## SEAI 2021 - Python - Lab 3
# ESDA & Spatial Econometrics Models

Vincenzo Nardelli - vincnardelli@gmail.com - https://github.com/vincnardelli



Before to start... install Python Libraries!


```python
! pip install pysal
! pip install cartoframes 
! pip install --user urllib3==1.22
```

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Remember to restart the runtime to load correctly the installed libraries!

Now, we download from the web the dataset for this lab.


```python
! wget https://geodacenter.github.io/data-and-lab//data/columbus.zip
! unzip columbus.zip
```

    --2021-06-11 10:11:30--  https://geodacenter.github.io/data-and-lab//data/columbus.zip
    Resolving geodacenter.github.io (geodacenter.github.io)... 185.199.108.153, 185.199.109.153, 185.199.110.153, ...
    Connecting to geodacenter.github.io (geodacenter.github.io)|185.199.108.153|:443... connected.
    HTTP request sent, awaiting response... 200 OK
    Length: 472230 (461K) [application/zip]
    Saving to: ‘columbus.zip’
    
    columbus.zip        100%[===================>] 461.16K  --.-KB/s    in 0.01s   
    
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Let's start!

## Explorative Spatial Data Analysis


```python
import pandas as pd
import numpy as np
import geopandas as gpd

columbus = gpd.read_file("columbus/columbus.shp")
columbus.head()
```

    /usr/local/lib/python3.7/dist-packages/geopandas/_compat.py:110: UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1 ) is incompatible with the GEOS version PyGEOS was compiled with (3.9.1-CAPI-1.14.2). Conversions between both will be slow.
      shapely_geos_version, geos_capi_version_string





<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>AREA</th>
      <th>PERIMETER</th>
      <th>COLUMBUS_</th>
      <th>COLUMBUS_I</th>
      <th>POLYID</th>
      <th>NEIG</th>
      <th>HOVAL</th>
      <th>INC</th>
      <th>CRIME</th>
      <th>OPEN</th>
      <th>PLUMB</th>
      <th>DISCBD</th>
      <th>X</th>
      <th>Y</th>
      <th>NSA</th>
      <th>NSB</th>
      <th>EW</th>
      <th>CP</th>
      <th>THOUS</th>
      <th>NEIGNO</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0.309441</td>
      <td>2.440629</td>
      <td>2</td>
      <td>5</td>
      <td>1</td>
      <td>5</td>
      <td>80.467003</td>
      <td>19.531</td>
      <td>15.725980</td>
      <td>2.850747</td>
      <td>0.217155</td>
      <td>5.03</td>
      <td>38.799999</td>
      <td>44.070000</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1000.0</td>
      <td>1005.0</td>
      <td>POLYGON ((8.62413 14.23698, 8.55970 14.74245, ...</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0.259329</td>
      <td>2.236939</td>
      <td>3</td>
      <td>1</td>
      <td>2</td>
      <td>1</td>
      <td>44.567001</td>
      <td>21.232</td>
      <td>18.801754</td>
      <td>5.296720</td>
      <td>0.320581</td>
      <td>4.27</td>
      <td>35.619999</td>
      <td>42.380001</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1000.0</td>
      <td>1001.0</td>
      <td>POLYGON ((8.25279 14.23694, 8.28276 14.22994, ...</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0.192468</td>
      <td>2.187547</td>
      <td>4</td>
      <td>6</td>
      <td>3</td>
      <td>6</td>
      <td>26.350000</td>
      <td>15.956</td>
      <td>30.626781</td>
      <td>4.534649</td>
      <td>0.374404</td>
      <td>3.89</td>
      <td>39.820000</td>
      <td>41.180000</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1000.0</td>
      <td>1006.0</td>
      <td>POLYGON ((8.65331 14.00809, 8.81814 14.00205, ...</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0.083841</td>
      <td>1.427635</td>
      <td>5</td>
      <td>2</td>
      <td>4</td>
      <td>2</td>
      <td>33.200001</td>
      <td>4.477</td>
      <td>32.387760</td>
      <td>0.394427</td>
      <td>1.186944</td>
      <td>3.70</td>
      <td>36.500000</td>
      <td>40.520000</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1000.0</td>
      <td>1002.0</td>
      <td>POLYGON ((8.45950 13.82035, 8.47341 13.83227, ...</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0.488888</td>
      <td>2.997133</td>
      <td>6</td>
      <td>7</td>
      <td>5</td>
      <td>7</td>
      <td>23.225000</td>
      <td>11.252</td>
      <td>50.731510</td>
      <td>0.405664</td>
      <td>0.624596</td>
      <td>2.83</td>
      <td>40.009998</td>
      <td>38.000000</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1000.0</td>
      <td>1007.0</td>
      <td>POLYGON ((8.68527 13.63952, 8.67758 13.72221, ...</td>
    </tr>
  </tbody>
</table>
</div>




```python
columbus.plot(column="CRIME", figsize=(10, 6),legend=True)
```


```python
columbus.plot(column="CRIME", figsize=(10, 6), scheme='Quantiles', k=7, cmap='Blues',legend=True);
```


```python
from cartoframes.viz import Layer, color_bins_style
Layer(
    columbus,
    color_bins_style('CRIME', palette='CB_BLUES', bins=5, opacity=0.8)
)
```

