Albino 724.511924 0.0 0.00 0.00 0.00 0.00 0.00 0.0259 0.0000 0.0000 0.0000 0.0
35 65037.0 Cava de' Tirreni 3653.112071 0.0 1.55 0.00 0.04 1.48 -0.07 54.0169 1.6314 0.0000 56.7053 0.0
37 65052.0 Fisciano 3168.742849 0.0 4.15 0.00 0.00 1.43 -2.72 45.1761 0.0000 0.0000 131.4723 0.0

In questa analisi ci sono in 6 delle prime 10 posizioni alcuni dei Comuni interessati dall’incendio del 2017.

2.3.5 Aree adibite a discarica

Codice

# Dati 1990
dump_surface_1990 = focus_1990_group[focus_1990_group['label3'].isin(['Dump sites'])]
dump_surface_1990_group = pd.DataFrame(dump_surface_1990.groupby(['pro_com'])['surface_ha_1990'].sum())
dump_surface_1990_group.reset_index(inplace=True)

# Dati 2000
dump_surface_2000 = focus_2000_group[focus_2000_group['label3'].isin(['Dump sites'])]
dump_surface_2000_group = pd.DataFrame(dump_surface_2000.groupby(['pro_com'])['surface_ha_2000'].sum())
dump_surface_2000_group.reset_index(inplace=True)

# Dati 2006
dump_surface_2006 = focus_2006_group[focus_2006_group['label3'].isin(['Dump sites'])]
dump_surface_2006_group = pd.DataFrame(dump_surface_2006.groupby(['pro_com'])['surface_ha_2006'].sum())
dump_surface_2006_group.reset_index(inplace=True)

# Dati 2012
dump_surface_2012 = focus_2012_group[focus_2012_group['label3'].isin(['Dump sites'])]
dump_surface_2012_group = pd.DataFrame(dump_surface_2012.groupby(['pro_com'])['surface_ha_2012'].sum())
dump_surface_2012_group.reset_index(inplace=True)

# Dati 2018
dump_surface_2018 = focus_2018_group[focus_2018_group['label3'].isin(['Dump sites'])]
dump_surface_2018_group = pd.DataFrame(dump_surface_2018.groupby(['pro_com', 'comune', 'sup_com_ha'])['surface_ha_2018'].sum())
dump_surface_2018_group.reset_index(inplace=True)

# Dati aggregati
focus_arable_lands = [dump_surface_2018_group, dump_surface_2012_group, dump_surface_2006_group, dump_surface_2000_group, dump_surface_1990_group]
focus_dump_df = reduce(lambda  left,right: pd.merge(left,right,on=['pro_com'], how='outer'), focus_arable_lands)
focus_dump_df = focus_dump_df[focus_dump_df['sup_com_ha'] > 0]
focus_dump_df.fillna(0, inplace=True)
focus_dump_df.insert(loc=3, column='perc_1990', value=round(focus_dump_df.surface_ha_1990 / focus_dump_df.sup_com_ha, 4) * 100)
focus_dump_df.insert(loc=4, column='perc_2000', value=round(focus_dump_df.surface_ha_2000 / focus_dump_df.sup_com_ha, 4) * 100)
focus_dump_df.insert(loc=5, column='perc_2006', value=round(focus_dump_df.surface_ha_2006 / focus_dump_df.sup_com_ha, 4) * 100)
focus_dump_df.insert(loc=6, column='perc_2012', value=round(focus_dump_df.surface_ha_2012 / focus_dump_df.sup_com_ha, 4) * 100)
focus_dump_df.insert(loc=7, column='perc_2018', value=round(focus_dump_df.surface_ha_2018 / focus_dump_df.sup_com_ha, 4) * 100)
focus_dump_df.insert(loc=8, column='Perc.Diff. | 2018-2000', value=(focus_dump_df.perc_2018 - focus_dump_df.perc_2000))
focus_dump_df.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=False, inplace=True)
focus_dump_df
pro_com comune sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
4 63034.0 Giugliano in Campania 9463.207795 0.01 0.01 0.16 1.89 1.89 1.88 179.2958 179.2958 14.7856 0.9317 0.9314
1 61085.0 San Tammaro 3696.678415 0.00 0.00 0.00 2.09 1.63 1.63 60.2521 77.3722 0.0000 0.0000 0.0000
3 63011.0 Caivano 2722.117368 1.34 0.00 1.32 1.32 1.32 1.32 35.9863 35.9863 35.9863 0.0000 36.5076
2 61099.0 Villa Literno 6182.490260 0.00 0.00 1.15 1.19 1.19 1.19 73.7065 73.7065 70.9893 0.0000 0.0000
0 61084.0 Santa Maria la Fossa 2972.966831 0.00 0.00 0.00 1.56 1.07 1.07 31.7251 46.3154 0.0000 0.0000 0.0000
5 64005.0 Ariano Irpino 18673.653095 0.00 0.00 0.14 0.14 0.14 0.14 25.9872 25.9872 25.9872 0.0000 0.0000

Qui abbiamo le più grandi discariche campane, cresciute con la crisi dei rifiuti di inizio anni 2000.

2.3.6 Aree verdi urbane

Codice
# Dati 1990
green_urban_surface_1990 = focus_1990_group[focus_1990_group['label3'].isin(['Green urban areas'])]
green_urban_surface_1990_group = pd.DataFrame(green_urban_surface_1990.groupby(['pro_com'])['surface_ha_1990'].sum())
green_urban_surface_1990_group.reset_index(inplace=True)

# Dati 2000
green_urban_surface_2000 = focus_2000_group[focus_2000_group['label3'].isin(['Green urban areas'])]
green_urban_surface_2000_group = pd.DataFrame(green_urban_surface_2000.groupby(['pro_com'])['surface_ha_2000'].sum())
green_urban_surface_2000_group.reset_index(inplace=True)

# Dati 2006
green_urban_surface_2006 = focus_2006_group[focus_2006_group['label3'].isin(['Green urban areas'])]
green_urban_surface_2006_group = pd.DataFrame(green_urban_surface_2006.groupby(['pro_com'])['surface_ha_2006'].sum())
green_urban_surface_2006_group.reset_index(inplace=True)

# Dati 2012
green_urban_surface_2012 = focus_2012_group[focus_2012_group['label3'].isin(['Green urban areas'])]
green_urban_surface_2012_group = pd.DataFrame(green_urban_surface_2012.groupby(['pro_com'])['surface_ha_2012'].sum())
green_urban_surface_2012_group.reset_index(inplace=True)

# Dati 2018
green_urban_surface_2018 = focus_2018_group[focus_2018_group['label3'].isin(['Green urban areas'])]
green_urban_surface_2018_group = pd.DataFrame(green_urban_surface_2018.groupby(['pro_com', 'comune', 'sup_com_ha'])['surface_ha_2018'].sum())
green_urban_surface_2018_group.reset_index(inplace=True)

# Dati aggregati
focus_green_urban = [green_urban_surface_2018_group, green_urban_surface_2012_group, green_urban_surface_2006_group, green_urban_surface_2000_group, green_urban_surface_1990_group]
focus_green_urban_df = reduce(lambda  left,right: pd.merge(left,right,on=['pro_com'], how='outer'), focus_green_urban)
focus_green_urban_df = focus_green_urban_df[focus_green_urban_df['sup_com_ha'] > 0]
focus_green_urban_df.fillna(0, inplace=True)
focus_green_urban_df.insert(loc=3, column='perc_1990', value=round(focus_green_urban_df.surface_ha_1990 / focus_green_urban_df.sup_com_ha, 4) * 100)
focus_green_urban_df.insert(loc=4, column='perc_2000', value=round(focus_green_urban_df.surface_ha_2000 / focus_green_urban_df.sup_com_ha, 4) * 100)
focus_green_urban_df.insert(loc=5, column='perc_2006', value=round(focus_green_urban_df.surface_ha_2006 / focus_green_urban_df.sup_com_ha, 4) * 100)
focus_green_urban_df.insert(loc=6, column='perc_2012', value=round(focus_green_urban_df.surface_ha_2012 / focus_green_urban_df.sup_com_ha, 4) * 100)
focus_green_urban_df.insert(loc=7, column='perc_2018', value=round(focus_green_urban_df.surface_ha_2018 / focus_green_urban_df.sup_com_ha, 4) * 100)
focus_green_urban_df.insert(loc=8, column='Perc.Diff. | 2018-2000', value=(focus_green_urban_df.perc_2018 - focus_green_urban_df.perc_2000))
focus_green_urban_df.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=False, inplace=True)
focus_green_urban_df
pro_com comune sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
3 62062.0 San Lorenzo Maggiore 1629.550368 0.00 0.00 0.00 0.00 11.89 11.89 193.6792 0.0000 0.0000 0.0000 0.0000
2 62037.0 Guardia Sanframondi 2109.748292 0.00 0.00 0.00 0.00 9.63 9.63 203.2666 0.0000 0.0000 0.0000 0.0000
8 65078.0 Nocera Inferiore 2094.467250 0.00 0.00 1.91 1.91 1.91 1.91 39.9739 39.9739 39.9740 0.0000 0.0000
6 63060.0 Pozzuoli 4343.389600 0.96 0.61 0.68 0.68 0.68 0.07 29.4516 29.4516 29.4517 26.6666 41.7480
0 61018.0 Casagiove 635.694991 0.79 0.79 0.79 0.79 0.79 0.00 5.0499 5.0499 5.0499 5.0499 5.0496
1 61022.0 Caserta 5406.845910 2.50 2.50 2.50 2.50 2.50 0.00 135.0386 135.0386 135.0386 135.0386 135.0388
5 63059.0 Portici 460.130721 0.00 12.99 12.99 12.99 12.99 0.00 59.7546 59.7546 59.7546 59.7546 0.0000
7 63064.0 Ercolano 1989.359843 0.00 0.21 0.21 0.21 0.21 0.00 4.2403 4.2403 4.2403 4.2404 0.0000
4 63049.0 Napoli 11894.407425 3.15 4.44 4.47 4.26 4.26 -0.18 506.6039 506.6039 531.7766 527.6737 374.2016

3. Analisi a scala nazionale

Fino a qui abbiamo visto una analisi focalizzata sulla Campania. Di seguito ripeterò tutte le analisi ma a scala nazionale.

