from google.colab import drive
drive.mount('/content/drive')

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).

import os
import geopandas as gpd
gpd.list_layers(os.path.join("/content/drive/MyDrive/ICDE/maps/maps","canadaMaps_ESRI_102001.gpkg"))

country_GADM=gpd.read_file(os.path.join("/content/drive/MyDrive/ICDE/maps/maps","canadaMaps_ESRI_102001.gpkg"), layer='country_GADM')
provincias_GADM=gpd.read_file(os.path.join("/content/drive/MyDrive/ICDE/maps/maps","canadaMaps_ESRI_102001.gpkg"), layer='provincias_GADM')

country_GADM

provincias_GADM

import pandas as pd
linkIDH="https://en.wikipedia.org/wiki/List_of_Canadian_provinces_and_territories_by_Human_Development_Index"
IDH=pd.read_html(linkIDH)[0]
IDH

IDH.drop(columns=['Rank'], inplace=True)
IDH.drop(index=3, inplace=True)
IDH.reset_index(drop=True, inplace=True)
IDH.rename(columns={'HDI (2022)[1]':'IDH_2022',"Province or territory":"PROVINCIA"}, inplace=True)
IDH['PROVINCIA']=IDH.PROVINCIA.str.upper()
IDH

IDH.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13 entries, 0 to 12
Data columns (total 2 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   PROVINCIA  13 non-null     object 
 1   IDH_2022   13 non-null     float64
dtypes: float64(1), object(1)
memory usage: 340.0+ bytes

linkPBI="https://en.wikipedia.org/wiki/List_of_Canadian_provinces_and_territories_by_gross_domestic_product"
PBI=pd.read_html(linkPBI)[0]      # se tendrá que trabjar con GDP per capita
PBI

PBI.drop(columns=["GDP (million CAD, 2023)","Share of national GDP (%, 2023)","Market income per capita (CAD, 2022)"], inplace=True)
PBI.drop(index=0, inplace=True)
PBI.reset_index(drop=True, inplace=True)
PBI.rename(columns={'Population (July 1, 2023)':'Poblacion',"Province or  territory":"PROVINCIA","GDP per capita (CAD, 2023)":"GDPperCapita_2023"}, inplace=True)
PBI['PROVINCIA']=PBI.PROVINCIA.str.upper()
PBI

linkHomRate="https://en.wikipedia.org/wiki/List_of_Canadian_provinces_and_territories_by_homicide_rate"
HomRate=pd.read_html(linkHomRate)[0]
HomRate

HOMRATE=HomRate.loc[:,["Province/territory","2023"]]
HOMRATE

HOMRATE.rename(columns={'Province/territory':'PROVINCIA',"2023":"HomicideRate_2023"}, inplace=True)
HOMRATE['PROVINCIA']=HOMRATE.PROVINCIA.str.upper()
HOMRATE

# se hace merge entre los 3 DF
IDH_PBI=pd.merge(IDH,PBI, on='PROVINCIA')
dataVariables=pd.merge(IDH_PBI,HOMRATE, on='PROVINCIA')
dataVariables

provincias_GADM

provincias_GADM.columns.to_list()

['GID_1',
 'GID_0',
 'COUNTRY',
 'NAME_1',
 'VARNAME_1',
 'NL_NAME_1',
 'TYPE_1',
 'ENGTYPE_1',
 'CC_1',
 'HASC_1',
 'ISO_1',
 'geometry']

# nos quedamos con las columnas importantes
provinciasGeometria=provincias_GADM.loc[:,['COUNTRY','NAME_1','geometry']]
provinciasGeometria

provinciasGeometria.rename(columns={"NAME_1":"PROVINCIA"}, inplace=True)
provinciasGeometria['PROVINCIA']=provinciasGeometria.PROVINCIA.str.upper()
provinciasGeometria['COUNTRY']=provinciasGeometria.COUNTRY.str.upper()
provinciasGeometria.loc[10,"PROVINCIA"]='QUEBEC'
provinciasGeometria

# finalmente se hace merge para tener la geometría y las variables en un solo GDF
data=pd.merge(provinciasGeometria,dataVariables, on='PROVINCIA')
data

# buscar vecinos de las 3 maneras, pero luego decido con cuál quedarme

#rojo es capital y amarillo son distritos

base=data.plot()
data[data.PROVINCIA=='ONTARIO'].plot(ax=base, color='red')

