notebook.community

Edit and run



In [1]:

    
import pysal.contrib.clusterpy as cp
%pylab inline









    



ClusterPy: Library of spatially constrained clustering algorithms
Populating the interactive namespace from numpy and matplotlib



In [2]:

    
import numpy as np
import pysal as ps
from collections import Counter



In [3]:

    
columbus = cp.loadArcData(ps.examples.get_path('columbus.shp'))
columbus.fieldNames
n = len(columbus.Wqueen)
#columbus.generateData('Uniform', 'rook', 1, 1, 10)
columbus.dataOperation("CONSTANT = 1")
columbus.cluster('maxpTabu', ['CRIME',  'CONSTANT'], threshold=4, dissolve=0, std=0)









    



Adding CONSTANT to fieldNames
Adding values from 1 to Y
Adding variables
Done
Getting variables
Variables successfully extracted
Running max-p-regions model (Duque, Anselin and Rey, 2010)
Local search method: Tabu Search
Number of areas:  49
threshold value:  4
FINAL SOLUTION:  [6, 6, 6, 6, 7, 1, 6, 6, 1, 1, 3, 3, 3, 3, 7, 3, 1, 8, 8, 5, 8, 1, 5, 8, 7, 7, 0, 0, 0, 0, 4, 5, 9, 4, 9, 4, 2, 0, 4, 5, 5, 4, 2, 9, 2, 4, 5, 2, 9]
FINAL OF:  2615.44707346
Done
Adding variables
Done



In [4]:

    
Counter(columbus.region2areas)









    Out[4]:





Counter({4: 6, 5: 6, 6: 6, 0: 5, 1: 5, 3: 5, 2: 4, 7: 4, 8: 4, 9: 4})



In [5]:

    
columbus.cluster('arisel', ['CRIME'], 5, wType='rook', inits=10, dissolve=0)
#calif.cluster('arisel', ['PCR2002'], 9, wType='rook', inits=10, dissolve=1)









    



Getting variables
Variables successfully extracted
Running original Arisel algorithm
Number of areas:  49
Number of regions:  5
initial Solution:  [2, 2, 2, 2, 0, 1, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 1, 4, 0, 1, 4, 1, 1, 4, 0, 0, 0, 0, 0, 0, 4, 1, 3, 4, 3, 4, 3, 0, 4, 1, 1, 4, 3, 3, 3, 4, 1, 3, 3]
initial O.F:  4292.34464226
FINAL SOLUTION:  [2, 2, 2, 2, 0, 1, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 4, 1, 0, 4, 1, 4, 3, 0, 4, 1, 1, 4, 3, 1, 1, 4, 1, 1, 1]
FINAL OF:  3695.95184733
Done
Adding variables
Done



In [6]:

    
# regionalization solutions are added as a list of region ids at the end
columbus.fieldNames









    Out[6]:





['ID',
 'AREA',
 'PERIMETER',
 'COLUMBUS_',
 'COLUMBUS_I',
 'POLYID',
 'NEIG',
 'HOVAL',
 'INC',
 'CRIME',
 'OPEN',
 'PLUMB',
 'DISCBD',
 'X',
 'Y',
 'NSA',
 'NSB',
 'EW',
 'CP',
 'THOUS',
 'NEIGNO',
 'CONSTANT',
 'maxpTabu_20140823211458',
 'arisel_20140823211459']



In [7]:

    
warisel = ps.block_weights(columbus.region2areas)



In [8]:

    
warisel.neighbors









    Out[8]:





