PyGIS Blog

Analisi della copertura del suolo in Italia

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:

In [1]:

from pathlib import Path
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.

In [2]:

main_folder = Path('/home/max/Downloads/clcitalia')
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.

In [3]:

%%time

# 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)

clc1990.head(10)

CPU times: user 1min 4s, sys: 687 ms, total: 1min 4s
Wall time: 1min 4s

Out[3]:

	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
5	242	Agricultural areas	Heterogeneous agricultural areas	Complex cultivation patterns	101005.0	Castelsilano	Crotone	Calabria	4006.296249	8.6550
6	243	Agricultural areas	Heterogeneous agricultural areas	Land principally occupied by agriculture, with...	101005.0	Castelsilano	Crotone	Calabria	4006.296249	37.2054
7	243	Agricultural areas	Heterogeneous agricultural areas	Land principally occupied by agriculture, with...	101005.0	Castelsilano	Crotone	Calabria	4006.296249	114.5143
8	311	Forest and semi natural areas	Forests	Broad-leaved forest	101005.0	Castelsilano	Crotone	Calabria	4006.296249	44.3502
9	311	Forest and semi natural areas	Forests	Broad-leaved forest	101005.0	Castelsilano	Crotone	Calabria	4006.296249	32.6148

In [4]:

%%time

# 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)

clc2000.head(10)

CPU times: user 1min 14s, sys: 388 ms, total: 1min 14s
Wall time: 1min 14s

Out[4]:

	code_00	label1	label2	label3	pro_com	comune	provincia	regione	sup_com_ha	surface_ha_2000
0	321	Forest and semi natural areas	Scrub and/or herbaceous vegetation associations	Natural grasslands	2170.0	Alto Sermenza	Vercelli	Piemonte	6032.586221	11.9494
1	321	Forest and semi natural areas	Scrub and/or herbaceous vegetation associations	Natural grasslands	2170.0	Alto Sermenza	Vercelli	Piemonte	6032.586221	42.8232
2	321	Forest and semi natural areas	Scrub and/or herbaceous vegetation associations	Natural grasslands	2170.0	Alto Sermenza	Vercelli	Piemonte	6032.586221	279.4458
3	321	Forest and semi natural areas	Scrub and/or herbaceous vegetation associations	Natural grasslands	2170.0	Alto Sermenza	Vercelli	Piemonte	6032.586221	120.3762
4	321	Forest and semi natural areas	Scrub and/or herbaceous vegetation associations	Natural grasslands	2170.0	Alto Sermenza	Vercelli	Piemonte	6032.586221	80.1545
5	311	Forest and semi natural areas	Forests	Broad-leaved forest	46008.0	Careggine	Lucca	Toscana	2408.101499	1664.3039
6	324	Forest and semi natural areas	Scrub and/or herbaceous vegetation associations	Transitional woodland-shrub	46008.0	Careggine	Lucca	Toscana	2408.101499	25.8278
7	324	Forest and semi natural areas	Scrub and/or herbaceous vegetation associations	Transitional woodland-shrub	46008.0	Careggine	Lucca	Toscana	2408.101499	73.5182
8	333	Forest and semi natural areas	Open spaces with little or no vegetation	Sparsely vegetated areas	46008.0	Careggine	Lucca	Toscana	2408.101499	34.7050
9	333	Forest and semi natural areas	Open spaces with little or no vegetation	Sparsely vegetated areas	46008.0	Careggine	Lucca	Toscana	2408.101499	101.5072

In [5]:

%%time

# 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)

clc2006.head(10)

CPU times: user 1min 10s, sys: 328 ms, total: 1min 11s
Wall time: 1min 11s

Out[5]:

	code_06	label1	label2	label3	pro_com	comune	provincia	regione	sup_com_ha	surface_ha_2006
0	211	Agricultural areas	Arable land	Non-irrigated arable land	101005.0	Castelsilano	Crotone	Calabria	4006.296249	99.9449
1	211	Agricultural areas	Arable land	Non-irrigated arable land	101005.0	Castelsilano	Crotone	Calabria	4006.296249	161.6097
2	223	Agricultural areas	Permanent crops	Olive groves	101005.0	Castelsilano	Crotone	Calabria	4006.296249	35.3999
3	223	Agricultural areas	Permanent crops	Olive groves	101005.0	Castelsilano	Crotone	Calabria	4006.296249	1.1332
4	223	Agricultural areas	Permanent crops	Olive groves	101005.0	Castelsilano	Crotone	Calabria	4006.296249	5.0278
5	241	Agricultural areas	Heterogeneous agricultural areas	Annual crops associated with permanent crops	101005.0	Castelsilano	Crotone	Calabria	4006.296249	2.8168
6	242	Agricultural areas	Heterogeneous agricultural areas	Complex cultivation patterns	101005.0	Castelsilano	Crotone	Calabria	4006.296249	0.1570
7	242	Agricultural areas	Heterogeneous agricultural areas	Complex cultivation patterns	101005.0	Castelsilano	Crotone	Calabria	4006.296249	165.7342
8	243	Agricultural areas	Heterogeneous agricultural areas	Land principally occupied by agriculture, with...	101005.0	Castelsilano	Crotone	Calabria	4006.296249	36.2394
9	311	Forest and semi natural areas	Forests	Broad-leaved forest	101005.0	Castelsilano	Crotone	Calabria	4006.296249	32.6261

