06_global_south.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "import folium\n",
      "import random\n",
      "import pandas as pd\n",
      "import pysal as ps\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Binding Attribute Data to the Map"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "\n",
      "f = open(r'data/south.json')\n",
      "q = json.load(f)\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "q.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "[u'type', u'features']"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "features = q['features']\n",
      "len(features)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "1412"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "feature_0 = features[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "feature_0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "{u'geometry': {u'coordinates': [[[-80.6280517578125, 40.39815902709961],\n",
        "    [-80.60203552246094, 40.480472564697266],\n",
        "    [-80.62545776367188, 40.504398345947266],\n",
        "    [-80.6336441040039, 40.53913879394531],\n",
        "    [-80.6688232421875, 40.568214416503906],\n",
        "    [-80.66793060302734, 40.58207321166992],\n",
        "    [-80.63754272460938, 40.61391830444336],\n",
        "    [-80.61175537109375, 40.619998931884766],\n",
        "    [-80.57462310791016, 40.615909576416016],\n",
        "    [-80.52220916748047, 40.63713836669922],\n",
        "    [-80.52456665039062, 40.47871780395508],\n",
        "    [-80.52377319335938, 40.4029655456543],\n",
        "    [-80.6280517578125, 40.39815902709961]]],\n",
        "  u'type': u'Polygon'},\n",
        " u'properties': {u'BLK60': 3.839454752,\n",
        "  u'BLK70': 3.2554278095,\n",
        "  u'BLK80': 2.5607402642,\n",
        "  u'BLK90': 2.5572616581,\n",
        "  u'CNTY_FIPS': u'029',\n",
        "  u'COFIPS': 29.0,\n",
        "  u'DNL60': 6.1681225056,\n",
        "  u'DNL70': 6.1714993547,\n",
        "  u'DNL80': 6.1714631077,\n",
        "  u'DNL90': 6.0508978146,\n",
        "  u'DV60': 2.2779893943,\n",
        "  u'DV70': 2.5591397849,\n",
        "  u'DV80': 5.0619350519,\n",
        "  u'DV90': 7.2636377003,\n",
        "  u'FH60': 9.9812973718,\n",
        "  u'FH70': 7.8,\n",
        "  u'FH80': 9.7857968181,\n",
        "  u'FH90': 12.604551644,\n",
        "  u'FIPS': u'54029',\n",
        "  u'FIPSNO': 54029.0,\n",
        "  u'FP59': 9.6,\n",
        "  u'FP69': 5.9,\n",
        "  u'FP79': 6.5327526442,\n",
        "  u'FP89': 10.17311807,\n",
        "  u'GI59': 0.2236450331,\n",
        "  u'GI69': 0.2953773833,\n",
        "  u'GI79': 0.3322512119,\n",
        "  u'GI89': 0.3639335641,\n",
        "  u'HC60': 0.6666666667,\n",
        "  u'HC70': 1.6666666667,\n",
        "  u'HC80': 2.6666666667,\n",
        "  u'HC90': 0.3333333333,\n",
        "  u'HR60': 1.6828642349,\n",
        "  u'HR70': 4.1929776011,\n",
        "  u'HR80': 6.5977204876,\n",
        "  u'HR90': 0.9460827444,\n",
        "  u'MA60': 28.9,\n",
        "  u'MA70': 30.0,\n",
        "  u'MA80': 31.4,\n",
        "  u'MA90': 37.7,\n",
        "  u'MFIL59': 8.8410143105,\n",
        "  u'MFIL69': 9.2471543451,\n",
        "  u'MFIL79': 10.073356901,\n",
        "  u'MFIL89': 10.327970666,\n",
        "  u'NAME': u'Hancock',\n",
        "  u'PO60': 39615,\n",
        "  u'PO70': 39749,\n",
        "  u'PO80': 40418,\n",
        "  u'PO90': 35233,\n",
        "  u'POL60': 10.586963113,\n",
        "  u'POL70': 10.590339963,\n",
        "  u'POL80': 10.607030509,\n",
        "  u'POL90': 10.469738422,\n",
        "  u'PS60': 1.218684208,\n",
        "  u'PS70': 1.1368342185,\n",
        "  u'PS80': 1.0385705291,\n",
        "  u'PS90': 0.8964534429,\n",
        "  u'RD60': -1.394676863,\n",
        "  u'RD70': -1.307438562,\n",
        "  u'RD80': -1.159302086,\n",
        "  u'RD90': -0.399028376,\n",
        "  u'SOUTH': 1,\n",
        "  u'STATE_FIPS': u'54',\n",
        "  u'STATE_NAME': u'West Virginia',\n",
        "  u'STFIPS': 54.0,\n",
        "  u'UE60': 3.1,\n",
        "  u'UE70': 2.7,\n",
        "  u'UE80': 7.0763827919,\n",
        "  u'UE90': 6.8578070515},\n",
        " u'type': u'Feature'}"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "indices = []\n",
