Exercise 1¶
You will make two maps:
- Plot your country as the background. Use cx to plot some points (the cities, airports, etc.) below the centroid.
- Plot your country as the background. Select with cx all the first administrative divisions above the centroid. Then, use clip to show some lines (rivers, railroads, etc) that cross those divisions.
In [171]:
import geopandas as gpd
countries=gpd.read_file('https://github.com/LeonarddoCLS/Avance-2/raw/refs/heads/main/World%20Country/World_Countries.shp')
argentina = countries[countries.COUNTRY=='Argentina']
centrales_electricas=gpd.read_file('https://github.com/LeonarddoCLS/Avance-2/raw/refs/heads/main/Energ%C3%ADa/puntos_de_energia_AD010Point.shp')
In [172]:
argentina.crs
Out[172]:
<Geographic 2D CRS: EPSG:4326> Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World. - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 ensemble - Ellipsoid: WGS 84 - Prime Meridian: Greenwich
In [173]:
argentina=argentina.to_crs(5343)
In [174]:
argentina.total_bounds
Out[174]:
array([1385481.70808148, 3884871.45452092, 3352642.62786363,
7581252.17870236])
In [175]:
argentina.centroid
Out[175]:
| 0 | |
|---|---|
| 10 | POINT (2166252.13 6144387.961) |
In [176]:
centrales_electricas=centrales_electricas.to_crs(5343)
In [177]:
base = argentina.plot(facecolor="greenyellow", edgecolor='black', linewidth=0.4, figsize=(10, 10))
centroid_lon = argentina.centroid.iloc[0].x
centroid_lat = argentina.centroid.iloc[0].y
centrales_abajo_centroid = centrales_electricas.cx[:, :centroid_lat]
centrales_abajo_centroid.plot(marker='o', color='red', markersize=15, ax=base)
Out[177]:
<Axes: >
Segundo apartado
In [178]:
prov = gpd.read_file('https://github.com/LeonarddoCLS/Avance-2/raw/refs/heads/main/provincias/provinciaPolygon.shp')
ferrovias = gpd.read_file('https://github.com/LeonarddoCLS/Avance-2/raw/refs/heads/main/Ferroviales/lineas_de_transporte_ferroviario_AN010Line.shp')
In [179]:
prov=prov.to_crs(5343)
ferrovias=ferrovias.to_crs(5343)
In [180]:
base2 = prov.plot(facecolor="greenyellow", edgecolor='black', linewidth=0.4, figsize=(10, 10))
divisions_above_centroid = prov.cx[:, centroid_lat:]
clipped_ferrovias = gpd.clip(gdf=ferrovias, mask=divisions_above_centroid)
clipped_ferrovias.plot(edgecolor='blue', linewidth=0.5, ax=base2)
Out[180]:
<Axes: >
Exercise 2¶
- Create some subset of polygons with your country data at the municipal (or similar level). Use Unary UNION with those polygons, and create a geoDF with the result.
Con ayuda de cx.(o.O) Solo voy a unir el norte con el sur}
- Dissolve your municipalities by another higher level administrative level. Plot the result.
- If possible, color some areas of your country by aggregating; if not, plot the "median" values in the indicators map.