<Axes: >

!pip install folium matplotlib mapclassify

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menor_GADM=gpd.read_file(os.path.join("/content/drive/MyDrive/ICDE/maps/maps","canadaMaps_ESRI_102001.gpkg"), layer='menor_GADM')
menor_GADM.head()

# en este caso, se trabajará con la capa con las geometrías más pequeñas, no con el GDF trabajado en la anterior actividad
from libpysal.weights import Queen, Rook, KNN

# rook
w_rook = Rook.from_dataframe(menor_GADM,use_index=False)

w_queen = Queen.from_dataframe(menor_GADM,use_index=False)

w_knn20 = KNN.from_dataframe(menor_GADM, k=20)

menor_GADM[menor_GADM.NAME_2=="Ottawa"]

menor_GADM.iloc[w_rook.neighbors[2713] ,]

# para metodo rook
base=menor_GADM.iloc[w_rook.neighbors[2713],].plot(facecolor="yellow",edgecolor='k')
menor_GADM.loc[menor_GADM.NAME_2=='Ottawa'].plot(ax=base,facecolor="red")

<Axes: >

menor_GADM.iloc[w_queen.neighbors[2713] ,]

# para metodo queen
base=menor_GADM.iloc[w_queen.neighbors[2713],].plot(facecolor="yellow",edgecolor='k')
menor_GADM.loc[menor_GADM.NAME_2=='Ottawa'].plot(ax=base,facecolor="red")

<Axes: >

menor_GADM.iloc[w_knn20.neighbors[2713] ,]

# para metodo knn
base=menor_GADM.iloc[w_knn20.neighbors[2713] ,].plot(facecolor="yellow",edgecolor='k')
menor_GADM.loc[menor_GADM.NAME_2=='Ottawa'].plot(ax=base,facecolor="red")

<Axes: >

# hacer merge con nivel de contaminacion, # niños en edad escola, etc
# se hace merge con 3 VARIABLES, y solo con una se hace el analisis este de correlacion

pd.DataFrame(*w_knn20.full())

w_knn20.transform = 'R'

pd.DataFrame(*w_knn20.full()).sum(axis=1)

!pip install esda

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w_knn8 = KNN.from_dataframe(data, k=8)

from esda.moran import Moran

moranIDH = Moran(data['IDH_2022'], w_knn8)
moranIDH.I,moranIDH.p_sim
# se muestra que casi no hay correlación y que este resultado no es coincidencia. Esto se debe a que
# estamos trabajando con muy poca data

(np.float64(0.0552915250637212), np.float64(0.04))

!pip install splot

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from splot.esda import moran_scatterplot

fig, ax = moran_scatterplot(moranIDH, aspect_equal=True)
ax.set_xlabel('IDH_std')
ax.set_ylabel('SpatialLag_IDH_std');

moranPBI = Moran(data['GDPperCapita_2023'], w_knn8)
moranPBI.I,moranPBI.p_sim

(np.float64(0.17491730909895079), np.float64(0.006))

fig, ax = moran_scatterplot(moranPBI, aspect_equal=True)
ax.set_xlabel('PBI_std')
ax.set_ylabel('SpatialLag_PBI_std');

moranHomRate = Moran(data['HomicideRate_2023'], w_knn8)
moranHomRate.I,moranHomRate.p_sim

(np.float64(0.13839147448544573), np.float64(0.002))

fig, ax = moran_scatterplot(moranHomRate, aspect_equal=True)
ax.set_xlabel('Homicide_Rate_std')
ax.set_ylabel('SpatialLag_Homicide_Rate_std');

from esda.moran import Moran_Local
lisaIDH = Moran_Local(y=data['IDH_2022'], w=w_knn8,seed=2022)

fig, ax = moran_scatterplot(lisaIDH,p=0.05)
ax.set_xlabel('IDH_std')
ax.set_ylabel('SpatialLag_IDH_std');

data['IDH_quadrant']=[l if p <0.05 else 0 for l,p in zip(lisaIDH.q,lisaIDH.p_sim)]
data['IDH_quadrant'].value_counts()

labels = ['1 hotSpot', '2 coldOutlier', '3 coldSpot', '4 hotOutlier']
data['IDH_quadrant_names']=[labels[i-1] for i in data['IDH_quadrant']]
data['IDH_quadrant_names'].value_counts()

import matplotlib.pyplot as plt
from matplotlib import colors
myColMap = colors.ListedColormap(['pink', 'cyan', 'azure','red'])