      "values = []\n",
      "for feature in features:\n",
      "    indices.append(str(feature['properties']['FIPS']))\n",
      "    values.append(feature['properties']['HR80'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame({'HR80': values,\n",
      "                   'FIPS': indices} )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "map_osm = folium.Map(location=[33.7550, -84.3900], zoom_start=7,)\n",
      "map_osm.geo_json(geo_path='data/south.json',\n",
      "                 key_on='feature.properties.FIPS',\n",
      "                 data_out='data.json', data=df,\n",
      "                 columns=['FIPS','HR80'],\n",
      "                 fill_color='YlGn', fill_opacity=0.7,\n",
      "                 line_opacity=0.2,\n",
      "                 legend_name='Homicide Rate HR80')\n",
      "map_osm.create_map('homicide_1.html')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = np.array(df.HR80.tolist())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Function that shows the map - call this function to redraw the map.\n",
      "def showmap(name):\n",
      "    sint = str(random.randint(0,10000))\n",
      "    out_file = name+\"_\"+sint+\".html\"\n",
      "    map_osm.create_map(path=out_file)\n",
      "    iframe = \"<iframe src=http://localhost:8015/%s width=1000 height=500></iframe>\"%out_file\n",
      "    print iframe\n",
      "    return HTML(iframe)\n",
      "\n",
      "def resetmap():\n",
      "    return folium.Map(location=[40, -99], zoom_start=4)\n",
      "\n",
      "map_osm = resetmap()\n",
      "showmap('map_0')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<iframe src=http://localhost:8015/map_0_6461.html width=1000 height=500></iframe>\n"
       ]
      },
      {
       "html": [
        "<iframe src=http://localhost:8015/map_0_6461.html width=1000 height=500></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "<IPython.core.display.HTML at 0x10a12cd10>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins= ps.Quantiles(y, 5).bins.tolist()\n",
      "map_osm = folium.Map(location=[33.7550, -84.3900], zoom_start=5,)\n",
      "map_osm.geo_json(geo_path='data/south.json',\n",
      "                 key_on='feature.properties.FIPS',\n",
      "                 data_out='data.json', data=df,\n",
      "                 columns=['FIPS','HR80'],\n",
      "                 fill_color='YlGn', fill_opacity=0.7,\n",
      "                 line_opacity=0.2,\n",
      "                 threshold_scale=bins[:-1],\n",
      "                 legend_name='Homicide Rate HR80 (Quantiles k=5)')\n",
      "showmap('test')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<iframe src=http://localhost:8015/test_4362.html width=1000 height=500></iframe>\n"
       ]
      },
      {
       "html": [
        "<iframe src=http://localhost:8015/test_4362.html width=1000 height=500></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<IPython.core.display.HTML at 0x10bc260d0>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Global Spatial Autocorrelation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w = ps.queen_from_shapefile('data/south.shp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w.n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "1412"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w.transform = 'r'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.seed(12345)\n",
      "mi_hr80 = ps.Moran(y, w)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mi_hr80.p_sim"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "0.001"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mi_hr80.I"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "0.2521152652175852"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mi_hr80.EI"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "-0.0007087172218284905"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mi_hr80.p_norm"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "0.0"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Create a random map"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "yr = np.random.permutation(y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['yrandom'] = yr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "numpy.ndarray"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins= ps.Quantiles(y, 5).bins.tolist()\n",