In [181]:
import geopandas as gpd
provincias=gpd.read_file('https://github.com/LeonarddoCLS/Avance-2/raw/refs/heads/main/provincias/provinciaPolygon.shp')
In [182]:
provincias.crs
Out[182]:
<Geographic 2D CRS: EPSG:4326> Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World. - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 ensemble - Ellipsoid: WGS 84 - Prime Meridian: Greenwich
In [183]:
provincias=provincias.to_crs(5343)
In [184]:
provincias.total_bounds
Out[184]:
array([1.38772426e+06, 5.64183001e-01, 4.12249404e+06, 7.58120702e+06])
In [185]:
provincias.plot()
Out[185]:
<Axes: >
Unary UNION¶
We can combine all these polygons into one:
In [186]:
!pip install shapely
Requirement already satisfied: shapely in /usr/local/lib/python3.11/dist-packages (2.0.7) Requirement already satisfied: numpy<3,>=1.14 in /usr/local/lib/python3.11/dist-packages (from shapely) (1.26.4)
In [187]:
centroid_lat = provincias.centroid.iloc[0].y
prov_sur=provincias.cx[:,:centroid_lat]
In [188]:
prov_norte = provincias.drop(prov_sur.index)
In [189]:
prov_sur.plot()
Out[189]:
<Axes: >
Propuesta: Intersecar mis provincias con el border del world countries, para quitar a antartida y malvidas que fastidian
In [190]:
prov_norte.plot()
Out[190]:
<Axes: >
In [191]:
provincias['region'] = 'norte'
provincias.loc[provincias.cx[:,:centroid_lat].index, 'region'] = 'sur'
provincias.plot(column='region', legend=True)
Out[191]:
<Axes: >
In [192]:
unidos_norte=gpd.GeoDataFrame(index=[0],data={'ADM':'Norte'},
crs=provincias.crs,
geometry=[provincias[provincias.region=='norte'].union_all()])
unidos_sur=gpd.GeoDataFrame(index=[0],data={'ADM':'Sur'},
crs=provincias.crs,
geometry=[provincias[provincias.region=='sur'].union_all()])
unidos_norte_sur = gpd.pd.concat([unidos_norte, unidos_sur], ignore_index=True)
unidos_norte_sur.plot(column='ADM')
Out[192]:
<Axes: >
DISSOLVE¶
In [193]:
departamentos = gpd.read_file('https://github.com/LeonarddoCLS/Avance-2/raw/refs/heads/main/departamentos/departamentoPolygon.shp')
departamentos=departamentos.to_crs(5343)
In [194]:
departamentos.head()
Out[194]:
| gid | objeto | fna | gna | nam | in1 | fdc | sag | geometry | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 38 | Departamento | Departamento Ojo de Agua | Departamento | Ojo de Agua | 86126 | Catastro Provinciales | IGN | POLYGON ((2276075.519 6782525.446, 2276264.373... |
| 1 | 39 | Departamento | Departamento Choya | Departamento | Choya | 86063 | Catastro Provinciales | IGN | POLYGON ((2225816.929 6862993.756, 2233663.751... |
| 2 | 65 | Departamento | Partido de Lezama | Partido | Lezama | 06466 | ARBA - Gerencia de Servicios Catastrales | IGN | POLYGON ((2784038.593 5957686.537, 2784665.484... |
| 3 | 102 | Departamento | Departamento Pilcaniyeu | Departamento | Pilcaniyeu | 62070 | IDE Rio Negro | IGN | POLYGON ((1692055.687 5583574.853, 1692055.041... |
| 4 | 118 | Departamento | Comuna 1 | Comuna | Comuna 1 | 02007 | Direc. de Catastro | IGN | POLYGON ((2753297.347 6088702.481, 2753313.38 ... |
In [195]:
prov.head()
Out[195]:
| gid | entidad | fna | gna | nam | in1 | fdc | sag | geometry | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.0 | Ciudad Autónoma de Buenos Aires | Ciudad Autónoma | Ciudad Autónoma de Buenos Aires | 02 | Geografía | IGN | POLYGON ((2747652.364 6095235.901, 2747750.867... |
| 1 | 2 | 0.0 | Provincia de San Luis | Provincia | San Luis | 74 | Geografía | IGN | POLYGON ((1968211.939 6465396.366, 1968723.496... |
| 2 | 3 | 0.0 | Provincia de Santa Fe | Provincia | Santa Fe | 82 | Geografía | IGN | POLYGON ((2581134.497 6854846.203, 2584940.824... |
| 3 | 4 | 0.0 | Provincia de La Rioja | Provincia | La Rioja | 46 | Geografía | IGN | POLYGON ((1842859.281 6918841.663, 1842839.746... |
| 4 | 5 | 0.0 | Provincia de Catamarca | Provincia | Catamarca | 10 | Geografía | IGN | POLYGON ((1852441.661 7212670.781, 1852593.364... |
In [196]:
departamentos.fdc.unique()
Out[196]:
array(['Catastro Provinciales',
'ARBA - Gerencia de Servicios Catastrales', 'IDE Rio Negro',
'Direc. de Catastro', 'SCAR', 'IDE Salta',
'Direc. Grl. de Inmuebles',
'Servicio de Catastro e Información Territorial', 'IDE Mendoza',
'IGN', 'Gerencia de Catastro Pcial.',
'Direc. Pcial. de Catastro e Inf. Territorial',
'Direc. de Geodesia y Catastro', 'Direc. Grl. de Catastro',
'Ministerio de Ecología', 'IDE Tucuman',
'Direc. Pcial. de Catastro y Cartografía',
'ATER - Direc. de Catastro', 'IDE Chaco', 'IDE Cordoba',
'IDE Catamarca', 'Catastro'], dtype=object)
In [197]:
departamentos[~departamentos.is_valid]
Out[197]:
| gid | objeto | fna | gna | nam | in1 | fdc | sag | geometry | |
|---|---|---|---|---|---|---|---|---|---|
| 6 | 153 | Departamento | Departamento Antártida Argentina | Departamento | Antártida Argentina | 94028 | SCAR | IGN | MULTIPOLYGON (((2654168.387 1087347.883, 26349... |
| 510 | 525 | Departamento | Departamento Islas del Atlántico Sur | Departamento | Islas del Atlántico Sur | 94021 | IGN | IGN | MULTIPOLYGON (((2441787.736 4227942.649, 24417... |
In [198]:
departamentos["geometry"] = departamentos["geometry"].buffer(0)
In [199]:
departamentos.dissolve(by='fdc').plot(facecolor='yellow', edgecolor='black',linewidth=0.2)
Out[199]:
<Axes: >
In [200]:
Argentina_fdc_diss=departamentos.dissolve(by='fdc')
In [201]:
!pip install fiona
Requirement already satisfied: fiona in /usr/local/lib/python3.11/dist-packages (1.10.1) Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.11/dist-packages (from fiona) (25.1.0) Requirement already satisfied: certifi in /usr/local/lib/python3.11/dist-packages (from fiona) (2025.1.31) Requirement already satisfied: click~=8.0 in /usr/local/lib/python3.11/dist-packages (from fiona) (8.1.8) Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.11/dist-packages (from fiona) (1.1.1) Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.11/dist-packages (from fiona) (0.7.2)
In [202]:
import geopandas as gpd
from fiona import listlayers
linkWorldMap="https://github.com/CienciaDeDatosEspacial/intro_geodataframe/raw/main/maps/worldMaps.gpkg"
indicators=gpd.read_file(linkWorldMap,layer='indicators')
In [203]:
indicators.head()
Out[203]:
| COUNTRY | Officialstatename | InternetccTLD | iso2 | iso3 | fragility_date | fragility | co2 | co2_date | region | ForestRev_gdp | ForestRev_date | fragility_Qt | fragility_Qt_jc5 | fragility_Qt_jc5_cat | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ANTIGUA AND BARBUDA | Antigua and Barbuda | .ag | AG | ATG | 2019 | 54.4 | 729000.0 | 2019 | CENTRAL AMERICA AND THE CARIBBEAN | 0.00 | 2018 | -0.530113 | 1 | 1_Good | MULTIPOLYGON (((-61.73889 17.54055, -61.75195 ... |
| 1 | AFGHANISTAN | The Islamic Republic of Afghanistan | .af | AF | AFG | 2019 | 105.0 | 7893000.0 | 2019 | SOUTH ASIA | 0.20 | 2018 | 1.827146 | 4 | 4_Poor | POLYGON ((61.27656 35.60725, 61.29638 35.62853... |
| 2 | ALGERIA | The People's Democratic Republic of Algeria | .dz | DZ | DZA | 2019 | 75.4 | 151633000.0 | 2019 | AFRICA | 0.10 | 2018 | 0.304520 | 3 | 3_Bad | POLYGON ((-5.15213 30.18047, -5.13917 30.19236... |
| 3 | AZERBAIJAN | The Republic of Azerbaijan | .az | AZ | AZE | 2019 | 73.2 | 35389000.0 | 2019 | MIDDLE EAST | 0.02 | 2018 | 0.233695 | 2 | 2_Middle | MULTIPOLYGON (((46.54037 38.87559, 46.49554 38... |
| 4 | ALBANIA | The Republic of Albania | .al | AL | ALB | 2019 | 58.9 | 3794000.0 | 2019 | EUROPE | 0.18 | 2018 | -0.424447 | 1 | 1_Good | POLYGON ((20.79192 40.43154, 20.78722 40.39472... |
In [204]:
new_indicators=indicators.dissolve(
by="region",
aggfunc={
"COUNTRY": "count",
"fragility": ["median"],
"co2": ["median"],
"ForestRev_gdp": ["median"]
},as_index=False,
)
new_indicators
Out[204]:
| region | geometry | (COUNTRY, count) | (fragility, median) | (co2, median) | (ForestRev_gdp, median) | |
|---|---|---|---|---|---|---|
| 0 | AFRICA | MULTIPOLYGON (((-2.92813 5.10022, -2.93222 5.1... | 51 | 87.40 | 4444000.0 | 2.470 |
| 1 | AUSTRALIA AND OCEANIA | MULTIPOLYGON (((-176.51779 -43.90778, -176.532... | 6 | 67.95 | 1023000.0 | 0.385 |
| 2 | CENTRAL AMERICA AND THE CARIBBEAN | MULTIPOLYGON (((-81.71556 7.44681, -81.71362 7... | 16 | 61.00 | 7873500.0 | 0.115 |
| 3 | CENTRAL ASIA | MULTIPOLYGON (((50.03472 45.04833, 50.0425 45.... | 6 | 75.20 | 106001000.0 | 0.005 |
| 4 | EAST AND SOUTHEAST ASIA | MULTIPOLYGON (((105.26361 -6.54472, 105.26138 ... | 15 | 70.40 | 238983000.0 | 0.180 |
| 5 | EUROPE | MULTIPOLYGON (((-18.13223 27.77264, -18.1132 2... | 39 | 40.50 | 38739000.0 | 0.150 |
| 6 | MIDDLE EAST | MULTIPOLYGON (((42.76819 14.06653, 42.7868 14.... | 16 | 72.60 | 68706500.0 | 0.000 |
| 7 | NORTH AMERICA | MULTIPOLYGON (((-155.211 20.00166, -155.18335 ... | 3 | 38.00 | 612084000.0 | 0.080 |
| 8 | SOUTH AMERICA | MULTIPOLYGON (((-74.93431 -51.85501, -74.93251... | 11 | 68.20 | 36051000.0 | 0.490 |
| 9 | SOUTH ASIA | MULTIPOLYGON (((73.10193 -0.58431, 73.09915 -0... | 8 | 84.35 | 15916000.0 | 0.120 |
In [205]:
new_indicators.plot(column=('fragility', 'median'), legend=True, cmap='viridis')
Out[205]:
<Axes: >
In [206]:
new_indicators.plot(column=('co2', 'median'), legend=True, cmap='viridis')
Out[206]:
<Axes: >
In [207]:
new_indicators.plot(column=('ForestRev_gdp', 'median'), legend=True, cmap='viridis')
Out[207]:
<Axes: >
Exercise 3¶
Select some points from your maps.
Create the convex hull for those points.
Turn the hull into a GDF.
Plot the hull on top of the country.