    NumExpr defaulting to 2 threads.









Let's create the weight matrix


```python
from libpysal.weights import Queen
w = Queen.from_dataframe(columbus)
w.transform = 'r'
```

### Spatial Lag

Compute the lagged value from the W matrix

$y l a g_{i}=\sum_{j} w_{i, j} y_{j}$


```python
from libpysal.weights import lag_spatial

y = columbus['CRIME']
ylag = lag_spatial(w, y)

ylag
```




    array([24.7142675 , 26.24684033, 29.411751  , 34.64647575, 40.4653275 ,
           40.6237135 , 49.728447  , 41.49913083, 36.9477815 , 25.328384  ,
           49.857447  , 43.25009117, 38.093982  , 42.82053817, 50.10508383,
           52.36547225, 18.09104533, 51.18116975, 46.442163  , 32.4463615 ,
           43.72131433, 35.90408433, 18.74605433, 54.90555729, 49.97125037,
           48.62930967, 33.20419825, 47.42423011, 51.79066157, 48.5733284 ,
           19.12681567, 13.8546865 , 37.27826625, 22.1371835 , 33.098551  ,
           18.7549056 , 50.99421883, 43.65270583, 16.171101  , 21.0544898 ,
           21.06991733, 19.139792  , 36.16118067, 35.747272  , 32.07386125,
           16.7032095 , 17.5732225 , 28.5489635 , 27.21200567])




```python
columbus['CRIME_W'] = ylag
```


```python
columbus.plot(column="CRIME_W", figsize=(10, 6), scheme='Quantiles', k=7, cmap='Blues',legend=True)
```


```python
import matplotlib.pyplot as plt

f,ax = plt.subplots(1,2,figsize=(16, 8))
columbus.plot(column='CRIME', ax=ax[0], edgecolor='k',
        scheme='Quantiles', k=7, cmap='Blues',legend=True)
ax[0].set_title("Crime")
columbus.plot(column='CRIME_W', ax=ax[1], edgecolor='k',
        scheme='Quantiles', k=7, cmap='Blues',legend=True)
ax[1].set_title("Crime Spatial Lag");
```

### Moran's Index
Now we can compute the Moran's Index


```python
from esda.moran import Moran
mi = Moran(y, w)
mi.I
```




    0.5001885571828611




```python
import seaborn as sbn
sbn.kdeplot(mi.sim, shade=True)
plt.vlines(mi.I, 0, 1, color='r')
plt.vlines(mi.EI, 0,1)
plt.xlabel("Moran's I");
```


```python
mi.p_norm
```




    1.7988309952698955e-08




```python
mi.p_sim
```




    0.001



https://pysal.org/esda/generated/esda.Moran.html


```python
from splot.esda import moran_scatterplot
moran_scatterplot(mi, aspect_equal=True);
```


```python
moran_scatterplot(mi, aspect_equal=True, zstandard=False);
```

### Local Moran

We can also compute the local moran in a similar way




```python
from esda.moran import Moran_Local
mi_local = Moran_Local(y, w)
```


```python
mi_local.p_sim
```




    array([0.18 , 0.145, 0.232, 0.46 , 0.153, 0.346, 0.055, 0.16 , 0.388,
           0.118, 0.014, 0.098, 0.342, 0.11 , 0.007, 0.001, 0.033, 0.029,
           0.116, 0.242, 0.185, 0.435, 0.044, 0.001, 0.001, 0.009, 0.442,
           0.008, 0.001, 0.015, 0.044, 0.006, 0.391, 0.061, 0.39 , 0.008,
           0.006, 0.085, 0.026, 0.018, 0.072, 0.083, 0.426, 0.474, 0.367,
           0.048, 0.067, 0.226, 0.206])




```python
moran_scatterplot(mi_local, p=0.05);
```


```python
from splot.esda import lisa_cluster

lisa_cluster(mi_local, columbus, p=0.05, figsize = (10,10));
```


```python
from splot.esda import plot_local_autocorrelation
plot_local_autocorrelation(mi_local, columbus, 'CRIME');
```

