import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt

countries = gpd.read_file("countries.geojson")

countries.plot()

<Axes: >

countries

countries = countries.set_index("name")

countries = countries.rename(index={
    "United States of America": "United States",
    "Democratic Republic of the Congo": "Democratic Republic of Congo",
    "Ivory Coast": "Cote d\'Ivoire",
    "Republic of the Congo": "Congo",
    "Czech Republic": "Czechia",
    "The Bahamas": "Bahamas",
    "Guinea Bissau": "Guinea-Bissau",
    "Federated States of Micronesia": "Micronesia (country)",
    "Macedonia": "North Macedonia",
    "eSwatini": "Eswatini",
    "Republic of Serbia": "Serbia",
    "United Republic of Tanzania": "Tanzania",
    "São Tomé and Principe": "Sao Tome and Principe",
    "Cape Verde": "Cabo Verde"
}
)

life_expectancy = pd.read_csv("life-expectancy.csv")

life_expectancy.head()

life_expectancy = life_expectancy.set_index("Entity")

life_expectancy.head()

countries["life_expectancy"]=np.arange(len(countries))
countries.head()

life_expectancy["Period life expectancy at birth"]

countries["life_expectancy"]=life_expectancy["Period life expectancy at birth"]
countries.head()

countries.plot(column="life_expectancy", figsize=(12,6), legend=True).set_title("life expectancy")

Text(0.5, 1.0, 'life expectancy')

countries["population"] = pd.read_csv("population.csv").set_index("Entity")["Population (historical)"]

countries.plot(column="population", figsize=(12,6), legend=True).set_title("population")

Text(0.5, 1.0, 'population')

countries["log_population"] = np.log(countries["population"])

countries["co2_emissions"] = pd.read_csv("co-emissions-per-capita.csv").set_index("Entity")["Annual CO₂ emissions (per capita)"]
countries.plot(column="co2_emissions", figsize=(12,6), legend=True).set_title("Emissions annuelles de CO₂ par habitant")

Text(0.5, 1.0, 'Emissions annuelles de CO₂ par habitant')

countries["child_mortality"] = pd.read_csv("child-mortality.csv").set_index("Entity")["Child mortality rate"]
countries.plot(column="child_mortality", figsize=(12,6), legend=True).set_title("Taux de mortalité infantile")

Text(0.5, 1.0, 'Taux de mortalité infantile')

countries["log_child_mortality"] = np.log(1+countries["child_mortality"])

hdi = pd.read_csv("human-development-index.csv")

hdi.head()

hdi_by_year = hdi.pivot_table(index='Entity', columns='Year', values='Human Development Index')
hdi_by_year.head()

def function_to_rename_columns_name(column_name):
    return f"hdi_{column_name}"

hdi_by_year = hdi_by_year.rename(columns=function_to_rename_columns_name)
hdi_by_year.head()

countries[["hdi_2023", "hdi_2013"]]=hdi_by_year[["hdi_2023", "hdi_2013"]]

countries

countries.plot(column='hdi_2023', figsize=(12,6), legend=True).set_title("Human Development Index (2023)")

Text(0.5, 1.0, 'Human Development Index (2023)')

countries = countries.dropna()
countries

pd.plotting.scatter_matrix(countries[["population", "log_population", "life_expectancy", "child_mortality", "log_child_mortality", "hdi_2023", "co2_emissions"]], figsize=(15, 15))

Text(0.5, 1.0, 'Scatter Matrix')