{0: [1L, 2L, 3L, 6L, 7L],
 1: [0L, 2L, 3L, 6L, 7L],
 2: [0L, 1L, 3L, 6L, 7L],
 3: [0L, 1L, 2L, 6L, 7L],
 4: [10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 5: [8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 6: [0L, 1L, 2L, 3L, 7L],
 7: [0L, 1L, 2L, 3L, 6L],
 8: [5L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 9: [5L, 8L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 10: [4L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 11: [4L,
  10L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 12: [4L,
  10L,
  11L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 13: [4L,
  10L,
  11L,
  12L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 14: [4L,
  10L,
  11L,
  12L,
  13L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 15: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 16: [5L, 8L, 9L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 17: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 18: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 19: [5L, 8L, 9L, 16L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 20: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 21: [5L, 8L, 9L, 16L, 19L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 22: [5L, 8L, 9L, 16L, 19L, 21L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 23: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 24: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 25: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 26: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  27L,
  28L,
  29L,
  32L,
  37L],
 27: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  28L,
  29L,
  32L,
  37L],
 28: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  29L,
  32L,
  37L],
 29: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  32L,
  37L],
 30: [33L, 35L, 38L, 41L, 45L],
 31: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 32: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  37L],
 33: [30L, 35L, 38L, 41L, 45L],
 34: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 35: [30L, 33L, 38L, 41L, 45L],
 36: [42L],
 37: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L],
 38: [30L, 33L, 35L, 41L, 45L],
 39: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 40L, 43L, 44L, 46L, 47L, 48L],
 40: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 43L, 44L, 46L, 47L, 48L],
 41: [30L, 33L, 35L, 38L, 45L],
 42: [36L],
 43: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 44L, 46L, 47L, 48L],
 44: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 46L, 47L, 48L],
 45: [30L, 33L, 35L, 38L, 41L],
 46: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 47L, 48L],
 47: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 48L],
 48: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L]}



In [9]:

    
wregimes = ps.block_weights(columbus.region2areas)



In [10]:

    
columbus.region2areas[5]









    Out[10]:





1



In [11]:

    
wregimes.n









    Out[11]:





49



In [12]:

    
wregimes.neighbors









    Out[12]:





{0: [1L, 2L, 3L, 6L, 7L],
 1: [0L, 2L, 3L, 6L, 7L],
 2: [0L, 1L, 3L, 6L, 7L],
 3: [0L, 1L, 2L, 6L, 7L],
 4: [10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 5: [8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 6: [0L, 1L, 2L, 3L, 7L],
 7: [0L, 1L, 2L, 3L, 6L],
 8: [5L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 9: [5L, 8L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 10: [4L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 11: [4L,
  10L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 12: [4L,
  10L,
  11L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 13: [4L,
  10L,
  11L,
  12L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 14: [4L,
  10L,
  11L,
  12L,
  13L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 15: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 16: [5L, 8L, 9L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 17: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 18: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 19: [5L, 8L, 9L, 16L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 20: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 21: [5L, 8L, 9L, 16L, 19L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 22: [5L, 8L, 9L, 16L, 19L, 21L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 23: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 24: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 25: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  26L,
  27L,
  28L,
  29L,
  32L,
  37L],
 26: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  27L,
  28L,
  29L,
  32L,
  37L],
 27: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  28L,
  29L,
  32L,
  37L],
 28: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  29L,
  32L,
  37L],
 29: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  32L,
  37L],
 30: [33L, 35L, 38L, 41L, 45L],
 31: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 34L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 32: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  37L],
 33: [30L, 35L, 38L, 41L, 45L],
 34: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 39L, 40L, 43L, 44L, 46L, 47L, 48L],
 35: [30L, 33L, 38L, 41L, 45L],
 36: [42L],
 37: [4L,
  10L,
  11L,
  12L,
  13L,
  14L,
  15L,
  17L,
  18L,
  20L,
  23L,
  24L,
  25L,
  26L,
  27L,
  28L,
  29L,
  32L],
 38: [30L, 33L, 35L, 41L, 45L],
 39: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 40L, 43L, 44L, 46L, 47L, 48L],
 40: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 43L, 44L, 46L, 47L, 48L],
 41: [30L, 33L, 35L, 38L, 45L],
 42: [36L],
 43: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 44L, 46L, 47L, 48L],
 44: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 46L, 47L, 48L],
 45: [30L, 33L, 35L, 38L, 41L],
 46: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 47L, 48L],
 47: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 48L],
 48: [5L, 8L, 9L, 16L, 19L, 21L, 22L, 31L, 34L, 39L, 40L, 43L, 44L, 46L, 47L]}

Attrribute data from a csv file and a W from a gal file



In [13]:

    
mexico = cp.importCsvData(ps.examples.get_path('mexico.csv'))



In [14]:

    
mexico.fieldNames









    Out[14]:





['ID',
 'State',
 'pcgdp1940',
 'pcgdp1950',
 'pcgdp1960',
 'pcgdp1970',
 'pcgdp1980',
 'pcgdp1990',
 'pcgdp2000',
 'hanson03',
 'hanson98',
 'esquivel99',
 'inegi',
 'inegi2']



In [15]:

    
w = ps.open(ps.examples.get_path('mexico.gal')).read()



In [16]:

    
w.n









    Out[16]:





