Solution 01

Setup

try:
  import geopandas as gpd
except ModuleNotFoundError as e:
  !pip install geopandas==0.10.0
  import geopandas as gpd
  if gpd.__version__ != "0.10.0":
    !pip install -U geopandas==0.10.0

---

Exercise

1. load the shapefile of ISTAT with the information of the provinces
   • filter it for an italian province at your choice (eg. Trento)
   • plot it
2. load the shapefile of ISTAT with the informations of the municipalities
   • identify the cities of the province selected with the biggest and smallest area
   • extract all the centroids of the areas expressed in WGS84
   • select all the municipalities of the Province of Trento
   • extract a representative point for the area of each municipality converted in WGS84
     suggestion: .representative_point()
   • save the points in a GeoJSON file
   • calculate the distance on the geodetic between the municipality with the big area and smallest area by using the centroid
3. download the shapefile of the lakes and bodies of water of Trentino
   • plot the geometries where Fktuso is “02”
   • convert in WGS84 and create a geojson

   note: projection Monte Mario zone 1

---

Solutions

learning objectives

• repeat the concepts on the previous lesson
• introduce geopackage
• centroid vs representative point

---

Import of the packages

import geopandas as gpd
import pandas as pd
import os
import urllib
from matplotlib import pyplot as plt  #to avoid the warning message by plotting the geometries
import warnings
warnings.simplefilter("ignore")

1. load the shapefile of ISTAT with the information of the provinces

This request is replaced with the use of geopackage

In the course material a geopackage file is available with all the shapefiles of the administrative limits of ISTAT (2022) with generalized geometries

download the file istat_administrative_units_generalized_2022.gpkg from the repository from directory “data”

url = 'https://github.com/napo/geospatial_course_unitn/raw/master/data/istat/istat_administrative_units_generalized_2022.gpkg'

urllib.request.urlretrieve(url ,"istat_administrative_units_generalized_2022.gpkg")

Geopackage

GeoPackage is used for storing and accessing:

• Vector feature data
• Imagery tile matrix sets
• Raster map tile matrix sets
• non-spatial tabular data
• Metadata that describes other stored data

To have a look at the structure of the files, download the files and open them using the basic SQLite3 command-line utility.

sqlite3 istat_administrative_units_generalized_2022.gpkg

SQLite version 3.37.2 2022-01-06 13:25:41
Enter ".help" for usage hints.
sqlite>.table

gpkg_contents                     rtree_macroregions_geom_parent  
gpkg_extensions                   rtree_macroregions_geom_rowid   
gpkg_geometry_columns             rtree_municipalities_geom       
gpkg_metadata                     rtree_municipalities_geom_node  
gpkg_metadata_reference           rtree_municipalities_geom_parent
gpkg_ogr_contents                 rtree_municipalities_geom_rowid 
gpkg_spatial_ref_sys              rtree_provincies_geom           
gpkg_tile_matrix                  rtree_provincies_geom_node      
gpkg_tile_matrix_set              rtree_provincies_geom_parent    
macroregions                      rtree_provincies_geom_rowid     
municipalities                    rtree_regions_geom              
provincies                        rtree_regions_geom_node         
regions                           rtree_regions_geom_parent       
rtree_macroregions_geom           rtree_regions_geom_rowid        
rtree_macroregions_geom_node     

sqlite> .header on

sqlite> select * from macroregions;

fid|geom|COD_RIP|DEN_RIP|Shape_Leng|Shape_Area
1|GP|1|Nord-Ovest|2330182.61891|57929577720.9
2|GP|2|Nord-Est|2327764.9178|62385087837.3
3|GP|3|Centro|2010203.10916|58018645204.4
4|GP|4|Sud|2517096.68356|73777950167.2
5|GP|5|Isole|2775538.337|49917777679.9

sqlite> .q

Geopandas can manage geopackage by using fiona

import fiona

fiona.supported_drivers

{'ARCGEN': 'r',
 'DXF': 'rw',
 'CSV': 'raw',
 'OpenFileGDB': 'r',
 'ESRIJSON': 'r',
 'ESRI Shapefile': 'raw',
 'FlatGeobuf': 'rw',
 'GeoJSON': 'raw',
 'GeoJSONSeq': 'rw',
 'GPKG': 'raw',
 'GML': 'rw',
 'OGR_GMT': 'rw',
 'GPX': 'rw',
 'GPSTrackMaker': 'rw',
 'Idrisi': 'r',
 'MapInfo File': 'raw',
 'DGN': 'raw',
 'OGR_PDS': 'r',
 'S57': 'r',
 'SQLite': 'raw',
 'TopoJSON': 'r'}

