indiaMapsLink = 'https://github.com/FabioGM-02/Tarea-5/raw/refs/heads/main/Maps/indiaMaps_7755.gpkg'

import geopandas as gpd

gpd.list_layers(indiaMapsLink)

states=gpd.read_file(indiaMapsLink,layer='states')

countries = gpd.read_file(indiaMapsLink, layer='country')

# Reprojecting the states
states_fixed = states.copy()
states_fixed = states_fixed.set_crs(epsg=32643, allow_override=True)
states_fixed = states_fixed.to_crs(countries.crs)

print("Fixed States bounds:", states_fixed.total_bounds)
print("Countries bounds:", countries.total_bounds)

states = states_fixed

Fixed States bounds: [2819242.64544075 2177754.67403902 5679168.18314899 5260289.41141887]
Countries bounds: [2814964.32964461 2176798.65900902 5676285.14421876 5260475.76432007]

import pandas as pd

# Loading an excel where all our variables are at district-level
hdi_url = 'https://github.com/alonso-mendoza/Tarea-6/raw/refs/heads/main/data/india%20HDI.xlsx'
hdi = pd.read_excel(hdi_url)

# Merging our geometry with the data
districts=hdi.merge(states,left_on='District', right_on='NAME_2')
districts.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 633 entries, 0 to 632
Data columns (total 23 columns):
 #   Column                  Non-Null Count  Dtype   
---  ------                  --------------  -----   
 0   State/Union Territory   633 non-null    object  
 1   District                633 non-null    object  
 2   L                       633 non-null    float64 
 3   E                       633 non-null    float64 
 4   H                       633 non-null    float64 
 5   HDIa                    633 non-null    float64 
 6   Rank in HDIa            633 non-null    int64   
 7   Inequality in Progress  633 non-null    float64 
 8   ID_0                    633 non-null    int64   
 9   ISO                     633 non-null    object  
 10  NAME_0                  633 non-null    object  
 11  ID_1                    633 non-null    int64   
 12  NAME_1                  633 non-null    object  
 13  ID_2                    633 non-null    int64   
 14  NAME_2                  633 non-null    object  
 15  HASC_2                  600 non-null    object  
 16  CCN_2                   633 non-null    int64   
 17  CCA_2                   0 non-null      object  
 18  TYPE_2                  633 non-null    object  
 19  ENGTYPE_2               633 non-null    object  
 20  NL_NAME_2               0 non-null      object  
 21  VARNAME_2               184 non-null    object  
 22  geometry                633 non-null    geometry
dtypes: float64(5), geometry(1), int64(5), object(12)
memory usage: 113.9+ KB

# We'll keep only the districts and the variables
districts = districts[['NAME_1','District', 'L', 'E', 'H', 'HDIa', 'geometry']]

# Creating the GeoDataFrame
districts = gpd.GeoDataFrame(districts, geometry='geometry', crs=7755)
districts.head()

districts.rename(columns={'NAME_1':'State/Union','L':'Wealth_Index_Above_2nd_Quintile','E':'Secondary_School_Attendance','H':'Prob_Survival_First5Yrs','HDIa':'HDI'},inplace=True)
districts.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 633 entries, 0 to 632
Data columns (total 7 columns):
 #   Column                           Non-Null Count  Dtype   
---  ------                           --------------  -----   
 0   State/Union                      633 non-null    object  
 1   District                         633 non-null    object  
 2   Wealth_Index_Above_2nd_Quintile  633 non-null    float64 
 3   Secondary_School_Attendance      633 non-null    float64 
 4   Prob_Survival_First5Yrs          633 non-null    float64 
 5   HDI                              633 non-null    float64 
 6   geometry                         633 non-null    geometry
dtypes: float64(4), geometry(1), object(2)
memory usage: 34.7+ KB

!pip install mapclassify

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# statistics
districts.HDI.describe()

count    633.000000
mean       0.760378
std        0.135514
min        0.429000
25%        0.658000
50%        0.773000
75%        0.879000
max        1.000000
Name: HDI, dtype: float64

import seaborn as sea

sea.histplot(districts.HDI, color='yellow')

<Axes: xlabel='HDI', ylabel='Count'>

districts.explore(
    column="HDI",
    scheme="fisherjenks",
    legend=True,
    tooltip=False,
    popup=['State/Union', 'District'],  # show popup (on-click)
    legend_kwds=dict(colorbar=False)
)

!pip install libpysal

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from libpysal.weights import Queen, Rook, KNN

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

C:\Users\mendo\anaconda3\envs\dataespacial__31111\Lib\site-packages\libpysal\weights\contiguity.py:61: UserWarning: The weights matrix is not fully connected: 
 There are 6 disconnected components.
 There are 4 islands with ids: 0, 1, 127, 282.
  W.__init__(self, neighbors, ids=ids, **kw)

w_rook.islands

[0, 1, 127, 282]

# queen
w_queen = Queen.from_dataframe(districts,use_index=False)

C:\Users\mendo\anaconda3\envs\dataespacial__31111\Lib\site-packages\libpysal\weights\contiguity.py:347: UserWarning: The weights matrix is not fully connected: 
 There are 5 disconnected components.
 There are 3 islands with ids: 0, 1, 282.
  W.__init__(self, neighbors, ids=ids, **kw)

w_queen.islands

[0, 1, 282]

districts.iloc[w_queen.islands,:].explore()

# k=8 nearest neighbors
w_knn8 = KNN.from_dataframe(districts, k=8)

w_knn8.islands

[]

capital = districts[districts['District'] == 'West']
display(capital)

# amount of neighbors of that district
len(w_rook.neighbors[126]),len(w_queen.neighbors[126])

(7, 7)

districts.iloc[w_rook.neighbors[126],]

# see the neighbor
districts.iloc[w_rook.neighbors[126],].plot(facecolor="yellow")

<Axes: >

# see whole area
base=districts.iloc[w_rook.neighbors[126] ,].plot(facecolor="yellow",edgecolor='k')
districts.loc[districts.District=='West'].plot(ax=base,facecolor="red")

<Axes: >

# details
districts.iloc[w_queen.neighbors[126],]

# whole area
# see whole area
base=districts.iloc[w_queen.neighbors[126] ,].plot(facecolor="yellow",edgecolor='k')
districts.loc[districts.District=='West'].plot(ax=base,facecolor="red")

<Axes: >

w_knn8.neighbors[126]