f, ax = plt.subplots(1, figsize=(6,6))
# Plot unique values choropleth including
# a legend and with no boundary lines

plt.title('Spots and Outliers for IDH')

data.plot(column='IDH_quadrant_names',
                categorical=True,
                cmap=myColMap,
                linewidth=0.1,
                edgecolor='k',
                legend=True,
                legend_kwds={'loc': 'center left',
                             'bbox_to_anchor': (0.7, 0.6)},
                ax=ax)
# Remove axis
ax.set_axis_off()
# Display the map
plt.show()

from esda.moran import Moran_Local
lisaPBI = Moran_Local(y=data['GDPperCapita_2023'], w=w_knn8,seed=2022)

data['PBI_quadrant']=[l if p <0.05 else 0 for l,p in zip(lisaPBI.q,lisaPBI.p_sim)]
data['PBI_quadrant'].value_counts()

labels = ["0 no_sig",'1 hotSpot', '2 coldOutlier', '3 coldSpot', '4 hotOutlier']
data['PBI_quadrant_names']=[labels[i] for i in data['PBI_quadrant']]
data['PBI_quadrant_names'].value_counts()

import matplotlib.pyplot as plt
from matplotlib import colors
myColMap = colors.ListedColormap(['white','pink', 'cyan', 'azure','red'])

f, ax = plt.subplots(1, figsize=(6,6))

plt.title('Spots and Outliers for PBI')

data.plot(column='PBI_quadrant_names',
                categorical=True,
                cmap=myColMap,
                linewidth=0.1,
                edgecolor='k',
                legend=True,
                legend_kwds={'loc': 'center left',
                             'bbox_to_anchor': (0.7, 0.6)},
                ax=ax)
# Remove axis
ax.set_axis_off()
# Display the map
plt.show()

from esda.moran import Moran_Local
lisaHomRate = Moran_Local(y=data['HomicideRate_2023'], w=w_knn8,seed=2022)

data['HomRate_quadrant']=[l if p <0.05 else 0 for l,p in zip(lisaHomRate.q,lisaHomRate.p_sim)]
data['HomRate_quadrant'].value_counts()

labels = ["0 no_sig",'1 hotSpot', '2 coldOutlier', '3 coldSpot', '4 hotOutlier']
data['HomRate_quadrant_names']=[labels[i] for i in data['HomRate_quadrant']]
data['HomRate_quadrant_names'].value_counts()

myColMap = colors.ListedColormap([ 'white', 'pink', 'cyan', 'azure','red'])

f, ax = plt.subplots(1, figsize=(6,6))

plt.title('Spots and Outliers for Homicide Rate')

data.plot(column='HomRate_quadrant_names',
                categorical=True,
                cmap=myColMap,
                linewidth=0.1,
                edgecolor='k',
                legend=True,
                legend_kwds={'loc': 'center left',
                             'bbox_to_anchor': (0.7, 0.6)},
                ax=ax)
# Remove axis
ax.set_axis_off()
# Display the map
plt.show()

data.head()

selected_variables = ['GDPperCapita_2023','HomicideRate_2023','IDH_2022']

data[selected_variables].corr()

import seaborn as sea

sea.pairplot(data[selected_variables], kind="reg", diag_kind="kde")

<seaborn.axisgrid.PairGrid at 0x792902601250>

# hay que invertir Homicide Rate porque se espera que a mayor IDH o GDP, el Homicide Rate sea menor
from sklearn.preprocessing import StandardScaler


scaler = StandardScaler()
normalized_data = scaler.fit_transform(data[selected_variables])
sea.displot(pd.melt(pd.DataFrame(normalized_data,columns=selected_variables)),
            x="value", hue="variable",kind="kde",
            log_scale=(False,False))

<seaborn.axisgrid.FacetGrid at 0x7928f0716090>

# new names
selected_variables_new_std=[s+'_std' for s in selected_variables]

# add colunms
data[selected_variables_new_std]=normalized_data

data.head()

data['HomicideRate_Inverso_2023_std']=-1*data.HomicideRate_2023_std

# as a result:
selected_variables_new_std = ['HomicideRate_Inverso_2023_std',
                     'GDPperCapita_2023_std',
                     'IDH_2022_std']
sea.pairplot(
    data[selected_variables_new_std], kind="reg", diag_kind="kde"
)