'GPKG': 'raw',

raw => read append write

geopandas can:

• read geopackage files
• aappend data to a geopackage file
• write data to a geopackage file

geopackage can store more layers, so we have to investigate the contents

fiona.listlayers('istat_administrative_units_generalized_2022.gpkg')

['macroregions', 'regions', 'provincies', 'municipalities']

provincies = gpd.read_file("istat_administrative_units_generalized_2022.gpkg",layer="provincies")

TIP: you can also download the file directly from geopandas with this function

provincies = gpd.read_file(url,layer="provincies")

WARNING:fiona._env:File /vsimem/a00e8be4b8444101b0507c606466fb12 has GPKG application_id, but non conformant file extension

provincies.head(3)

	COD_RIP	COD_REG	COD_PROV	COD_CM	COD_UTS	DEN_PROV	DEN_CM	DEN_UTS	SIGLA	TIPO_UTS	Shape_Leng	Shape_Area	geometry
0	1	1	1	201	201	-	Torino	Torino	TO	Citta metropolitana	539249.811270	6.828137e+09	MULTIPOLYGON (((411015.006 5049970.983, 411266...
1	1	1	2	0	2	Vercelli	-	Vercelli	VC	Provincia	418078.067821	2.082097e+09	MULTIPOLYGON (((438328.612 5087208.215, 439028...
2	1	1	3	0	3	Novara	-	Novara	NO	Provincia	250242.030547	1.341318e+09	MULTIPOLYGON (((460929.542 5076320.298, 461165...

provincies.columns

Index(['COD_RIP', 'COD_REG', 'COD_PROV', 'COD_CM', 'COD_UTS', 'DEN_PROV',
       'DEN_CM', 'DEN_UTS', 'SIGLA', 'TIPO_UTS', 'Shape_Leng', 'Shape_Area',
       'geometry'],
      dtype='object')

COD_RIP
codice ripartizione
numeric code of the macroregion of belonging

COD_REG
codice regione
numeric code of the region of belonging

COD_PROV
codice provincia
numeric code of the region of belonging

COD_CM
codice comune
Istat code of the metropolitan city (three characters in reference to all official statistics are numeric format) obtained by adding the value 200 to adopted the statistical codes of the cities corresponding code of the province.

COD_UTS
codice unità territoriali sovracomunali
Numeric code that uniquely identifies the Units territorial supra-municipal on the national territory.

DEN_PROV
denominazione provincia
name of the province

DEN_CM
denominazione città metropolitana
name of the metropolitan city

DEN_UTS
denominazione unità territoriale sovracomunale
Denomination of the supra-municipal territorial units.

SIGLA
sigla
abbreviation

TIPO_UTS
tipologia unità territoriale sovracomunale
kind of supra-municipal territorial units.

filter it for an italian provice at your choice (eg. Trento)

obtain the list of all the provincies

provincies['DEN_PROV'].unique()

array(['-', 'Vercelli', 'Novara', 'Cuneo', 'Asti', 'Alessandria', 'Aosta',
       'Imperia', 'Savona', 'La Spezia', 'Varese', 'Como', 'Sondrio',
       'Bergamo', 'Brescia', 'Pavia', 'Cremona', 'Mantova', 'Bolzano',
       'Trento', 'Verona', 'Vicenza', 'Belluno', 'Treviso', 'Padova',
       'Rovigo', 'Udine', 'Gorizia', 'Trieste', 'Piacenza', 'Parma',
       "Reggio nell'Emilia", 'Modena', 'Ferrara', 'Ravenna',
       "Forli'-Cesena", 'Pesaro e Urbino', 'Ancona', 'Macerata',
       'Ascoli Piceno', 'Massa Carrara', 'Lucca', 'Pistoia', 'Livorno',
       'Pisa', 'Arezzo', 'Siena', 'Grosseto', 'Perugia', 'Terni',
       'Viterbo', 'Rieti', 'Latina', 'Frosinone', 'Caserta', 'Benevento',
       'Avellino', 'Salerno', "L'Aquila", 'Teramo', 'Pescara', 'Chieti',
       'Campobasso', 'Foggia', 'Taranto', 'Brindisi', 'Lecce', 'Potenza',
       'Matera', 'Cosenza', 'Catanzaro', 'Trapani', 'Agrigento',
       'Caltanissetta', 'Enna', 'Ragusa', 'Siracusa', 'Sassari', 'Nuoro',
       'Pordenone', 'Isernia', 'Oristano', 'Biella', 'Lecco', 'Lodi',
       'Rimini', 'Prato', 'Crotone', 'Vibo Valentia',
       'Verbano-Cusio-Ossola', 'Monza e della Brianza', 'Fermo',
       'Barletta-Andria-Trani', 'Sud Sardegna'], dtype=object)