[np.int64(167),
 np.int64(537),
 np.int64(162),
 np.int64(171),
 np.int64(539),
 np.int64(538),
 np.int64(164),
 np.int64(166)]

base=districts.iloc[w_knn8.neighbors[126] ,].plot(facecolor="yellow",edgecolor='k')
districts.loc[districts.District=='West'].plot(ax=base,facecolor="red")

<Axes: >

!pip install esda

Collecting esda
  Downloading esda-2.7.0-py3-none-any.whl.metadata (2.0 kB)
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Installing collected packages: esda
Successfully installed esda-2.7.0

pd.DataFrame(*w_knn8.full()) # 1 means both are neighbors

# needed for spatial correlation
w_knn8.transform = 'R'

# after transformation
pd.DataFrame(*w_knn8.full()).sum(axis=1)

0      1.0
1      1.0
2      1.0
3      1.0
4      1.0
      ... 
628    1.0
629    1.0
630    1.0
631    1.0
632    1.0
Length: 633, dtype: float64

from esda.moran import Moran

moranHDI = Moran(districts['HDI'], w_knn8)
moranHDI.I,moranHDI.p_sim

(np.float64(0.6434254521432141), np.float64(0.001))

!pip install splot

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Installing collected packages: mpmath, sympy, quantecon, spreg, giddy, splot
Successfully installed giddy-2.3.6 mpmath-1.3.0 quantecon-0.8.1 splot-1.1.7 spreg-1.8.3 sympy-1.14.0

from splot.esda import moran_scatterplot

fig, ax = moran_scatterplot(moranHDI, aspect_equal=True)
ax.set_xlabel('HDI_std')
ax.set_ylabel('SpatialLag_HDI_std')

Text(0, 0.5, 'SpatialLag_HDI_std')

from splot.esda import moran_scatterplot

fig, ax = moran_scatterplot(moranHDI, aspect_equal=True)
ax.set_xlabel('HDI_std')
ax.set_ylabel('SpatialLag_HDI_std')

Text(0, 0.5, 'SpatialLag_HDI_std')

moranSSA = Moran(districts['Secondary_School_Attendance'], w_knn8)

fig, ax = moran_scatterplot(moranSSA, aspect_equal=True)
ax.set_xlabel('Secondary_School_Attendance_std')
ax.set_ylabel('SpatialLag_SSA_std')

Text(0, 0.5, 'SpatialLag_SSA_std')

moranPSF = Moran(districts['Prob_Survival_First5Yrs'], w_knn8)

fig, ax = moran_scatterplot(moranPSF, aspect_equal=True)
ax.set_xlabel('Prob_Survival_First5Yrs_std')
ax.set_ylabel('SpatialLag_PSF_std')

Text(0, 0.5, 'SpatialLag_PSF_std')

# A LISA for each district using IDH2019
from esda.moran import Moran_Local
lisaHDI = Moran_Local(y=districts['HDI'], w=w_knn8,seed=2022)

fig, ax = moran_scatterplot(lisaHDI,p=0.05)
ax.set_xlabel('HDI_std')
ax.set_ylabel('SpatialLag_HDI_std');