import statsmodels.api as sm
explicative_vars = ["population", "life_expectancy", "log_child_mortality", "hdi_2023"]
target = "co2_emissions"
X = sm.add_constant(countries[explicative_vars])
y = countries[target]
#Ordinary Least Square
model = sm.OLS(y, X)
results = model.fit()
print(results.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:          co2_emissions   R-squared:                       0.303
Model:                            OLS   Adj. R-squared:                  0.288
Method:                 Least Squares   F-statistic:                     19.86
Date:                Thu, 05 Mar 2026   Prob (F-statistic):           1.34e-13
Time:                        13:23:47   Log-Likelihood:                -554.61
No. Observations:                 188   AIC:                             1119.
Df Residuals:                     183   BIC:                             1135.
Df Model:                           4                                         
Covariance Type:            nonrobust                                         
=======================================================================================
                          coef    std err          t      P>|t|      [0.025      0.975]
---------------------------------------------------------------------------------------
const                 -23.6306     10.692     -2.210      0.028     -44.726      -2.535
population           5.968e-10   2.25e-09      0.265      0.791   -3.84e-09    5.03e-09
life_expectancy         0.0883      0.136      0.647      0.518      -0.181       0.357
log_child_mortality     2.4131      1.623      1.487      0.139      -0.789       5.615
hdi_2023               25.6472      6.123      4.188      0.000      13.566      37.729
==============================================================================
Omnibus:                      171.228   Durbin-Watson:                   1.660
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2597.328
Skew:                           3.515   Prob(JB):                         0.00
Kurtosis:                      19.797   Cond. No.                     5.02e+09
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 5.02e+09. This might indicate that there are
strong multicollinearity or other numerical problems.

y_pred = results.predict(X)
countries["predicted_co2_emissions"] = y_pred
countries["residus"] = results.resid

/usr/local/lib/python3.12/dist-packages/geopandas/geodataframe.py:1969: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)

#calcul des valeurs min et max pour avoir une échelle commune
vmin = min(countries["co2_emissions"].min(), countries["predicted_co2_emissions"].min())
vmax = max(countries["co2_emissions"].max(), countries["predicted_co2_emissions"].max())

countries.plot(column="co2_emissions", figsize=(12,6), legend=True, vmin=vmin, vmax=vmax).set_title("Emissions annuelles de CO₂ par habitant")
countries.plot(column="predicted_co2_emissions", figsize=(12,6), legend=True, vmin=vmin, vmax=vmax).set_title("Emissions annuelles de CO₂ par habitant prédites")
countries.plot(column="residus", figsize=(12,6), legend=True, cmap="coolwarm").set_title("Résidus")

Text(0.5, 1.0, 'Résidus')

import statsmodels.api as sm
explicative_vars = ["population", "life_expectancy", "log_child_mortality", "hdi_2023"]
target = "co2_emissions"

countries_without_high_residus = countries[countries["residus"]<15]

X2 = sm.add_constant(countries_without_high_residus[explicative_vars])
y2 = countries_without_high_residus[target]
#Ordinary Least Square
model2 = sm.OLS(y2, X2)
results2 = model2.fit()
print(results2.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:          co2_emissions   R-squared:                       0.455
Model:                            OLS   Adj. R-squared:                  0.443
Method:                 Least Squares   F-statistic:                     37.21
Date:                Thu, 05 Mar 2026   Prob (F-statistic):           1.34e-22
Time:                        13:24:03   Log-Likelihood:                -448.35
No. Observations:                 183   AIC:                             906.7
Df Residuals:                     178   BIC:                             922.7
Df Model:                           4                                         
Covariance Type:            nonrobust                                         
=======================================================================================
                          coef    std err          t      P>|t|      [0.025      0.975]
---------------------------------------------------------------------------------------
const                  -9.3549      6.582     -1.421      0.157     -22.345       3.635
population           1.747e-09   1.37e-09      1.279      0.203   -9.49e-10    4.44e-09
life_expectancy        -0.0568      0.084     -0.678      0.499      -0.222       0.109
log_child_mortality     0.7364      0.994      0.741      0.460      -1.225       2.698
hdi_2023               22.1422      3.736      5.927      0.000      14.770      29.514
==============================================================================
Omnibus:                      101.890   Durbin-Watson:                   1.847
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              456.080
Skew:                           2.217   Prob(JB):                    9.19e-100
Kurtosis:                       9.336   Cond. No.                     5.09e+09
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 5.09e+09. This might indicate that there are
strong multicollinearity or other numerical problems.

y_pred2 = results.predict(X)
countries["predicted_co2_emissions2"] = y_pred2
countries["residus2"] = countries["co2_emissions"] - countries["predicted_co2_emissions2"]

/usr/local/lib/python3.12/dist-packages/geopandas/geodataframe.py:1969: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)