obtain the list of all the metropolitan ciies

provincies.DEN_CM.unique()

array(['Torino', '-', 'Genova', 'Milano', 'Venezia', 'Bologna', 'Firenze',
       'Roma', 'Napoli', 'Bari', 'Reggio di Calabria', 'Palermo',
       'Messina', 'Catania', 'Cagliari'], dtype=object)

choose the province of Trento

province_of_trento = provincies[provincies['DEN_PROV']=='Trento']

province_of_trento

	COD_RIP	COD_REG	COD_PROV	COD_CM	COD_UTS	DEN_PROV	DEN_CM	DEN_UTS	SIGLA	TIPO_UTS	Shape_Leng	Shape_Area	geometry
21	2	4	22	0	22	Trento	-	Trento	TN	Provincia autonoma	594758.785501	6.208170e+09	MULTIPOLYGON (((716676.337 5153931.623, 716029...

plot it

province_of_trento.plot(figsize=(10,10))
plt.show()

2. load the shapefile of ISTAT with the informations of the muncipalities

identify the municipalities of the province selected with the biggest and smallest area

this means we need to use another layer / dataset

municipalities = gpd.read_file(url,layer="municipalities")

WARNING:fiona._env:File /vsimem/bff5372ff2dc43c3b114ee0fc1f5f392 has GPKG application_id, but non conformant file extension

municipalities.columns

Index(['COD_RIP', 'COD_REG', 'COD_PROV', 'COD_CM', 'COD_UTS', 'PRO_COM',
       'PRO_COM_T', 'COMUNE', 'COMUNE_A', 'CC_UTS', 'Shape_Leng', 'Shape_Area',
       'geometry'],
      dtype='object')

COD_RIP
codice ripartizione
numeric code of the macroregion of belonging

COD_REG
codice regione
numeric code of the region of belonging

COD_PROV
codice provincia
numeric code of the region of belonging

COD_CM
codice comune
unique numeric identification code of the municipality within the province of belonging

COD_UTS
codice unità territoriali sovracomunali
Numeric code that uniquely identifies the Units territorial supra-municipal on the national territory.

PRO_COM
*provincia comune**
Numeric code that uniquely identifies the Municipality on the national territory. (= COD_PROV & COD_COM)

PRO_COM_T
*provincia comune territorio**
Alphanumeric code that uniquely identifies the Municipality on the national territory.
Like PRO_COM but definied in 6 fixed characters.

COMUNE
comune
Name of the Municipality

COMUNE_A
comune alternativa
Name of the Municipality in a language other than Italian

CC_UTS
comune capoluogo
Provincial capital or metropolitan city
1 => True
2 => False

the COD_PROV of the Province of Trento is 22

# filter the province
municipalities_province_trento = municipalities[municipalities.COD_PROV==22]

# plot it
municipalities_province_trento.plot(figsize=(10,10))
plt.show()

identify the cities of the province selected with the biggest and smallest area

CAUTION:

we are using generalized boundaries !!!

finding the max area

max_area = municipalities_province_trento.geometry.area.max()

max_area

199625902.75494903

finding the min area

min_area = municipalities_province_trento.geometry.area.min()

min_area

1652100.49475859

… you can obtain the same in another way (combination of the requests)

municipalities[municipalities.COD_PROV==22].geometry.area.min()

1652100.49475859

identify the municipality with the biggest area

maxarea_municipality_trentino = municipalities_province_trento[municipalities_province_trento.geometry.area == max_area]

maxarea_municipality_trentino.PRO_COM_T

3025    022245
Name: PRO_COM_T, dtype: object

maxarea_municipality_trentino.COMUNE

3025    Primiero San Martino di Castrozza
Name: COMUNE, dtype: object

the municipality with the bigger area is Primiero San Martino di Castrozza (022245)

maxarea_municipality_trentino.plot(figsize=(10,10))
plt.show()

identify the municipality with the smallest area

minarea_municipality_trentino = municipalities_province_trento[municipalities_province_trento.geometry.area == min_area]

minarea_municipality_trentino.COMUNE

2896    Carzano
Name: COMUNE, dtype: object

minarea_municipality_trentino.PRO_COM_T

2896    022043
Name: PRO_COM_T, dtype: object

the municipality with the smallest area is Carzano (022243)

minarea_municipality_trentino.plot(figsize=(10,10))
plt.show()

extract all the centroids of the areas expressed in WGS84

maxarea_municipality_trentino.geometry.centroid.to_crs(epsg=4326)