# quadrant, # significance
lisaHDI.q, lisaHDI.p_sim

(array([1, 1, 1, 1, 1, 4, 1, 1, 4, 4, 1, 1, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4,
        3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3,
        3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
        3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 4, 1, 1, 1, 4, 3, 2, 2, 3, 2,
        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 4, 4, 1, 4, 1, 1, 1, 4, 1, 1, 1, 1, 4, 1, 4, 3, 2, 3, 2,
        2, 3, 2, 3, 3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 2, 4, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 4, 4, 4, 4,
        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 3, 3,
        3, 3, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4,
        4, 4, 4, 1, 4, 1, 2, 3, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3,
        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3,
        3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 4, 1, 1, 1, 4, 1,
        1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 4, 3, 4, 4, 3, 3, 2,
        3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 1, 4, 1, 1, 1, 1, 4, 4, 4, 4,
        4, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3,
        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 4, 4, 1, 4, 1, 2, 2,
        2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4,
        1, 1, 2, 4, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 4, 1, 1, 4, 4, 1, 4, 4,
        1, 4, 4, 1, 4, 1, 4, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
        3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]),
 array([0.207, 0.107, 0.432, 0.002, 0.002, 0.317, 0.005, 0.002, 0.022,
        0.064, 0.003, 0.056, 0.004, 0.182, 0.022, 0.062, 0.096, 0.119,
        0.116, 0.11 , 0.062, 0.121, 0.197, 0.144, 0.037, 0.005, 0.332,
        0.455, 0.214, 0.179, 0.213, 0.01 , 0.004, 0.046, 0.005, 0.009,
        0.056, 0.009, 0.01 , 0.021, 0.011, 0.039, 0.066, 0.185, 0.045,
        0.015, 0.354, 0.023, 0.008, 0.096, 0.015, 0.182, 0.083, 0.029,
        0.035, 0.017, 0.024, 0.163, 0.257, 0.024, 0.156, 0.005, 0.248,
        0.269, 0.001, 0.126, 0.033, 0.127, 0.099, 0.033, 0.174, 0.03 ,
        0.011, 0.024, 0.074, 0.004, 0.011, 0.274, 0.094, 0.005, 0.005,
        0.001, 0.006, 0.032, 0.003, 0.001, 0.005, 0.002, 0.005, 0.032,
        0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.331,
        0.491, 0.199, 0.373, 0.222, 0.223, 0.221, 0.158, 0.002, 0.34 ,
        0.092, 0.008, 0.182, 0.18 , 0.311, 0.182, 0.02 , 0.011, 0.003,
        0.001, 0.001, 0.011, 0.004, 0.001, 0.011, 0.005, 0.467, 0.054,
        0.001, 0.071, 0.366, 0.01 , 0.009, 0.115, 0.435, 0.131, 0.069,
        0.29 , 0.225, 0.475, 0.039, 0.053, 0.13 , 0.043, 0.311, 0.272,
        0.113, 0.066, 0.121, 0.246, 0.132, 0.229, 0.417, 0.406, 0.21 ,
        0.054, 0.49 , 0.032, 0.437, 0.027, 0.21 , 0.029, 0.011, 0.382,
        0.001, 0.001, 0.002, 0.003, 0.001, 0.002, 0.001, 0.001, 0.001,
        0.006, 0.002, 0.003, 0.002, 0.002, 0.002, 0.001, 0.001, 0.002,
        0.002, 0.031, 0.002, 0.072, 0.007, 0.005, 0.002, 0.007, 0.08 ,
        0.003, 0.016, 0.001, 0.002, 0.003, 0.017, 0.027, 0.099, 0.016,
        0.012, 0.025, 0.002, 0.025, 0.023, 0.007, 0.04 , 0.017, 0.006,
        0.025, 0.005, 0.011, 0.005, 0.103, 0.034, 0.017, 0.058, 0.012,
        0.001, 0.008, 0.005, 0.102, 0.016, 0.098, 0.036, 0.047, 0.001,
        0.013, 0.006, 0.036, 0.006, 0.001, 0.001, 0.001, 0.002, 0.001,
        0.001, 0.003, 0.001, 0.002, 0.001, 0.002, 0.001, 0.002, 0.002,
        0.002, 0.002, 0.001, 0.002, 0.001, 0.002, 0.002, 0.001, 0.006,
        0.042, 0.006, 0.069, 0.01 , 0.003, 0.188, 0.122, 0.042, 0.055,
        0.457, 0.398, 0.001, 0.24 , 0.176, 0.292, 0.248, 0.001, 0.001,
        0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001,
        0.001, 0.001, 0.001, 0.001, 0.119, 0.255, 0.157, 0.298, 0.435,
        0.437, 0.446, 0.328, 0.347, 0.494, 0.073, 0.401, 0.387, 0.235,
        0.34 , 0.48 , 0.256, 0.067, 0.298, 0.036, 0.02 , 0.446, 0.051,
        0.205, 0.004, 0.122, 0.265, 0.114, 0.001, 0.361, 0.16 , 0.199,
        0.009, 0.159, 0.007, 0.004, 0.05 , 0.38 , 0.024, 0.004, 0.004,
        0.044, 0.462, 0.009, 0.007, 0.009, 0.094, 0.018, 0.009, 0.48 ,
        0.036, 0.058, 0.007, 0.334, 0.026, 0.103, 0.032, 0.182, 0.044,
        0.03 , 0.024, 0.242, 0.002, 0.06 , 0.23 , 0.063, 0.218, 0.393,
        0.174, 0.208, 0.342, 0.356, 0.159, 0.014, 0.066, 0.126, 0.126,
        0.129, 0.137, 0.104, 0.411, 0.195, 0.214, 0.137, 0.009, 0.006,
        0.09 , 0.112, 0.145, 0.148, 0.308, 0.159, 0.247, 0.196, 0.259,
        0.014, 0.009, 0.008, 0.006, 0.008, 0.019, 0.02 , 0.017, 0.276,
        0.207, 0.126, 0.087, 0.301, 0.277, 0.479, 0.046, 0.033, 0.007,
        0.13 , 0.445, 0.069, 0.059, 0.002, 0.016, 0.017, 0.019, 0.34 ,
        0.446, 0.178, 0.359, 0.374, 0.057, 0.317, 0.365, 0.033, 0.041,
        0.157, 0.218, 0.057, 0.194, 0.231, 0.001, 0.15 , 0.008, 0.01 ,
        0.031, 0.054, 0.049, 0.022, 0.001, 0.016, 0.015, 0.019, 0.008,
        0.001, 0.001, 0.01 , 0.493, 0.026, 0.002, 0.002, 0.002, 0.001,
        0.001, 0.001, 0.002, 0.001, 0.001, 0.002, 0.001, 0.001, 0.002,
        0.001, 0.002, 0.002, 0.001, 0.002, 0.002, 0.002, 0.002, 0.005,
        0.139, 0.198, 0.002, 0.313, 0.003, 0.097, 0.002, 0.011, 0.01 ,
        0.004, 0.011, 0.062, 0.293, 0.021, 0.481, 0.434, 0.089, 0.351,
        0.168, 0.463, 0.207, 0.101, 0.137, 0.394, 0.459, 0.452, 0.391,
        0.341, 0.41 , 0.444, 0.025, 0.296, 0.059, 0.041, 0.003, 0.001,
        0.002, 0.003, 0.001, 0.002, 0.001, 0.005, 0.001, 0.001, 0.008,
        0.001, 0.002, 0.006, 0.002, 0.002, 0.007, 0.002, 0.021, 0.024,
        0.014, 0.01 , 0.008, 0.003, 0.014, 0.021, 0.002, 0.006, 0.021,
        0.014, 0.075, 0.008, 0.064, 0.277, 0.039, 0.053, 0.021, 0.053,
        0.016, 0.215, 0.315, 0.129, 0.133, 0.338, 0.004, 0.012, 0.008,
        0.032, 0.014, 0.002, 0.027, 0.054, 0.148, 0.388, 0.142, 0.162,
        0.156, 0.008, 0.073, 0.39 , 0.105, 0.487, 0.17 , 0.139, 0.491,
        0.006, 0.126, 0.234, 0.189, 0.241, 0.042, 0.034, 0.212, 0.215,
        0.308, 0.357, 0.459, 0.038, 0.417, 0.231, 0.042, 0.045, 0.019,
        0.058, 0.002, 0.193, 0.111, 0.275, 0.278, 0.423, 0.095, 0.191,
        0.013, 0.313, 0.219, 0.003, 0.058, 0.1  , 0.412, 0.105, 0.027,
        0.049, 0.052, 0.01 , 0.19 , 0.016, 0.008, 0.001, 0.015, 0.043,
        0.041, 0.015, 0.005, 0.011, 0.021, 0.265, 0.026, 0.026, 0.041,
        0.438, 0.049, 0.006, 0.047, 0.027, 0.352, 0.076, 0.07 , 0.192,
        0.074, 0.049, 0.001, 0.001, 0.001, 0.072, 0.001, 0.001, 0.169,
        0.226, 0.001, 0.027]))

# quadrant: 1 HH,  2 LH,  3 LL,  4 HL
pd.Series(lisaHDI.q).value_counts()

1    263
3    263
4     76
2     31
Name: count, dtype: int64

districts['HDI_quadrant']=[l if p <0.05 else 0 for l,p in zip(lisaHDI.q,lisaHDI.p_sim)  ]
districts['HDI_quadrant'].value_counts()

HDI_quadrant
0    279
1    180
3    151
4     18
2      5
Name: count, dtype: int64

labels = [ '0 no_sig', '1 hotSpot', '2 coldOutlier', '3 coldSpot', '4 hotOutlier']

districts['HDI_quadrant_names']=[labels[i] for i in districts['HDI_quadrant']]

districts['HDI_quadrant_names'].value_counts()

