In [62]:

    

import sys
import os
import matplotlib.pyplot as plt
%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [63]:

    

sys.path.append(os.path.abspath('..'))
from pysal.explore import inequality
import pysal.lib


    

In [64]:

    

pysal.lib.examples.available()


    

    
Out[64]:





['georgia',
 '__pycache__',
 'tests',
 'newHaven',
 'Polygon_Holes',
 'nat',
 'Polygon',
 '10740',
 'berlin',
 'rio_grande_do_sul',
 'sids2',
 'sacramento2',
 'burkitt',
 'arcgis',
 'calemp',
 'stl',
 'virginia',
 'geodanet',
 'desmith',
 'book',
 'nyc_bikes',
 'Line',
 'south',
 'snow_maps',
 'Point',
 'street_net_pts',
 'guerry',
 '__pycache__',
 'baltim',
 'networks',
 'us_income',
 'taz',
 'columbus',
 'tokyo',
 'mexico',
 '__pycache__',
 'chicago',
 'wmat',
 'juvenile',
 'clearwater']

In [65]:

    

pysal.lib.examples.explain('mexico')


    

    
Out[65]:





{'name': 'mexico',
 'description': 'Decennial per capita incomes of Mexican states 1940-2000',
 'explanation': ['* mexico.csv: attribute data. (n=32, k=13)',
  '* mexico.gal: spatial weights in GAL format.',
  '* mexicojoin.shp: Polygon shapefile. (n=32)',
  'Data used in Rey, S.J. and M.L. Sastre Gutierrez. (2010) "Interregional inequality dynamics in Mexico." Spatial Economic Analysis, 5: 277-298.']}

In [66]:

    

import geopandas
pth = pysal.lib.examples.get_path("mexicojoin.shp")
gdf = geopandas.read_file(pth)

from pysal.lib.weights import Queen, Rook, KNN


    

In [67]:

    

%matplotlib inline
import matplotlib.pyplot as plt


    

In [68]:

    

ax = gdf.plot()
ax.set_axis_off()


    

In [69]:

    

gdf.head()


    

    
Out[69]:







  

    

      

      
POLY_ID
      
AREA
      
CODE
      
NAME
      
PERIMETER
      
ACRES
      
HECTARES
      
PCGDP1940
      
PCGDP1950
      
PCGDP1960
      
...
      
GR9000
      
LPCGDP40
      
LPCGDP50
      
LPCGDP60
      
LPCGDP70
      
LPCGDP80
      
LPCGDP90
      
LPCGDP00
      
TEST
      
geometry
    
  
  

    

      
0
      
1
      
7.252751e+10
      
MX02
      
Baja California Norte
      
2040312.385
      
1.792187e+07
      
7252751.376
      
22361.0
      
20977.0
      
17865.0
      
...
      
0.05
      
4.35
      
4.32
      
4.25
      
4.40
      
4.47
      
4.43
      
4.48
      
1.0
      
(POLYGON ((-113.1397171020508 29.0177764892578...
    
    

      
1
      
2
      
7.225988e+10
      
MX03
      
Baja California Sur
      
2912880.772
      
1.785573e+07
      
7225987.769
      
9573.0
      
16013.0
      
16707.0
      
...
      
0.00
      
3.98
      
4.20
      
4.22
      
4.39
      
4.46
      
4.41
      
4.42
      
2.0
      
(POLYGON ((-111.2061233520508 25.8027763366699...
    
    

      
2
      
3
      
2.731957e+10
      
MX18
      
Nayarit
      
1034770.341
      
6.750785e+06
      
2731956.859
      
4836.0
      
7515.0
      
7621.0
      
...
      
-0.05
      
3.68
      
3.88
      
3.88
      
4.04
      
4.13
      
4.11
      
4.06
      
3.0
      
(POLYGON ((-106.6210784912109 21.5653114318847...
    
    

      
3
      
4
      
7.961008e+10
      
MX14
      
Jalisco
      
2324727.436
      
1.967200e+07
      
7961008.285
      
5309.0
      
8232.0
      
9953.0
      
...
      
0.03
      
3.73
      
3.92
      
4.00
      
4.21
      
4.32
      
4.30
      
4.33
      
4.0
      
POLYGON ((-101.52490234375 21.85663986206055, ...
    