<seaborn.axisgrid.PairGrid at 0x7928d351e450>

from scipy.cluster import hierarchy as hc


Z = hc.linkage(data[selected_variables_new_std], 'ward')
# calculate full dendrogram
plt.figure(figsize=(25, 10))
plt.title('Hierarchical Clustering Dendrogram')
plt.xlabel('cases')
plt.ylabel('distance')
hc.dendrogram(
    Z,
    leaf_rotation=90.,  # rotates the x axis labels
    leaf_font_size=1,  # font size for the x axis labels
)
plt.show()

from sklearn.cluster import AgglomerativeClustering as agnes

import numpy as np
np.random.seed(12345)# Set seed for reproducibility

# Initialize the algorithm, requesting 3 clusters
model = agnes(linkage="ward", n_clusters=3).fit(data[selected_variables_new_std])

# Assign labels to main data table
data["hc_ag3"] = model.labels_

data["hc_ag3"].value_counts()

# Set up figure and ax
f, ax = plt.subplots(1, figsize=(9, 9))
# Plot unique values choropleth including
# a legend and with no boundary lines
data.plot(
    column="hc_ag3", categorical=True, legend=True, linewidth=0, ax=ax
)
# Remove axis
ax.set_axis_off()
# Display the map
plt.show()

w_queen = Queen.from_dataframe(data,use_index=False)

model_queen = agnes(linkage="ward", n_clusters=3, connectivity=w_queen.sparse).fit(data[selected_variables_new_std])
data["hc_ag3_wQueen"] = model_queen.labels_

# Set up figure and ax
f, ax = plt.subplots(1, figsize=(9, 9))
# Plot unique values choropleth including a legend and with no boundary lines
data.plot(
    column="hc_ag3_wQueen",
    categorical=True,
    legend=True,
    linewidth=0,
    ax=ax,
)
# Remove axis
ax.set_axis_off()
# Display the map
plt.show()

# para ver qué clustering es el más adecuado, podemos ver la compacidad de cada método
from esda import shape as shapestats
results={}
for cluster_type in ("hc_ag3_wQueen", "hc_ag3"):
    # compute the region polygons using a dissolve
    regions = data[[cluster_type, "geometry"]].to_crs("ESRI:102001").dissolve(by=cluster_type)
    # compute the actual convex hull quotient for these regions
    chullr = shapestats.convex_hull_ratio(regions)
    # cast to a dataframe
    result = {cluster_type:chullr}
    results.update(result)
# stack the series together along columns
pd.DataFrame(results)

# segun este criterio, se puede decir que spatial clustering fue más efectivo para Canada

from sklearn import metrics

fit_scores = []
for cluster_type in ("hc_ag3_wQueen", "hc_ag3"):
    # compute the CH score
    ch_score = metrics.calinski_harabasz_score(
        # using scaled variables
        data[selected_variables_new_std],
        # using these labels
        data[cluster_type],
    )
    sil_score = metrics.silhouette_score(
        # using scaled variables
        data[selected_variables_new_std],
        # using these labels
        data[cluster_type],
    )
    # and append the cluster type with the CH score
    fit_scores.append((cluster_type, ch_score,sil_score))


# re-arrange the scores into a dataframe for display
pd.DataFrame(
    fit_scores, columns=["cluster type", "CH score", "SIL score"]
).set_index("cluster type")

# segun este criterio, el clustering convencional es el más adecuado

!pip install pysal

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data.head()

from pysal.model import spreg

dep_var_name=['HomicideRate_2023']
ind_vars_names=['GDPperCapita_2023','IDH_2022']
labels=['Homicide Rate %','GDP per capita', 'Indice desarrollo humano %']

ols_model = spreg.OLS(
    # Dependent variable
    data[dep_var_name].values,
    # Independent variables
    data[ind_vars_names].values,
    # Dependent variable name
    name_y=labels[0],
    # Independent variable name
    name_x=labels[1:],
    name_ds='data')

print(ols_model.summary)

REGRESSION RESULTS
------------------

SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES
-----------------------------------------
Data set            :        data
Weights matrix      :        None
Dependent Variable  :Homicide Rate %                Number of Observations:          13
Mean dependent var  :      3.7762                Number of Variables   :           3
S.D. dependent var  :      3.7205                Degrees of Freedom    :          10
R-squared           :      0.6273
Adjusted R-squared  :      0.5528
Sum squared residual:     61.9058                F-statistic           :      8.4162
Sigma-square        :       6.191                Prob(F-statistic)     :     0.00719
S.E. of regression  :       2.488                Log likelihood        :     -28.591
Sigma-square ML     :       4.762                Akaike info criterion :      63.181
S.E of regression ML:      2.1822                Schwarz criterion     :      64.876