#calcul des valeurs min et max pour avoir une échelle commune
vmin = min(countries["co2_emissions"].min(), countries["predicted_co2_emissions2"].min())
vmax = max(countries["co2_emissions"].max(), countries["predicted_co2_emissions2"].max())
countries.plot(column="co2_emissions", figsize=(12,6), legend=True, vmin=vmin, vmax=vmax).set_title("Emissions annuelles de CO₂ par habitant")
countries.plot(column="predicted_co2_emissions2", figsize=(12,6), legend=True, vmin=vmin, vmax=vmax).set_title("Emissions annuelles de CO₂ par habitant prédites")
countries.plot(column="residus2", figsize=(12,6), legend=True, cmap="coolwarm").set_title("Résidus")

Text(0.5, 1.0, 'Résidus')

import statsmodels.api as sm
explicative_vars = ["hdi_2023"]
target = "co2_emissions"

countries_without_high_residus = countries[countries["residus"]<15]

X3 = sm.add_constant(countries_without_high_residus[explicative_vars])
y3 = countries_without_high_residus[target]
#Ordinary Least Square
model3 = sm.OLS(y2, X3)
results3 = model3.fit()
print(results3.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:          co2_emissions   R-squared:                       0.444
Model:                            OLS   Adj. R-squared:                  0.441
Method:                 Least Squares   F-statistic:                     144.4
Date:                Thu, 05 Mar 2026   Prob (F-statistic):           7.71e-25
Time:                        13:22:48   Log-Likelihood:                -450.27
No. Observations:                 183   AIC:                             904.5
Df Residuals:                     181   BIC:                             911.0
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const         -8.7836      1.057     -8.308      0.000     -10.870      -6.697
hdi_2023      16.9033      1.406     12.018      0.000      14.128      19.678
==============================================================================
Omnibus:                      102.644   Durbin-Watson:                   1.807
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              456.434
Skew:                           2.243   Prob(JB):                    7.70e-100
Kurtosis:                       9.304   Cond. No.                         10.4
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

W = countries.geometry.apply(lambda geom: countries.geometry.touches(geom)).astype(int)

W

voisins_france = W.loc["France"][W.loc["France"]==1]
voisins_france

print(countries.loc["France"][target])
print(countries.loc[voisins_france.index][target])

4.067842
name
Suriname        4.561335
Brazil          2.292272
Germany         7.022808
Luxembourg     10.285255
Belgium         7.231636
Spain           4.497049
Italy           5.248631
Switzerland     3.604899
Andorra         5.170065
Name: co2_emissions, dtype: float64

Wtilde = W/W.sum(axis=0)
x = countries[target]
xc = x - x.mean()

Wtilde = Wtilde.fillna(0)

plt.scatter(xc, Wtilde @ xc)
plt.xlabel("xc")
plt.ylabel("Wtilde @ xc")
plt.axvline(0)
plt.axhline(0)

#Afficher le point de la France
plt.plot(xc.loc["France"], (Wtilde @ xc).loc["France"], "or")
plt.text(xc.loc["France"], (Wtilde @ xc).loc["France"], "France")

Text(-0.25431276723404306, 3.411865224021276, 'France')

Ii = xc * (Wtilde @ xc) / x.var()

countries["$I_i$ "+target] = Ii
ax = countries[countries["$I_i$ "+target]>0].plot(column="$I_i$ "+target, figsize=(18,6), legend=True, cmap="Reds")
countries[countries["$I_i$ "+target]<=0].plot(column="$I_i$ "+target, figsize=(18,6), legend=True, cmap="Blues_r", ax=ax)
ax.set_title("$I_i$ "+target)

/usr/local/lib/python3.12/dist-packages/geopandas/geodataframe.py:1969: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)

Text(0.5, 1.0, '$I_i$ co2_emissions')