HDI_quadrant_names
0 no_sig         279
1 hotSpot        180
3 coldSpot       151
4 hotOutlier      18
2 coldOutlier      5
Name: count, dtype: int64

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

# Set up figure and ax
f, ax = plt.subplots(1, figsize=(12,12))
# Plot unique values choropleth including
# a legend and with no boundary lines

plt.title('Spots and Outliers')

districts.plot(column='HDI_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
lisaSSA = Moran_Local(y=districts['Secondary_School_Attendance'], w=w_knn8,seed=2022)

fig, ax = moran_scatterplot(lisaHDI,p=0.05)
ax.set_xlabel('Secondary_School_Attendance_std')
ax.set_ylabel('SpatialLag_SSA_std');

# quadrant, # significance
lisaSSA.q, lisaSSA.p_sim

(array([1, 1, 4, 1, 1, 4, 1, 2, 4, 4, 1, 2, 4, 2, 4, 1, 1, 1, 1, 1, 1, 2,
        1, 1, 4, 3, 1, 1, 1, 2, 2, 3, 4, 1, 3, 4, 2, 1, 3, 1, 1, 3, 4, 1,
        3, 1, 2, 2, 1, 2, 3, 4, 3, 2, 1, 3, 3, 2, 2, 3, 1, 3, 1, 2, 3, 4,
        3, 1, 1, 3, 4, 4, 3, 3, 3, 4, 3, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,
        3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 2, 2, 4, 1,
        4, 2, 2, 4, 4, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 1, 1, 3,
        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
        3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 4, 2, 4, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 1,
        1, 1, 4, 3, 4, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 3, 3,
        3, 3, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 3,
        3, 2, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 4, 3, 3, 3, 2, 3,
        3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,
        3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1,
        1, 3, 1, 1, 1, 1, 3, 3, 1, 1, 1, 3, 1, 2, 1, 3, 3, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 3, 1, 4, 1, 2, 2, 2, 1, 4, 1, 1, 1, 1, 1, 1, 1, 4,
        1, 2, 2, 1, 2, 2, 3, 2, 4, 1, 3, 2, 1, 4, 4, 1, 3, 1, 4, 2, 4, 3,
        2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 1, 1, 3, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1,
        4, 1, 1, 1, 4, 1, 1, 2, 3, 1, 2, 3, 4, 1, 3, 4, 2, 3, 4, 1, 3, 4,
        3, 2, 4, 4, 3, 4, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4,
        2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4,
        3, 4, 4, 3, 3, 3, 4, 3, 3, 3, 2, 4, 4, 4, 3, 4, 3, 4, 3, 2, 3, 1,
        4, 4, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 4, 1, 1, 1, 3, 1, 1, 1,
        3, 4, 1, 1, 2, 1, 4, 4, 3, 3, 1, 4, 3, 1, 1, 3, 1]),
 array([0.006, 0.015, 0.155, 0.02 , 0.005, 0.088, 0.139, 0.078, 0.013,
        0.046, 0.019, 0.435, 0.001, 0.471, 0.429, 0.409, 0.471, 0.034,
        0.23 , 0.095, 0.21 , 0.034, 0.053, 0.116, 0.5  , 0.151, 0.264,
        0.119, 0.145, 0.161, 0.054, 0.168, 0.176, 0.323, 0.415, 0.368,
        0.267, 0.303, 0.301, 0.408, 0.183, 0.401, 0.397, 0.346, 0.437,
        0.263, 0.029, 0.131, 0.31 , 0.257, 0.297, 0.419, 0.434, 0.126,
        0.404, 0.314, 0.341, 0.322, 0.344, 0.383, 0.304, 0.073, 0.355,
        0.191, 0.002, 0.275, 0.309, 0.326, 0.218, 0.269, 0.37 , 0.169,
        0.111, 0.097, 0.268, 0.035, 0.13 , 0.413, 0.375, 0.04 , 0.055,
        0.028, 0.062, 0.299, 0.046, 0.003, 0.05 , 0.062, 0.079, 0.093,
        0.002, 0.001, 0.008, 0.003, 0.001, 0.001, 0.001, 0.016, 0.035,
        0.17 , 0.489, 0.142, 0.017, 0.226, 0.371, 0.495, 0.021, 0.449,
        0.409, 0.097, 0.152, 0.44 , 0.467, 0.483, 0.186, 0.122, 0.024,
        0.001, 0.001, 0.033, 0.076, 0.041, 0.001, 0.009, 0.38 , 0.018,
        0.038, 0.001, 0.025, 0.01 , 0.007, 0.001, 0.001, 0.001, 0.001,
        0.001, 0.001, 0.013, 0.001, 0.001, 0.001, 0.001, 0.003, 0.001,
        0.001, 0.002, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.007,
        0.001, 0.001, 0.001, 0.031, 0.001, 0.002, 0.001, 0.001, 0.026,
        0.007, 0.023, 0.173, 0.211, 0.007, 0.376, 0.018, 0.01 , 0.007,
        0.436, 0.157, 0.173, 0.029, 0.327, 0.121, 0.029, 0.004, 0.09 ,
        0.153, 0.135, 0.29 , 0.41 , 0.037, 0.032, 0.024, 0.039, 0.117,
        0.009, 0.051, 0.018, 0.007, 0.003, 0.023, 0.01 , 0.01 , 0.017,
        0.004, 0.018, 0.004, 0.013, 0.009, 0.014, 0.004, 0.004, 0.007,
        0.007, 0.009, 0.007, 0.007, 0.004, 0.007, 0.007, 0.016, 0.004,
        0.418, 0.475, 0.404, 0.225, 0.444, 0.422, 0.212, 0.462, 0.315,
        0.5  , 0.326, 0.41 , 0.081, 0.006, 0.051, 0.014, 0.396, 0.004,
        0.227, 0.472, 0.122, 0.185, 0.035, 0.004, 0.001, 0.004, 0.004,
        0.004, 0.009, 0.001, 0.004, 0.002, 0.007, 0.005, 0.001, 0.014,
        0.059, 0.014, 0.074, 0.018, 0.004, 0.277, 0.153, 0.153, 0.08 ,
        0.454, 0.227, 0.001, 0.132, 0.078, 0.412, 0.147, 0.001, 0.001,
        0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001,
        0.001, 0.001, 0.001, 0.001, 0.008, 0.105, 0.078, 0.066, 0.419,
        0.304, 0.233, 0.227, 0.303, 0.248, 0.105, 0.28 , 0.338, 0.449,
        0.108, 0.347, 0.086, 0.139, 0.08 , 0.026, 0.001, 0.479, 0.114,
        0.41 , 0.024, 0.138, 0.126, 0.338, 0.002, 0.104, 0.102, 0.188,
        0.118, 0.061, 0.103, 0.032, 0.441, 0.358, 0.036, 0.047, 0.054,
        0.002, 0.438, 0.089, 0.004, 0.02 , 0.382, 0.001, 0.001, 0.182,
        0.463, 0.399, 0.006, 0.306, 0.045, 0.068, 0.062, 0.374, 0.419,
        0.098, 0.013, 0.276, 0.004, 0.419, 0.293, 0.173, 0.376, 0.353,
        0.014, 0.287, 0.118, 0.484, 0.021, 0.014, 0.207, 0.225, 0.225,
        0.122, 0.112, 0.103, 0.033, 0.113, 0.402, 0.093, 0.001, 0.001,
        0.029, 0.022, 0.025, 0.02 , 0.019, 0.014, 0.034, 0.138, 0.014,
        0.191, 0.251, 0.354, 0.48 , 0.424, 0.098, 0.394, 0.319, 0.279,
        0.477, 0.177, 0.041, 0.116, 0.037, 0.172, 0.152, 0.199, 0.395,
        0.294, 0.015, 0.179, 0.087, 0.489, 0.347, 0.191, 0.463, 0.451,
        0.345, 0.263, 0.317, 0.171, 0.438, 0.394, 0.202, 0.217, 0.429,
        0.392, 0.302, 0.359, 0.393, 0.481, 0.459, 0.438, 0.023, 0.117,
        0.234, 0.062, 0.089, 0.006, 0.001, 0.007, 0.004, 0.481, 0.002,
        0.001, 0.001, 0.003, 0.34 , 0.003, 0.02 , 0.002, 0.035, 0.024,
        0.014, 0.027, 0.027, 0.006, 0.027, 0.221, 0.018, 0.015, 0.049,
        0.018, 0.3  , 0.049, 0.086, 0.42 , 0.421, 0.115, 0.42 , 0.489,
        0.165, 0.445, 0.014, 0.325, 0.022, 0.166, 0.137, 0.484, 0.131,
        0.072, 0.198, 0.482, 0.362, 0.364, 0.04 , 0.372, 0.201, 0.033,
        0.202, 0.33 , 0.142, 0.07 , 0.095, 0.073, 0.418, 0.028, 0.324,
        0.151, 0.163, 0.294, 0.009, 0.427, 0.145, 0.047, 0.004, 0.001,
        0.005, 0.005, 0.001, 0.003, 0.001, 0.005, 0.001, 0.001, 0.004,
        0.001, 0.002, 0.005, 0.006, 0.001, 0.011, 0.001, 0.004, 0.005,
        0.002, 0.005, 0.006, 0.005, 0.004, 0.005, 0.004, 0.002, 0.004,
        0.001, 0.199, 0.122, 0.179, 0.466, 0.011, 0.245, 0.14 , 0.103,
        0.422, 0.28 , 0.156, 0.279, 0.228, 0.182, 0.457, 0.275, 0.235,
        0.059, 0.23 , 0.001, 0.13 , 0.011, 0.067, 0.157, 0.008, 0.486,
        0.133, 0.199, 0.099, 0.433, 0.052, 0.001, 0.076, 0.432, 0.147,
        0.39 , 0.211, 0.451, 0.483, 0.481, 0.078, 0.085, 0.391, 0.001,
        0.087, 0.001, 0.471, 0.004, 0.395, 0.383, 0.127, 0.102, 0.017,
        0.09 , 0.023, 0.163, 0.149, 0.006, 0.199, 0.001, 0.361, 0.327,
        0.001, 0.205, 0.299, 0.001, 0.03 , 0.035, 0.002, 0.071, 0.003,
        0.086, 0.03 , 0.014, 0.001, 0.088, 0.009, 0.157, 0.006, 0.051,
        0.003, 0.001, 0.001, 0.165, 0.09 , 0.364, 0.067, 0.001, 0.001,
        0.101, 0.003, 0.001, 0.002, 0.31 , 0.152, 0.071, 0.213, 0.116,
        0.295, 0.339, 0.032, 0.006, 0.041, 0.497, 0.001, 0.138, 0.243,
        0.25 , 0.002, 0.308]))