32



In [17]:

    
cp.addRook2Layer(ps.examples.get_path('mexico.gal'), mexico)



In [18]:

    
mexico.Wrook









    Out[18]:





{0: [31, 13],
 1: [2, 25],
 2: [1],
 3: [30, 22, 26],
 4: [19, 26, 29],
 5: [6, 9, 24, 25],
 6: [18, 23, 31, 9, 5],
 7: [13, 15],
 8: [16, 14],
 9: [5, 6, 31, 13, 17, 24],
 10: [23, 21, 31, 15, 13],
 11: [15, 14, 16, 20, 19],
 12: [21, 23, 29, 20, 28, 14],
 13: [17, 31, 0, 23, 10, 15, 7, 9],
 14: [21, 12, 28, 20, 16, 11, 15, 8],
 15: [7, 13, 10, 21, 14, 11],
 16: [14, 8, 20, 11],
 17: [24, 9, 31, 13],
 18: [6, 27, 23, 31],
 19: [11, 20, 29, 4],
 20: [29, 19, 11, 16, 14, 28, 12],
 21: [23, 12, 14, 15, 10],
 22: [30, 3],
 23: [18, 27, 29, 12, 21, 10, 31, 6, 13],
 24: [25, 5, 9, 17],
 25: [1, 5, 24],
 26: [3, 4, 29],
 27: [18, 29, 23],
 28: [12, 20, 14],
 29: [26, 4, 19, 20, 12, 23, 27],
 30: [3, 22],
 31: [18, 23, 10, 0, 13, 17, 9, 6]}



In [19]:

    
mexico.cluster('arisel', ['pcgdp1940'], 5, wType='rook', inits=10, dissolve=0)









    



Getting variables
Variables successfully extracted
Running original Arisel algorithm
Number of areas:  32
Number of regions:  5
initial Solution:  [2, 3, 3, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 0, 2]
initial O.F:  287200188.741
FINAL SOLUTION:  [2, 3, 3, 0, 0, 2, 2, 2, 4, 2, 0, 0, 2, 2, 0, 0, 0, 0, 2, 0, 0, 2, 1, 0, 0, 2, 0, 2, 0, 2, 0, 0]
FINAL OF:  186036319.667
Done
Adding variables
Done



In [20]:

    
mexico.fieldNames









    Out[20]:





['ID',
 'State',
 'pcgdp1940',
 'pcgdp1950',
 'pcgdp1960',
 'pcgdp1970',
 'pcgdp1980',
 'pcgdp1990',
 'pcgdp2000',
 'hanson03',
 'hanson98',
 'esquivel99',
 'inegi',
 'inegi2',
 'arisel_20140823211523']



In [21]:

    
mexico.getVars('pcgdp1940')









    



Getting variables
Variables successfully extracted






    Out[21]:





{0: [10384.0],
 1: [22361.0],
 2: [9573.0],
 3: [3758.0],
 4: [2934.0],
 5: [8578.0],
 6: [8537.0],
 7: [6909.0],
 8: [17816.0],
 9: [12132.0],
 10: [4359.0],
 11: [2181.0],
 12: [4414.0],
 13: [5309.0],
 14: [3408.0],
 15: [3327.0],
 16: [6936.0],
 17: [4836.0],
 18: [9073.0],
 19: [1892.0],
 20: [3569.0],
 21: [11016.0],
 22: [21965.0],
 23: [4372.0],
 24: [4840.0],
 25: [6399.0],
 26: [2459.0],
 27: [7508.0],
 28: [3605.0],
 29: [5203.0],
 30: [7990.0],
 31: [3734.0]}



In [22]:

    
# mexico example all together

csvfile = ps.examples.get_path('mexico.csv')
galfile = ps.examples.get_path('mexico.gal')

mexico = cp.importCsvData(csvfile)
cp.addRook2Layer(galfile, mexico)
mexico.cluster('arisel', ['pcgdp1940'], 5, wType='rook', inits=10, dissolve=0)









    



Getting variables
Variables successfully extracted
Running original Arisel algorithm
Number of areas:  32
Number of regions:  5
initial Solution:  [2, 0, 0, 1, 1, 2, 2, 2, 4, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 2, 3, 2, 2, 2, 1, 2, 1, 2, 1, 2]
initial O.F:  228683641.198
FINAL SOLUTION:  [2, 0, 0, 1, 1, 2, 2, 2, 4, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 3, 1, 1, 2, 1, 2, 1, 2, 1, 1]
FINAL OF:  186036319.667
Done
Adding variables
Done