I = Ii.sum()/Wtilde.sum().sum()
I

np.float64(0.48546924209009396)

def compute_I(var):
    x = countries[var]
    xc = x - x.mean()
    Ii = xc * (Wtilde @ xc) / x.var()
    ax = countries[Ii>0].plot(column=Ii[Ii>0], figsize=(18,6), legend=True, cmap="Reds")
    countries[Ii<=0].plot(ax=ax, column=Ii[Ii<=0], legend=True, cmap="Blues_r").set_title("I_i "+ var)
    I = Ii.sum()/Wtilde.sum().sum()
    return I

for var in explicative_vars:
    print(var, compute_I(var))
print("co2_emissions", compute_I(target))
print("residus", compute_I("residus"))

hdi_2023 0.810427511484669
co2_emissions 0.48546924209009396
residus 0.4365841553095059

def distances(pays_cible):
    R = 6371000
    #On calcule les centroides en coordonnées projetées
    pays_cible = countries.to_crs(epsg="3857").loc[pays_cible]
    centroid_cible = pays_cible.geometry.centroid
    centroid_cible = gpd.GeoSeries([centroid_cible], crs="EPSG:3857").to_crs(epsg="4326")#On retourne sur les coordonnées lat lon
    centroids = countries.to_crs(epsg="3857").geometry.centroid.to_crs(epsg="4326")

    #On calcule les distances avec la formule de Haversine
    lat1 = centroid_cible.y * np.pi/180
    lon1 = centroid_cible.x * np.pi/180
    lat2 = centroids.y * np.pi/180
    lon2 = centroids.x * np.pi/180
    delta_lat = lat1.values - lat2.values
    delta_lon = lon1.values - lon2.values
    a = np.sin(delta_lat/2)**2 + np.cos(lat1.values) * np.cos(lat2.values) * np.sin(delta_lon/2)**2
    distances = 2*R*np.arcsin(np.sqrt(a))
    return pd.Series(distances, index=centroids.index)
distances("France").sort_values()

#fonction Kernel
def K(x):
    return np.exp(-0.5*x**2)

def w(pays_1, pays_2, h):
    distances_pays1 = distances(pays_1)
    d = distances_pays1.loc[pays_2]
    return K(d/h)

print(w("France", "Spain", 1000000))#h = 1000km
print(w("France", "Chile", 1000000))

0.895747518969046
1.38397335406039e-30

def Wi(pays_cible, h):
    distances_pays_cible = distances(pays_cible)
    wik = K(distances_pays_cible/h)
    return pd.DataFrame(np.diag(wik), index=wik.index, columns=wik.index)
Wi("France", 1000000)

def Betas_i(pays_cible, h):
    Wi_pays_cible = Wi(pays_cible, h)
    return np.linalg.solve(X.T @ Wi_pays_cible @ X, X.T @ Wi_pays_cible @ y)

Betas_i("France", 1000000)

array([ 1.25866334e+01, -7.28745501e-09, -2.52405679e-01, -2.33875524e+00,
        1.56641687e+01])

results.params

def predire(pays_cible, h):
    Betas_i_pays_cible = Betas_i(pays_cible, h)
    return (X @ Betas_i_pays_cible).loc[pays_cible]

predire("France", 1000000)

np.float64(4.645206727664256)

countries.loc["France", target]

np.float64(4.067842)

y_pred = [predire(pays, 1000000) for pays in countries.index]

countries["predicted_co2_emissions_gwr"] = y_pred

/usr/local/lib/python3.12/dist-packages/geopandas/geodataframe.py:1969: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)

residus = countries["co2_emissions"] - countries["predicted_co2_emissions_gwr"]

vmin = min(countries["co2_emissions"].min(), countries["predicted_co2_emissions_gwr"].min())
vmax = max(countries["co2_emissions"].max(), countries["predicted_co2_emissions_gwr"].max())
countries.plot(column="co2_emissions", figsize=(12,6), legend=True, vmin=vmin, vmax=vmax).set_title("Emissions annuelles de CO₂ par habitant")
countries.plot(column="predicted_co2_emissions_gwr", figsize=(12,6), legend=True, vmin=vmin, vmax=vmax).set_title("Emissions annuelles de CO₂ par habitant prédites par GWR, pour h=1000000")
countries.plot(column=residus, figsize=(12,6), legend=True, cmap="coolwarm").set_title("Résidus")