In [208]:
aeropuertos=gpd.read_file('https://github.com/LeonarddoCLS/Avance-2/raw/refs/heads/main/aeropuertos/puntos_de_transporte_aereo_GB005Point.shp')
aeropuertos=aeropuertos.to_crs(5343)
In [209]:
aeropuertos_IGN=aeropuertos[aeropuertos.fdc=='IGN/ORSNA']
aeropuertos_IGN.plot()
# ME QUEDO CON LOS AEROPUERTOS REGISTRADOS POR IGN
Out[209]:
<Axes: >
In [210]:
aeropuertos_IGN.union_all().convex_hull
Out[210]:
In [211]:
aeropuertos_hull= gpd.GeoDataFrame(index=[0],
crs=aeropuertos_IGN.crs,
geometry=[aeropuertos_IGN.union_all().convex_hull])
aeropuertos_hull
Out[211]:
| geometry | |
|---|---|
| 0 | POLYGON ((1736873.028 3916366.243, 1484220.121... |
In [212]:
base=prov.plot(facecolor='yellow')
aeropuertos_hull.plot(ax=base, alpha=0.4)
aeropuertos_hull.plot(ax=base,facecolor='green',
edgecolor='white',alpha=0.4,
hatch='X')
aeropuertos_IGN.plot(ax=base, marker='o', color='blue', markersize=8)
Out[212]:
<Axes: >
Exercise 4¶
Apply two spatial overlays to your maps. If possible. If not, try the codes below.
Spatial Overlay¶
In [213]:
departamentos=departamentos.to_crs(5343)
In [214]:
# the north
DepaN_argentina=departamentos.cx[:,centroid_lat:]
# the south
DepaS_argentina=departamentos.cx[:,:centroid_lat]
# the west
DepaW_argentina=departamentos.cx[:centroid_lon,:]
# the east
DepaE_argentina=departamentos.cx[centroid_lon:,:]
In [215]:
base=DepaN_argentina.plot(facecolor='yellow', edgecolor='black',linewidth=0.2, alpha=0.6)
DepaS_argentina.plot(facecolor='grey', edgecolor='black',linewidth=0.2,ax=base, alpha=0.4)
Out[215]:
<Axes: >
In [216]:
base=DepaE_argentina.plot(facecolor='yellow', edgecolor='black',linewidth=0.2, alpha=0.6)
DepaW_argentina.plot(facecolor='grey', edgecolor='black',linewidth=0.2,ax=base, alpha=0.4)
Out[216]:
<Axes: >
Intersection¶
In [217]:
depaNS_argentina=DepaN_argentina.overlay(DepaS_argentina, how="intersection",keep_geom_type=True, make_valid=True)
depaNS_argentina.plot()
Out[217]:
<Axes: >
In [218]:
depaWE_argentina=DepaW_argentina.overlay(DepaE_argentina, how="intersection",keep_geom_type=True)
depaWE_argentina.plot(edgecolor='white',linewidth=0.1)
Out[218]:
<Axes: >
Union¶
In [219]:
keep=['fna_1','gna_1','nam_1','geometry']
depaNS_argentina=depaNS_argentina.loc[:,keep]
depaWE_argentina=depaWE_argentina.loc[:,keep]
In [220]:
depaNS_argentina.overlay(depaWE_argentina,how="union",keep_geom_type=True)
Out[220]:
| fna_1_1 | gna_1_1 | nam_1_1 | fna_1_2 | gna_1_2 | nam_1_2 | geometry | |
|---|---|---|---|---|---|---|---|
| 0 | Departamento Rancul | Departamento | Rancul | Departamento Rancul | Departamento | Rancul | POLYGON ((2180328.024 6100644.134, 2180587.411... |
| 1 | Comuna 1 | Comuna | Comuna 1 | NaN | NaN | NaN | POLYGON ((2753313.38 6088699.601, 2753333.834 ... |
| 2 | Departamento Realicó | Departamento | Realicó | NaN | NaN | NaN | POLYGON ((2235740.179 6082213.857, 2235304.088... |
| 3 | Departamento San Rafael | Departamento | San Rafael | NaN | NaN | NaN | POLYGON ((1946220.069 6194841.405, 1946222.513... |