3.1.1 Stato al 1990

Codice
clc1990_label1 = pd.DataFrame(clc1990.groupby('label1')['surface_ha_1990'].sum())
clc1990_label1.reset_index(inplace=True)
clc1990_label1.rename(columns={'label1': 'CORINE Land Cover - level 1'}, inplace=True)
clc1990_label1
CORINE Land Cover - level 1 surface_ha_1990
0 Agricultural areas 1.582623e+07
1 Artificial surfaces 1.347486e+06
2 Forest and semi natural areas 1.263937e+07
3 Water bodies 3.122569e+05
4 Wetlands 6.897894e+04
Codice
clc1990_label2 = pd.DataFrame(clc1990.groupby('label2')['surface_ha_1990'].sum())
clc1990_label2.reset_index(inplace=True)
clc1990_label2.rename(columns={'label2': 'CORINE Land Cover - level 2'}, inplace=True)
clc1990_label2
CORINE Land Cover - level 2 surface_ha_1990
0 Arable land 8.392567e+06
1 Artificial, non-agricultural vegetated areas 2.798779e+04
2 Forests 7.831590e+06
3 Heterogeneous agricultural areas 4.778702e+06
4 Industrial, commercial and transport units 2.363946e+05
5 Inland waters 2.175910e+05
6 Inland wetlands 1.585394e+04
7 Marine waters 9.466595e+04
8 Maritime wetlands 5.312500e+04
9 Mine, dump and construction sites 5.153499e+04
10 Open spaces with little or no vegetation 1.106064e+06
11 Pastures 4.560942e+05
12 Permanent crops 2.198869e+06
13 Scrub and/or herbaceous vegetation associations 3.701717e+06
14 Urban fabric 1.031569e+06
Codice
clc1990_label3 = pd.DataFrame(clc1990.groupby('label3')['surface_ha_1990'].sum())
clc1990_label3.reset_index(inplace=True)
clc1990_label3.rename(columns={'label3': 'CORINE Land Cover - level 3'}, inplace=True)
clc1990_label3
CORINE Land Cover - level 3 surface_ha_1990
0 Agro-forestry areas 1.875953e+05
1 Airports 2.061481e+04
2 Annual crops associated with permanent crops 3.948806e+05
3 Bare rocks 4.823748e+05
4 Beaches, dunes, sands 8.237502e+04
5 Broad-leaved forest 5.477418e+06
6 Burnt areas 3.688807e+03
7 Coastal lagoons 9.441910e+04
8 Complex cultivation patterns 2.208152e+06
9 Coniferous forest 1.322878e+06
10 Construction sites 6.608806e+03
11 Continuous urban fabric 1.465191e+05
12 Discontinuous urban fabric 8.850498e+05
13 Dump sites 1.813232e+03
14 Estuaries 2.468468e+02
15 Fruit trees and berry plantations 3.995981e+05
16 Glaciers and perpetual snow 5.296383e+04
17 Green urban areas 1.029782e+04
18 Industrial or commercial units 1.935121e+05
19 Inland marshes 1.581604e+04
20 Land principally occupied by agriculture, with... 1.988074e+06
21 Mineral extraction sites 4.311295e+04
22 Mixed forest 1.031295e+06
23 Moors and heathland 2.759264e+05
24 Natural grasslands 1.452501e+06
25 Non-irrigated arable land 8.075700e+06
26 Olive groves 1.263777e+06
27 Pastures 4.560942e+05
28 Peat bogs 3.790290e+01
29 Permanently irrigated land 4.081855e+04
30 Port areas 9.735064e+03
31 Rice fields 2.760486e+05
32 Road and rail networks and associated land 1.253256e+04
33 Salines 1.000881e+04
34 Salt marshes 4.311619e+04
35 Sclerophyllous vegetation 9.609517e+05
36 Sparsely vegetated areas 4.846612e+05
37 Sport and leisure facilities 1.768997e+04
38 Transitional woodland-shrub 1.012338e+06
39 Vineyards 5.354933e+05
40 Water bodies 1.681098e+05
41 Water courses 4.948117e+04

3.1.2 Stato al 2000

Codice
clc2000_label1 = pd.DataFrame(clc2000.groupby('label1')['surface_ha_2000'].sum())
clc2000_label1.reset_index(inplace=True)
clc2000_label1.rename(columns={'label1': 'CORINE Land Cover - level 1'}, inplace=True)

clc2000_label2 = pd.DataFrame(clc2000.groupby('label2')['surface_ha_2000'].sum())
clc2000_label2.reset_index(inplace=True)
clc2000_label2.rename(columns={'label2': 'CORINE Land Cover - level 2'}, inplace=True)

clc2000_label3 = pd.DataFrame(clc2000.groupby('label3')['surface_ha_2000'].sum())
clc2000_label3.reset_index(inplace=True)
clc2000_label3.rename(columns={'label3': 'CORINE Land Cover - level 3'}, inplace=True)

3.1.3 Stato al 2006

Codice
clc2006_label1 = pd.DataFrame(clc2006.groupby('label1')['surface_ha_2006'].sum())
clc2006_label1.reset_index(inplace=True)
clc2006_label1.rename(columns={'label1': 'CORINE Land Cover - level 1'}, inplace=True)

clc2006_label2 = pd.DataFrame(clc2006.groupby('label2')['surface_ha_2006'].sum())
clc2006_label2.reset_index(inplace=True)
clc2006_label2.rename(columns={'label2': 'CORINE Land Cover - level 2'}, inplace=True)

clc2006_label3 = pd.DataFrame(clc2006.groupby('label3')['surface_ha_2006'].sum())
clc2006_label3.reset_index(inplace=True)
clc2006_label3.rename(columns={'label3': 'CORINE Land Cover - level 3'}, inplace=True)

3.1.4 Stato al 2012

Codice
clc2012_label1 = pd.DataFrame(clc2012.groupby('label1')['surface_ha_2012'].sum())
clc2012_label1.reset_index(inplace=True)
clc2012_label1.rename(columns={'label1': 'CORINE Land Cover - level 1'}, inplace=True)

clc2012_label2 = pd.DataFrame(clc2012.groupby('label2')['surface_ha_2012'].sum())
clc2012_label2.reset_index(inplace=True)
clc2012_label2.rename(columns={'label2': 'CORINE Land Cover - level 2'}, inplace=True)

clc2012_label3 = pd.DataFrame(clc2012.groupby('label3')['surface_ha_2012'].sum())
clc2012_label3.reset_index(inplace=True)
clc2012_label3.rename(columns={'label3': 'CORINE Land Cover - level 3'}, inplace=True)

3.1.5 Stato al 2018

Codice
clc2018_label1 = pd.DataFrame(clc2018.groupby('label1')['surface_ha_2018'].sum())
clc2018_label1.reset_index(inplace=True)
clc2018_label1.rename(columns={'label1': 'CORINE Land Cover - level 1'}, inplace=True)

clc2018_label2 = pd.DataFrame(clc2018.groupby('label2')['surface_ha_2018'].sum())
clc2018_label2.reset_index(inplace=True)
clc2018_label2.rename(columns={'label2': 'CORINE Land Cover - level 2'}, inplace=True)

clc2018_label3 = pd.DataFrame(clc2018.groupby('label3')['surface_ha_2018'].sum())
clc2018_label3.reset_index(inplace=True)
clc2018_label3.rename(columns={'label3': 'CORINE Land Cover - level 3'}, inplace=True)

3.2 Grafici

Mi limiterò a commentare solo la analisi a scala nazionale perchè quelle a scala comunale hanno risultati che, sebbene intuibili dal punto di vista delle variazioni, hanno una valenza territoriale molto forte(come lo è stato per la Campania) e non conoscendo tutti quei territori preferisco lasciare a chi è del posto le considerazioni del caso.

3.2.1 Grafici per il livello 1

Codice
lvl1_naz = [clc1990_label1, clc2000_label1, clc2006_label1, clc2012_label1, clc2018_label1]
lvl1_naz_df = reduce(lambda  left,right: pd.merge(left,right,on=['CORINE Land Cover - level 1'], how='outer'), lvl1_naz)
lvl1_naz_df.insert(loc=6, column='Surf.Diff. | 2018-1990', value=(lvl1_naz_df.surface_ha_2018 - lvl1_naz_df.surface_ha_1990))
lvl1_naz_df.fillna(0, inplace=True)
lvl1_naz_df.set_index('CORINE Land Cover - level 1', inplace=True)
lvl1_naz_df.rename(columns={'surface_ha_1990': '1990', 'surface_ha_2000': '2000', 'surface_ha_2006': '2006', 'surface_ha_2012': '2012', 'surface_ha_2018': '2018'}, inplace=True)
lvl1_naz_df
1990 2000 2006 2012 2018 Surf.Diff. | 2018-1990
CORINE Land Cover - level 1
Agricultural areas 1.582623e+07 1.580692e+07 1.578178e+07 1.569692e+07 1.568826e+07 -137975.0803
Artificial surfaces 1.347486e+06 1.443652e+06 1.562164e+06 1.646988e+06 1.655775e+06 308288.5459
Forest and semi natural areas 1.263937e+07 1.256172e+07 1.246520e+07 1.246121e+07 1.245564e+07 -183733.2104
Water bodies 3.122569e+05 3.152274e+05 3.145802e+05 3.228559e+05 3.226620e+05 10405.1059
Wetlands 6.897894e+04 6.780469e+04 7.179663e+04 6.758186e+04 6.796058e+04 -1018.3605
Codice
fig1_naz = plt.figure(figsize=(20, 10), dpi=100)
sub_fig_naz = GridSpec(nrows=1, ncols=1, figure=fig1_naz, hspace=0.35)
ax_naz = fig1_naz.add_subplot(sub_fig_naz[0, 0])
lvl1_naz_df.plot.bar(ax=ax_naz, rot=0)

ax_naz.yaxis.set_label_text('ha')
ax_naz.set_xlabel('')

plt.legend(loc='best')
<matplotlib.legend.Legend at 0x7fe2d08eedc0>

In questo livello di CLC si nota una certa somiglianza con ciò che abbiamo visto per la Campania: aree artificiali in aumento, aree forestate ed agricole in regressione.