3025    POINT (11.82683 46.24190)
dtype: geometry

minarea_municipality_trentino.geometry.centroid.to_crs(epsg=4326)

2896    POINT (11.49436 46.07497)
dtype: geometry

extract a rappresenative point for the area of the smallest and bigger municipality in WGS84

representative_point_minarea_municipality = minarea_municipality_trentino.geometry.representative_point()

representative_point_maxarea_municipality = maxarea_municipality_trentino.geometry.representative_point()

representative_point_minarea_municipality.to_crs(epsg=4326)

2896    POINT (11.49403 46.07235)
dtype: geometry

representative_point_maxarea_municipality.to_crs(epsg=4326)

3025    POINT (11.83795 46.24792)
dtype: geometry

save the data in GeoJSON files

we can save each point in geojson

representative_point_maxarea_municipality.to_crs(epsg=4326).to_file("point.geojson",driver="GeoJSON")

… but we need to create an only one file with all the data in a geojson file

points = representative_point_maxarea_municipality.append(representative_point_minarea_municipality)

points.to_crs(epsg=4326).to_file("points.geojson",driver="GeoJSON")

and we can do the same with the polygons

polygons = maxarea_municipality_trentino.append(minarea_municipality_trentino)

polygons.to_crs(epsg=4326).to_file("polygons.geojson",driver="GeoJSON")

# you need to use this function only if you are working with Google Colab
# uncomment if you want use it
# from google.colab import files
#files.download("points.geojson")

<IPython.core.display.Javascript object>



<IPython.core.display.Javascript object>

#files.download("polygons.geojson")

<IPython.core.display.Javascript object>



<IPython.core.display.Javascript object>

tip 1:
you can download, open with geojson.io and create a gist resource to distribute

tip 2:
you can download the files and create a map with umap and create an interactive map online with the data

calculate the distance on the geodetic between the municipality with the big area and smallest area by using the centroid

to_point = minarea_municipality_trentino.geometry.centroid.values[0]

maxarea_municipality_trentino.geometry.centroid.distance(to_point)

3025    31686.63797
dtype: float64

distance = maxarea_municipality_trentino.geometry.centroid.distance(to_point).values[0]

distance

31686.637969815347

the distance is in meters due the CRS used on the dataset

to_city = minarea_municipality_trentino.COMUNE.values[0]
from_city = maxarea_municipality_trentino.COMUNE.values[0]
distance_km = distance/1000

print("the distance from %s to %s is %.1f km" % (from_city, to_city, round(distance_km,1)))

the distance from Primiero San Martino di Castrozza to Carzano is 31.7 km

Why a representative point?

Where is the centroid of Liguria?

regions = gpd.read_file(url,layer="regions")

WARNING:fiona._env:File /vsimem/1023372f228c4f2f8ea69e6cbbc51fa5 has GPKG application_id, but non conformant file extension

regions.DEN_REG.unique()

array(['Piemonte', "Valle d'Aosta", 'Lombardia', 'Trentino-Alto Adige',
       'Veneto', 'Friuli Venezia Giulia', 'Liguria', 'Emilia-Romagna',
       'Toscana', 'Umbria', 'Marche', 'Lazio', 'Abruzzo', 'Molise',
       'Campania', 'Puglia', 'Basilicata', 'Calabria', 'Sicilia',
       'Sardegna'], dtype=object)

regions[regions.DEN_REG=='Liguria'].plot(figsize=(10,10))
plt.show()

regions[regions.DEN_REG=='Liguria'].to_crs(epsg=4326).to_file("liguria.geojson",driver='GeoJSON')

regions[regions.DEN_REG=='Liguria'].centroid.to_crs(epsg=4326).to_file("liguria_centroid.geojson",driver='GeoJSON')

regions[regions.DEN_REG=='Liguria'].representative_point().to_crs(epsg=4326).to_file("liguria_representative_point.geojson",driver='GeoJSON')

# uncomment if you want download from Colab
# files.download("liguria.geojson")

# uncomment if you want download from Colab
# files.download("liguria_centroid.geojson")

# uncomment if you want download from Colab
# files.download('liguria_representative_point.geojson')

you can upload all the geojson on uMap to see the result

3. download the shapefile of the lakes and bodies of water of Trentino

note: the shapefile is in Monte Mario 1

dataurl='https://github.com/napo/geospatial_course_unitn/raw/master/data/trentino/lakes_bodies_of_water_trentino.zip'
lakes_bodies_of_water_trentino = gpd.read_file(dataurl)

plot the geometries where Fktuso is “02”

lakes_bodies_of_water_trentino.head(3)

	OBJECTID	NOMESPACQ	geometry
0	29690.0	LAGO DI MOLVENO	POLYGON ((1652079.952 5111676.932, 1652079.586...
1	29743.0	LAGO BACINO S.I.T. - BACINO DI PONTARSO	POLYGON ((1692832.012 5109957.392, 1692832.114...
2	29723.0	LAGO DEL CARESER	POLYGON ((1630312.511 5142839.286, 1630311.588...

lakes_bodies_of_water_trentino.columns

Index(['OBJECTID', 'NOMESPACQ', 'geometry'], dtype='object')

where is “Fktuso” ???

We have to analyze the ESRI Shapefile

os.mkdir("shapefile")

os.chdir("shapefile")

urllib.request.urlretrieve(dataurl ,"lakes_bodies_of_water_trentino.zip")

('lakes_bodies_of_water_trentino.zip',
 <http.client.HTTPMessage at 0x7f5dcf5444d0>)

import zipfile
z = zipfile.ZipFile('lakes_bodies_of_water_trentino.zip')
z.extractall()

os.listdir()

['lakes_bodies_of_water_trentino_use.dbf',
 'lakes_bodies_of_water_trentino.shx',
 'lakes_bodies_of_water_trentino.zip',
 'lakes_bodies_of_water_trentino.dbf',
 'lakes_bodies_of_water_trentino.shp']

filenname	description	note
lakes_bodies_of_water_trentino.shp	the file with the shapes (geometries)	ok
lakes_bodies_of_water_trentino_use.dbf	the file with the attributes	the name is NOT the same of the .shp
lakes_bodies_of_water_trentino.dbf	the file with the attributes	the name is the same of the .shp
lakes_bodies_of_water_trentino.zip	the zip archive	it’s present because downloaded
lakes_bodies_of_water_trentino.shx	the file to associate the attributes to the geometries	the name is the the of the .shp

How we can use the file lakes_bodies_of_water_trentino_use.dbf?

Try to create a shapefile from the other

from shutil import copyfile

copyfile("lakes_bodies_of_water_trentino.shp","lakes_bodies_of_water_trentino_use.shp")

'lakes_bodies_of_water_trentino_use.shp'

copyfile("lakes_bodies_of_water_trentino.shx","lakes_bodies_of_water_trentino_use.shx")

'lakes_bodies_of_water_trentino_use.shx'

lakes_bodies_of_water_trentino_use =  gpd.read_file("lakes_bodies_of_water_trentino_use.shp")

lakes_bodies_of_water_trentino_use.columns

Index(['FKTUSO', 'geometry'], dtype='object')

there is the attribute “FTKUSO” …

lakes_bodies_of_water_trentino_use.FKTUSO.unique()

array(['0101', '92', '0191', '0192', '02', '0102'], dtype=object)

… and there is also the value “02”

ftkuso02 = lakes_bodies_of_water_trentino_use[lakes_bodies_of_water_trentino_use.FKTUSO=='02']

ftkuso02.plot(figsize=(10,10))
plt.show()

convert in WGS84 and create a geojson

ftkuso02.to_crs(epsg=4326)

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-84-cce14c4aa588> in <module>
----> 1 ftkuso02.to_crs(epsg=4326)


/usr/local/lib/python3.7/dist-packages/geopandas/geodataframe.py in to_crs(self, crs, epsg, inplace)
   1273         else:
   1274             df = self.copy()
-> 1275         geom = df.geometry.to_crs(crs=crs, epsg=epsg)
   1276         df.geometry = geom
   1277         df.crs = geom.crs


/usr/local/lib/python3.7/dist-packages/geopandas/geoseries.py in to_crs(self, crs, epsg)
   1118         """
   1119         return GeoSeries(
-> 1120             self.values.to_crs(crs=crs, epsg=epsg), index=self.index, name=self.name
   1121         )
   1122 


/usr/local/lib/python3.7/dist-packages/geopandas/array.py in to_crs(self, crs, epsg)
    762         if self.crs is None:
    763             raise ValueError(
--> 764                 "Cannot transform naive geometries.  "
    765                 "Please set a crs on the object first."
    766             )


ValueError: Cannot transform naive geometries.  Please set a crs on the object first.

ERROR: Cannot transform naive geometries. Please set a crs on the object first.

The geodataframe is without the CRS

If you check again all the files contained in the zip archive the .prj file is not present!!!

You need to create it!

THe note says “projection Monte Mario zone 1”

This is enough to search the right epsg code on https://epsg.io with the query “Monte Mario 1”

where the EPSG code is 3003

the solution can be possible in two ways:

1 - assign the CRS to the geodataframe

ftkuso02.set_crs(epsg=3003,inplace=True,allow_override=True)

	FKTUSO	geometry
43	02	POLYGON ((1620506.057 5076599.354, 1620506.479...
56	02	POLYGON ((1620419.156 5075908.883, 1620417.568...

ftkuso02.to_crs(epsg=4326)

	FKTUSO	geometry
43	02	POLYGON ((10.55121 45.83224, 10.55122 45.83226...
56	02	POLYGON ((10.54992 45.82605, 10.54990 45.82604...

this approach solves the problem but keeps the error in the data source.
Keeping this approach, it is advisable to apply the assignment of the CRS to the initial geodataframe and, if necessary, to export the geodatrafame in ESRi Shapefile (or other format)

2 - create a file with the name lakes_bodies_of_water_trentino_use.prj that contains the right string in WKT for the proj conversion.

You can obtain the right file by simple download the right string from epsg.io in this way

https://epsg.io/3003.wkt

simple by adding .wkt to the url

this is the answer

PROJCS["Monte Mario / Italy zone 1",
    GEOGCS["Monte Mario",
        DATUM["Monte_Mario",
            SPHEROID["International 1924",6378388,297,
                AUTHORITY["EPSG","7022"]],
            TOWGS84[-104.1,-49.1,-9.9,0.971,-2.917,0.714,-11.68],
            AUTHORITY["EPSG","6265"]],
        PRIMEM["Greenwich",0,
            AUTHORITY["EPSG","8901"]],
        UNIT["degree",0.0174532925199433,
            AUTHORITY["EPSG","9122"]],
        AUTHORITY["EPSG","4265"]],
    PROJECTION["Transverse_Mercator"],
    PARAMETER["latitude_of_origin",0],
    PARAMETER["central_meridian",9],
    PARAMETER["scale_factor",0.9996],
    PARAMETER["false_easting",1500000],
    PARAMETER["false_northing",0],
    UNIT["metre",1,
        AUTHORITY["EPSG","9001"]],
    AXIS["X",EAST],
    AXIS["Y",NORTH],
    AUTHORITY["EPSG","3003"]]

url = "https://epsg.io/3003.wkt"
opener = urllib.request.URLopener()
opener.addheader('User-Agent', 'Geospatial Course Unitn')
filename, headers = opener.retrieve(url, 'lakes_bodies_of_water_trentino_use.prj')

.. it’s better create a copy also for the original .shp file

copyfile("lakes_bodies_of_water_trentino_use.prj","lakes_bodies_of_water_trentino.prj")

'lakes_bodies_of_water_trentino.prj'

.. and now it’s necessary to recreate the geodataframe and repeat the operations

lakes_bodies_of_water_trentino_use = gpd.read_file("lakes_bodies_of_water_trentino_use.shp")

lakes_bodies_of_water_trentino_use.crs

<Projected CRS: EPSG:3003>
Name: Monte Mario / Italy zone 1
Axis Info [cartesian]:
- X[east]: Easting (metre)
- Y[north]: Northing (metre)
Area of Use:
- name: Italy - onshore and offshore - west of 12°E.
- bounds: (5.94, 36.53, 12.0, 47.04)
Coordinate Operation:
- name: Italy zone 1
- method: Transverse Mercator
Datum: Monte Mario
- Ellipsoid: International 1924
- Prime Meridian: Greenwich

Now it’s ok ;)

ftkuso02 = lakes_bodies_of_water_trentino_use[lakes_bodies_of_water_trentino_use.FKTUSO=='02']

… and create the geojson file in wgs84

ftkuso02.to_crs(epsg=4326).to_file("ftkuso02.geojson",driver="GeoJSON")

# uncomment if you want download from Colab
# files.download("ftkuso02.geojson")

… and here the areas over a satellite image