------------------------------------------------------------------------------------
            Variable     Coefficient       Std.Error     t-Statistic     Probability
------------------------------------------------------------------------------------
            CONSTANT        44.71528        45.54150         0.98186         0.34933
      GDP per capita         0.00014         0.00003         4.06715         0.00226
Indice desarrollo humano %       -56.39365        50.36316        -1.11974         0.28900
------------------------------------------------------------------------------------

REGRESSION DIAGNOSTICS
MULTICOLLINEARITY CONDITION NUMBER         162.176

TEST ON NORMALITY OF ERRORS
TEST                             DF        VALUE           PROB
Jarque-Bera                       2          0.224           0.8939

DIAGNOSTICS FOR HETEROSKEDASTICITY
RANDOM COEFFICIENTS
TEST                             DF        VALUE           PROB
Breusch-Pagan test                2          7.276           0.0263
Koenker-Bassett test              2         10.290           0.0058
================================ END OF REPORT =====================================

	name	geometry_type
0	border	MultiLineString
1	country	MultiPolygon
2	cities	Point
3	rivers	MultiLineString
4	centroid	Point
5	airports	Point
6	country_GADM	MultiPolygon
7	provincias_GADM	MultiPolygon
8	municipios_GADM	MultiPolygon
9	menor_GADM	MultiPolygon
10	indicators	MultiPolygon

	GID_1	GID_0	COUNTRY	NAME_1	VARNAME_1	NL_NAME_1	TYPE_1	ENGTYPE_1	CC_1	HASC_1	ISO_1	geometry
0	CAN.1_1	CAN	Canada	Alberta	NA	NA	Province	Province	48	CA.AB	CA-AB	MULTIPOLYGON (((-1191696.513 1131094.24, -1193...
1	CAN.2_1	CAN	Canada	British Columbia	Colombie britannique\|New Caledon	NA	Province	Province	59	CA.BC	CA-BC	MULTIPOLYGON (((-1994216.432 1323427.567, -199...
2	CAN.3_1	CAN	Canada	Manitoba	NA	NA	Province	Province	46	CA.MB	CA-MB	MULTIPOLYGON (((-225729.524 989985.252, -22714...
3	CAN.4_1	CAN	Canada	New Brunswick	Nouveau-Brunswick\|Acadia	NA	Province	Province	13	CA.NB	CA-NB	MULTIPOLYGON (((2280813.977 989844.48, 2280788...
4	CAN.5_1	CAN	Canada	Newfoundland and Labrador	Newfoundland\|Terre-Neuve\|Terre-N	NA	Province	Province	10	CA.NF	CA-NL	MULTIPOLYGON (((3062487.374 1723165.753, 30624...
5	CAN.6_1	CAN	Canada	Northwest Territories	Territoires du Nord-Ouest	NA	Territoire	Territory	61	CA.NT	CA-NT	MULTIPOLYGON (((-932976.987 3668489.676, -9330...
6	CAN.7_1	CAN	Canada	Nova Scotia	Acadia\|Nouvelle-Écosse	NA	Province	Province	12	CA.NS	CA-NS	MULTIPOLYGON (((2419990.709 926493.096, 242000...
7	CAN.8_1	CAN	Canada	Nunavut	NA	NA	Territoire	Territory	62	CA.NU	CA-NU	MULTIPOLYGON (((1581589.008 2742840.281, 15815...
8	CAN.9_1	CAN	Canada	Ontario	Upper Canada	NA	Province	Province	35	CA.ON	CA-ON	MULTIPOLYGON (((1409005.46 492629.458, 1408574...
9	CAN.10_1	CAN	Canada	Prince Edward Island	Île de Saint-Jean\|Île du Prince-	NA	Province	Province	11	CA.PE	CA-PE	MULTIPOLYGON (((2505095.555 1289997.862, 25051...
10	CAN.11_1	CAN	Canada	Québec	Lower Canada	NA	Province	Province	24	CA.QC	NA	MULTIPOLYGON (((1678607.031 812858.207, 167861...
11	CAN.12_1	CAN	Canada	Saskatchewan	NA	NA	Province	Province	47	CA.SK	CA-SK	MULTIPOLYGON (((-479648.471 1008211.632, -4796...
12	CAN.13_1	CAN	Canada	Yukon	Yukon Territory\|Territoire du Yu	NA	Territoire	Territory	60	CA.YT	CA-YT	MULTIPOLYGON (((-1485495.114 2531187.928, -148...