3.2.2 Grafici per il livello 2

Codice
lvl2_naz = [clc1990_label2, clc2000_label2, clc2006_label2, clc2012_label2, clc2018_label2]
lvl2_naz_df = reduce(lambda  left,right: pd.merge(left,right,on=['CORINE Land Cover - level 2'], how='outer'), lvl2_naz)
lvl2_naz_df.insert(loc=6, column='Surf.Diff. | 2000-1990', value=(lvl2_naz_df.surface_ha_2000 - lvl2_naz_df.surface_ha_1990))
lvl2_naz_df.insert(loc=7, column='Surf.Diff. | 2006-2000', value=(lvl2_naz_df.surface_ha_2006 - lvl2_naz_df.surface_ha_2000))
lvl2_naz_df.insert(loc=8, column='Surf.Diff. | 2012-2006', value=(lvl2_naz_df.surface_ha_2012 - lvl2_naz_df.surface_ha_2006))
lvl2_naz_df.insert(loc=9, column='Surf.Diff. | 2018-2012', value=(lvl2_naz_df.surface_ha_2018 - lvl2_naz_df.surface_ha_2012))
lvl2_naz_df.insert(loc=9, column='Surf.Diff. | 2018-1990', value=(lvl2_naz_df.surface_ha_2018 - lvl2_naz_df.surface_ha_1990))
lvl2_naz_df.fillna(0, inplace=True)
lvl2_naz_df.set_index('CORINE Land Cover - level 2', inplace=True)
lvl2_naz_df = lvl2_naz_df[['Surf.Diff. | 2000-1990', 'Surf.Diff. | 2006-2000', 'Surf.Diff. | 2012-2006', 'Surf.Diff. | 2018-2012', 'Surf.Diff. | 2018-1990']]
lvl2_naz_df
Surf.Diff. | 2000-1990 Surf.Diff. | 2006-2000 Surf.Diff. | 2012-2006 Surf.Diff. | 2018-2012 Surf.Diff. | 2018-1990
CORINE Land Cover - level 2
Arable land 55207.0658 -10271.7226 -90231.5817 -12360.8275 -57657.0660
Artificial, non-agricultural vegetated areas 5554.0023 4316.9704 4861.1250 662.4738 15394.5715
Forests 42767.2886 86747.2016 -16412.5022 -6762.5701 106339.4179
Heterogeneous agricultural areas 3141.8711 -80525.4734 48412.0084 -629.6565 -29601.2504
Industrial, commercial and transport units 34135.2946 44503.3245 38255.6049 6654.8030 123549.0270
Inland waters 683.7863 1188.3191 3352.2153 -24.4133 5199.9074
Inland wetlands 1132.9251 1989.9128 227.6485 252.6951 3603.1815
Marine waters 2286.6599 -1835.4393 4923.4371 -169.4592 5205.1985
Maritime wetlands -2307.1754 2002.0212 -4442.4099 126.0221 -4621.5420
Mine, dump and construction sites 4408.4545 733.9772 1327.1069 -373.1135 6096.4251
Open spaces with little or no vegetation -114342.2191 -78796.3505 650778.9658 24555.2063 482195.6025
Pastures -28097.0989 2041.0694 -15847.9600 -97.1172 -42001.1067
Permanent crops -49565.7883 63620.5499 -27194.6323 4424.2135 -8715.6572
Scrub and/or herbaceous vegetation associations -6080.8338 -104468.5696 -638349.3736 -23369.4538 -772268.2308
Urban fabric 52068.0313 68957.2822 40380.6911 1842.5177 163248.5223
Codice
fig2_naz = plt.figure(figsize=(20, 10), dpi=100)
sub_fig_naz = GridSpec(nrows=1, ncols=1, figure=fig2_naz, hspace=0.35)
ax_naz = fig2_naz.add_subplot(sub_fig_naz[0, 0])
lvl2_naz_df.plot.bar(ax=ax_naz, rot=0)

ax_naz.yaxis.set_label_text('ha')
ax_naz.set_xlabel('')

plt.xticks(rotation = 90)
plt.legend(loc='best')
<matplotlib.legend.Legend at 0x7fe2cdb88ee0>

Anche qui c’è una certa somiglianza con ciò che accade in Campania.

3.2.3 Grafici per il livello 3

Codice
lvl3_naz = [clc1990_label3, clc2000_label3, clc2006_label3, clc2012_label3, clc2018_label3]
lvl3_naz_df = reduce(lambda  left,right: pd.merge(left,right,on=['CORINE Land Cover - level 3'], how='outer'), lvl3_naz)
lvl3_naz_df.insert(loc=6, column='Surf.Diff. | 2000-1990', value=(lvl3_naz_df.surface_ha_2000 - lvl3_naz_df.surface_ha_1990))
lvl3_naz_df.insert(loc=7, column='Surf.Diff. | 2006-2000', value=(lvl3_naz_df.surface_ha_2006 - lvl3_naz_df.surface_ha_2000))
lvl3_naz_df.insert(loc=8, column='Surf.Diff. | 2012-2006', value=(lvl3_naz_df.surface_ha_2012 - lvl3_naz_df.surface_ha_2006))
lvl3_naz_df.insert(loc=9, column='Surf.Diff. | 2018-2012', value=(lvl3_naz_df.surface_ha_2018 - lvl3_naz_df.surface_ha_2012))
lvl3_naz_df.insert(loc=9, column='Surf.Diff. | 2018-1990', value=(lvl3_naz_df.surface_ha_2018 - lvl3_naz_df.surface_ha_1990))
lvl3_naz_df.set_index('CORINE Land Cover - level 3', inplace=True)
lvl3_naz_df.fillna(0, inplace=True)
lvl3_naz_df
surface_ha_1990 surface_ha_2000 surface_ha_2006 surface_ha_2012 surface_ha_2018 Surf.Diff. | 2000-1990 Surf.Diff. | 2006-2000 Surf.Diff. | 2012-2006 Surf.Diff. | 2018-1990 Surf.Diff. | 2018-2012
CORINE Land Cover - level 3
Agro-forestry areas 1.875953e+05 1.752959e+05 1.703568e+05 1.690878e+05 1.688554e+05 -12299.3238 -4939.1076 -1269.0034 -18739.8517 -232.4169
Airports 2.061481e+04 2.141885e+04 2.264239e+04 2.283700e+04 2.288896e+04 804.0433 1223.5421 194.6092 2274.1471 51.9525
Annual crops associated with permanent crops 3.948806e+05 3.827196e+05 2.056113e+05 2.313810e+05 2.311865e+05 -12160.9920 -177108.3339 25769.7259 -163694.1511 -194.5511
Bare rocks 4.823748e+05 4.475284e+05 4.351080e+05 4.347627e+05 4.348511e+05 -34846.4100 -12420.4185 -345.3238 -47523.7192 88.4331
Beaches, dunes, sands 8.237502e+04 7.823843e+04 7.584526e+04 8.212965e+04 8.230547e+04 -4136.5882 -2393.1717 6284.3971 -69.5490 175.8138
Broad-leaved forest 5.477418e+06 5.490370e+06 5.558573e+06 5.681647e+06 5.674656e+06 12952.5900 68202.4264 123074.4047 197238.3262 -6991.0949
Burnt areas 3.688807e+03 7.726265e+03 3.048895e+03 1.040139e+04 2.990311e+04 4037.4575 -4677.3703 7352.4912 26214.3071 19501.7287
Coastal lagoons 9.441910e+04 9.670576e+04 9.487032e+04 9.993340e+04 9.976394e+04 2286.6592 -1835.4391 5063.0745 5344.8354 -169.4592
Complex cultivation patterns 2.208152e+06 2.171692e+06 2.202763e+06 2.204137e+06 2.198169e+06 -36460.6309 31070.7372 1374.0283 -9983.7268 -5967.8614
Coniferous forest 1.322878e+06 1.289463e+06 1.298869e+06 1.290058e+06 1.290082e+06 -33414.6622 9406.4133 -8811.0217 -32795.3931 23.8775
Construction sites 6.608806e+03 5.547377e+03 5.315068e+03 4.014755e+03 3.311441e+03 -1061.4296 -232.3081 -1300.3139 -3297.3650 -703.3134
Continuous urban fabric 1.465191e+05 1.436331e+05 1.408018e+05 1.588690e+05 1.589181e+05 -2886.0313 -2831.2332 18067.1411 12399.0267 49.1501
Discontinuous urban fabric 8.850498e+05 9.400039e+05 1.011792e+06 1.034106e+06 1.035899e+06 54954.0626 71788.5154 22313.5500 150849.4956 1793.3676
Dump sites 1.813232e+03 1.799607e+03 2.339274e+03 4.358176e+03 4.343699e+03 -13.6242 539.6663 2018.9020 2530.4679 -14.4762
Estuaries 2.468468e+02 2.468475e+02 2.468473e+02 1.072099e+02 1.072099e+02 0.0007 -0.0002 -139.6374 -139.6369 0.0000
Fruit trees and berry plantations 3.995981e+05 4.020846e+05 4.244182e+05 3.778050e+05 3.775497e+05 2486.5267 22333.5844 -46613.1517 -22048.3769 -255.3363
Glaciers and perpetual snow 5.296383e+04 4.158192e+04 3.930079e+04 3.905328e+04 3.879693e+04 -11381.9118 -2281.1240 -247.5108 -14166.9013 -256.3547
Green urban areas 1.029782e+04 1.003812e+04 1.107030e+04 1.095570e+04 1.134447e+04 -259.6952 1032.1803 -114.6074 1046.6534 388.7757
Industrial or commercial units 1.935121e+05 2.272337e+05 2.679521e+05 3.023072e+05 3.069798e+05 33721.5546 40718.4407 34355.0673 113467.6588 4672.5962
Inland marshes 1.581604e+04 1.662644e+04 1.856711e+04 1.879475e+04 1.904745e+04 810.4000 1940.6703 227.6486 3231.4140 252.6951
Land principally occupied by agriculture, with significant areas of natural vegetation 1.988074e+06 2.052137e+06 2.122588e+06 2.145125e+06 2.150890e+06 64062.8178 70451.2309 22537.2576 162816.4792 5765.1729
Mineral extraction sites 4.311295e+04 4.859646e+04 4.902308e+04 4.963160e+04 4.997628e+04 5483.5083 426.6190 608.5188 6863.3222 344.6761
Mixed forest 1.031295e+06 1.094524e+06 1.103663e+06 9.729868e+05 9.731915e+05 63229.3608 9138.3619 -130675.8852 -58103.5152 204.6473
Moors and heathland 2.759264e+05 1.444961e+05 1.486737e+05 1.640305e+05 1.640305e+05 -131430.3790 4177.6625 15356.7528 -111895.9638 -0.0001
Natural grasslands 1.452501e+06 1.471513e+06 1.384153e+06 7.696045e+05 7.621346e+05 19011.7396 -87360.1247 -614548.1465 -690366.4484 -7469.9168
Non-irrigated arable land 8.075700e+06 8.119901e+06 8.102169e+06 7.985138e+06 7.970837e+06 44200.6553 -17731.6848 -117031.0990 -104863.2290 -14301.1005
Olive groves 1.263777e+06 1.218749e+06 1.211951e+06 1.190108e+06 1.189869e+06 -45028.7668 -6797.4286 -21842.8690 -73908.7495 -239.6851
Pastures 4.560942e+05 4.279971e+05 4.300382e+05 4.141902e+05 4.140931e+05 -28097.0989 2041.0694 -15847.9600 -42001.1067 -97.1172
Peat bogs 3.790290e+01 3.604280e+02 4.096705e+02 4.096704e+02 4.096704e+02 322.5251 49.2425 -0.0001 371.7675 0.0000
Permanently irrigated land 4.081855e+04 4.092019e+04 4.131372e+04 6.817000e+04 6.996687e+04 101.6408 393.5360 26856.2729 29148.3233 1796.8736
Port areas 9.735064e+03 1.054597e+04 1.073864e+04 1.059175e+04 1.070018e+04 810.9039 192.6727 -146.8916 965.1133 108.4283
Rice fields 2.760486e+05 2.869534e+05 2.940198e+05 2.939631e+05 2.941065e+05 10904.7697 7066.4262 -56.7556 18057.8397 143.3994
Road and rail networks and associated land 1.253256e+04 1.133135e+04 1.370002e+04 1.755284e+04 1.937467e+04 -1201.2072 2368.6690 3852.8200 6842.1078 1821.8260
Salines 1.000881e+04 9.634204e+03 9.372738e+03 9.278927e+03 9.278927e+03 -374.6057 -261.4668 -93.8107 -729.8832 0.0000
Salt marshes 4.311619e+04 4.118362e+04 4.344711e+04 3.909851e+04 3.922454e+04 -1932.5697 2263.4880 -4348.5992 -3891.6588 126.0221
Sclerophyllous vegetation 9.609517e+05 1.006894e+06 1.015687e+06 1.014778e+06 1.002021e+06 45941.8202 8793.0178 -908.2755 41069.1211 -12757.4414
Sparsely vegetated areas 4.846612e+05 4.166465e+05 3.596222e+05 9.973571e+05 1.002403e+06 -68014.7666 -57024.2660 637734.9121 517741.4649 5045.5854
Sport and leisure facilities 1.768997e+04 2.350367e+04 2.678846e+04 3.176419e+04 3.203789e+04 5813.6975 3284.7901 4975.7324 14347.9181 273.6981
Transitional woodland-shrub 1.012338e+06 1.072734e+06 1.042655e+06 1.004405e+06 1.001263e+06 60395.9854 -30079.1252 -38249.7044 -11074.9397 -3142.0955
Vineyards 5.354933e+05 5.284697e+05 5.765541e+05 6.178155e+05 6.227348e+05 -7023.5482 48084.3941 41261.3884 87241.4692 4919.2349
Water bodies 1.681098e+05 1.698191e+05 1.727610e+05 1.755170e+05 1.755253e+05 1709.2964 2941.8555 2756.0595 7415.4779 8.2665
Water courses 4.948117e+04 4.845566e+04 4.670212e+04 4.729828e+04 4.726560e+04 -1025.5101 -1753.5364 596.1558 -2215.5705 -32.6798
Codice
fig3_naz = plt.figure(figsize=(20, 10), dpi=100)
sub_fig_naz = GridSpec(nrows=1, ncols=1, figure=fig3_naz, hspace=0.35)
ax_naz = fig3_naz.add_subplot(sub_fig_naz[0, 0])
lvl3_df['Surf.Diff. | 2018-1990'].plot.bar(ax=ax_naz, rot=0)