# quadrant: 1 HH,  2 LH,  3 LL,  4 HL
pd.Series(lisaSSA.q).value_counts()

1    273
3    231
4     73
2     56
Name: count, dtype: int64

districts['SSA_quadrant']=[l if p <0.05 else 0 for l,p in zip(lisaSSA.q,lisaSSA.p_sim)  ]
districts['SSA_quadrant'].value_counts()

SSA_quadrant
0    355
1    153
3    109
4     11
2      5
Name: count, dtype: int64

labels = [ '0 no_sig', '1 hotSpot', '2 coldOutlier', '3 coldSpot', '4 hotOutlier']

districts['SSA_quadrant_names']=[labels[i] for i in districts['SSA_quadrant']]

districts['SSA_quadrant_names'].value_counts()

SSA_quadrant_names
0 no_sig         355
1 hotSpot        153
3 coldSpot       109
4 hotOutlier      11
2 coldOutlier      5
Name: count, dtype: int64

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

# Set up figure and ax
f, ax = plt.subplots(1, figsize=(12,12))
# Plot unique values choropleth including
# a legend and with no boundary lines

plt.title('Spots and Outliers')

districts.plot(column='SSA_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()

selected_variables = ['Wealth_Index_Above_2nd_Quintile',
                     'Secondary_School_Attendance',
                     'Prob_Survival_First5Yrs']

# see distribution
sea.boxplot(districts[selected_variables])

<Axes: >

districts[selected_variables].corr()

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

<seaborn.axisgrid.PairGrid at 0x25891ba8790>

from sklearn.preprocessing import StandardScaler


scaler = StandardScaler()
normalized_data = scaler.fit_transform(districts[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 0x25892ee6e50>