In [6]:

%%time

# 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)

clc2012.head(10)

CPU times: user 1min 20s, sys: 588 ms, total: 1min 21s
Wall time: 1min 21s

Out[6]:

	code_12	label1	label2	label3	pro_com	comune	provincia	regione	sup_com_ha	surface_ha_2012
0	243	Agricultural areas	Heterogeneous agricultural areas	Land principally occupied by agriculture, with...	101005.0	Castelsilano	Crotone	Calabria	4006.296249	0.5968
1	313	Forest and semi natural areas	Forests	Mixed forest	101005.0	Castelsilano	Crotone	Calabria	4006.296249	187.4011
2	324	Forest and semi natural areas	Scrub and/or herbaceous vegetation associations	Transitional woodland-shrub	101005.0	Castelsilano	Crotone	Calabria	4006.296249	29.7040
3	211	Agricultural areas	Arable land	Non-irrigated arable land	101005.0	Castelsilano	Crotone	Calabria	4006.296249	99.9449
4	211	Agricultural areas	Arable land	Non-irrigated arable land	101005.0	Castelsilano	Crotone	Calabria	4006.296249	161.6097
5	211	Agricultural areas	Arable land	Non-irrigated arable land	101005.0	Castelsilano	Crotone	Calabria	4006.296249	420.1994
6	223	Agricultural areas	Permanent crops	Olive groves	101005.0	Castelsilano	Crotone	Calabria	4006.296249	35.3999
7	223	Agricultural areas	Permanent crops	Olive groves	101005.0	Castelsilano	Crotone	Calabria	4006.296249	1.1332
8	223	Agricultural areas	Permanent crops	Olive groves	101005.0	Castelsilano	Crotone	Calabria	4006.296249	5.0278
9	223	Agricultural areas	Permanent crops	Olive groves	101005.0	Castelsilano	Crotone	Calabria	4006.296249	1.6430

In [7]:

%%time

# 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)

clc2018.head(10)

CPU times: user 1min 12s, sys: 560 ms, total: 1min 12s
Wall time: 1min 12s

Out[7]:

	code_18	label1	label2	label3	pro_com	comune	provincia	regione	sup_com_ha	surface_ha_2018
0	242	Agricultural areas	Heterogeneous agricultural areas	Complex cultivation patterns	101005.0	Castelsilano	Crotone	Calabria	4006.296249	0.1570
1	211	Agricultural areas	Arable land	Non-irrigated arable land	101005.0	Castelsilano	Crotone	Calabria	4006.296249	161.6097
2	211	Agricultural areas	Arable land	Non-irrigated arable land	101005.0	Castelsilano	Crotone	Calabria	4006.296249	420.1994
3	223	Agricultural areas	Permanent crops	Olive groves	101005.0	Castelsilano	Crotone	Calabria	4006.296249	5.0278
4	223	Agricultural areas	Permanent crops	Olive groves	101005.0	Castelsilano	Crotone	Calabria	4006.296249	1.6430
5	223	Agricultural areas	Permanent crops	Olive groves	101005.0	Castelsilano	Crotone	Calabria	4006.296249	34.7345
6	241	Agricultural areas	Heterogeneous agricultural areas	Annual crops associated with permanent crops	101005.0	Castelsilano	Crotone	Calabria	4006.296249	2.8168
7	241	Agricultural areas	Heterogeneous agricultural areas	Annual crops associated with permanent crops	101005.0	Castelsilano	Crotone	Calabria	4006.296249	1.3899
8	241	Agricultural areas	Heterogeneous agricultural areas	Annual crops associated with permanent crops	101005.0	Castelsilano	Crotone	Calabria	4006.296249	13.0463
9	243	Agricultural areas	Heterogeneous agricultural areas	Land principally occupied by agriculture, with...	101005.0	Castelsilano	Crotone	Calabria	4006.296249	1.1525

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.