Text(0.5, 1.0, 'Résidus')

sum_of_squares_errors = (residus**2).sum()
RMSE = np.sqrt(sum_of_squares_errors/len(countries))
print(RMSE)

2.4714162052837563

sum_of_squares_distance_to_mean = ((countries[target]-countries[target].mean())**2).sum()
R_2 = 1 - sum_of_squares_errors / sum_of_squares_distance_to_mean
print(R_2)

0.800751344305064

Adjusted_R_2 = 1 - (1 - R_2) * (len(countries) - 1) / (len(countries) - X.shape[1] - 1)
print(Adjusted_R_2)

0.7952774801376207

betas_i_all_countries = [Betas_i(pays, 1000000) for pays in countries.index]

gdf_betas_i_all_countries = gpd.GeoDataFrame(
    betas_i_all_countries,
    index=countries.index,
    columns=X.columns,
    geometry=countries.geometry
)

gdf_betas_i_all_countries

for var in X.columns:
    gdf_betas_i_all_countries.plot(column=var, figsize=(12,6), legend=True).set_title(
       f"Beta_{var}(i)"
    )

y_pred = [predire(pays, 5000000) for pays in countries.index] # h=5000km

countries["predicted_co2_emissions_gwr"] = y_pred

/usr/local/lib/python3.12/dist-packages/geopandas/geodataframe.py:1969: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)

residus = countries["co2_emissions"] - countries["predicted_co2_emissions_gwr"]

vmin = min(countries["co2_emissions"].min(), countries["predicted_co2_emissions_gwr"].min())
vmax = max(countries["co2_emissions"].max(), countries["predicted_co2_emissions_gwr"].max())
countries.plot(column="co2_emissions", figsize=(12,6), legend=True, vmin=vmin, vmax=vmax).set_title("Emissions annuelles de CO₂ par habitant")
countries.plot(column="predicted_co2_emissions_gwr", figsize=(12,6), legend=True, vmin=vmin, vmax=vmax).set_title("Emissions annuelles de CO₂ par habitant prédites par GWR, pour h=5000000")
countries.plot(column=residus, figsize=(12,6), legend=True, cmap="coolwarm").set_title("Résidus")

Text(0.5, 1.0, 'Résidus')

	name	ISO3166-1-Alpha-3	ISO3166-1-Alpha-2	geometry
0	Indonesia	IDN	ID	MULTIPOLYGON (((117.70361 4.16342, 117.70361 4...
1	Malaysia	MYS	MY	MULTIPOLYGON (((117.70361 4.16342, 117.69711 4...
2	Chile	CHL	CL	MULTIPOLYGON (((-69.51009 -17.50659, -69.50611...
3	Bolivia	BOL	BO	POLYGON ((-69.51009 -17.50659, -69.51009 -17.5...
4	Peru	PER	PE	MULTIPOLYGON (((-69.51009 -17.50659, -69.63832...
...	...	...	...	...
253	Macao S.A.R	MAC	MO	MULTIPOLYGON (((113.5586 22.16303, 113.56943 2...
254	Ashmore and Cartier Islands	-99	-99	POLYGON ((123.59702 -12.42832, 123.59775 -12.4...
255	Bajo Nuevo Bank (Petrel Is.)	-99	-99	POLYGON ((-79.98929 15.79495, -79.98782 15.796...
256	Serranilla Bank	-99	-99	POLYGON ((-78.63707 15.86209, -78.64041 15.864...
257	Scarborough Reef	-99	-99	POLYGON ((117.75389 15.15437, 117.75569 15.151...

	Entity	Code	Year	Period life expectancy at birth	time
0	Afghanistan	AFG	2023	66.0346	2023
1	Albania	ALB	2023	79.6019	2023
2	Algeria	DZA	2023	76.2610	2023
3	Andorra	AND	2023	84.0406	2023
4	Angola	AGO	2023	64.6170	2023

	Code	Year	Period life expectancy at birth	time
Entity
Afghanistan	AFG	2023	66.0346	2023
Albania	ALB	2023	79.6019	2023
Algeria	DZA	2023	76.2610	2023
Andorra	AND	2023	84.0406	2023
Angola	AGO	2023	64.6170	2023