    

      
4
      
5
      
5.467030e+09
      
MX01
      
Aguascalientes
      
313895.530
      
1.350927e+06
      
546702.985
      
10384.0
      
6234.0
      
8714.0
      
...
      
0.13
      
4.02
      
3.79
      
3.94
      
4.21
      
4.32
      
4.32
      
4.44
      
5.0
      
POLYGON ((-101.8461990356445 22.01176071166992...
    
  

5 rows × 35 columns

In [99]:

    

ax = gdf.plot(column='PCGDP1940',k=5,scheme='Quantiles',legend=True)
ax.set_axis_off()
#ax.set_title("PC GDP 1940")
plt.savefig('1940.png')


    

    




/home/serge/anaconda3/envs/pysal.lib/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval






    

In [71]:

    

gini_1940 = inequality.gini.Gini(gdf['PCGDP1940'])


    

In [72]:

    

gini_1940.g


    

    
Out[72]:





0.3537237117345285

In [73]:

    

decades = range(1940, 2010, 10)
decades


    

    
Out[73]:





range(1940, 2010, 10)

In [74]:

    

ginis = [ inequality.gini.Gini(gdf["PCGDP%s"%decade]).g for decade in decades]


    

In [75]:

    

ginis


    

    
Out[75]:





[0.3537237117345285,
 0.29644613439022827,
 0.2537183285655905,
 0.25513356494927303,
 0.24505338049421577,
 0.25181825879538217,
 0.2581130824882791]

In [76]:

    

inequality.gini.Gini_Spatial


    

    
Out[76]:





inequality.gini.Gini_Spatial

In [77]:

    

regimes = gdf['HANSON98']


    

In [78]:

    

w = pysal.lib.weights.block_weights(regimes)


    

    




/home/serge/Dropbox/p/pysal/src/subpackages/pysal.lib/pysal.lib/weights/weights.py:170: UserWarning: The weights matrix is not fully connected. There are 5 components
  warnings.warn("The weights matrix is not fully connected. There are %d components" % self.n_components)

In [98]:

    

ax = gdf.plot(column='HANSON98', categorical=True)
#ax.set_title('Regions')
ax.set_axis_off()
plt.savefig('regions.png')


    

In [80]:

    

import numpy as np
np.random.seed(12345)
gs = inequality.gini.Gini_Spatial(gdf['PCGDP1940'],w)


    

In [81]:

    

gs.p_sim


    

    
Out[81]:





0.01

In [82]:

    

gs_all = [ inequality.gini.Gini_Spatial(gdf["PCGDP%s"%decade], w) for decade in decades]


    

In [83]:

    

p_values = [gs.p_sim for gs in gs_all]


    

In [84]:

    

p_values


    

    
Out[84]:





[0.04, 0.01, 0.01, 0.01, 0.02, 0.01, 0.01]

In [85]:

    

wgs = [gs.wcg_share for gs in gs_all]


    

In [86]:

    

wgs


    

    
Out[86]:





[0.2940179879590452,
 0.24885041274552472,
 0.21715641601961586,
 0.2212882581200239,
 0.20702733316567423,
 0.21270360014540865,
 0.2190953550725723]

In [87]:

    

bgs = [ 1 - wg for wg in wgs]


    

In [88]:

    

bgs


    

    
Out[88]:





[0.7059820120409548,
 0.7511495872544753,
 0.7828435839803841,
 0.778711741879976,
 0.7929726668343258,
 0.7872963998545913,
 0.7809046449274277]

In [89]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [90]:

    

years = np.array(decades)


    

In [91]:

    

years


    

    
Out[91]:





array([1940, 1950, 1960, 1970, 1980, 1990, 2000])

In [97]:

    

fig, ax1 = plt.subplots()
t = years
s1 = ginis
ax1.plot(t, s1, 'b-')
ax1.set_xlabel('Year')
# Make the y-axis label, ticks and tick labels match the line color.
ax1.set_ylabel('Gini', color='b')
ax1.tick_params('y', colors='b')

ax2 = ax1.twinx()
s2 = bgs
ax2.plot(t, s2, 'r-.')
ax2.set_ylabel('Spatial Inequality Share', color='r')
ax2.tick_params('y', colors='r')

fig.tight_layout()

plt.savefig('share.png')


    

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