In [23]:

    
mexico.region2areas.index(2)









    Out[23]:





0



In [24]:

    
mexico.Wrook[0]









    Out[24]:





[31, 13]



In [25]:

    
mexico.getVars('State')









    



Getting variables
Variables successfully extracted






    Out[25]:





{0: ['Aguascalientes'],
 1: ['Baja California'],
 2: ['Baja California Sur'],
 3: ['Campeche'],
 4: ['Chiapas'],
 5: ['Chihuahua'],
 6: ['Coahuila'],
 7: ['Colima'],
 8: ['Distrito Federal'],
 9: ['Durango'],
 10: ['Guanajuato'],
 11: ['Guerrero'],
 12: ['Hidalgo'],
 13: ['Jalisco'],
 14: ['Mexico'],
 15: ['Michoacan'],
 16: ['Morelos'],
 17: ['Nayarit'],
 18: ['Nuevo Leon'],
 19: ['Oaxaca'],
 20: ['Puebla'],
 21: ['Quertaro'],
 22: ['Quintana Roo'],
 23: ['San Luis Potosi'],
 24: ['Sinaloa'],
 25: ['Sonora'],
 26: ['Tabasco'],
 27: ['Tamaulipas'],
 28: ['Tlaxcala'],
 29: ['Veracruz'],
 30: ['Yucatan'],
 31: ['Zacatecas']}



In [26]:

    
regions = np.array(mexico.region2areas)



In [27]:

    
regions









    Out[27]:





array([2, 0, 0, 1, 1, 2, 2, 2, 4, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 3,
       1, 1, 2, 1, 2, 1, 2, 1, 1])



In [28]:

    
Counter(regions)









    Out[28]:





Counter({1: 16, 2: 12, 0: 2, 3: 1, 4: 1})

Shapefile and mapping results with PySAL Viz



In [29]:

    
usf = ps.examples.get_path('us48.shp')



In [30]:

    
us = cp.loadArcData(usf.split(".")[0])



In [31]:

    
us.Wqueen









    Out[31]:





{0: [10, 7],
 1: [3, 4, 5, 7],
 2: [11],
 3: [1, 4, 9],
 4: [1, 3, 5, 9, 12, 14],
 5: [1, 4, 7, 14, 22, 29],
 6: [9, 12, 25, 47],
 7: [0, 1, 5, 10, 21, 22],
 8: [11, 13, 15],
 9: [12, 3, 4, 6],
 10: [0, 23, 21, 7],
 11: [8, 2, 13],
 12: [33, 4, 6, 9, 14, 25],
 13: [8, 17, 18, 11, 15],
 14: [33, 4, 5, 12, 29, 31],
 15: [8, 16, 19, 13, 17],
 16: [15, 19, 24, 26, 27, 28],
 17: [18, 13, 15],
 18: [17, 13],
 19: [16, 26, 15],
 20: [24, 25, 30, 47],
 21: [10, 23, 34, 22, 7],
 22: [34, 5, 39, 7, 21, 29],
 23: [10, 34, 21],
 24: [16, 27, 20, 30, 47],
 25: [20, 12, 30, 6, 33],
 26: [16, 19, 28],
 27: [16, 24, 28, 30, 32],
 28: [16, 32, 26, 27],
 29: [34, 35, 5, 39, 14, 22, 31],
 30: [32, 33, 37, 20, 24, 25, 27],
 31: [33, 35, 29, 14],
 32: [36, 27, 28, 37, 30],
 33: [35, 37, 44, 12, 14, 25, 30, 31],
 34: [39, 29, 21, 22, 23],
 35: [33, 38, 39, 44, 29, 31],
 36: [32, 42, 43, 37],
 37: [32, 33, 36, 40, 41, 42, 44, 30],
 38: [35, 44, 45, 39],
 39: [38, 34, 35, 29, 22],
 40: [41, 42, 37, 46],
 41: [40, 44, 37, 45],
 42: [40, 43, 36, 37, 46],
 43: [42, 36],
 44: [33, 35, 37, 38, 41, 45],
 45: [41, 44, 38],
 46: [40, 42],
 47: [24, 20, 6]}



In [32]:

    
us.fieldNames









    Out[32]:





['ID',
 'AREA',
 'PERIMETER',
 'STATE_',
 'STATE_ID',
 'STATE_NAME',
 'STATE_FIPS',
 'SUB_REGION',
 'STATE_ABBR']