	Rank	Province or territory	HDI (2022)[1]
0	1	Alberta	0.947
1	2	British Columbia	0.946
2	3	Ontario	0.942
3	—	Canada (average)	0.935
4	4	Prince Edward Island	0.929
5	4	Northwest Territories	0.929
6	4	Yukon	0.929
7	4	Nunavut	0.929
8	8	Quebec	0.926
9	9	Saskatchewan	0.920
10	10	Nova Scotia	0.909
11	11	Manitoba	0.907
12	12	New Brunswick	0.904
13	13	Newfoundland and Labrador	0.901

	PROVINCIA	IDH_2022
0	ALBERTA	0.947
1	BRITISH COLUMBIA	0.946
2	ONTARIO	0.942
3	PRINCE EDWARD ISLAND	0.929
4	NORTHWEST TERRITORIES	0.929
5	YUKON	0.929
6	NUNAVUT	0.929
7	QUEBEC	0.926
8	SASKATCHEWAN	0.920
9	NOVA SCOTIA	0.909
10	MANITOBA	0.907
11	NEW BRUNSWICK	0.904
12	NEWFOUNDLAND AND LABRADOR	0.901

	Province or territory	GDP (million CAD, 2023)	Share of national GDP (%, 2023)	Population (July 1, 2023)	GDP per capita (CAD, 2023)	Market income per capita (CAD, 2022)
0	Canada	2933810	100.00	40083484	73192	39741
1	Alberta	452410	15.42	4684514	96576	43434
2	British Columbia	409881	13.97	5531553	74099	42094
3	Manitoba	91872	3.13	1454743	63153	33057
4	Newfoundland and Labrador	38959	1.33	538907	72293	33871
5	New Brunswick	47035	1.60	832190	56520	32145
6	Northwest Territories	5478	0.19	44681	122602	47470
7	Nova Scotia	59574	2.03	1056486	56389	33866
8	Nunavut	4825	0.16	40700	118550	29475
9	Ontario	1119545	38.16	15623207	71659	40940
10	Prince Edward Island	9924	0.34	173713	57129	32329
11	Quebec	579460	19.75	8848020	65490	37819
12	Saskatchewan	109702	3.74	1209307	90715	35744
13	Yukon	4330	0.15	45463	95242	48232

Práctica calificada 6 - Parte II¶

Ejercicio 5¶

Ejercicio 6¶

Ejercicio 7¶

Ejercicio 8¶

Ejercicio 9¶

Ejercicio 10¶

	Province/territory	1990	2000	2010	2015	2016	2017	2018	2019	2020	2021	2022	2023	2015 –2023
0	Ontario	1.77	1.35	1.44	1.27	1.48	1.41	1.90	1.74	1.65	1.91	1.93	1.68	1.66
1	Quebec	2.63	2.00	1.08	0.97	0.81	1.12	1.00	0.92	1.02	1.05	1.28	1.14	1.03
2	British Columbia	3.34	2.10	1.86	2.01	1.85	2.45	1.83	1.78	1.93	2.39	2.89	2.23	2.15
3	Alberta	3.20	1.96	2.09	3.21	2.76	2.81	1.93	2.30	3.24	2.69	2.64	2.45	2.67
4	Manitoba	3.44	2.61	3.69	3.71	3.27	3.52	4.07	5.40	4.49	4.45	6.30	5.09	4.48
5	Saskatchewan	3.57	2.58	3.23	4.10	5.02	3.31	3.03	4.72	5.48	6.08	6.19	4.88	4.76
6	Nova Scotia	0.88	1.71	2.34	1.28	1.38	2.21	1.14	0.61	3.74[n 1]	2.30	1.85	1.32	1.86
7	New Brunswick	1.49	1.33	1.20	1.45	1.44	1.30	1.69	2.19	1.79	1.39	1.85	1.08	1.58
8	Newfoundland and Labrador	0.00	1.14	0.77	0.57	1.51	0.76	0.38	0.95	0.76	1.52	0.56	1.49	0.94
9	Prince Edward Island	0.77	2.20	0.00	0.69	0.00	1.34	0.00	1.28	0.63	0.62	0.00	0.58	0.72
10	Northwest Territories	20.40	2.47	2.31	13.56	6.72	4.48	13.47	4.50	13.48	2.24	6.71	13.34	7.32
11	Yukon	3.60	6.57	2.89	2.65	10.38	20.24	7.43	2.43	0.00	9.31	4.56	8.89	7.32
12	Nunavut	NaN	10.91	17.99	5.48	2.70	15.93	20.88	18.02	7.60	4.99	2.47	4.92	9.22