## Spatial Econometrics Models
Let's start with some data manipulation with numpy


```python
columbus['CRIME']
```




    0     15.725980
    1     18.801754
    2     30.626781
    3     32.387760
    4     50.731510
    5     26.066658
    6      0.178269
    7     38.425858
    8     30.515917
    9     34.000835
    10    62.275448
    11    56.705669
    12    46.716129
    13    57.066132
    14    48.585487
    15    54.838711
    16    36.868774
    17    43.962486
    18    54.521965
    19     0.223797
    20    40.074074
    21    33.705048
    22    20.048504
    23    38.297871
    24    61.299175
    25    40.969742
    26    52.794430
    27    56.919785
    28    60.750446
    29    68.892044
    30    17.677214
    31    19.145592
    32    41.968163
    33    23.974028
    34    39.175053
    35    14.305556
    36    42.445076
    37    53.710938
    38    19.100863
    39    16.241299
    40    18.905146
    41    16.491890
    42    36.663612
    43    25.962263
    44    29.028488
    45    16.530533
    46    27.822861
    47    26.645266
    48    22.541491
    Name: CRIME, dtype: float64




```python
columbus['CRIME'].values
```




    array([15.72598 , 18.801754, 30.626781, 32.38776 , 50.73151 , 26.066658,
            0.178269, 38.425858, 30.515917, 34.000835, 62.275448, 56.705669,
           46.716129, 57.066132, 48.585487, 54.838711, 36.868774, 43.962486,
           54.521965,  0.223797, 40.074074, 33.705048, 20.048504, 38.297871,
           61.299175, 40.969742, 52.79443 , 56.919785, 60.750446, 68.892044,
           17.677214, 19.145592, 41.968163, 23.974028, 39.175053, 14.305556,
           42.445076, 53.710938, 19.100863, 16.241299, 18.905146, 16.49189 ,
           36.663612, 25.962263, 29.028488, 16.530533, 27.822861, 26.645266,
           22.541491])




```python
y = columbus['CRIME'].values
y_name = 'CRIME'
```


```python
x = np.array([columbus.INC]).T
x_name = 'INC'
x
```




    array([[19.531   ],
           [21.232   ],
           [15.956   ],
           [ 4.477   ],
           [11.252   ],
           [16.028999],
           [ 8.438   ],
           [11.337   ],
           [17.586   ],
           [13.598   ],
           [ 7.467   ],
           [10.048   ],
           [ 9.549   ],
           [ 9.963   ],
           [ 9.873   ],
           [ 7.625   ],
           [ 9.798   ],
           [13.185   ],
           [11.618   ],
           [31.07    ],
           [10.655   ],
           [11.709   ],
           [21.155001],
           [14.236   ],
           [ 8.461   ],
           [ 8.085   ],
           [10.822   ],
           [ 7.856   ],
           [ 8.681   ],
           [13.906   ],
           [16.940001],
           [18.941999],
           [ 9.918   ],
           [14.948   ],
           [12.814   ],
           [18.739   ],
           [17.017   ],
           [11.107   ],
           [18.476999],
           [29.833   ],
           [22.207001],
           [25.872999],
           [13.38    ],
           [16.961   ],
           [14.135   ],
           [18.323999],
           [18.950001],
           [11.813   ],
           [18.796   ]])




```python
X_names = ['INC', 'HOVAL']
X = columbus[X_names].values
X
```




    array([[19.531   , 80.467003],
           [21.232   , 44.567001],
           [15.956   , 26.35    ],
           [ 4.477   , 33.200001],
           [11.252   , 23.225   ],
           [16.028999, 28.75    ],
           [ 8.438   , 75.      ],
           [11.337   , 37.125   ],
           [17.586   , 52.599998],
           [13.598   , 96.400002],
           [ 7.467   , 19.700001],
           [10.048   , 19.9     ],
           [ 9.549   , 41.700001],
           [ 9.963   , 42.900002],
           [ 9.873   , 18.      ],
           [ 7.625   , 18.799999],
           [ 9.798   , 41.75    ],
           [13.185   , 60.      ],
           [11.618   , 30.6     ],
           [31.07    , 81.266998],
           [10.655   , 19.975   ],
           [11.709   , 30.450001],
           [21.155001, 47.733002],
           [14.236   , 53.200001],
           [ 8.461   , 17.9     ],
           [ 8.085   , 20.299999],
           [10.822   , 34.099998],
           [ 7.856   , 22.85    ],
           [ 8.681   , 32.5     ],
           [13.906   , 22.5     ],
           [16.940001, 31.799999],
           [18.941999, 40.299999],
           [ 9.918   , 23.6     ],
           [14.948   , 28.450001],
           [12.814   , 27.      ],
           [18.739   , 36.299999],
           [17.017   , 43.299999],
           [11.107   , 22.700001],
           [18.476999, 39.599998],
           [29.833   , 61.950001],
           [22.207001, 42.099998],
           [25.872999, 44.333   ],
           [13.38    , 25.700001],
           [16.961   , 33.5     ],
           [14.135   , 27.733   ],
           [18.323999, 76.099998],
           [18.950001, 42.5     ],
           [11.813   , 26.799999],
           [18.796   , 35.799999]])