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

# add colunms
districts[selected_variables_new_std]=normalized_data

# as a result:
selected_variables_new_std = ['Wealth_Index_Above_2nd_Quintile_std',
                     'Secondary_School_Attendance_std',
                     'Prob_Survival_First5Yrs_std']
sea.pairplot(
    districts[selected_variables_new_std], kind="reg", diag_kind="kde"
)

<seaborn.axisgrid.PairGrid at 0x2588f415dd0>

from scipy.cluster import hierarchy as hc

Z = hc.linkage(districts[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 6 clusters
model = agnes(linkage="ward", n_clusters=6).fit(districts[selected_variables_new_std])

# Assign labels to main data table
districts["hc_ag6"] = model.labels_

# see distribution of districts
districts["hc_ag6"].value_counts()

hc_ag6
0    255
2    129
3    111
4     57
1     56
5     25
Name: count, dtype: int64

districts.groupby("hc_ag6")[selected_variables_new_std].mean()

# 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
districts.plot(
    column="hc_ag6", categorical=True, legend=True, linewidth=0, ax=ax
)
# Remove axis
ax.set_axis_off()
# Display the map
plt.show()

# modify previous funtion call to specify cluster model with spatial constraint

model_queen = agnes(linkage="ward",
                    n_clusters=6,
                    connectivity=w_queen.sparse).fit(districts[selected_variables_new_std])
# Fit algorithm to the data
districts["hc_ag6_wQueen"] = model_queen.labels_

C:\Users\mendo\anaconda3\envs\dataespacial__31111\Lib\site-packages\sklearn\cluster\_agglomerative.py:325: UserWarning: the number of connected components of the connectivity matrix is 5 > 1. Completing it to avoid stopping the tree early.
  connectivity, n_connected_components = _fix_connectivity(

# 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
districts.plot(
    column="hc_ag6_wQueen",
    categorical=True,
    legend=True,
    linewidth=0,
    ax=ax,
)
# Remove axis
ax.set_axis_off()
# Display the map
plt.show()

model_wknn8 = agnes(linkage="ward",
                    n_clusters=6,
                    connectivity=w_knn8.sparse).fit(districts[selected_variables_new_std])
districts["hc_ag6_wknn8"] = model_wknn8.labels_

# Set up figure and ax
f, ax = plt.subplots(1, figsize=(10, 12))
# Plot unique values choropleth including a legend and with no boundary lines
districts.plot(
    column="hc_ag6_wknn8",
    categorical=True,
    legend=True,
    linewidth=0,
    ax=ax,
)
# Remove axis
ax.set_axis_off()
# Display the map
plt.show()

from esda import shape as shapestats
results={}
for cluster_type in ("hc_ag6_wknn8", "hc_ag6"):
    # compute the region polygons using a dissolve
    regions = districts[[cluster_type, "geometry"]].to_crs(7755).dissolve(by=cluster_type)
    # compute the actual isoperimetric quotient for these regions
    ipqs = shapestats.isoperimetric_quotient(regions)
    # cast to a dataframe
    result = {cluster_type:ipqs}
    results.update(result)
# stack the series together along columns
pd.DataFrame(results)

from esda import shape as shapestats
results={}
for cluster_type in ("hc_ag6_wknn8", "hc_ag6"):
    # compute the region polygons using a dissolve
    regions = districts[[cluster_type, "geometry"]].to_crs(7755).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)

from sklearn import metrics

fit_scores = []
for cluster_type in ("hc_ag6_wknn8", "hc_ag6"):
    # compute the CH score
    ch_score = metrics.calinski_harabasz_score(
        # using scaled variables
        districts[selected_variables_new_std],
        # using these labels
        districts[cluster_type],
    )
    sil_score = metrics.silhouette_score(
        # using scaled variables
        districts[selected_variables_new_std],
        # using these labels
        districts[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")

!pip install pysal

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Building wheels for collected packages: spint
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Installing collected packages: tqdm, simplejson, rtree, pulp, patsy, deprecation, affine, statsmodels, rasterio, rasterstats, access, tobler, segregation, pointpats, momepy, inequality, spglm, spaghetti, spopt, spint, mgwr, pysal
Successfully installed access-1.1.9 affine-2.4.0 deprecation-2.1.0 inequality-1.1.1 mgwr-2.2.1 momepy-0.10.0 patsy-1.0.1 pointpats-2.5.1 pulp-3.2.1 pysal-25.1 rasterio-1.4.3 rasterstats-0.20.0 rtree-1.4.0 segregation-2.5.2 simplejson-3.20.1 spaghetti-1.7.6 spglm-1.1.0 spint-1.0.7 spopt-0.6.1 statsmodels-0.14.4 tobler-0.12.1 tqdm-4.67.1

from pysal.model import spreg

dep_var_name=['Wealth_Index_Above_2nd_Quintile']
ind_vars_names=['Secondary_School_Attendance','Prob_Survival_First5Yrs']
labels=['Wealth Index Above 2nd Quintile %','Secondary-School Attendance %', 'Probability of Survival in the First 5Yrs %']

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

print(ols_model.summary)

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

SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES
-----------------------------------------
Data set            :   districts
Weights matrix      :        None
Dependent Variable  :Wealth Index Above 2nd Quintile %                Number of Observations:         633
Mean dependent var  :      0.5690                Number of Variables   :           3
S.D. dependent var  :      0.2539                Degrees of Freedom    :         630
R-squared           :      0.2752
Adjusted R-squared  :      0.2729
Sum squared residual:     29.5313                F-statistic           :    119.6281
Sigma-square        :       0.047                Prob(F-statistic)     :   9.135e-45
S.E. of regression  :       0.217                Log likelihood        :      71.891
Sigma-square ML     :       0.047                Akaike info criterion :    -137.781
S.E of regression ML:      0.2160                Schwarz criterion     :    -124.430

------------------------------------------------------------------------------------
            Variable     Coefficient       Std.Error     t-Statistic     Probability
------------------------------------------------------------------------------------
            CONSTANT        -2.62387         0.38785        -6.76519         0.00000
Secondary-School Attendance %         0.92627         0.09159        10.11329         0.00000
Probability of Survival in the First 5Yrs %         2.51955         0.43245         5.82620         0.00000
------------------------------------------------------------------------------------

REGRESSION DIAGNOSTICS
MULTICOLLINEARITY CONDITION NUMBER         114.055

TEST ON NORMALITY OF ERRORS
TEST                             DF        VALUE           PROB
Jarque-Bera                       2         11.758           0.0028

DIAGNOSTICS FOR HETEROSKEDASTICITY
RANDOM COEFFICIENTS
TEST                             DF        VALUE           PROB
Breusch-Pagan test                2          4.616           0.0994
Koenker-Bassett test              2          6.913           0.0315
================================ END OF REPORT =====================================

# the dependent variable
moranWI = Moran(districts[dep_var_name], w_knn8)
moranWI.I,moranWI.p_sim

(np.float64(0.6755988730591118), np.float64(0.001))

# the error term
moranError = Moran(ols_model.u, w_knn8)
moranError.I,moranError.p_sim

(np.float64(0.6669397918119403), np.float64(0.001))

SAC_model = spreg.ML_Lag(
    # Dependent variable
    districts[dep_var_name].values,
    # Independent variables
    districts[ind_vars_names].values,
    w=w_knn8,
    # Dependent variable name
    name_y=labels[0],
    # Independent variable name
    name_x=labels[1:],
    name_w='KNN8',
    name_ds='districts'
    )

print(SAC_model.summary)

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

SUMMARY OF OUTPUT: MAXIMUM LIKELIHOOD SPATIAL LAG (METHOD = FULL)
-----------------------------------------------------------------
Data set            :   districts
Weights matrix      :        KNN8
Dependent Variable  :Wealth Index Above 2nd Quintile %                Number of Observations:         633
Mean dependent var  :      0.5690                Number of Variables   :           4
S.D. dependent var  :      0.2539                Degrees of Freedom    :         629
Pseudo R-squared    :      0.7186
Spatial Pseudo R-squared:  0.2557
Log likelihood      :    334.9282
Sigma-square ML     :      0.0183                Akaike info criterion :    -661.856
S.E of regression   :      0.1353                Schwarz criterion     :    -644.055

------------------------------------------------------------------------------------
            Variable     Coefficient       Std.Error     z-Statistic     Probability
------------------------------------------------------------------------------------
            CONSTANT        -1.80993         0.24958        -7.25179         0.00000
Secondary-School Attendance %         0.41817         0.06383         6.55095         0.00000
Probability of Survival in the First 5Yrs %         1.64886         0.27522         5.99115         0.00000
W_Wealth Index Above 2nd Quintile %         0.78410         0.02467        31.77713         0.00000
------------------------------------------------------------------------------------

SPATIAL LAG MODEL IMPACTS
Impacts computed using the 'simple' method.
            Variable         Direct        Indirect          Total
Secondary-School Attendance %         0.4182          1.5187          1.9369
Probability of Survival in the First 5Yrs %         1.6489          5.9883          7.6372
================================ END OF REPORT =====================================

SER_model = spreg.ML_Error(
    # Dependent variable
    districts[dep_var_name].values,
    # Independent variables
    districts[ind_vars_names].values,
    w=w_knn8,
    # Dependent variable name
    name_y=labels[0],
    # Independent variable name
    name_x=labels[1:],
    name_w='KNN8',
    name_ds='districts'
    )

print(SER_model.summary)

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

SUMMARY OF OUTPUT: ML SPATIAL ERROR (METHOD = full)
---------------------------------------------------
Data set            :   districts
Weights matrix      :        KNN8
Dependent Variable  :Wealth Index Above 2nd Quintile %                Number of Observations:         633
Mean dependent var  :      0.5690                Number of Variables   :           3
S.D. dependent var  :      0.2539                Degrees of Freedom    :         630
Pseudo R-squared    :      0.2750
Log likelihood      :    361.6024
Sigma-square ML     :      0.0163                Akaike info criterion :    -717.205
S.E of regression   :      0.1277                Schwarz criterion     :    -703.853

------------------------------------------------------------------------------------
            Variable     Coefficient       Std.Error     z-Statistic     Probability
------------------------------------------------------------------------------------
            CONSTANT        -1.99741         0.25990        -7.68545         0.00000
Secondary-School Attendance %         0.81484         0.07657        10.64143         0.00000
Probability of Survival in the First 5Yrs %         1.96555         0.27779         7.07559         0.00000
              lambda         0.85301         0.02416        35.31175         0.00000
------------------------------------------------------------------------------------
================================ END OF REPORT =====================================

C:\Users\mendo\anaconda3\envs\dataespacial__31111\Lib\site-packages\spreg\ml_error.py:184: RuntimeWarning: Method 'bounded' does not support relative tolerance in x; defaulting to absolute tolerance.
  res = minimize_scalar(

SAC_model = spreg.GM_Combo_Het(
    # Dependent variable
    districts[dep_var_name].values,
    # Independent variables
    districts[ind_vars_names].values,
    w=w_knn8,
    # Dependent variable name
    name_y=labels[0],
    # Independent variable name
    name_x=labels[1:],
    name_w='KNN8',
    name_ds='districts'
)

# Print results
print(SAC_model.summary)

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

SUMMARY OF OUTPUT: SPATIALLY WEIGHTED 2SLS- GM-COMBO MODEL (HET)
----------------------------------------------------------------
Data set            :   districts
Weights matrix      :        KNN8
Dependent Variable  :Wealth Index Above 2nd Quintile %                Number of Observations:         633
Mean dependent var  :      0.5690                Number of Variables   :           4
S.D. dependent var  :      0.2539                Degrees of Freedom    :         629
Pseudo R-squared    :      0.5238
Spatial Pseudo R-squared:  0.2829
N. of iterations    :           1                Step1c computed       :          No

------------------------------------------------------------------------------------
            Variable     Coefficient       Std.Error     z-Statistic     Probability
------------------------------------------------------------------------------------
            CONSTANT        -2.19490         0.28020        -7.83320         0.00000
Secondary-School Attendance %         0.79369         0.08852         8.96590         0.00000
Probability of Survival in the First 5Yrs %         2.03657         0.29858         6.82088         0.00000
W_Wealth Index Above 2nd Quintile %         0.25672         0.17012         1.50899         0.13130
              lambda         0.82484         0.06037        13.66235         0.00000
------------------------------------------------------------------------------------
Instrumented: W_Wealth Index Above 2nd Quintile %
Instruments: W_Probability of Survival in the First 5Yrs %, W_Secondary-
             School Attendance %
================================ END OF REPORT =====================================

	name	geometry_type
0	country	MultiPolygon
1	cities	Point
2	rivers	MultiLineString
3	centroid	Point
4	airports	Point
5	states	MultiPolygon

	NAME_1	District	L	E	H	HDIa	geometry
0	Andaman and Nicobar	South Andaman	0.919	0.952	0.979	0.950	MULTIPOLYGON (((5503734.054 2797754.016, 55059...
1	Andaman and Nicobar	North and Middle Andaman	0.526	0.909	0.993	0.788	MULTIPOLYGON (((5455144.764 3082535.015, 54552...
2	Andhra Pradesh	West Godavari	0.867	0.871	0.981	0.905	MULTIPOLYGON (((4164885.629 3285520.16, 416540...
3	Andhra Pradesh	Y.S.R.	0.864	0.872	0.928	0.888	MULTIPOLYGON (((3887677.643 3040941.348, 38881...
4	Andhra Pradesh	Chittoor	0.768	0.897	0.970	0.875	MULTIPOLYGON (((3836090.177 2903126.194, 38367...

	State/Union	District	Wealth_Index_Above_2nd_Quintile	Secondary_School_Attendance	Prob_Survival_First5Yrs	HDI	geometry
162	Haryana	Jhajjar	0.954	0.971	0.991	0.972	MULTIPOLYGON (((3703135.5 4528351.619, 3703663...
164	Haryana	Sonipat	0.940	0.937	0.965	0.947	MULTIPOLYGON (((3679488.454 4577392.127, 36807...
167	Haryana	Gurgaon	0.940	0.911	0.966	0.939	MULTIPOLYGON (((3715858.418 4494004.054, 37166...
539	Uttar Pradesh	Baghpat	0.824	0.814	0.951	0.861	MULTIPOLYGON (((3725134.787 4557017.481, 37244...
537	Uttar Pradesh	Ghaziabad	0.968	0.844	0.970	0.926	MULTIPOLYGON (((3777001.971 4520467.486, 37758...
538	Uttar Pradesh	Gautam Buddha Nagar	0.929	0.850	0.947	0.908	MULTIPOLYGON (((3747063.972 4512105.936, 37474...
171	Haryana	Faridabad	0.949	0.873	0.967	0.929	MULTIPOLYGON (((3726361.466 4458097.746, 37271...

	State/Union	District	Wealth_Index_Above_2nd_Quintile	Secondary_School_Attendance	Prob_Survival_First5Yrs	HDI	geometry
162	Haryana	Jhajjar	0.954	0.971	0.991	0.972	MULTIPOLYGON (((3703135.5 4528351.619, 3703663...
164	Haryana	Sonipat	0.940	0.937	0.965	0.947	MULTIPOLYGON (((3679488.454 4577392.127, 36807...
167	Haryana	Gurgaon	0.940	0.911	0.966	0.939	MULTIPOLYGON (((3715858.418 4494004.054, 37166...
539	Uttar Pradesh	Baghpat	0.824	0.814	0.951	0.861	MULTIPOLYGON (((3725134.787 4557017.481, 37244...
537	Uttar Pradesh	Ghaziabad	0.968	0.844	0.970	0.926	MULTIPOLYGON (((3777001.971 4520467.486, 37758...
538	Uttar Pradesh	Gautam Buddha Nagar	0.929	0.850	0.947	0.908	MULTIPOLYGON (((3747063.972 4512105.936, 37474...
171	Haryana	Faridabad	0.949	0.873	0.967	0.929	MULTIPOLYGON (((3726361.466 4458097.746, 37271...

	Wealth_Index_Above_2nd_Quintile_std	Secondary_School_Attendance_std	Prob_Survival_First5Yrs_std
hc_ag6
0	0.810192	0.760727	0.678130
1	0.992289	-0.642456	-0.326209
2	-0.657353	0.066438	0.320661
3	-0.875546	-0.376034	-0.707919
4	-1.161041	-1.928298	-0.968072
5	-0.560150	-0.597021	-2.490463

	0	1	2	3	4	5	6	7	8	9	...	623	624	625	626	627	628	629	630	631	632
0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	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	0.0	0.0	0.0	0.0	0.0	0.0
2	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	0.0	0.0	0.0	0.0	1.0	0.0	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	0.0	0.0	0.0	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	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
628	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	0.0	0.0	0.0	0.0	0.0
629	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	0.0	0.0	0.0	0.0	0.0	0.0
630	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	0.0	0.0	0.0	0.0	0.0	0.0
631	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	0.0	1.0	0.0	0.0	0.0	0.0	0.0
632	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0

	Wealth_Index_Above_2nd_Quintile	Secondary_School_Attendance	Prob_Survival_First5Yrs
Wealth_Index_Above_2nd_Quintile	1.000000	0.485996	0.396964
Secondary_School_Attendance	0.485996	1.000000	0.454648
Prob_Survival_First5Yrs	0.396964	0.454648	1.000000

	hc_ag6_wknn8	hc_ag6
0	0.006588	0.005475
1	0.015873	0.008415
2	0.028184	0.003860
3	0.011220	0.004861
4	0.010901	0.009011
5	0.044095	0.013651

	hc_ag6_wknn8	hc_ag6
0	0.207758	0.202565
1	0.423602	0.157215
2	0.586189	0.147140
3	0.427325	0.172362
4	0.360999	0.115156
5	0.628876	0.056561

	CH score	SIL score
cluster type
hc_ag6_wknn8	126.443557	0.042646
hc_ag6	245.201020	0.249418

	0	1	2	3	4	5	6	7	8	9	...	623	624	625	626	627	628	629	630	631	632
0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	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	0.0	0.0	0.0	0.0	0.0	0.0
2	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	0.0	0.0	0.0	0.0	1.0	0.0	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	0.0	0.0	0.0	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	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
628	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	0.0	0.0	0.0	0.0	0.0
629	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	0.0	0.0	0.0	0.0	0.0	0.0
630	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	0.0	0.0	0.0	0.0	0.0	0.0
631	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	0.0	1.0	0.0	0.0	0.0	0.0	0.0
632	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0

	0	1	2	3	4	5	6	7	8	9	...	623	624	625	626	627	628	629	630	631	632
0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	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	0.0	0.0	0.0	0.0	0.0	0.0
2	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	0.0	0.0	0.0	0.0	1.0	0.0	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	0.0	0.0	0.0	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	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
628	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	0.0	0.0	0.0	0.0	0.0
629	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	0.0	0.0	0.0	0.0	0.0	0.0
630	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	0.0	0.0	0.0	0.0	0.0	0.0
631	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	0.0	1.0	0.0	0.0	0.0	0.0	0.0
632	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0