In [33]:

    
uscsv = ps.examples.get_path("usjoin.csv")



In [34]:

    
f = ps.open(uscsv)
pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)]).T



In [35]:

    
pci









    Out[35]:





array([[  323,   267,   224, ..., 31988, 32819, 32274],
       [  600,   520,   429, ..., 33470, 33445, 32077],
       [  310,   228,   215, ..., 31070, 31800, 31493],
       ..., 
       [  460,   408,   356, ..., 29769, 31265, 31843],
       [  673,   588,   469, ..., 35839, 36594, 35676],
       [  675,   585,   476, ..., 43453, 45177, 42504]])



In [36]:

    
usy = cp.Layer()



In [37]:

    
cp.addQueen2Layer(ps.examples.get_path('states48.gal'), usy)



In [38]:

    
names = ["Y_%d"%v for v in range(1929,2010)]
cp.addArray2Layer(pci, usy, names)









    



Adding variables
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In [39]:

    
names









    Out[39]:





['Y_1929',
 'Y_1930',
 'Y_1931',
 'Y_1932',
 'Y_1933',
 'Y_1934',
 'Y_1935',
 'Y_1936',
 'Y_1937',
 'Y_1938',
 'Y_1939',
 'Y_1940',
 'Y_1941',
 'Y_1942',
 'Y_1943',
 'Y_1944',
 'Y_1945',
 'Y_1946',
 'Y_1947',
 'Y_1948',
 'Y_1949',
 'Y_1950',
 'Y_1951',
 'Y_1952',
 'Y_1953',
 'Y_1954',
 'Y_1955',
 'Y_1956',
 'Y_1957',
 'Y_1958',
 'Y_1959',
 'Y_1960',
 'Y_1961',
 'Y_1962',
 'Y_1963',
 'Y_1964',
 'Y_1965',
 'Y_1966',
 'Y_1967',
 'Y_1968',
 'Y_1969',
 'Y_1970',
 'Y_1971',
 'Y_1972',
 'Y_1973',
 'Y_1974',
 'Y_1975',
 'Y_1976',
 'Y_1977',
 'Y_1978',
 'Y_1979',
 'Y_1980',
 'Y_1981',
 'Y_1982',
 'Y_1983',
 'Y_1984',
 'Y_1985',
 'Y_1986',
 'Y_1987',
 'Y_1988',
 'Y_1989',
 'Y_1990',
 'Y_1991',
 'Y_1992',
 'Y_1993',
 'Y_1994',
 'Y_1995',
 'Y_1996',
 'Y_1997',
 'Y_1998',
 'Y_1999',
 'Y_2000',
 'Y_2001',
 'Y_2002',
 'Y_2003',
 'Y_2004',
 'Y_2005',
 'Y_2006',
 'Y_2007',
 'Y_2008',
 'Y_2009']



In [40]:

    
usy.fieldNames









    Out[40]:





['Y_1929',
 'Y_1930',
 'Y_1931',
 'Y_1932',
 'Y_1933',
 'Y_1934',
 'Y_1935',
 'Y_1936',
 'Y_1937',
 'Y_1938',
 'Y_1939',
 'Y_1940',
 'Y_1941',
 'Y_1942',
 'Y_1943',
 'Y_1944',
 'Y_1945',
 'Y_1946',
 'Y_1947',
 'Y_1948',
 'Y_1949',
 'Y_1950',
 'Y_1951',
 'Y_1952',
 'Y_1953',
 'Y_1954',
 'Y_1955',
 'Y_1956',
 'Y_1957',
 'Y_1958',
 'Y_1959',
 'Y_1960',
 'Y_1961',
 'Y_1962',
 'Y_1963',
 'Y_1964',
 'Y_1965',
 'Y_1966',
 'Y_1967',
 'Y_1968',
 'Y_1969',
 'Y_1970',
 'Y_1971',
 'Y_1972',
 'Y_1973',
 'Y_1974',
 'Y_1975',
 'Y_1976',
 'Y_1977',
 'Y_1978',
 'Y_1979',
 'Y_1980',
 'Y_1981',
 'Y_1982',
 'Y_1983',
 'Y_1984',
 'Y_1985',
 'Y_1986',
 'Y_1987',
 'Y_1988',
 'Y_1989',
 'Y_1990',
 'Y_1991',
 'Y_1992',
 'Y_1993',
 'Y_1994',
 'Y_1995',
 'Y_1996',
 'Y_1997',
 'Y_1998',
 'Y_1999',
 'Y_2000',
 'Y_2001',
 'Y_2002',
 'Y_2003',
 'Y_2004',
 'Y_2005',
 'Y_2006',
 'Y_2007',
 'Y_2008',
 'Y_2009']