ax_naz.yaxis.set_label_text('ha')
ax_naz.set_xlabel('')

plt.xticks(rotation = 90)
plt.legend(loc='best')
<matplotlib.legend.Legend at 0x7fe2d2f7ddc0>

Purtroppo il “trend campano” è visibile anche qui. Il fatto è che ciò che accade a scala nazionale si ripete a scala regionale e scommetto anche provinciale in un po’ tutto il territorio italiano.

3.3 Focus sui Comuni

Codice
# Dati 1990
focus_1990_naz = clc1990[['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha', 'label3', 'surface_ha_1990']]
focus_1990_naz.set_index('label3', inplace=True)
focus_naz_1990_group = pd.DataFrame(focus_1990_naz.groupby(['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha', 'label3'])['surface_ha_1990'].sum())
focus_naz_1990_group.reset_index(inplace=True)

# Dati 2000
focus_2000_naz = clc2000[['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha', 'label3', 'surface_ha_2000']]
focus_2000_naz.set_index('label3', inplace=True)
focus_naz_2000_group = pd.DataFrame(focus_2000_naz.groupby(['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha', 'label3'])['surface_ha_2000'].sum())
focus_naz_2000_group.reset_index(inplace=True)

# Dati 2006
focus_2006_naz = clc2006[['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha', 'label3', 'surface_ha_2006']]
focus_2006_naz.set_index('label3', inplace=True)
focus_naz_2006_group = pd.DataFrame(focus_2006_naz.groupby(['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha', 'label3'])['surface_ha_2006'].sum())
focus_naz_2006_group.reset_index(inplace=True)

# Dati 2012
focus_2012_naz = clc2012[['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha', 'label3', 'surface_ha_2012']]
focus_2012_naz.set_index('label3', inplace=True)
focus_naz_2012_group = pd.DataFrame(focus_2012_naz.groupby(['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha', 'label3'])['surface_ha_2012'].sum())
focus_naz_2012_group.reset_index(inplace=True)

# Dati 2018
focus_2018_naz = clc2018[['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha', 'label3', 'surface_ha_2018']]
focus_2018_naz.set_index('label3', inplace=True)
focus_naz_2018_group = pd.DataFrame(focus_2018_naz.groupby(['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha', 'label3'])['surface_ha_2018'].sum())
focus_naz_2018_group.reset_index(inplace=True)
Codice
focus_naz_2018_group
pro_com comune provincia regione sup_com_ha label3 surface_ha_2018
0 1001.0 Agliè Torino Piemonte 1314.622087 Broad-leaved forest 218.6224
1 1001.0 Agliè Torino Piemonte 1314.622087 Discontinuous urban fabric 64.7110
2 1001.0 Agliè Torino Piemonte 1314.622087 Green urban areas 51.8549
3 1001.0 Agliè Torino Piemonte 1314.622087 Industrial or commercial units 25.4731
4 1001.0 Agliè Torino Piemonte 1314.622087 Land principally occupied by agriculture, with... 24.4334
... ... ... ... ... ... ... ...
64052 111107.0 Villaspeciosa Sud Sardegna Sardegna 2719.371313 Industrial or commercial units 25.7592
64053 111107.0 Villaspeciosa Sud Sardegna Sardegna 2719.371313 Land principally occupied by agriculture, with... 216.6219
64054 111107.0 Villaspeciosa Sud Sardegna Sardegna 2719.371313 Non-irrigated arable land 1675.3119
64055 111107.0 Villaspeciosa Sud Sardegna Sardegna 2719.371313 Sclerophyllous vegetation 44.4901
64056 111107.0 Villaspeciosa Sud Sardegna Sardegna 2719.371313 Water bodies 176.2525

64057 rows × 7 columns

3.3.1 Aree artificiali

I gruppi 1.1 ed 1.2 della CLC

Codice
# Dati 1990
artificial_surface_naz_1990 = focus_naz_1990_group[focus_naz_1990_group['label3'].isin(['Continuous urban fabric', 'Discontinuous urban fabric', 'Industrial or commercial units', 'Road and rail networks and associated land', 'Port areas', 'Airports'])]
artificial_surface_naz_1990_group = pd.DataFrame(artificial_surface_naz_1990.groupby(['pro_com'])['surface_ha_1990'].sum())
artificial_surface_naz_1990_group.reset_index(inplace=True)

# Dati 2000
artificial_surface_naz_2000 = focus_naz_2000_group[focus_naz_2000_group['label3'].isin(['Continuous urban fabric', 'Discontinuous urban fabric', 'Industrial or commercial units', 'Road and rail networks and associated land', 'Port areas', 'Airports'])]
artificial_surface_naz_2000_group = pd.DataFrame(artificial_surface_naz_2000.groupby(['pro_com'])['surface_ha_2000'].sum())
artificial_surface_naz_2000_group.reset_index(inplace=True)

# Dati 2006
artificial_surface_naz_2006 = focus_naz_2006_group[focus_naz_2006_group['label3'].isin(['Continuous urban fabric', 'Discontinuous urban fabric', 'Industrial or commercial units', 'Road and rail networks and associated land', 'Port areas', 'Airports'])]
artificial_surface_naz_2006_group = pd.DataFrame(artificial_surface_naz_2006.groupby(['pro_com'])['surface_ha_2006'].sum())
artificial_surface_naz_2006_group.reset_index(inplace=True)

# Dati 2012
artificial_surface_naz_2012 = focus_naz_2012_group[focus_naz_2012_group['label3'].isin(['Continuous urban fabric', 'Discontinuous urban fabric', 'Industrial or commercial units', 'Road and rail networks and associated land', 'Port areas', 'Airports'])]
artificial_surface_naz_2012_group = pd.DataFrame(artificial_surface_naz_2012.groupby(['pro_com'])['surface_ha_2012'].sum())
artificial_surface_naz_2012_group.reset_index(inplace=True)

# Dati 2018
artificial_surface_naz_2018 = focus_naz_2018_group[focus_naz_2018_group['label3'].isin(['Continuous urban fabric', 'Discontinuous urban fabric', 'Industrial or commercial units', 'Road and rail networks and associated land', 'Port areas', 'Airports'])]
artificial_surface_naz_2018_group = pd.DataFrame(artificial_surface_naz_2018.groupby(['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha'])['surface_ha_2018'].sum())
artificial_surface_naz_2018_group.reset_index(inplace=True)

# Dati aggregati
focus_artificial_naz = [artificial_surface_naz_2018_group, artificial_surface_naz_2012_group, artificial_surface_naz_2006_group, artificial_surface_naz_2000_group, artificial_surface_naz_1990_group]
focus_artificial_naz_df = reduce(lambda  left,right: pd.merge(left,right,on=['pro_com'], how='outer'), focus_artificial_naz)
focus_artificial_naz_df = focus_artificial_naz_df[focus_artificial_naz_df['sup_com_ha'] > 0]
focus_artificial_naz_df.fillna(0, inplace=True)
focus_artificial_naz_df.insert(loc=5, column='perc_1990', value=round(focus_artificial_naz_df.surface_ha_1990 / focus_artificial_naz_df.sup_com_ha, 4) * 100)
focus_artificial_naz_df.insert(loc=6, column='perc_2000', value=round(focus_artificial_naz_df.surface_ha_2000 / focus_artificial_naz_df.sup_com_ha, 4) * 100)
focus_artificial_naz_df.insert(loc=7, column='perc_2006', value=round(focus_artificial_naz_df.surface_ha_2006 / focus_artificial_naz_df.sup_com_ha, 4) * 100)
focus_artificial_naz_df.insert(loc=8, column='perc_2012', value=round(focus_artificial_naz_df.surface_ha_2012 / focus_artificial_naz_df.sup_com_ha, 4) * 100)
focus_artificial_naz_df.insert(loc=9, column='perc_2018', value=round(focus_artificial_naz_df.surface_ha_2018 / focus_artificial_naz_df.sup_com_ha, 4) * 100)
focus_artificial_naz_df.insert(loc=10, column='Perc.Diff. | 2018-2000', value=(focus_artificial_naz_df.perc_2018 - focus_artificial_naz_df.perc_2000))
focus_artificial_naz_df.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=True, inplace=True)
focus_artificial_naz_df
pro_com comune provincia regione sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
899 12023.0 Brunello Varese Lombardia 161.998833 60.01 60.01 44.49 44.49 44.49 -15.52 72.0654 72.0654 72.0655 97.2179 97.2181
4298 63036.0 Grumo Nevano Napoli Campania 287.688759 47.45 64.93 64.93 51.31 51.31 -13.62 147.6065 147.6065 186.8019 186.8019 136.5066
882 12006.0 Azzate Varese Lombardia 450.621469 46.75 46.75 38.01 38.01 38.01 -8.74 171.2779 171.2779 171.2779 210.6471 210.6469
953 12080.0 Gornate Olona Varese Lombardia 469.573308 31.05 31.05 22.32 22.32 22.32 -8.73 104.8177 104.8177 104.8177 145.8012 145.8012
4329 63067.0 San Giorgio a Cremano Napoli Campania 415.383936 60.96 76.57 68.51 68.51 68.51 -8.06 284.5767 284.5767 284.5768 318.0615 253.2174
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
372 3114.0 Paruzzaro Novara Piemonte 522.716138 13.89 11.57 23.24 35.11 35.11 23.54 183.5155 183.5155 121.4790 60.5013 72.6050
5506 80097.0 San Ferdinando Reggio di Calabria Calabria 1419.798819 16.31 15.22 40.20 40.20 40.20 24.98 570.7536 570.7536 570.7535 216.1294 231.5077
257 2009.0 Balocco Vercelli Piemonte 1680.794846 10.41 0.00 2.97 28.22 28.22 28.22 474.3195 474.3195 49.8811 0.0000 175.0071
336 3043.0 Castelletto sopra Ticino Novara Piemonte 1464.109249 22.01 20.80 23.45 50.63 50.63 29.83 741.2521 741.2521 343.3539 304.5441 322.2282
4477 65011.0 Atrani Salerno Campania 12.061182 0.00 0.00 52.01 52.01 52.01 52.01 6.2730 6.2730 6.2730 0.0000 0.0000