	GID_3	GID_0	COUNTRY	GID_1	NAME_1	NL_NAME_1	GID_2	NAME_2	NL_NAME_2	NAME_3	VARNAME_3	NL_NAME_3	TYPE_3	ENGTYPE_3	CC_3	HASC_3	geometry
0	CAN.1.11.1_1	CAN	Canada	CAN.1_1	Alberta	NA	CAN.1.11_1	Division No. 1	NA	Bow Island	NA	NA	Town	Town	NA	NA	MULTIPOLYGON (((-1095252.637 1208455.549, -109...
1	CAN.1.11.2_1	CAN	Canada	CAN.1_1	Alberta	NA	CAN.1.11_1	Division No. 1	NA	Burdett	NA	NA	Village	Village	NA	NA	MULTIPOLYGON (((-1106734.051 1207041.712, -110...
2	CAN.1.11.3_1	CAN	Canada	CAN.1_1	Alberta	NA	CAN.1.11_1	Division No. 1	NA	Cypress County	NA	NA	Municipal District	Municipal District	NA	NA	MULTIPOLYGON (((-1114033.57 1214677.478, -1114...
3	CAN.1.11.4_1	CAN	Canada	CAN.1_1	Alberta	NA	CAN.1.11_1	Division No. 1	NA	Foremost	NA	NA	Village	Village	NA	NA	MULTIPOLYGON (((-1110213.091 1166590.676, -111...
4	CAN.1.11.5_1	CAN	Canada	CAN.1_1	Alberta	NA	CAN.1.11_1	Division No. 1	NA	Forty Mile County No. 8	NA	NA	County Municipality	County Municipality	NA	NA	MULTIPOLYGON (((-1114033.57 1214677.478, -1113...