      "map_osm = folium.Map(location=[33.7550, -84.3900], zoom_start=5,)\n",
      "map_osm.geo_json(geo_path='data/south.json',\n",
      "                 key_on='feature.properties.FIPS',\n",
      "                 data_out='data.json', data=df,\n",
      "                 columns=['FIPS','HR80'],\n",
      "                 fill_color='YlGn', fill_opacity=0.7,\n",
      "                 line_opacity=0.2,\n",
      "                 threshold_scale=bins[:-1],\n",
      "                 legend_name='Homicide Rate HR80 (Quantiles k=5)')\n",
      "showmap('test')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<iframe src=http://localhost:8015/test_811.html width=1000 height=500></iframe>\n"
       ]
      },
      {
       "html": [
        "<iframe src=http://localhost:8015/test_811.html width=1000 height=500></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "<IPython.core.display.HTML at 0x10a12fbd0>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins= ps.Quantiles(y, 5).bins.tolist()\n",
      "map_osm = folium.Map(location=[33.7550, -84.3900], zoom_start=5,)\n",
      "map_osm.geo_json(geo_path='data/south.json',\n",
      "                 key_on='feature.properties.FIPS',\n",
      "                 data_out='data.json', data=df,\n",
      "                 columns=['FIPS','yrandom'],\n",
      "                 fill_color='YlGn', fill_opacity=0.7,\n",
      "                 line_opacity=0.2,\n",
      "                 threshold_scale=bins[:-1],\n",
      "                 legend_name='Spatially Random Homicide Rate HR80 (Quantiles k=5)')\n",
      "showmap('test')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<iframe src=http://localhost:8015/test_3910.html width=1000 height=500></iframe>\n"
       ]
      },
      {
       "html": [
        "<iframe src=http://localhost:8015/test_3910.html width=1000 height=500></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "<IPython.core.display.HTML at 0x10bc26090>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mi_hr80_random = ps.Moran(df['yrandom'],w)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mi_hr80_random.I"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "-0.0075100395407744691"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mi_hr80_random.p_sim"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "0.34799999999999998"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import gaussian_kde"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "density = gaussian_kde(mi_hr80.sim)\n",
      "xs = linspace(mi_hr80.sim.min(),mi_hr80.sim.max(),200)\n",
      "plot(xs, density(xs))\n",
      "axvline(x=mi_hr80.I, color='r')\n",
      "xlabel('I')\n",
      "ylabel('f(I)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "<matplotlib.text.Text at 0x10b718710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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s5DQ3Nwcf98tf/lIsXbrUsGz9nTeg+wJuVVVVcJEslGNVyHn58mVx8eJFIYS+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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bc26f10>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "density = gaussian_kde(mi_hr80.sim)\n",
      "xs = linspace(mi_hr80.sim.min(),mi_hr80.sim.max(),200)\n",
      "plot(xs, density(xs))\n",
      "axvline(x=mi_hr80.I, color='r')\n",
      "axvline(x=mi_hr80_random.I, color='g')\n",
      "xlabel('I')\n",
      "ylabel('f(I)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "<matplotlib.text.Text at 0x10a12c710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7wt5WhZyqHc/PPvtMdHd3CyGEOHDggMjIyAh5256klcKFCxfEnDlzRFZWligt\nLRWXLl0SQgjR3Nws5s2bF3jdokWLREpKioiPjxdOp1O8+uqr/W4vK+dbb70lsrOzRWZmpli3bl3g\n+SVLloi8vDyRn58vvvvd7wqfz2dovr72u2XLFrFly5bAa3784x+LzMxMkZ+fLw4cOHDbzGYYaM6G\nhgbhcrmEy+USU6dONTXn7TK2trYKp9MpRowYIUaNGiXS0tLE5cuXg26rWk4rj2UoOSsqKsTo0aNF\nQUGBKCgoEN/4xjf63Va1nKodz/Xr14upU6eKgoICUVRUJPbv39/vtsHw5DUiIgrg7TiJiCiApUBE\nRAEsBSIiCmApEBFRAEuBiIgCWApERBTAUiAKQ0JCguwIRKZiKRCFwcrr8hDJwFIgIqIAlgIREQWw\nFIiIKIClQEREASwFIiIKYCkQhYHfPqJox0tnExFRAEcKREQUwFIgIqIAlgIREQWwFIiIKIClQERE\nASwFIiIKYCkQEVEAS4GIiAL+H/blWAdYqt6dAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b0d0050>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}