6773 rows × 16 columns

Codice
# Superfici in arretramento
naz_reduction_artificial_area = focus_artificial_naz_df.iloc[0:10]
naz_reduction_artificial_area
pro_com comune provincia regione sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
899 12023.0 Brunello Varese Lombardia 161.998833 60.01 60.01 44.49 44.49 44.49 -15.52 72.0654 72.0654 72.0655 97.2179 97.2181
4298 63036.0 Grumo Nevano Napoli Campania 287.688759 47.45 64.93 64.93 51.31 51.31 -13.62 147.6065 147.6065 186.8019 186.8019 136.5066
882 12006.0 Azzate Varese Lombardia 450.621469 46.75 46.75 38.01 38.01 38.01 -8.74 171.2779 171.2779 171.2779 210.6471 210.6469
953 12080.0 Gornate Olona Varese Lombardia 469.573308 31.05 31.05 22.32 22.32 22.32 -8.73 104.8177 104.8177 104.8177 145.8012 145.8012
4329 63067.0 San Giorgio a Cremano Napoli Campania 415.383936 60.96 76.57 68.51 68.51 68.51 -8.06 284.5767 284.5767 284.5768 318.0615 253.2174
5710 83108.0 Torrenova Messina Sicilia 1292.677225 22.03 22.62 15.29 14.94 14.94 -7.68 193.1105 193.1105 197.6093 292.4331 284.7380
5708 83106.0 Terme Vigliatore Messina Sicilia 1322.797657 25.43 25.81 19.42 18.33 18.33 -7.48 242.5046 242.5046 256.9306 341.4543 336.4462
801 9049.0 Pietra Ligure Savona Liguria 987.811402 34.90 34.90 34.90 27.46 27.46 -7.44 271.2950 271.2950 344.7029 344.7031 344.7031
1049 13058.0 Castelmarte Como Lombardia 197.225522 47.17 47.17 39.83 39.83 39.83 -7.34 78.5500 78.5500 78.5500 93.0399 93.0399
4294 63032.0 Frattamaggiore Napoli Campania 538.816178 58.54 71.14 72.46 63.97 63.97 -7.17 344.6705 344.6705 390.4469 383.2980 315.4185
Codice
# Superfici in avanzamento
naz_increase_artificial_area = focus_artificial_naz_df.iloc[-10:]
naz_increase_artificial_area.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=False, inplace=True)
naz_increase_artificial_area
pro_com comune provincia regione sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
4477 65011.0 Atrani Salerno Campania 12.061182 0.00 0.00 52.01 52.01 52.01 52.01 6.2730 6.2730 6.2730 0.0000 0.0000
336 3043.0 Castelletto sopra Ticino Novara Piemonte 1464.109249 22.01 20.80 23.45 50.63 50.63 29.83 741.2521 741.2521 343.3539 304.5441 322.2282
257 2009.0 Balocco Vercelli Piemonte 1680.794846 10.41 0.00 2.97 28.22 28.22 28.22 474.3195 474.3195 49.8811 0.0000 175.0071
5506 80097.0 San Ferdinando Reggio di Calabria Calabria 1419.798819 16.31 15.22 40.20 40.20 40.20 24.98 570.7536 570.7536 570.7535 216.1294 231.5077
372 3114.0 Paruzzaro Novara Piemonte 522.716138 13.89 11.57 23.24 35.11 35.11 23.54 183.5155 183.5155 121.4790 60.5013 72.6050
5705 83103.0 Valdina Messina Sicilia 260.160806 16.64 16.64 35.93 39.55 39.55 22.91 102.8957 102.8957 93.4653 43.3003 43.3026
371 3112.0 Orta San Giulio Novara Piemonte 664.998756 0.00 0.00 0.00 22.62 22.62 22.62 150.3928 150.3928 0.0000 0.0000 0.0000
4287 63025.0 Castello di Cisterna Napoli Campania 392.135970 21.89 46.04 68.31 68.31 68.31 22.27 267.8514 267.8514 267.8514 180.5292 85.8311
6271 96083.0 Zumaglia Biella Piemonte 261.365071 14.82 4.43 4.43 26.59 26.59 22.16 69.5047 69.5047 11.5807 11.5807 38.7374
3499 46013.0 Forte dei Marmi Lucca Toscana 918.715130 56.53 59.08 63.30 80.45 80.45 21.37 739.1208 739.1208 581.5429 542.7534 519.3559

3.3.2 Aree forestate

Il gruppo 3.1 della CLC

Codice
# Dati 1990
forest_surface_naz_1990 = focus_naz_1990_group[focus_naz_1990_group['label3'].isin(['Broad-leaved forest', 'Coniferous forest', 'Mixed forest'])]
forest_surface_naz_1990_group = pd.DataFrame(forest_surface_naz_1990.groupby(['pro_com'])['surface_ha_1990'].sum())
forest_surface_naz_1990_group.reset_index(inplace=True)

# Dati 2000
forest_surface_naz_2000 = focus_naz_2000_group[focus_naz_2000_group['label3'].isin(['Broad-leaved forest', 'Coniferous forest', 'Mixed forest'])]
forest_surface_naz_2000_group = pd.DataFrame(forest_surface_naz_2000.groupby(['pro_com'])['surface_ha_2000'].sum())
forest_surface_naz_2000_group.reset_index(inplace=True)

# Dati 2006
forest_surface_naz_2006 = focus_naz_2006_group[focus_naz_2006_group['label3'].isin(['Broad-leaved forest', 'Coniferous forest', 'Mixed forest'])]
forest_surface_naz_2006_group = pd.DataFrame(forest_surface_naz_2006.groupby(['pro_com'])['surface_ha_2006'].sum())
forest_surface_naz_2006_group.reset_index(inplace=True)

# Dati 2012
forest_surface_naz_2012 = focus_naz_2012_group[focus_naz_2012_group['label3'].isin(['Broad-leaved forest', 'Coniferous forest', 'Mixed forest'])]
forest_surface_naz_2012_group = pd.DataFrame(forest_surface_naz_2012.groupby(['pro_com'])['surface_ha_2012'].sum())
forest_surface_naz_2012_group.reset_index(inplace=True)

# Dati 2018
forest_surface_naz_2018 = focus_naz_2018_group[focus_naz_2018_group['label3'].isin(['Broad-leaved forest', 'Coniferous forest', 'Mixed forest'])]
forest_surface_naz_2018_group = pd.DataFrame(forest_surface_naz_2018.groupby(['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha'])['surface_ha_2018'].sum())
forest_surface_naz_2018_group.reset_index(inplace=True)

# Dati aggregati
focus_forest_naz = [forest_surface_naz_2018_group, forest_surface_naz_2012_group, forest_surface_naz_2006_group, forest_surface_naz_2000_group, forest_surface_naz_1990_group]
focus_forest_naz_df = reduce(lambda  left,right: pd.merge(left,right,on=['pro_com'], how='outer'), focus_forest_naz)
focus_forest_naz_df = focus_forest_naz_df[focus_forest_naz_df['sup_com_ha'] > 0]
focus_forest_naz_df.fillna(0, inplace=True)
focus_forest_naz_df.insert(loc=5, column='perc_1990', value=round(focus_forest_naz_df.surface_ha_1990 / focus_forest_naz_df.sup_com_ha, 4) * 100)
focus_forest_naz_df.insert(loc=6, column='perc_2000', value=round(focus_forest_naz_df.surface_ha_2000 / focus_forest_naz_df.sup_com_ha, 4) * 100)
focus_forest_naz_df.insert(loc=7, column='perc_2006', value=round(focus_forest_naz_df.surface_ha_2006 / focus_forest_naz_df.sup_com_ha, 4) * 100)
focus_forest_naz_df.insert(loc=8, column='perc_2012', value=round(focus_forest_naz_df.surface_ha_2012 / focus_forest_naz_df.sup_com_ha, 4) * 100)
focus_forest_naz_df.insert(loc=9, column='perc_2018', value=round(focus_forest_naz_df.surface_ha_2018 / focus_forest_naz_df.sup_com_ha, 4) * 100)
focus_forest_naz_df.insert(loc=10, column='Perc.Diff. | 2018-2000', value=(focus_forest_naz_df.perc_2018 - focus_forest_naz_df.perc_2000))
focus_forest_naz_df.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=True, inplace=True)
focus_forest_naz_df
pro_com comune provincia regione sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
4010 62073.0 Solopaca Benevento Campania 3112.664003 43.63 43.63 43.46 11.69 11.69 -31.94 363.9901 363.9901 1352.7210 1358.0359 1358.0275
2005 18162.0 Travacò Siccomario Pavia Lombardia 1704.486441 35.67 34.72 8.46 6.86 5.13 -29.59 87.4269 116.9231 144.1941 591.7315 607.9471
2066 20022.0 Dosolo Mantova Lombardia 2553.627074 36.66 33.26 15.80 28.04 5.45 -27.81 139.2560 716.0698 403.4684 849.4448 936.1148
5387 83054.0 Monforte San Giorgio Messina Sicilia 3225.987912 20.15 38.03 11.27 11.27 11.27 -26.76 363.6191 363.6191 363.6193 1226.7195 650.1504
4014 62077.0 Vitulano Benevento Campania 3599.050416 50.86 50.81 50.81 26.82 26.82 -23.99 965.1276 965.1276 1828.5273 1828.5272 1830.3353
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
6035 97078.0 Suello Lecco Lombardia 262.901636 3.74 3.74 30.07 30.07 30.07 26.33 79.0515 79.0515 79.0515 9.8363 9.8363
4727 71047.0 San Marco in Lamis Foggia Puglia 23419.726063 12.66 12.66 12.30 39.01 39.01 26.35 9136.7031 9136.7031 2879.7020 2965.3694 2965.3412
5343 83002.0 Alì Messina Sicilia 1593.861487 11.13 11.13 42.21 42.20 42.20 31.07 672.5921 672.5921 672.7852 177.3506 177.3474
4645 70041.0 Montagano Campobasso Molise 2662.359533 12.81 13.18 49.30 49.17 49.17 35.99 1309.1148 1309.1148 1312.6691 350.7878 341.0156
5378 83042.0 Longi Messina Sicilia 4210.989035 31.58 31.58 65.31 68.51 68.51 36.93 2885.0294 2885.0294 2750.1191 1329.8307 1329.8237