	ISO3166-1-Alpha-3	ISO3166-1-Alpha-2	geometry	life_expectancy
name
Indonesia	IDN	ID	MULTIPOLYGON (((117.70361 4.16342, 117.70361 4...	0
Malaysia	MYS	MY	MULTIPOLYGON (((117.70361 4.16342, 117.69711 4...	1
Chile	CHL	CL	MULTIPOLYGON (((-69.51009 -17.50659, -69.50611...	2
Bolivia	BOL	BO	POLYGON ((-69.51009 -17.50659, -69.51009 -17.5...	3
Peru	PER	PE	MULTIPOLYGON (((-69.51009 -17.50659, -69.63832...	4

	Period life expectancy at birth
Entity
Afghanistan	66.0346
Albania	79.6019
Algeria	76.2610
Andorra	84.0406
Angola	64.6170
...	...
Vietnam	74.5883
Western Sahara	71.3850
Yemen	69.2952
Zambia	66.3487
Zimbabwe	62.7748

TP4 : Régression linéaire multiple, auto-corrélation spatiale et GWR¶

Données¶

Chargement des contours des pays¶

Chargement des données¶

Cas 1: On a téléchargé des données pour une seule date¶

Cas 2 : on a téléchargé la série temporelle¶

Gestion des valeurs NaN¶

2) Régression linéaire multiple¶

3) Autocorrélation spatiale¶

4) GWR¶

	Entity	Code	Year	Human Development Index	World regions according to OWID
0	Afghanistan	AFG	1990	0.285	NaN
1	Afghanistan	AFG	1991	0.291	NaN
2	Afghanistan	AFG	1992	0.301	NaN
3	Afghanistan	AFG	1993	0.311	NaN
4	Afghanistan	AFG	1994	0.305	NaN

	France
name
Suriname	1
Brazil	1
Germany	1
Luxembourg	1
Belgium	1
Spain	1
Italy	1
Switzerland	1
Andorra	1

	co2_emissions
name
Indonesia	-1.610972
Malaysia	3.543656
Chile	-0.374605
Bolivia	-2.311358
Peru	-2.370211
...	...
Nauru	0.611038
Micronesia (country)	-3.043485
Vanuatu	-3.733999
Palau	7.857947
Bahrain	20.387875

	0
name
France	0.000000e+00
Andorra	2.887777e+05
Spain	4.692477e+05
Portugal	7.890318e+05
Switzerland	7.917678e+05
...	...
Samoa	1.652552e+07
Fiji	1.671480e+07
Vanuatu	1.673755e+07
Tonga	1.727393e+07
New Zealand	1.936601e+07

name	Indonesia	Malaysia	Chile	Bolivia	Peru	Argentina	Cyprus	India	China	Israel	...	Tonga	Samoa	Solomon Islands	Tuvalu	Maldives	Nauru	Micronesia (country)	Vanuatu	Palau	Bahrain
name
Indonesia	3.307843e-34	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000
Malaysia	0.000000e+00	1.036276e-28	0.000000e+00	0.000000e+00	0.000000e+00	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000
Chile	0.000000e+00	0.000000e+00	1.383973e-30	0.000000e+00	0.000000e+00	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000
Bolivia	0.000000e+00	0.000000e+00	0.000000e+00	1.230765e-19	0.000000e+00	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000
Peru	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	5.888055e-20	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Nauru	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	9.870316e-50	0.000000e+00	0.000000e+00	0.000000e+00	0.000000
Micronesia (country)	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000e+00	1.997287e-42	0.000000e+00	0.000000e+00	0.000000
Vanuatu	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000e+00	0.000000e+00	1.469473e-61	0.000000e+00	0.000000
Palau	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000e+00	0.000000e+00	0.000000e+00	3.620417e-36	0.000000
Bahrain	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000003

	0
const	-2.363059e+01
population	5.968180e-10
life_expectancy	8.826256e-02
log_child_mortality	2.413059e+00
hdi_2023	2.564717e+01

	const	population	life_expectancy	log_child_mortality	hdi_2023	geometry
name
Indonesia	266.116496	-4.312500e-08	-4.190585	-28.178468	106.246494	MULTIPOLYGON (((117.70361 4.16342, 117.70361 4...
Malaysia	240.994841	-5.098763e-08	-4.208162	-19.270307	131.076197	MULTIPOLYGON (((117.70361 4.16342, 117.69711 4...
Chile	-67.635768	-8.604978e-09	0.166714	12.428998	58.611579	MULTIPOLYGON (((-69.51009 -17.50659, -69.50611...
Bolivia	-19.550895	1.265115e-09	-0.112319	3.579118	34.188579	POLYGON ((-69.51009 -17.50659, -69.51009 -17.5...
Peru	-4.188316	-8.626947e-09	-0.215201	1.480612	28.110437	MULTIPOLYGON (((-69.51009 -17.50659, -69.63832...
...	...	...	...	...	...	...
Nauru	17.092764	-3.923703e-08	-0.338677	-0.694999	13.095048	POLYGON ((166.93881 -0.49041, 166.95558 -0.497...
Micronesia (country)	-21.695788	2.087967e-08	0.049062	-1.182948	34.391673	MULTIPOLYGON (((163.02605 5.34089, 163.03045 5...
Vanuatu	16.979232	2.980912e-07	-0.255604	-3.199578	7.853731	MULTIPOLYGON (((169.84034 -20.1408, 169.86052 ...
Palau	-1.895352	-4.024749e-08	-0.099196	-10.408483	42.271629	MULTIPOLYGON (((134.2715 7.07453, 134.27931 7....
Bahrain	-175.934514	-1.592962e-08	2.032739	13.850842	28.074399	MULTIPOLYGON (((50.55161 26.19424, 50.59474 26...

Year	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	...	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
Entity
Afghanistan	0.285	0.291	0.301	0.311	0.305	0.329	0.334	0.338	0.338	0.347	...	0.497000	0.49600	0.495000	0.4960	0.498000	0.507000	0.501000	0.486000	0.495000	0.496000
Africa	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	0.536723	0.54259	0.546639	0.5504	0.555833	0.560354	0.559465	0.561104	0.571485	0.576059
Albania	0.654	0.638	0.622	0.624	0.629	0.638	0.647	0.645	0.659	0.671	...	0.797000	0.79700	0.797000	0.7980	0.801000	0.805000	0.794000	0.794000	0.806000	0.810000
Algeria	0.595	0.596	0.601	0.603	0.603	0.608	0.615	0.624	0.634	0.643	...	0.732000	0.73700	0.743000	0.7460	0.749000	0.753000	0.742000	0.755000	0.761000	0.763000
Andorra	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	0.866000	0.86900	0.872000	0.8730	0.875000	0.876000	0.851000	0.871000	0.893000	0.913000

Year	hdi_1990	hdi_1991	hdi_1992	hdi_1993	hdi_1994	hdi_1995	hdi_1996	hdi_1997	hdi_1998	hdi_1999	...	hdi_2014	hdi_2015	hdi_2016	hdi_2017	hdi_2018	hdi_2019	hdi_2020	hdi_2021	hdi_2022	hdi_2023
Entity
Afghanistan	0.285	0.291	0.301	0.311	0.305	0.329	0.334	0.338	0.338	0.347	...	0.497000	0.49600	0.495000	0.4960	0.498000	0.507000	0.501000	0.486000	0.495000	0.496000
Africa	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	0.536723	0.54259	0.546639	0.5504	0.555833	0.560354	0.559465	0.561104	0.571485	0.576059
Albania	0.654	0.638	0.622	0.624	0.629	0.638	0.647	0.645	0.659	0.671	...	0.797000	0.79700	0.797000	0.7980	0.801000	0.805000	0.794000	0.794000	0.806000	0.810000
Algeria	0.595	0.596	0.601	0.603	0.603	0.608	0.615	0.624	0.634	0.643	...	0.732000	0.73700	0.743000	0.7460	0.749000	0.753000	0.742000	0.755000	0.761000	0.763000
Andorra	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	0.866000	0.86900	0.872000	0.8730	0.875000	0.876000	0.851000	0.871000	0.893000	0.913000