| 4 | Partido de Lincoln | Partido | Lincoln | NaN | NaN | NaN | POLYGON ((2489326.755 6108996.521, 2489983.652... |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 72 | NaN | NaN | NaN | Departamento Dr. Manuel Belgrano | Departamento | Dr. Manuel Belgrano | POLYGON ((2152488.909 7332264.103, 2152509.629... |
| 73 | NaN | NaN | NaN | Departamento Graneros | Departamento | Graneros | POLYGON ((2157991.138 6938532.378, 2157993.459... |
| 74 | NaN | NaN | NaN | Departamento Simoca | Departamento | Simoca | POLYGON ((2155102.825 6977734.368, 2155122.305... |
| 75 | NaN | NaN | NaN | Departamento Leales | Departamento | Leales | POLYGON ((2166918.969 7005177.525, 2166928.861... |
| 76 | NaN | NaN | NaN | Departamento Utracán | Departamento | Utracán | POLYGON ((2190410.926 5801413.002, 2190087.949... |
77 rows × 7 columns
In [221]:
import pandas as pd
pd.concat([depaNS_argentina,depaWE_argentina],ignore_index=True)
Out[221]:
| fna_1 | gna_1 | nam_1 | geometry | |
|---|---|---|---|---|
| 0 | Comuna 1 | Comuna | Comuna 1 | POLYGON ((2753313.38 6088699.601, 2753333.834 ... |
| 1 | Departamento Realicó | Departamento | Realicó | POLYGON ((2235754.902 6082380.797, 2235740.34 ... |
| 2 | Departamento San Rafael | Departamento | San Rafael | POLYGON ((1946220.069 6194841.405, 1946222.513... |
| 3 | Partido de Lincoln | Partido | Lincoln | POLYGON ((2489326.755 6108996.521, 2489983.652... |
| 4 | Partido de Moreno | Partido | Moreno | POLYGON ((2715092.629 6099911.817, 2715269.499... |
| ... | ... | ... | ... | ... |
| 73 | Departamento Dr. Manuel Belgrano | Departamento | Dr. Manuel Belgrano | POLYGON ((2152488.909 7332264.103, 2152509.629... |
| 74 | Departamento Graneros | Departamento | Graneros | POLYGON ((2157991.138 6938532.378, 2157993.459... |
| 75 | Departamento Simoca | Departamento | Simoca | POLYGON ((2155102.825 6977734.368, 2155122.305... |
| 76 | Departamento Leales | Departamento | Leales | POLYGON ((2166918.969 7005177.525, 2166928.861... |
| 77 | Departamento Utracán | Departamento | Utracán | POLYGON ((2190410.926 5801413.002, 2190087.949... |
78 rows × 4 columns
In [222]:
depaNS_argentina.overlay(depaWE_argentina, how="union",keep_geom_type=True).dissolve().plot()
Out[222]:
<Axes: >
In [223]:
depaMidArgentina=depaNS_argentina.overlay(depaWE_argentina, how="union",keep_geom_type=True).dissolve()
depaMidArgentina
Out[223]:
| geometry | fna_1_1 | gna_1_1 | nam_1_1 | fna_1_2 | gna_1_2 | nam_1_2 | |
|---|---|---|---|---|---|---|---|
| 0 | MULTIPOLYGON (((1465907.039 1392130.708, 14661... | Departamento Rancul | Departamento | Rancul | Departamento Rancul | Departamento | Rancul |
In [224]:
depaMidArgentina['zone']='middles'
depaMidArgentina=depaMidArgentina.loc[:,['fna_1_1','zone','geometry']]
depaMidArgentina
Out[224]:
| fna_1_1 | zone | geometry | |
|---|---|---|---|
| 0 | Departamento Rancul | middles | MULTIPOLYGON (((1465907.039 1392130.708, 14661... |
Difference¶
In [225]:
departamentos.overlay(depaMidArgentina, how='difference').plot()