### OLS


```python
from spreg import OLS
ols = OLS(y=y, x=x)
print(ols.summary)
```

    REGRESSION
    ----------
    SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES
    -----------------------------------------
    Data set            :     unknown
    Weights matrix      :        None
    Dependent Variable  :     dep_var                Number of Observations:          49
    Mean dependent var  :     35.1288                Number of Variables   :           2
    S.D. dependent var  :     16.7321                Degrees of Freedom    :          47
    R-squared           :      0.4838
    Adjusted R-squared  :      0.4729
    Sum squared residual:    6936.202                F-statistic           :     44.0579
    Sigma-square        :     147.579                Prob(F-statistic)     :   2.901e-08
    S.E. of regression  :      12.148                Log likelihood        :    -190.869
    Sigma-square ML     :     141.555                Akaike info criterion :     385.738
    S.E of regression ML:     11.8977                Schwarz criterion     :     389.521
    
    ------------------------------------------------------------------------------------
                Variable     Coefficient       Std.Error     t-Statistic     Probability
    ------------------------------------------------------------------------------------
                CONSTANT      64.4632283       4.7479549      13.5770516       0.0000000
                   var_1      -2.0406629       0.3074391      -6.6376157       0.0000000
    ------------------------------------------------------------------------------------
    
    REGRESSION DIAGNOSTICS
    MULTICOLLINEARITY CONDITION NUMBER            5.282
    
    TEST ON NORMALITY OF ERRORS
    TEST                             DF        VALUE           PROB
    Jarque-Bera                       2          36.549           0.0000
    
    DIAGNOSTICS FOR HETEROSKEDASTICITY
    RANDOM COEFFICIENTS
    TEST                             DF        VALUE           PROB
    Breusch-Pagan test                1           5.072           0.0243
    Koenker-Bassett test              1           1.723           0.1893
    ================================ END OF REPORT =====================================



```python
?ols
```


```python
ols = OLS(y=y, x=x, 
          name_y=y_name, name_x=[x_name], name_ds='columbus')
print(ols.summary)
```

    REGRESSION
    ----------
    SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES
    -----------------------------------------
    Data set            :    columbus
    Weights matrix      :        None
    Dependent Variable  :       CRIME                Number of Observations:          49
    Mean dependent var  :     35.1288                Number of Variables   :           2
    S.D. dependent var  :     16.7321                Degrees of Freedom    :          47
    R-squared           :      0.4838
    Adjusted R-squared  :      0.4729
    Sum squared residual:    6936.202                F-statistic           :     44.0579
    Sigma-square        :     147.579                Prob(F-statistic)     :   2.901e-08
    S.E. of regression  :      12.148                Log likelihood        :    -190.869
    Sigma-square ML     :     141.555                Akaike info criterion :     385.738
    S.E of regression ML:     11.8977                Schwarz criterion     :     389.521
    
    ------------------------------------------------------------------------------------
                Variable     Coefficient       Std.Error     t-Statistic     Probability
    ------------------------------------------------------------------------------------
                CONSTANT      64.4632283       4.7479549      13.5770516       0.0000000
                     INC      -2.0406629       0.3074391      -6.6376157       0.0000000
    ------------------------------------------------------------------------------------
    
    REGRESSION DIAGNOSTICS
    MULTICOLLINEARITY CONDITION NUMBER            5.282
    
    TEST ON NORMALITY OF ERRORS
    TEST                             DF        VALUE           PROB
    Jarque-Bera                       2          36.549           0.0000
    
    DIAGNOSTICS FOR HETEROSKEDASTICITY
    RANDOM COEFFICIENTS
    TEST                             DF        VALUE           PROB
    Breusch-Pagan test                1           5.072           0.0243
    Koenker-Bassett test              1           1.723           0.1893
    ================================ END OF REPORT =====================================