In [41]:

    
usy.getVars('Y_1929')









    



Getting variables
Variables successfully extracted






    Out[41]:





{0: [323],
 1: [600],
 2: [310],
 3: [991],
 4: [634],
 5: [1024],
 6: [1032],
 7: [518],
 8: [347],
 9: [507],
 10: [948],
 11: [607],
 12: [581],
 13: [532],
 14: [393],
 15: [414],
 16: [601],
 17: [768],
 18: [906],
 19: [790],
 20: [599],
 21: [286],
 22: [621],
 23: [592],
 24: [596],
 25: [868],
 26: [686],
 27: [918],
 28: [410],
 29: [1152],
 30: [332],
 31: [382],
 32: [771],
 33: [455],
 34: [668],
 35: [772],
 36: [874],
 37: [271],
 38: [426],
 39: [378],
 40: [479],
 41: [551],
 42: [634],
 43: [434],
 44: [741],
 45: [460],
 46: [673],
 47: [675]}



In [42]:

    
usy.Wrook









    Out[42]:





{}



In [43]:

    
usy.cluster('arisel', ['Y_1980'], 8, wType='queen', inits=10, dissolve=0)
#mexico.cluster('arisel', ['pcgdp1940'], 5, wType='rook', inits=10, dissolve=0)









    



Getting variables
Variables successfully extracted
Running original Arisel algorithm
Number of areas:  48
Number of regions:  8
initial Solution:  [4, 0, 4, 2, 0, 6, 0, 4, 4, 1, 0, 0, 0, 0, 4, 4, 5, 0, 7, 0, 0, 4, 0, 1, 0, 2, 5, 7, 1, 7, 4, 1, 0, 0, 1, 0, 3, 4, 1, 4, 0, 1, 5, 0, 1, 4, 0, 0]
initial O.F:  23338316.0
FINAL SOLUTION:  [4, 1, 4, 2, 0, 6, 7, 4, 4, 1, 0, 0, 1, 0, 4, 1, 5, 0, 7, 0, 0, 4, 0, 1, 1, 2, 5, 7, 1, 7, 4, 1, 0, 1, 1, 0, 3, 4, 1, 4, 1, 1, 5, 0, 1, 4, 0, 0]
FINAL OF:  21804253.0
Done
Adding variables
Done



In [43]:



In [44]:

    
us = cp.Layer()



In [45]:

    
cp.addQueen2Layer(ps.examples.get_path('states48.gal'), us)



In [46]:

    
uscsv = ps.examples.get_path("usjoin.csv")



In [47]:

    
f = ps.open(uscsv)
pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)]).T
names = ["Y_%d"%v for v in range(1929,2010)]
cp.addArray2Layer(pci, us, names)









    



Adding variables
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In [48]:

    
usy.cluster('arisel', ['Y_1980'], 8, wType='queen', inits=10, dissolve=0)









    



Getting variables
Variables successfully extracted
Running original Arisel algorithm
Number of areas:  48
Number of regions:  8
initial Solution:  [0, 6, 0, 2, 6, 5, 6, 0, 0, 7, 6, 6, 6, 6, 0, 0, 4, 6, 4, 6, 6, 0, 6, 7, 6, 2, 4, 1, 7, 4, 0, 7, 6, 6, 2, 6, 4, 0, 7, 0, 6, 7, 3, 6, 2, 0, 6, 6]
initial O.F:  21166215.0
FINAL SOLUTION:  [0, 6, 0, 2, 6, 5, 1, 0, 0, 7, 6, 6, 6, 6, 0, 0, 4, 6, 4, 6, 6, 0, 6, 7, 7, 2, 4, 1, 7, 1, 0, 7, 6, 6, 2, 6, 4, 0, 7, 0, 6, 7, 3, 6, 2, 0, 6, 6]
FINAL OF:  19355956.0
Done
Adding variables
Done



In [49]:

    
us_alpha = cp.importCsvData(ps.examples.get_path('usjoin.csv'))



In [50]:

    
alpha_fips = us_alpha.getVars('STATE_FIPS')