In [8]:

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

In [9]:

clc2000_campania

Out[9]:

	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

In [10]:

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

Out[10]:

	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

In [11]:

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

Out[11]:

	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

In [12]:

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

Out[12]:

	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¶

In [13]:

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¶

In [14]:

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¶

In [15]:

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¶

In [16]:

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¶

In [17]:

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

Out[17]:

	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

In [18]:

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')

Out[18]:

<matplotlib.legend.Legend at 0x7f13b28b6da0>

No description has been provided for this image

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¶

In [19]:

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

Out[19]:

	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

In [20]:

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')

Out[20]:

<matplotlib.legend.Legend at 0x7f13ab7d1fc0>

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¶

In [21]:

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

Out[21]:

	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

In [22]:

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')

Out[22]:

<matplotlib.legend.Legend at 0x7f13b28b4fd0>

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.

In [23]:

# 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)

In [24]:

focus_1990_group

Out[24]:

	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

In [25]:

# 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

Out[25]:

	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

In [26]:

# Superfici in arretramento
reduction_artificial_area = focus_artificial_df.iloc[0:10]
reduction_artificial_area

Out[26]:

	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

In [27]:

# 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

Out[27]:

	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

In [28]:

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')

Out[28]:

<matplotlib.legend.Legend at 0x7f13b50e6e60>

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

In [29]:

# 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

Out[29]:

	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

In [30]:

# Superfici in arretramento
reduction_forest_area = focus_forest_df.iloc[0:10]
reduction_forest_area

Out[30]:

	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

In [31]:

# 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

Out[31]:

	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

In [32]:

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')

Out[32]:

<matplotlib.legend.Legend at 0x7f13b1292cb0>

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

In [33]:

# 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

Out[33]:

	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

In [34]:

# Superfici in arretramento
reduction_arable_lands_area = focus_arable_lands_df.iloc[0:10]
reduction_arable_lands_area

Out[34]:

	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

In [35]:

# 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

Out[35]:

	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

In [36]:

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')

Out[36]:

<matplotlib.legend.Legend at 0x7f13aa26b460>

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¶

In [37]:

# 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

Out[37]:

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

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¶

In [38]:

# 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

Out[38]:

	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¶

In [39]:

# 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

Out[39]:

	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¶

In [40]:

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

Out[40]:

	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

In [41]:

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

Out[41]:

	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

In [42]:

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

Out[42]:

	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¶

In [43]:

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¶

In [44]:

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¶

In [45]:

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¶

In [46]:

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¶

In [47]:

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

Out[47]:

	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

In [48]:

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')

Out[48]:

<matplotlib.legend.Legend at 0x7f13ad7f9540>

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¶

In [49]:

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

Out[49]:

	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

In [50]:

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')

Out[50]:

<matplotlib.legend.Legend at 0x7f13b21f3940>

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

3.2.3 Grafici per il livello 3¶

In [51]:

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

Out[51]:

	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

In [52]:

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')

Out[52]:

<matplotlib.legend.Legend at 0x7f13ab9c75b0>

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¶

In [53]:

# 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)

In [54]:

focus_naz_2018_group

Out[54]:

	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

In [55]:

# 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

Out[55]:

	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

In [56]:

# Superfici in arretramento
naz_reduction_artificial_area = focus_artificial_naz_df.iloc[0:10]
naz_reduction_artificial_area

Out[56]:

	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

In [57]:

# 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

Out[57]:

	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

In [58]:

# 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

Out[58]:

	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

In [59]:

# Superfici in arretramento
naz_reduction_forest_area = focus_forest_naz_df.iloc[0:10]
naz_reduction_forest_area

Out[59]:

	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

In [60]:

# 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

Out[60]:

	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

In [61]:

# 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

Out[61]:

	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

In [62]:

# Superfici in arretramento
naz_reduction_arable_lands_area = focus_arable_lands_naz_df.iloc[0:10]
naz_reduction_arable_lands_area

Out[62]:

	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

In [63]:

# 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

Out[63]:

	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¶

In [64]:

# 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

Out[64]:

	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¶

In [65]:

# 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

Out[65]:

	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

In [66]:

# 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

Out[66]:

	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.

Offrimi una birra!

Se ti e piaciuto aiutami a gestire il sito

Offrimi una birra usando PayPal

Aggiornato il 23 febbraio 2026