Out[225]:
<Axes: >
Symmetric Difference¶
In [226]:
DepaN_argentina.overlay(DepaS_argentina, how="symmetric_difference",keep_geom_type=True).plot()
Out[226]:
<Axes: >
In [227]:
DepaW_argentina.overlay(depaMidArgentina, how="symmetric_difference",keep_geom_type=False).plot()
Out[227]:
<Axes: >
Validity of Geometry¶
In [228]:
DepaS_argentina[~DepaS_argentina.is_valid]
Out[228]:
| gid | objeto | fna | gna | nam | in1 | fdc | sag | geometry |
|---|
In [229]:
DepaS_argentina[~DepaS_argentina.is_valid].plot()
Out[229]:
<Axes: >
In [230]:
#from shapely.validation import explain_validity, make_valid
#explain_validity(DepaS_argentina[~DepaS_argentina.is_valid].geometry)
Out[230]:
| geometry |
|---|
In [231]:
#DepaS_argentina['validity']=[x.split('[')[0] for x in DepaS_argentina.geometry.apply(lambda x: explain_validity(x))]
#DepaS_argentina['validity'].value_counts()
Out[231]:
| count | |
|---|---|
| validity | |
| Valid Geometry | 197 |
In [232]:
#DepaS_argentina.drop(columns=['validity'],inplace=True)
#DepaS_argentina_valid=DepaS_argentina.copy()
#DepaS_argentina_valid['geometry'] = [make_valid(row) if not row.is_valid else row for row in DepaS_argentina_valid['geometry'] ]
#any invalid?
#DepaS_argentina_valid[~DepaS_argentina_valid.is_valid]
Out[232]:
| gid | objeto | fna | gna | nam | in1 | fdc | sag | geometry |
|---|
In [233]:
pd.Series([type(x) for x in DepaS_argentina_valid.geometry]).value_counts()
Out[233]:
| count | |
|---|---|
| <class 'shapely.geometry.polygon.Polygon'> | 184 |
| <class 'shapely.geometry.multipolygon.MultiPolygon'> | 13 |
In [234]:
DepaN_argentina_valid=DepaN_argentina.copy()
DepaE_argentina_valid=DepaE_argentina.copy()
DepaW_argentina_valid=DepaE_argentina.copy()
DepaN_argentina_valid['geometry'] = [make_valid(row) if not row.is_valid else row for row in DepaN_argentina_valid['geometry'] ]
DepaE_argentina_valid['geometry'] = [make_valid(row) if not row.is_valid else row for row in DepaE_argentina_valid['geometry'] ]
DepaW_argentina_valid['geometry'] = [make_valid(row) if not row.is_valid else row for row in DepaW_argentina_valid['geometry'] ]
In [235]:
DepaN_argentina_cleaned = DepaN_argentina_valid.copy()
DepaS_argentina_cleaned = DepaS_argentina_valid.copy()
DepaE_argentina_cleaned = DepaE_argentina_valid.copy()
DepaW_argentina_cleaned = DepaW_argentina_valid.copy()
# Apply buffer(0) to potentially fix remaining issues
DepaN_argentina_cleaned['geometry'] = DepaN_argentina_cleaned.geometry.buffer(0)
DepaS_argentina_cleaned['geometry'] = DepaS_argentina_cleaned.geometry.buffer(0)
DepaE_argentina_cleaned['geometry'] = DepaE_argentina_cleaned.geometry.buffer(0)
DepaW_argentina_cleaned['geometry'] = DepaW_argentina_cleaned.geometry.buffer(0)
In [236]:
DepaN_argentina_cleaned.overlay(DepaS_argentina_cleaned, how="symmetric_difference", keep_geom_type=True).plot()
Out[236]:
<Axes: >
In [238]:
DepaE_argentina_cleaned.overlay(DepaW_argentina_cleaned, how="symmetric_difference", keep_geom_type=True).plot()
Out[238]:
<Axes: >