6400 rows × 16 columns

Codice
# Superfici in arretramento
naz_reduction_forest_area = focus_forest_naz_df.iloc[0:10]
naz_reduction_forest_area
pro_com comune provincia regione sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
4010 62073.0 Solopaca Benevento Campania 3112.664003 43.63 43.63 43.46 11.69 11.69 -31.94 363.9901 363.9901 1352.7210 1358.0359 1358.0275
2005 18162.0 Travacò Siccomario Pavia Lombardia 1704.486441 35.67 34.72 8.46 6.86 5.13 -29.59 87.4269 116.9231 144.1941 591.7315 607.9471
2066 20022.0 Dosolo Mantova Lombardia 2553.627074 36.66 33.26 15.80 28.04 5.45 -27.81 139.2560 716.0698 403.4684 849.4448 936.1148
5387 83054.0 Monforte San Giorgio Messina Sicilia 3225.987912 20.15 38.03 11.27 11.27 11.27 -26.76 363.6191 363.6191 363.6193 1226.7195 650.1504
4014 62077.0 Vitulano Benevento Campania 3599.050416 50.86 50.81 50.81 26.82 26.82 -23.99 965.1276 965.1276 1828.5273 1828.5272 1830.3353
1354 13059.0 Castelnuovo Bozzente Como Lombardia 362.195167 74.76 74.76 51.14 51.14 51.14 -23.62 185.2128 185.2128 185.2127 270.7783 270.7781
1975 18114.0 Pieve Porto Morone Pavia Lombardia 1639.626801 23.53 23.53 24.21 20.04 0.00 -23.53 0.0002 328.5814 396.9912 385.7940 385.7940
1788 17027.0 Braone Brescia Lombardia 1335.673792 61.74 61.74 61.69 39.25 39.25 -22.49 524.1978 524.1978 823.9188 824.5857 824.5859
5410 83080.0 San Pier Niceto Messina Sicilia 3667.976954 27.71 38.63 16.27 16.27 16.27 -22.36 596.7868 596.7868 596.7872 1416.8175 1016.4019
1321 13009.0 Anzano del Parco Como Lombardia 324.503555 29.46 29.46 7.69 7.69 7.69 -21.77 24.9485 24.9485 24.9485 95.6024 95.6025
Codice
# Superfici in avanzamento
naz_increase_forest_area = focus_forest_naz_df.iloc[-10:]
naz_increase_forest_area.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=False, inplace=True)
naz_increase_forest_area
pro_com comune provincia regione sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
5378 83042.0 Longi Messina Sicilia 4210.989035 31.58 31.58 65.31 68.51 68.51 36.93 2885.0294 2885.0294 2750.1191 1329.8307 1329.8237
4645 70041.0 Montagano Campobasso Molise 2662.359533 12.81 13.18 49.30 49.17 49.17 35.99 1309.1148 1309.1148 1312.6691 350.7878 341.0156
5343 83002.0 Alì Messina Sicilia 1593.861487 11.13 11.13 42.21 42.20 42.20 31.07 672.5921 672.5921 672.7852 177.3506 177.3474
4727 71047.0 San Marco in Lamis Foggia Puglia 23419.726063 12.66 12.66 12.30 39.01 39.01 26.35 9136.7031 9136.7031 2879.7020 2965.3694 2965.3412
6035 97078.0 Suello Lecco Lombardia 262.901636 3.74 3.74 30.07 30.07 30.07 26.33 79.0515 79.0515 79.0515 9.8363 9.8363
5383 83050.0 Militello Rosmarino Messina Sicilia 2953.560321 30.59 30.80 55.18 56.89 56.89 26.09 1680.4067 1680.4067 1629.8563 909.6290 903.4505
1946 18071.0 Gerenzago Pavia Lombardia 541.125193 0.00 0.00 25.77 25.77 25.77 25.77 139.4217 139.4217 139.4217 0.0000 0.0000
5606 90044.0 Nughedu San Nicolò Sassari Sardegna 6789.378997 14.32 15.66 39.48 40.42 40.42 24.76 2744.1430 2744.1430 2680.5068 1062.9248 972.5207
5390 83057.0 Montalbano Elicona Messina Sicilia 6780.082531 52.78 30.74 53.69 53.50 55.09 24.35 3734.9590 3627.4330 3639.9593 2084.5071 3578.2554
5391 83058.0 Motta Camastra Messina Sicilia 2530.898840 36.11 36.11 54.05 59.70 59.70 23.59 1510.8548 1510.8548 1367.8344 913.8916 913.8926

3.3.3 Aree agricole

Il gruppo 2.1 della CLC

Codice
# Dati 1990
arable_lands_surface_naz_1990 = focus_naz_1990_group[focus_naz_1990_group['label3'].isin(['Non-irrigated arable land', 'Permanently irrigated land'])]
arable_lands_surface_naz_1990_group = pd.DataFrame(arable_lands_surface_naz_1990.groupby(['pro_com'])['surface_ha_1990'].sum())
arable_lands_surface_naz_1990_group.reset_index(inplace=True)

# Dati 2000
arable_lands_surface_naz_2000 = focus_naz_2000_group[focus_naz_2000_group['label3'].isin(['Non-irrigated arable land', 'Permanently irrigated land'])]
arable_lands_surface_naz_2000_group = pd.DataFrame(arable_lands_surface_naz_2000.groupby(['pro_com'])['surface_ha_2000'].sum())
arable_lands_surface_naz_2000_group.reset_index(inplace=True)

# Dati 2006
arable_lands_surface_naz_2006 = focus_naz_2006_group[focus_naz_2006_group['label3'].isin(['Non-irrigated arable land', 'Permanently irrigated land'])]
arable_lands_surface_naz_2006_group = pd.DataFrame(arable_lands_surface_naz_2006.groupby(['pro_com'])['surface_ha_2006'].sum())
arable_lands_surface_naz_2006_group.reset_index(inplace=True)

# Dati 2012
arable_lands_surface_naz_2012 = focus_naz_2012_group[focus_naz_2012_group['label3'].isin(['Non-irrigated arable land', 'Permanently irrigated land'])]
arable_lands_surface_naz_2012_group = pd.DataFrame(arable_lands_surface_naz_2012.groupby(['pro_com'])['surface_ha_2012'].sum())
arable_lands_surface_naz_2012_group.reset_index(inplace=True)

# Dati 2018
arable_lands_surface_naz_2018 = focus_naz_2018_group[focus_naz_2018_group['label3'].isin(['Non-irrigated arable land', 'Permanently irrigated land'])]
arable_lands_surface_naz_2018_group = pd.DataFrame(arable_lands_surface_naz_2018.groupby(['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha'])['surface_ha_2018'].sum())
arable_lands_surface_naz_2018_group.reset_index(inplace=True)

# Dati aggregati
focus_arable_lands_naz = [arable_lands_surface_naz_2018_group, arable_lands_surface_naz_2012_group, arable_lands_surface_naz_2006_group, arable_lands_surface_naz_2000_group, arable_lands_surface_naz_1990_group]
focus_arable_lands_naz_df = reduce(lambda  left,right: pd.merge(left,right,on=['pro_com'], how='outer'), focus_arable_lands_naz)
focus_arable_lands_naz_df = focus_arable_lands_naz_df[focus_arable_lands_naz_df['sup_com_ha'] > 0]
focus_arable_lands_naz_df.fillna(0, inplace=True)
focus_arable_lands_naz_df.insert(loc=5, column='perc_1990', value=round(focus_arable_lands_naz_df.surface_ha_1990 / focus_arable_lands_naz_df.sup_com_ha, 4) * 100)
focus_arable_lands_naz_df.insert(loc=6, column='perc_2000', value=round(focus_arable_lands_naz_df.surface_ha_2000 / focus_arable_lands_naz_df.sup_com_ha, 4) * 100)
focus_arable_lands_naz_df.insert(loc=7, column='perc_2006', value=round(focus_arable_lands_naz_df.surface_ha_2006 / focus_arable_lands_naz_df.sup_com_ha, 4) * 100)
focus_arable_lands_naz_df.insert(loc=8, column='perc_2012', value=round(focus_arable_lands_naz_df.surface_ha_2012 / focus_arable_lands_naz_df.sup_com_ha, 4) * 100)
focus_arable_lands_naz_df.insert(loc=9, column='perc_2018', value=round(focus_arable_lands_naz_df.surface_ha_2018 / focus_arable_lands_naz_df.sup_com_ha, 4) * 100)
focus_arable_lands_naz_df.insert(loc=10, column='Perc.Diff. | 2018-2000', value=(focus_arable_lands_naz_df.perc_2018 - focus_arable_lands_naz_df.perc_2000))
focus_arable_lands_naz_df.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=True, inplace=True)
focus_arable_lands_naz_df
pro_com comune provincia regione sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
1903 28080.0 Santa Giustina in Colle Padova Veneto 1796.754897 89.86 92.50 91.92 12.83 12.83 -79.67 230.5114 230.5115 1651.6402 1661.9126 1614.5434
1861 28036.0 Curtarolo Padova Veneto 1472.896746 89.61 84.87 83.69 9.24 9.24 -75.63 136.0505 136.0505 1232.6876 1250.0597 1319.9213
1626 24026.0 Cassola Vicenza Veneto 1273.950878 70.93 69.20 66.58 11.82 11.13 -58.07 141.8257 150.5180 848.1867 881.6361 903.5994
3451 64072.0 Pietradefusi Avellino Campania 924.149874 77.90 77.90 20.16 20.16 20.16 -57.74 186.3344 186.3344 186.3345 719.9375 719.9308
3442 64056.0 Montefusco Avellino Campania 823.823558 59.13 59.13 2.01 2.01 2.01 -57.12 16.5238 16.5238 16.5238 487.1327 487.1269
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3880 70046.0 Montenero di Bisaccia Campobasso Molise 9331.252115 27.05 26.67 70.13 70.13 70.13 43.46 6544.4633 6544.4633 6544.4634 2488.5836 2524.0691
3230 61020.0 Casaluce Caserta Campania 955.842829 0.70 0.70 0.70 60.53 60.53 59.83 578.6141 578.6141 6.7225 6.7226 6.7222
3903 70069.0 San Martino in Pensilis Campobasso Molise 10065.202788 23.10 23.10 83.46 83.46 83.46 60.36 8400.8974 8400.8974 8400.8973 2324.9437 2324.9034
3245 61037.0 Frignano Caserta Campania 985.795577 0.00 0.00 0.00 69.03 69.03 69.03 680.5149 680.5149 0.0000 0.0000 0.0000
3297 61098.0 Villa di Briano Caserta Campania 854.692612 3.91 3.91 3.91 74.03 74.03 70.12 632.7436 632.7436 33.4422 33.4424 33.4453

5585 rows × 16 columns

Codice
# Superfici in arretramento
naz_reduction_arable_lands_area = focus_arable_lands_naz_df.iloc[0:10]
naz_reduction_arable_lands_area
pro_com comune provincia regione sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
1903 28080.0 Santa Giustina in Colle Padova Veneto 1796.754897 89.86 92.50 91.92 12.83 12.83 -79.67 230.5114 230.5115 1651.6402 1661.9126 1614.5434
1861 28036.0 Curtarolo Padova Veneto 1472.896746 89.61 84.87 83.69 9.24 9.24 -75.63 136.0505 136.0505 1232.6876 1250.0597 1319.9213
1626 24026.0 Cassola Vicenza Veneto 1273.950878 70.93 69.20 66.58 11.82 11.13 -58.07 141.8257 150.5180 848.1867 881.6361 903.5994
3451 64072.0 Pietradefusi Avellino Campania 924.149874 77.90 77.90 20.16 20.16 20.16 -57.74 186.3344 186.3344 186.3345 719.9375 719.9308
3442 64056.0 Montefusco Avellino Campania 823.823558 59.13 59.13 2.01 2.01 2.01 -57.12 16.5238 16.5238 16.5238 487.1327 487.1269
1923 28101.0 Villa del Conte Padova Veneto 1734.840442 96.57 96.39 92.42 39.87 39.87 -56.52 691.7159 691.7159 1603.3313 1672.1357 1675.4122
2545 44036.0 Montefiore dell'Aso Ascoli Piceno Marche 2820.740109 31.58 52.36 51.94 1.96 1.96 -50.40 55.4167 55.4167 1465.2004 1477.0519 890.6685
1847 28020.0 Campo San Martino Padova Veneto 1315.986365 89.89 89.56 87.50 39.25 39.25 -50.31 516.4652 516.4652 1151.4484 1178.6033 1182.9783
4097 75041.0 Martignano Lecce Puglia 649.212863 55.63 55.62 7.23 7.23 7.23 -48.39 46.9307 46.9307 46.9307 361.0667 361.1841
4147 75094.0 Zollino Lecce Puglia 995.447907 73.07 73.07 25.56 25.56 25.56 -47.51 254.4702 254.4702 254.4701 727.3750 727.3907
Codice
# Superfici in avanzamento
naz_increase_arable_lands_area = focus_arable_lands_naz_df.iloc[-10:]
naz_increase_arable_lands_area.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=False, inplace=True)
naz_increase_arable_lands_area
pro_com comune provincia regione sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
3297 61098.0 Villa di Briano Caserta Campania 854.692612 3.91 3.91 3.91 74.03 74.03 70.12 632.7436 632.7436 33.4422 33.4424 33.4453
3245 61037.0 Frignano Caserta Campania 985.795577 0.00 0.00 0.00 69.03 69.03 69.03 680.5149 680.5149 0.0000 0.0000 0.0000
3903 70069.0 San Martino in Pensilis Campobasso Molise 10065.202788 23.10 23.10 83.46 83.46 83.46 60.36 8400.8974 8400.8974 8400.8973 2324.9437 2324.9034
3230 61020.0 Casaluce Caserta Campania 955.842829 0.70 0.70 0.70 60.53 60.53 59.83 578.6141 578.6141 6.7225 6.7226 6.7222
3880 70046.0 Montenero di Bisaccia Campobasso Molise 9331.252115 27.05 26.67 70.13 70.13 70.13 43.46 6544.4633 6544.4633 6544.4634 2488.5836 2524.0691
3912 70078.0 Termoli Campobasso Molise 5563.450730 16.95 16.76 57.90 59.83 59.83 43.07 3328.3420 3328.3420 3221.2335 932.5081 942.8076
5205 97072.0 Rogeno Lecco Lombardia 482.482168 0.70 0.70 42.34 42.34 42.34 41.64 204.2863 204.2863 204.2863 3.3881 3.3881
4668 83090.0 San Teodoro Messina Sicilia 1397.402292 29.16 29.16 67.97 67.97 67.97 38.81 949.7711 949.7711 949.7711 407.4871 407.4882
3889 70055.0 Portocannone Campobasso Molise 1311.357120 19.86 19.86 57.15 57.15 57.15 37.29 749.4987 749.4987 749.4987 260.4368 260.4467
3931 71014.0 Casalvecchio di Puglia Foggia Puglia 3193.205814 45.71 48.78 48.78 84.11 84.11 35.33 2685.9620 2685.9620 1557.6443 1557.6441 1459.5313

3.3.4 Aree bruciate

Codice
# Dati 1990
burnt_surface_naz_1990 = focus_naz_1990_group[focus_naz_1990_group['label3'].isin(['Burnt areas'])]
burnt_surface_naz_1990_group = pd.DataFrame(burnt_surface_naz_1990.groupby(['pro_com'])['surface_ha_1990'].sum())
burnt_surface_naz_1990_group.reset_index(inplace=True)

# Dati 2000
burnt_surface_naz_2000 = focus_naz_2000_group[focus_naz_2000_group['label3'].isin(['Burnt areas'])]
burnt_surface_naz_2000_group = pd.DataFrame(burnt_surface_naz_2000.groupby(['pro_com'])['surface_ha_2000'].sum())
burnt_surface_naz_2000_group.reset_index(inplace=True)

# Dati 2006
burnt_surface_naz_2006 = focus_naz_2006_group[focus_naz_2006_group['label3'].isin(['Burnt areas'])]
burnt_surface_naz_2006_group = pd.DataFrame(burnt_surface_naz_2006.groupby(['pro_com'])['surface_ha_2006'].sum())
burnt_surface_naz_2006_group.reset_index(inplace=True)

# Dati 2012
burnt_surface_naz_2012 = focus_naz_2012_group[focus_naz_2012_group['label3'].isin(['Burnt areas'])]
burnt_surface_naz_2012_group = pd.DataFrame(burnt_surface_naz_2012.groupby(['pro_com'])['surface_ha_2012'].sum())
burnt_surface_naz_2012_group.reset_index(inplace=True)

# Dati 2018
burnt_surface_naz_2018 = focus_naz_2018_group[focus_naz_2018_group['label3'].isin(['Burnt areas'])]
burnt_surface_naz_2018_group = pd.DataFrame(burnt_surface_naz_2018.groupby(['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha'])['surface_ha_2018'].sum())
burnt_surface_naz_2018_group.reset_index(inplace=True)

# Dati aggregati
focus_burnt_naz = [burnt_surface_naz_2018_group, burnt_surface_naz_2012_group, burnt_surface_naz_2006_group, burnt_surface_naz_2000_group, burnt_surface_naz_1990_group]
focus_burnt_naz_df = reduce(lambda  left,right: pd.merge(left,right,on=['pro_com'], how='outer'), focus_burnt_naz)
focus_burnt_naz_df = focus_burnt_naz_df[focus_burnt_naz_df['sup_com_ha'] > 0]
focus_burnt_naz_df.fillna(0, inplace=True)
focus_burnt_naz_df.insert(loc=5, column='perc_1990', value=round(focus_burnt_naz_df.surface_ha_1990 / focus_burnt_naz_df.sup_com_ha, 4) * 100)
focus_burnt_naz_df.insert(loc=6, column='perc_2000', value=round(focus_burnt_naz_df.surface_ha_2000 / focus_burnt_naz_df.sup_com_ha, 4) * 100)
focus_burnt_naz_df.insert(loc=7, column='perc_2006', value=round(focus_burnt_naz_df.surface_ha_2006 / focus_burnt_naz_df.sup_com_ha, 4) * 100)
focus_burnt_naz_df.insert(loc=8, column='perc_2012', value=round(focus_burnt_naz_df.surface_ha_2012 / focus_burnt_naz_df.sup_com_ha, 4) * 100)
focus_burnt_naz_df.insert(loc=9, column='perc_2018', value=round(focus_burnt_naz_df.surface_ha_2018 / focus_burnt_naz_df.sup_com_ha, 4) * 100)
focus_burnt_naz_df.insert(loc=10, column='Perc.Diff. | 2018-2000', value=(focus_burnt_naz_df.perc_2018 - focus_burnt_naz_df.perc_2000))
focus_burnt_naz_df.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=False, inplace=True)
focus_burnt_naz_df
pro_com comune provincia regione sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
114 81020.0 San Vito Lo Capo Trapani Sicilia 6011.867192 0.00 0.00 0.0 0.00 42.65 42.65 2564.0387 0.0000 0.0 0.0000 0.0000
141 83063.0 Oliveri Messina Sicilia 1043.475159 0.00 0.00 0.0 0.00 36.58 36.58 381.7231 0.0000 0.0 0.0000 0.0000
49 63082.0 Terzigno Napoli Campania 2346.480829 0.00 0.00 0.0 0.00 18.50 18.50 434.1155 0.0000 0.0 0.0000 0.0000
45 63064.0 Ercolano Napoli Campania 1989.359843 0.00 0.00 0.0 8.49 17.54 17.54 348.9169 168.9727 0.0 0.0000 0.0000
223 111046.0 Nurallao Sud Sardegna Sardegna 3476.167353 0.00 0.00 0.0 0.00 17.40 17.40 604.7035 0.0000 0.0 0.0000 0.0000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
140 83061.0 Nizza di Sicilia Messina Sicilia 1341.538156 0.00 1.97 0.0 0.00 0.02 -1.95 0.3277 0.0000 0.0 26.3822 0.0000
203 100003.0 Montemurlo Prato Toscana 3077.584750 0.00 3.78 0.0 1.64 1.64 -2.14 50.4463 50.4463 0.0 116.4644 0.0000
66 65052.0 Fisciano Salerno Campania 3168.742849 0.00 4.15 0.0 0.00 1.43 -2.72 45.1761 0.0000 0.0 131.4723 0.0000
4 10059.0 Sestri Levante Genova Liguria 3361.625421 12.95 9.23 0.0 0.00 3.04 -6.19 102.1619 0.0000 0.0 310.3360 435.4819
134 83045.0 Mandanici Messina Sicilia 1185.361172 0.00 27.54 0.0 0.00 5.16 -22.38 61.1878 0.0000 0.0 326.4113 0.0000

235 rows × 16 columns

3.3.5 Aree adibite a discarica

Codice
# Dati 1990
dump_surface_naz_1990 = focus_naz_1990_group[focus_naz_1990_group['label3'].isin(['Dump sites'])]
dump_surface_naz_1990_group = pd.DataFrame(dump_surface_naz_1990.groupby(['pro_com'])['surface_ha_1990'].sum())
dump_surface_naz_1990_group.reset_index(inplace=True)

# Dati 2000
dump_surface_naz_2000 = focus_naz_2000_group[focus_naz_2000_group['label3'].isin(['Dump sites'])]
dump_surface_naz_2000_group = pd.DataFrame(dump_surface_naz_2000.groupby(['pro_com'])['surface_ha_2000'].sum())
dump_surface_naz_2000_group.reset_index(inplace=True)

# Dati 2006
dump_surface_naz_2006 = focus_naz_2006_group[focus_naz_2006_group['label3'].isin(['Dump sites'])]
dump_surface_naz_2006_group = pd.DataFrame(dump_surface_naz_2006.groupby(['pro_com'])['surface_ha_2006'].sum())
dump_surface_naz_2006_group.reset_index(inplace=True)

# Dati 2012
dump_surface_naz_2012 = focus_naz_2012_group[focus_naz_2012_group['label3'].isin(['Dump sites'])]
dump_surface_naz_2012_group = pd.DataFrame(dump_surface_naz_2012.groupby(['pro_com'])['surface_ha_2012'].sum())
dump_surface_naz_2012_group.reset_index(inplace=True)

# Dati 2018
dump_surface_naz_2018 = focus_naz_2018_group[focus_naz_2018_group['label3'].isin(['Dump sites'])]
dump_surface_naz_2018_group = pd.DataFrame(dump_surface_naz_2018.groupby(['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha'])['surface_ha_2018'].sum())
dump_surface_naz_2018_group.reset_index(inplace=True)

# Dati aggregati
focus_dump_naz = [dump_surface_naz_2018_group, dump_surface_naz_2012_group, dump_surface_naz_2006_group, dump_surface_naz_2000_group, dump_surface_naz_1990_group]
focus_dump_naz_df = reduce(lambda  left,right: pd.merge(left,right,on=['pro_com'], how='outer'), focus_dump_naz)
focus_dump_naz_df = focus_dump_naz_df[focus_dump_naz_df['sup_com_ha'] > 0]
focus_dump_naz_df.fillna(0, inplace=True)
focus_dump_naz_df.insert(loc=5, column='perc_1990', value=round(focus_dump_naz_df.surface_ha_1990 / focus_dump_naz_df.sup_com_ha, 4) * 100)
focus_dump_naz_df.insert(loc=6, column='perc_2000', value=round(focus_dump_naz_df.surface_ha_2000 / focus_dump_naz_df.sup_com_ha, 4) * 100)
focus_dump_naz_df.insert(loc=7, column='perc_2006', value=round(focus_dump_naz_df.surface_ha_2006 / focus_dump_naz_df.sup_com_ha, 4) * 100)
focus_dump_naz_df.insert(loc=8, column='perc_2012', value=round(focus_dump_naz_df.surface_ha_2012 / focus_dump_naz_df.sup_com_ha, 4) * 100)
focus_dump_naz_df.insert(loc=9, column='perc_2018', value=round(focus_dump_naz_df.surface_ha_2018 / focus_dump_naz_df.sup_com_ha, 4) * 100)
focus_dump_naz_df.insert(loc=10, column='Perc.Diff. | 2018-2000', value=(focus_dump_naz_df.perc_2018 - focus_dump_naz_df.perc_2000))
focus_dump_naz_df.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=False, inplace=True)
focus_dump_naz_df
pro_com comune provincia regione sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
2 1273.0 Torrazza Piemonte Torino Piemonte 982.382979 0.0 0.00 0.00 6.72 7.64 7.64 75.0599 66.0309 0.0000 0.0000 0.0000
13 20034.0 Medole Mantova Lombardia 2572.857637 0.0 0.00 0.00 6.66 6.66 6.66 171.3837 171.3837 0.0000 0.0000 0.0000
82 87030.0 Motta Sant'Anastasia Catania Sicilia 3569.989125 0.0 0.00 1.62 2.16 2.16 2.16 77.0713 77.0713 57.8396 0.0000 0.0000
25 34041.0 Torrile Parma Emilia-Romagna 3714.699293 0.0 0.00 0.00 2.10 2.10 2.10 77.8524 77.8524 0.0000 0.0000 0.0000
52 57027.0 Fara in Sabina Rieti Lazio 5496.235909 0.0 0.00 0.00 1.98 1.98 1.98 108.5511 108.5511 0.0000 0.0000 0.0000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
16 26010.0 Casier Treviso Veneto 1342.173767 0.0 1.25 1.25 1.25 1.25 0.00 16.8000 16.8000 16.8000 16.8000 0.0000
35 37032.0 Imola Bologna Emilia-Romagna 20502.134650 0.0 0.18 0.18 0.18 0.18 0.00 37.1468 37.1468 37.1468 37.1468 0.0000
36 38006.0 Comacchio Ferrara Emilia-Romagna 28374.611068 0.1 0.10 0.10 0.10 0.10 0.00 28.5891 28.5891 28.5891 28.5898 28.5892
20 27042.0 Venezia Venezia Veneto 41589.266300 0.0 0.09 0.09 0.09 0.09 0.00 36.2536 36.2536 36.2536 36.2536 0.0000
80 85007.0 Gela Caltanissetta Sicilia 27921.954881 0.0 0.17 0.24 0.11 0.11 -0.06 29.9715 29.9715 65.9464 47.9113 0.0000

94 rows × 16 columns

Codice
# Dati 1990
green_urban_surface_surface_naz_1990 = focus_naz_1990_group[focus_naz_1990_group['label3'].isin(['Green urban areas'])]
green_urban_surface_surface_naz_1990_group = pd.DataFrame(green_urban_surface_surface_naz_1990.groupby(['pro_com'])['surface_ha_1990'].sum())
green_urban_surface_surface_naz_1990_group.reset_index(inplace=True)

# Dati 2000
green_urban_surface_surface_naz_2000 = focus_naz_2000_group[focus_naz_2000_group['label3'].isin(['Green urban areas'])]
green_urban_surface_surface_naz_2000_group = pd.DataFrame(green_urban_surface_surface_naz_2000.groupby(['pro_com'])['surface_ha_2000'].sum())
green_urban_surface_surface_naz_2000_group.reset_index(inplace=True)

# Dati 2006
green_urban_surface_surface_naz_2006 = focus_naz_2006_group[focus_naz_2006_group['label3'].isin(['Green urban areas'])]
green_urban_surface_surface_naz_2006_group = pd.DataFrame(green_urban_surface_surface_naz_2006.groupby(['pro_com'])['surface_ha_2006'].sum())
green_urban_surface_surface_naz_2006_group.reset_index(inplace=True)

# Dati 2012
green_urban_surface_surface_naz_2012 = focus_naz_2012_group[focus_naz_2012_group['label3'].isin(['Green urban areas'])]
green_urban_surface_surface_naz_2012_group = pd.DataFrame(green_urban_surface_surface_naz_2012.groupby(['pro_com'])['surface_ha_2012'].sum())
green_urban_surface_surface_naz_2012_group.reset_index(inplace=True)

# Dati 2018
green_urban_surface_surface_naz_2018 = focus_naz_2018_group[focus_naz_2018_group['label3'].isin(['Green urban areas'])]
green_urban_surface_surface_naz_2018_group = pd.DataFrame(green_urban_surface_surface_naz_2018.groupby(['pro_com', 'comune', 'provincia', 'regione', 'sup_com_ha'])['surface_ha_2018'].sum())
green_urban_surface_surface_naz_2018_group.reset_index(inplace=True)

# Dati aggregati
focus_green_urban_surface_naz = [green_urban_surface_surface_naz_2018_group, green_urban_surface_surface_naz_2012_group, green_urban_surface_surface_naz_2006_group, green_urban_surface_surface_naz_2000_group, green_urban_surface_surface_naz_1990_group]
focus_green_urban_surface_naz_df = reduce(lambda  left,right: pd.merge(left,right,on=['pro_com'], how='outer'), focus_green_urban_surface_naz)
focus_green_urban_surface_naz_df = focus_green_urban_surface_naz_df[focus_green_urban_surface_naz_df['sup_com_ha'] > 0]
focus_green_urban_surface_naz_df.fillna(0, inplace=True)
focus_green_urban_surface_naz_df.insert(loc=5, column='perc_1990', value=round(focus_green_urban_surface_naz_df.surface_ha_1990 / focus_green_urban_surface_naz_df.sup_com_ha, 4) * 100)
focus_green_urban_surface_naz_df.insert(loc=6, column='perc_2000', value=round(focus_green_urban_surface_naz_df.surface_ha_2000 / focus_green_urban_surface_naz_df.sup_com_ha, 4) * 100)
focus_green_urban_surface_naz_df.insert(loc=7, column='perc_2006', value=round(focus_green_urban_surface_naz_df.surface_ha_2006 / focus_green_urban_surface_naz_df.sup_com_ha, 4) * 100)
focus_green_urban_surface_naz_df.insert(loc=8, column='perc_2012', value=round(focus_green_urban_surface_naz_df.surface_ha_2012 / focus_green_urban_surface_naz_df.sup_com_ha, 4) * 100)
focus_green_urban_surface_naz_df.insert(loc=9, column='perc_2018', value=round(focus_green_urban_surface_naz_df.surface_ha_2018 / focus_green_urban_surface_naz_df.sup_com_ha, 4) * 100)
focus_green_urban_surface_naz_df.insert(loc=10, column='Perc.Diff. | 2018-2000', value=(focus_green_urban_surface_naz_df.perc_2018 - focus_green_urban_surface_naz_df.perc_2000))
focus_green_urban_surface_naz_df.sort_values(by=['Perc.Diff. | 2018-2000'], ascending=False, inplace=True)
focus_green_urban_surface_naz_df
pro_com comune provincia regione sup_com_ha perc_1990 perc_2000 perc_2006 perc_2012 perc_2018 Perc.Diff. | 2018-2000 surface_ha_2018 surface_ha_2012 surface_ha_2006 surface_ha_2000 surface_ha_1990
19 15032.0 Bresso Milano Lombardia 338.236565 0.00 0.00 10.09 19.93 19.93 19.93 67.4045 67.4045 34.1411 0.0000 0.0000
127 108046.0 Vedano al Lambro Monza e della Brianza Lombardia 197.928997 0.00 0.00 19.70 19.70 19.70 19.70 38.9824 38.9824 38.9825 0.0000 0.0000
125 108033.0 Monza Monza e della Brianza Lombardia 3308.665453 1.07 1.07 13.58 13.58 13.58 12.51 449.4274 449.4274 449.4274 35.4254 35.4254
95 62062.0 San Lorenzo Maggiore Benevento Campania 1629.550368 0.00 0.00 0.00 0.00 11.89 11.89 193.6792 0.0000 0.0000 0.0000 0.0000
20 15077.0 Cinisello Balsamo Milano Lombardia 1272.408112 0.00 0.00 0.00 9.68 9.68 9.68 123.1662 123.1662 0.0000 0.0000 0.0000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2 1090.0 Collegno Torino Piemonte 1809.931733 3.36 3.22 3.48 2.76 2.76 -0.46 50.0311 50.0311 62.9144 58.3309 60.8457
50 30027.0 Codroipo Udine Friuli Venezia Giulia 7520.098015 1.09 1.09 0.40 0.40 0.40 -0.69 30.4123 30.4123 30.4123 81.8193 81.8197
126 108040.0 Seveso Monza e della Brianza Lombardia 740.240733 0.00 8.95 8.23 8.23 8.23 -0.72 60.8857 60.8857 60.8857 66.2372 0.0000
82 48017.0 Firenze Firenze Toscana 10231.561540 3.25 3.09 2.44 2.14 2.14 -0.95 218.9329 218.9329 249.9431 315.8389 332.8009
70 42002.0 Ancona Ancona Marche 12486.826920 1.71 1.70 0.74 0.74 0.74 -0.96 92.4984 92.4984 92.1039 211.9722 213.0332

130 rows × 16 columns

Conclusione

Sembra abbastanza chiaro che in Italia negli ultimi trenta anni circa si sia preferito sacrificare il paesaggio agricolo e forestale in favore di quello urbano. Si parla molto di Urban Heat Island e della riduzione di questi fenomeni tipici delle aree urbane. Magari, se si fosse riammodernato il costruito negli ultimi trenta anni al posto di edificare in nuovi luoghi, il fenomeno delle isole urbane di calore ci avrebbe colpito in maniera molto minore di quanto non ci colpisca adesso(e nei prossimi anni!). Avremmo così preservato il nostro patrimonio boschivo ed agricolo rendendo il Bel Paese ancora più bello.

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