    



Getting variables
Variables successfully extracted



In [51]:

    
alpha_fips









    Out[51]:





{0: [1],
 1: [4],
 2: [5],
 3: [6],
 4: [8],
 5: [9],
 6: [10],
 7: [12],
 8: [13],
 9: [16],
 10: [17],
 11: [18],
 12: [19],
 13: [20],
 14: [21],
 15: [22],
 16: [23],
 17: [24],
 18: [25],
 19: [26],
 20: [27],
 21: [28],
 22: [29],
 23: [30],
 24: [31],
 25: [32],
 26: [33],
 27: [34],
 28: [35],
 29: [36],
 30: [37],
 31: [38],
 32: [39],
 33: [40],
 34: [41],
 35: [42],
 36: [44],
 37: [45],
 38: [46],
 39: [47],
 40: [48],
 41: [49],
 42: [50],
 43: [51],
 44: [53],
 45: [54],
 46: [55],
 47: [56]}



In [52]:

    
dbf = ps.open(ps.examples.get_path('us48.dbf'))



In [53]:

    
dbf.header









    Out[53]:





['AREA',
 'PERIMETER',
 'STATE_',
 'STATE_ID',
 'STATE_NAME',
 'STATE_FIPS',
 'SUB_REGION',
 'STATE_ABBR']



In [54]:

    
state_fips = dbf.by_col('STATE_FIPS')
names = dbf.by_col('STATE_NAME')



In [55]:

    
names









    Out[55]:





['Washington',
 'Montana',
 'Maine',
 'North Dakota',
 'South Dakota',
 'Wyoming',
 'Wisconsin',
 'Idaho',
 'Vermont',
 'Minnesota',
 'Oregon',
 'New Hampshire',
 'Iowa',
 'Massachusetts',
 'Nebraska',
 'New York',
 'Pennsylvania',
 'Connecticut',
 'Rhode Island',
 'New Jersey',
 'Indiana',
 'Nevada',
 'Utah',
 'California',
 'Ohio',
 'Illinois',
 'Delaware',
 'West Virginia',
 'Maryland',
 'Colorado',
 'Kentucky',
 'Kansas',
 'Virginia',
 'Missouri',
 'Arizona',
 'Oklahoma',
 'North Carolina',
 'Tennessee',
 'Texas',
 'New Mexico',
 'Alabama',
 'Mississippi',
 'Georgia',
 'South Carolina',
 'Arkansas',
 'Louisiana',
 'Florida',
 'Michigan']



In [56]:

    
state_fips = map(int, state_fips)



In [57]:

    
state_fips









    Out[57]:





[53,
 30,
 23,
 38,
 46,
 56,
 55,
 16,
 50,
 27,
 41,
 33,
 19,
 25,
 31,
 36,
 42,
 9,
 44,
 34,
 18,
 32,
 49,
 6,
 39,
 17,
 10,
 54,
 24,
 8,
 21,
 20,
 51,
 29,
 4,
 40,
 37,
 47,
 48,
 35,
 1,
 28,
 13,
 45,
 5,
 22,
 12,
 26]



In [58]:

    
# the csv file has the states ordered alphabetically, but this isn't the case for the order in the shapefile so we have to reorder before any choropleths are drawn
alpha_fips = [i[0] for i in alpha_fips.values()]
reorder = [ alpha_fips.index(s) for s in state_fips]



In [59]:

    
regions = usy.region2areas



In [60]:

    
regions









    Out[60]:





[0,
 6,
 0,
 2,
 6,
 5,
 1,
 0,
 0,
 7,
 6,
 6,
 6,
 6,
 0,
 0,
 4,
 6,
 4,
 6,
 6,
 0,
 6,
 7,
 7,
 2,
 4,
 1,
 7,
 1,
 0,
 7,
 6,
 6,
 2,
 6,
 4,
 0,
 7,
 0,
 6,
 7,
 3,
 6,
 2,
 0,
 6,
 6]



In [61]:

    
from pysal.contrib.viz import mapping as maps



In [62]:

    
shp = ps.examples.get_path('us48.shp')
regions = np.array(regions)



In [63]:

    
maps.plot_choropleth(shp, regions[reorder], 'unique_values')



In [64]:

    
usy.cluster('arisel', ['Y_1929'], 8, wType='queen', inits=10, dissolve=0)









    