	GID_3	GID_0	COUNTRY	GID_1	NAME_1	NL_NAME_1	GID_2	NAME_2	NL_NAME_2	NAME_3	VARNAME_3	NL_NAME_3	TYPE_3	ENGTYPE_3	CC_3	HASC_3	geometry
2819	CAN.9.44.13_1	CAN	Canada	CAN.9_1	Ontario	NA	CAN.9.44_1	Renfrew	NA	McNab/Braeside	NA	NA	Township	Township	NA	NA	MULTIPOLYGON (((1534436.789 813992.934, 153416...
2628	CAN.9.27.9_1	CAN	Canada	CAN.9_1	Ontario	NA	CAN.9.27_1	Leeds and Grenville	NA	Merrickville-Wolford	NA	NA	Village	Village	NA	NA	MULTIPOLYGON (((1578566.446 768358.064, 157863...
2629	CAN.9.27.10_1	CAN	Canada	CAN.9_1	Ontario	NA	CAN.9.27_1	Leeds and Grenville	NA	North Grenville	NA	NA	Township	Township	NA	NA	MULTIPOLYGON (((1605927.213 775203.269, 160548...
4298	CAN.11.85.9_1	CAN	Canada	CAN.11_1	Québec	NA	CAN.11.85_1	Papineau	NA	Lochaber-Partie-Ouest	NA	NA	Municipalité De Canton	Canton Municipality	NA	NA	MULTIPOLYGON (((1607168.778 851571.936, 160696...
3346	CAN.11.19.1_1	CAN	Canada	CAN.11_1	Québec	NA	CAN.11.19_1	Communauté-Urbaine-de-l'Outaouai	NA	Aylmer	NA	NA	Ville	Ville	NA	NA	MULTIPOLYGON (((1580023.424 828702.304, 157996...
3348	CAN.11.19.3_1	CAN	Canada	CAN.11_1	Québec	NA	CAN.11.19_1	Communauté-Urbaine-de-l'Outaouai	NA	Gatineau	NA	NA	Ville	Ville	NA	NA	MULTIPOLYGON (((1594697.151 843000.144, 159457...
3349	CAN.11.19.4_1	CAN	Canada	CAN.11_1	Québec	NA	CAN.11.19_1	Communauté-Urbaine-de-l'Outaouai	NA	Hull	NA	NA	Ville	Ville	NA	NA	MULTIPOLYGON (((1583424.937 834159.215, 158329...
3350	CAN.11.19.5_1	CAN	Canada	CAN.11_1	Québec	NA	CAN.11.19_1	Communauté-Urbaine-de-l'Outaouai	NA	Masson-Angers	NA	NA	Ville	Ville	NA	NA	MULTIPOLYGON (((1606967.254 851487.278, 160681...
2776	CAN.9.41.4_1	CAN	Canada	CAN.9_1	Ontario	NA	CAN.9.41_1	Prescott and Russell	NA	Clarence-Rockland	NA	NA	Cité	City	NA	NA	MULTIPOLYGON (((1620148.048 835829.62, 1620096...
2779	CAN.9.41.7_1	CAN	Canada	CAN.9_1	Ontario	NA	CAN.9.41_1	Prescott and Russell	NA	Russell	NA	NA	Township	Township	NA	NA	MULTIPOLYGON (((1618149.828 814194.153, 161786...
2780	CAN.9.41.8_1	CAN	Canada	CAN.9_1	Ontario	NA	CAN.9.41_1	Prescott and Russell	NA	The Nation Municipality	NA	NA	Township	Township	NA	NA	MULTIPOLYGON (((1618798.831 834437.131, 161879...
2848	CAN.9.46.3_1	CAN	Canada	CAN.9_1	Ontario	NA	CAN.9.46_1	Stormont, Dundas and Glengarry	NA	North Dundas	NA	NA	Township	Township	NA	NA	MULTIPOLYGON (((1615543.723 787415.214, 161553...
2616	CAN.9.26.6_1	CAN	Canada	CAN.9_1	Ontario	NA	CAN.9.26_1	Lanark	NA	Mississippi Mills	NA	NA	Town	Town	NA	NA	MULTIPOLYGON (((1562965.857 798093.91, 1562154...
3951	CAN.11.63.6_1	CAN	Canada	CAN.11_1	Québec	NA	CAN.11.63_1	Les Collines-de-l'Outaouais	NA	Pontiac	NA	NA	Municipalité	Municipality	NA	NA	MULTIPOLYGON (((1569848.222 824350.784, 156983...
2612	CAN.9.26.2_1	CAN	Canada	CAN.9_1	Ontario	NA	CAN.9.26_1	Lanark	NA	Beckwith	NA	NA	Township	Township	NA	NA	MULTIPOLYGON (((1558157.764 790253.978, 155845...
2808	CAN.9.44.2_1	CAN	Canada	CAN.9_1	Ontario	NA	CAN.9.44_1	Renfrew	NA	Arnprior	NA	NA	Town	Town	NA	NA	MULTIPOLYGON (((1534795.84 814658.656, 1534728...
2617	CAN.9.26.7_1	CAN	Canada	CAN.9_1	Ontario	NA	CAN.9.26_1	Lanark	NA	Montague	NA	NA	Township	Township	NA	NA	MULTIPOLYGON (((1568113.605 775753.877, 156811...

	0	1	2	3	4	5	6	7	8	9	...	5571	5572	5573	5574	5575	5576	5577	5578	5579	5580
0	0.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	1.0	0.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	1.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	1.0	1.0	1.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	1.0	1.0	1.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5576	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	1.0	1.0	1.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0
5577	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0
5578	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
5579	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
5580	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0

	count
IDH_quadrant_names
1 hotSpot	5
3 coldSpot	3
4 hotOutlier	3
2 coldOutlier	2

	count
PBI_quadrant_names
1 hotSpot	5
0 no_sig	5
2 coldOutlier	2
3 coldSpot	1

	count
HomRate_quadrant_names
1 hotSpot	5
0 no_sig	5
2 coldOutlier	2
3 coldSpot	1

	GDPperCapita_2023	HomicideRate_2023	IDH_2022
GDPperCapita_2023	1.000000	0.761963	0.396979
HomicideRate_2023	0.761963	1.000000	0.104080
IDH_2022	0.396979	0.104080	1.000000

	CH score	SIL score
cluster type
hc_ag3_wQueen	8.581915	0.293870
hc_ag3	13.048216	0.428276

	count
IDH_quadrant
1	5
3	3
4	3
2	2

	count
PBI_quadrant
1	5
0	5
2	2
3	1

	count
HomRate_quadrant
1	5
0	5
2	2
3	1

	hc_ag3_wQueen	hc_ag3
0	0.494340	0.494340
1	0.601777	0.462117
2	0.828507	0.223788