Array of betas


```python
ols.betas
```




    array([[64.46322833],
           [-2.04066292]])



Array of residuals


```python
ols.u
```




    array([[ -8.88106093],
           [ -2.33411931],
           [ -1.27562985],
           [-22.93942046],
           [  9.2298208 ],
           [ -5.6867865 ],
           [-47.06584565],
           [ -2.90237486],
           [  1.9397867 ],
           [ -2.713459  ],
           [ 13.04984966],
           [ 12.74702165],
           [  1.73919085],
           [ 12.9340283 ],
           [  4.26972364],
           [  5.9355374 ],
           [ -7.60003908],
           [  6.40539821],
           [ 13.76715842],
           [ -0.83603454],
           [ -2.64589096],
           [ -6.86405825],
           [ -1.24449831],
           [  2.88551994],
           [ 14.1019956 ],
           [ -6.99472666],
           [ 10.41525574],
           [  8.48800454],
           [ 14.00221244],
           [ 32.80627417],
           [-12.2171825 ],
           [ -6.66340142],
           [ -2.25577053],
           [ -9.98537107],
           [  0.86087927],
           [-11.91768995],
           [ 12.70780851],
           [ 11.91335267],
           [ -7.65703868],
           [ 12.65716743],
           [ -0.24107892],
           [  4.82673124],
           [ -0.49554652],
           [ -3.88928162],
           [ -6.58997002],
           [-10.53959011],
           [  2.03019696],
           [-13.71161131],
           [ -3.56543717]])



Predicted values


```python
ols.predy
```




    array([[24.60704093],
           [21.13587331],
           [31.90241085],
           [55.32718046],
           [41.5016892 ],
           [31.7534445 ],
           [47.24411465],
           [41.32823286],
           [28.5761303 ],
           [36.714294  ],
           [49.22559834],
           [43.95864735],
           [44.97693815],
           [44.1321037 ],
           [44.31576336],
           [48.9031736 ],
           [44.46881308],
           [37.55708779],
           [40.75480658],
           [ 1.05983154],
           [42.71996496],
           [40.56910625],
           [21.29300231],
           [35.41235106],
           [47.1971794 ],
           [47.96446866],
           [42.37917426],
           [48.43178046],
           [46.74823356],
           [36.08576983],
           [29.8943965 ],
           [25.80899342],
           [44.22393353],
           [33.95939907],
           [38.31417373],
           [26.22324595],
           [29.73726749],
           [41.79758533],
           [26.75790168],
           [ 3.58413157],
           [19.14622492],
           [11.66515876],
           [37.15915852],
           [29.85154462],
           [35.61845802],
           [27.07012311],
           [25.79266404],
           [40.35687731],
           [26.10692817]])



$R^2$



```python
ols.r2
```




    0.48384513293999487



Log-likelihood



```python
ols.logll
```




    -190.8688784063158



Jarque Bera


```python
ols.jarque_bera
```




    {'df': 2, 'jb': 36.5488718687176, 'pvalue': 1.1574795154584871e-08}



https://spreg.readthedocs.io/en/latest/generated/spreg.OLS.html

### Moran's I on residual
Using the W matrix in combination with the OLS is possible to compute the Moran's I on the residuals


```python
from spreg import MoranRes
m = MoranRes(ols, w, z=True)
```

Value of the Moran’s I statistic:


```python
print(round(m.I,4))
```

    0.1724


Value of the Moran’s I expectation:


```python
print(round(m.eI,4))
```

    -0.0301


Value of the Moran’s I variance:


```python
print(round(m.vI,4))
```

    0.0082


Value of the Moran’s I standardized value. This is distributed as a standard Normal(0, 1)


```python
print(round(m.zI,4))
```

    2.237


P-value of the standardized Moran’s I value (z):


```python
print(round(m.p_norm,4))
```

    0.0253


Otherwise, the preferred approach is to perform all the test usign OLS function:


```python
ols = OLS(y=y, x=x, w=w, 
          name_y=y_name, name_x = [x_name], name_ds='columbus', 
          white_test=True, spat_diag=True, moran=True)
print(ols.summary)
```

    REGRESSION
    ----------
    SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES
    -----------------------------------------
    Data set            :    columbus
    Weights matrix      :     unknown
    Dependent Variable  :       CRIME                Number of Observations:          49
    Mean dependent var  :     35.1288                Number of Variables   :           2
    S.D. dependent var  :     16.7321                Degrees of Freedom    :          47
    R-squared           :      0.4838
    Adjusted R-squared  :      0.4729
    Sum squared residual:    6936.202                F-statistic           :     44.0579
    Sigma-square        :     147.579                Prob(F-statistic)     :   2.901e-08
    S.E. of regression  :      12.148                Log likelihood        :    -190.869
    Sigma-square ML     :     141.555                Akaike info criterion :     385.738
    S.E of regression ML:     11.8977                Schwarz criterion     :     389.521
    
    ------------------------------------------------------------------------------------
                Variable     Coefficient       Std.Error     t-Statistic     Probability
    ------------------------------------------------------------------------------------
                CONSTANT      64.4632283       4.7479549      13.5770516       0.0000000
                     INC      -2.0406629       0.3074391      -6.6376157       0.0000000
    ------------------------------------------------------------------------------------
    
    REGRESSION DIAGNOSTICS
    MULTICOLLINEARITY CONDITION NUMBER            5.282
    
    TEST ON NORMALITY OF ERRORS
    TEST                             DF        VALUE           PROB
    Jarque-Bera                       2          36.549           0.0000
    
    DIAGNOSTICS FOR HETEROSKEDASTICITY
    RANDOM COEFFICIENTS
    TEST                             DF        VALUE           PROB
    Breusch-Pagan test                1           5.072           0.0243
    Koenker-Bassett test              1           1.723           0.1893
    
    SPECIFICATION ROBUST TEST
    TEST                             DF        VALUE           PROB
    White                             2           3.831           0.1473
    
    DIAGNOSTICS FOR SPATIAL DEPENDENCE
    TEST                           MI/DF       VALUE           PROB
    Moran's I (error)              0.1724         2.237           0.0253
    Lagrange Multiplier (lag)         1           7.777           0.0053
    Robust LM (lag)                   1           4.890           0.0270
    Lagrange Multiplier (error)       1           3.137           0.0765
    Robust LM (error)                 1           0.250           0.6170
    Lagrange Multiplier (SARMA)       2           8.027           0.0181
    
    ================================ END OF REPORT =====================================



```python
ols = OLS(y=y, x=X, w=w, 
          name_y=y_name, name_x = X_names, name_w="Q row std", name_ds='columbus', 
          white_test=True, spat_diag=True, moran=True)
print(ols.summary)
```

    REGRESSION
    ----------
    SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES
    -----------------------------------------
    Data set            :    columbus
    Weights matrix      :   Q row std
    Dependent Variable  :       CRIME                Number of Observations:          49
    Mean dependent var  :     35.1288                Number of Variables   :           3
    S.D. dependent var  :     16.7321                Degrees of Freedom    :          46
    R-squared           :      0.5524
    Adjusted R-squared  :      0.5329
    Sum squared residual:    6014.893                F-statistic           :     28.3856
    Sigma-square        :     130.759                Prob(F-statistic)     :   9.341e-09
    S.E. of regression  :      11.435                Log likelihood        :    -187.377
    Sigma-square ML     :     122.753                Akaike info criterion :     380.754
    S.E of regression ML:     11.0794                Schwarz criterion     :     386.430
    
    ------------------------------------------------------------------------------------
                Variable     Coefficient       Std.Error     t-Statistic     Probability
    ------------------------------------------------------------------------------------
                CONSTANT      68.6189611       4.7354861      14.4903731       0.0000000
                     INC      -1.5973108       0.3341308      -4.7804962       0.0000183
                   HOVAL      -0.2739315       0.1031987      -2.6544086       0.0108745
    ------------------------------------------------------------------------------------
    
    REGRESSION DIAGNOSTICS
    MULTICOLLINEARITY CONDITION NUMBER            6.542
    
    TEST ON NORMALITY OF ERRORS
    TEST                             DF        VALUE           PROB
    Jarque-Bera                       2           1.836           0.3994
    
    DIAGNOSTICS FOR HETEROSKEDASTICITY
    RANDOM COEFFICIENTS
    TEST                             DF        VALUE           PROB
    Breusch-Pagan test                2           7.900           0.0193
    Koenker-Bassett test              2           5.694           0.0580
    
    SPECIFICATION ROBUST TEST
    TEST                             DF        VALUE           PROB
    White                             5          19.946           0.0013
    
    DIAGNOSTICS FOR SPATIAL DEPENDENCE
    TEST                           MI/DF       VALUE           PROB
    Moran's I (error)              0.2221         2.839           0.0045
    Lagrange Multiplier (lag)         1           8.898           0.0029
    Robust LM (lag)                   1           3.736           0.0533
    Lagrange Multiplier (error)       1           5.206           0.0225
    Robust LM (error)                 1           0.044           0.8340
    Lagrange Multiplier (SARMA)       2           8.942           0.0114
    
    ================================ END OF REPORT =====================================


### How to select the spatial specification?

Spatial specification search (Anselin, 2015)

"However, this decision rule needs to be applied with caution, since there may not be sufficient evidence to clearly select one alternative over another"





### Spatial Lag

Reference: Anselin, 1998


```python
from spreg import ML_Lag
lag = ML_Lag(y=y, x=X, w=w, 
          name_y=y_name, name_x = X_names, name_w="Q row std", name_ds='columbus')
print(lag.summary)
```

    REGRESSION
    ----------
    SUMMARY OF OUTPUT: MAXIMUM LIKELIHOOD SPATIAL LAG (METHOD = FULL)
    -----------------------------------------------------------------
    Data set            :    columbus
    Weights matrix      :   Q row std
    Dependent Variable  :       CRIME                Number of Observations:          49
    Mean dependent var  :     35.1288                Number of Variables   :           4
    S.D. dependent var  :     16.7321                Degrees of Freedom    :          45
    Pseudo R-squared    :      0.6473
    Spatial Pseudo R-squared:  0.5806
    Sigma-square ML     :      96.857                Log likelihood        :    -182.674
    S.E of regression   :       9.842                Akaike info criterion :     373.348
                                                     Schwarz criterion     :     380.915
    
    ------------------------------------------------------------------------------------
                Variable     Coefficient       Std.Error     z-Statistic     Probability
    ------------------------------------------------------------------------------------
                CONSTANT      45.6032490       7.2574039       6.2836862       0.0000000
                     INC      -1.0487282       0.3074059      -3.4115419       0.0006460
                   HOVAL      -0.2663348       0.0890963      -2.9892918       0.0027962
                 W_CRIME       0.4233254       0.1195104       3.5421625       0.0003969
    ------------------------------------------------------------------------------------
    ================================ END OF REPORT =====================================


    /usr/local/lib/python3.7/dist-packages/scipy/optimize/_minimize.py:770: RuntimeWarning: Method 'bounded' does not support relative tolerance in x; defaulting to absolute tolerance.
      "defaulting to absolute tolerance.", RuntimeWarning)
    /usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:87: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
      return ufunc.reduce(obj, axis, dtype, out, **passkwargs)



```python
from spreg import GM_Lag
lag = GM_Lag(y=y, x=X, w=w,
          name_y=y_name, name_x = X_names, name_w="Q row std", name_ds='columbus')
print(lag.summary)
```

    REGRESSION
    ----------
    SUMMARY OF OUTPUT: SPATIAL TWO STAGE LEAST SQUARES
    --------------------------------------------------
    Data set            :    columbus
    Weights matrix      :   Q row std
    Dependent Variable  :       CRIME                Number of Observations:          49
    Mean dependent var  :     35.1288                Number of Variables   :           4
    S.D. dependent var  :     16.7321                Degrees of Freedom    :          45
    Pseudo R-squared    :      0.6494
    Spatial Pseudo R-squared:  0.5786
    
    ------------------------------------------------------------------------------------
                Variable     Coefficient       Std.Error     z-Statistic     Probability
    ------------------------------------------------------------------------------------
                CONSTANT      43.9631909      10.7680847       4.0827308       0.0000445
                     INC      -1.0096372       0.3723948      -2.7112007       0.0067040
                   HOVAL      -0.2657935       0.0886026      -2.9998391       0.0027012
                 W_CRIME       0.4534908       0.1834172       2.4724546       0.0134189
    ------------------------------------------------------------------------------------
    Instrumented: W_CRIME
    Instruments: W_HOVAL, W_INC
    ================================ END OF REPORT =====================================


### Spatial Error

Reference: Anselin, 1998


```python
from spreg import ML_Error
error = ML_Error(y=y, x=X, w=w, 
          name_y=y_name, name_x = X_names, name_w="Q row std", name_ds='columbus')
print(error.summary)
```

    REGRESSION
    ----------
    SUMMARY OF OUTPUT: MAXIMUM LIKELIHOOD SPATIAL ERROR (METHOD = FULL)
    -------------------------------------------------------------------
    Data set            :    columbus
    Weights matrix      :   Q row std
    Dependent Variable  :       CRIME                Number of Observations:          49
    Mean dependent var  :     35.1288                Number of Variables   :           3
    S.D. dependent var  :     16.7321                Degrees of Freedom    :          46
    Pseudo R-squared    :      0.5362
    Sigma-square ML     :      97.674                Log likelihood        :    -183.749
    S.E of regression   :       9.883                Akaike info criterion :     373.499
                                                     Schwarz criterion     :     379.174
    
    ------------------------------------------------------------------------------------
                Variable     Coefficient       Std.Error     z-Statistic     Probability
    ------------------------------------------------------------------------------------
                CONSTANT      60.2794691       5.3655939      11.2344448       0.0000000
                     INC      -0.9573053       0.3342308      -2.8642047       0.0041806
                   HOVAL      -0.3045593       0.0920473      -3.3087251       0.0009372
                  lambda       0.5467531       0.1380508       3.9605217       0.0000748
    ------------------------------------------------------------------------------------
    ================================ END OF REPORT =====================================


    /usr/local/lib/python3.7/dist-packages/scipy/optimize/_minimize.py:770: RuntimeWarning: Method 'bounded' does not support relative tolerance in x; defaulting to absolute tolerance.
      "defaulting to absolute tolerance.", RuntimeWarning)


Reference: Kelejian and Prucha (1998, 1999) 


```python
from spreg import GM_Error
error = GM_Error(y=y, x=X, w=w, 
          name_y=y_name, name_x = X_names, name_w="Q row std", name_ds='columbus')
print(error.summary)
```

    REGRESSION
    ----------
    SUMMARY OF OUTPUT: SPATIALLY WEIGHTED LEAST SQUARES
    ---------------------------------------------------
    Data set            :    columbus
    Weights matrix      :   Q row std
    Dependent Variable  :       CRIME                Number of Observations:          49
    Mean dependent var  :     35.1288                Number of Variables   :           3
    S.D. dependent var  :     16.7321                Degrees of Freedom    :          46
    Pseudo R-squared    :      0.5453
    
    ------------------------------------------------------------------------------------
                Variable     Coefficient       Std.Error     z-Statistic     Probability
    ------------------------------------------------------------------------------------
                CONSTANT      62.9187786       5.0108898      12.5564083       0.0000000
                     INC      -1.1500724       0.3347166      -3.4359582       0.0005905
                   HOVAL      -0.2982308       0.0948124      -3.1454814       0.0016581
                  lambda       0.3834563    
    ------------------------------------------------------------------------------------
    ================================ END OF REPORT =====================================


### Lag + Error

Reference: Kelejian and Prucha (1998, 1999)


```python
from spreg import GM_Combo
combo = GM_Combo(y=y, x=X, w=w, 
          name_y=y_name, name_x = X_names, name_w="Q row std", name_ds='columbus')
print(combo.summary)
```

    REGRESSION
    ----------
    SUMMARY OF OUTPUT: SPATIALLY WEIGHTED TWO STAGE LEAST SQUARES
    -------------------------------------------------------------
    Data set            :    columbus
    Weights matrix      :   Q row std
    Dependent Variable  :       CRIME                Number of Observations:          49
    Mean dependent var  :     35.1288                Number of Variables   :           4
    S.D. dependent var  :     16.7321                Degrees of Freedom    :          45
    Pseudo R-squared    :      0.6494
    Spatial Pseudo R-squared:  0.5787
    
    ------------------------------------------------------------------------------------
                Variable     Coefficient       Std.Error     z-Statistic     Probability
    ------------------------------------------------------------------------------------
                CONSTANT      43.9717784      10.7831309       4.0778303       0.0000455
                     INC      -1.0121280       0.3729276      -2.7140070       0.0066475
                   HOVAL      -0.2651583       0.0885408      -2.9947599       0.0027466
                 W_CRIME       0.4535726       0.1832568       2.4750651       0.0133212
                  lambda      -0.0069612    
    ------------------------------------------------------------------------------------
    Instrumented: W_CRIME
    Instruments: W_HOVAL, W_INC
    ================================ END OF REPORT =====================================



```python
combo
```

ipynb
e64706b4626a45a935f04c23dd34601d12f2c6a5
Jupyter Notebook
2021-05-22T16:22:23.000Z
2021-05-22T16:22:23.000Z
5dc72646654deffee0bfea59da9f5983f8c46033
Apache-2.0
vincnardelli/seai2021
lab3/seai_lab3.ipynb
5dc72646654deffee0bfea59da9f5983f8c46033
Apache-2.0
vincnardelli/seai2021
lab3/seai_lab3.ipynb
5dc72646654deffee0bfea59da9f5983f8c46033
Apache-2.0
vincnardelli/seai2021
lab3/seai_lab3.ipynb
2021-11-12T16:25:27.000Z
2021-05-24T21:38:12.000Z