Getting variables
Variables successfully extracted
Running original Arisel algorithm
Number of areas:  48
Number of regions:  8
initial Solution:  [0, 4, 0, 1, 4, 6, 6, 0, 0, 4, 5, 5, 4, 4, 2, 0, 3, 5, 6, 5, 4, 0, 4, 7, 4, 1, 3, 6, 0, 6, 0, 4, 5, 0, 4, 5, 6, 0, 4, 2, 0, 4, 3, 2, 4, 2, 4, 7]
initial O.F:  320792.0
FINAL SOLUTION:  [0, 4, 0, 1, 7, 6, 6, 0, 0, 4, 5, 5, 4, 4, 0, 0, 3, 5, 6, 5, 4, 0, 4, 4, 4, 1, 3, 6, 0, 6, 0, 4, 5, 0, 4, 5, 6, 0, 4, 0, 0, 4, 3, 2, 4, 2, 5, 7]
FINAL OF:  311057.0
Done
Adding variables
Done



In [65]:

    
regions = usy.region2areas



In [66]:

    
regions = np.array(regions)



In [67]:

    
maps.plot_choropleth(shp, regions[reorder], 'unique_values')



In [ ]:

    
names = ["Y_%d"%i for i in range(1929, 2010)]
#usy.cluster('arisel', ['Y_1929'], 8, wType='queen', inits=10, dissolve=0)
usy.cluster('arisel', names, 8, wType='queen', inits=10, dissolve=0)



In [ ]:

    
regions = usy.region2areas
regions = np.array(regions)
maps.plot_choropleth(shp, regions[reorder], 'unique_values', title='All Years')



In [ ]:

    
ps.version



In [ ]:

    
usy.cluster('arisel', names[:40], 8, wType='queen', inits=10, dissolve=0)
regions = usy.region2areas
regions = np.array(regions)
maps.plot_choropleth(shp, regions[reorder], 'unique_values', title='1929-68')



In [ ]:

    
usy.cluster('arisel', names[40:], 8, wType='queen', inits=10, dissolve=0)
regions = usy.region2areas
regions = np.array(regions)
maps.plot_choropleth(shp, regions[reorder], 'unique_values', title='1969-2009')



In [ ]:

    
usy.cluster('arisel', names[40:], 8, wType='queen', inits=10, dissolve=0)



In [ ]:

    
usy.dataOperation("CONSTANT = 1")
usy.Wrook = usy.Wqueen
usy.cluster('maxpTabu', ['Y_1929', 'Y_1929'], threshold=1000, dissolve=0)
regions = usy.region2areas
regions = np.array(regions)
maps.plot_choropleth(shp, regions[reorder], 'unique_values', title='maxp 1929')



In [ ]:

    
Counter(regions)



In [ ]:

    
usy.getVars('Y_1929')



In [ ]:

    
usy.Wrook



In [ ]:

    
usy.cluster('maxpTabu', ['Y_1929', 'CONSTANT'], threshold=8, dissolve=0)
regions = usy.region2areas
regions = np.array(regions)
maps.plot_choropleth(shp, regions[reorder], 'unique_values', title='maxp 1929')



In [ ]:

    
regions



In [ ]:

    
Counter(regions)



In [ ]:

    
vars = names



In [ ]:

    
vars.append('CONSTANT')



In [ ]:

    
vars



In [ ]:

    
usy.cluster('maxpTabu', vars, threshold=8, dissolve=0)
regions = usy.region2areas
regions = np.array(regions)
maps.plot_choropleth(shp, regions[reorder], 'unique_values', title='maxp 1929-2009')



In [ ]:

    
Counter(regions)



In [ ]:

    
south = cp.loadArcData(ps.examples.get_path("south.shp"))



In [ ]:

    
south.fieldNames



In [ ]:

    
# uncomment if you have some time ;->
#south.cluster('arisel', ['HR70'], 20, wType='queen', inits=10, dissolve=0)



In [ ]:

    
#regions = south.region2areas



In [ ]:

    
shp = ps.examples.get_path('south.shp')
#maps.plot_choropleth(shp, np.array(regions), 'unique_values')



In [ ]:

    
south.dataOperation("CONSTANT = 1")
south.cluster('maxpTabu', ['HR70', 'CONSTANT'], threshold=70, dissolve=0)
regions = south.region2areas
regions = np.array(regions)
maps.plot_choropleth(shp, regions, 'unique_values', title='maxp HR70 threshold=70')



In [ ]:

    
Counter(regions)



In [ ]: