GDELT correlations.ipynb

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By [Felipe Hoffa](https://plus.google.com/+FelipeHoffa/)\n",
      "([@felipehoffa](http://twitter.com/felipehoffa), [/r/bigquery](http://reddit.com/r/bigquery))\n\n",
      "\n",
      "To look further into how to read these results to understand history, look at:\n\n",
      "* Kalev Leetaru's [GDELT correlations post](http://blog.gdeltproject.org/towards-psychohistory-uncovering-the-patterns-of-world-history-with-google-bigquery/).\n",
      "* Andrew Gelman's [counterpoint](http://andrewgelman.com/2014/08/15/psychohistory-hype-paradox/).\n",
      "* Anthony A. Boyles's [counterpoint](http://aaboyles.com/2014/08/access-event-data-in-google-bigquery-from-r/).\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Some pre-flight work:\n",
      "# - Libraries.\n",
      "# - Patch to handle authentication automatically on Google Compute Engine",
      "from oauth2client.gce import AppAssertionCredentials\n",
      "from bigquery_client import BigqueryClient\n",
      "from pandas.io import gbq\n",
      "import pandas as pd\n",
      "\n",
      "def GetMetadata(path):\n",
      "  import urllib2\n",
      "  BASE_PATH = 'http://metadata/computeMetadata/v1/'\n",
      "  request = urllib2.Request(BASE_PATH + path, headers={'Metadata-Flavor': 'Google'})\n",
      "  return urllib2.urlopen(request).read()\n",
      "\n",
      "credentials = AppAssertionCredentials(scope='https://www.googleapis.com/auth/bigquery')\n",
      "\n",
      "client = BigqueryClient(credentials=credentials,\n",
      "                        api='https://www.googleapis.com',\n",
      "                        api_version='v2',\n",
      "                        project_id=GetMetadata('project/project-id'))\n",
      "\n",
      "gbq._authenticate = lambda: client"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How many rows GDELT has for each country?\n",
      "\n",
      "We are going to use a view specially defined for that. 'sample_views.country_date_matconf_numarts' counts the number of material conflicts per day per country.\n",
      "\n",
      "To see the view definition, go to https://bigquery.cloud.google.com/table/gdelt-bq:sample_views.country_date_matconf_numarts and click on 'Details'."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "query = \"\"\"\n",
      "SELECT country, SUM(c) AS count\n",
      "FROM [gdelt-bq:sample_views.country_date_matconf_numarts]\n",
      "GROUP BY country\n",
      "ORDER BY count DESC\n",
      "LIMIT 10\n",
      "\"\"\"\n",
      "gbq.read_gbq(query)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Waiting on bqjob_r3494ef2663a3c45e_00000147cc145563_1 ... (0s) Current status: DONE   \n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>country</th>\n",
        "      <th>count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>         Israel</td>\n",
        "      <td> 13138702</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>       Pakistan</td>\n",
        "      <td> 11547139</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>    Afghanistan</td>\n",
        "      <td> 11334579</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>           Iraq</td>\n",
        "      <td> 10181510</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>          Syria</td>\n",
        "      <td>  9538310</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> United Kingdom</td>\n",
        "      <td>  8764009</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>         Russia</td>\n",
        "      <td>  7569903</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>          India</td>\n",
        "      <td>  6988340</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>          Egypt</td>\n",
        "      <td>  6511839</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>          China</td>\n",
        "      <td>  6268915</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "          country     count\n",
        "0          Israel  13138702\n",
        "1        Pakistan  11547139\n",
        "2     Afghanistan  11334579\n",
        "3            Iraq  10181510\n",
        "4           Syria   9538310\n",
        "5  United Kingdom   8764009\n",
        "6          Russia   7569903\n",
        "7           India   6988340\n",
        "8           Egypt   6511839\n",
        "9           China   6268915"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Focusing solely in Egypt. Let's plot the number of material conflicts reported for each day over the last 35 years:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "query = \"\"\"\n",
      "SELECT date, c AS count\n",
      "FROM [gdelt-bq:sample_views.country_date_matconf_numarts]\n",
      "WHERE country='Egypt'\n",
      "ORDER BY date\n",
      "\"\"\"\n",
      "data=gbq.read_gbq(query)\n",
      "data.index=pd.to_datetime(data['date']*1000)\n",
      "x = data['count'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Waiting on bqjob_r46d7af66323c4ad9_00000147cc18e43f_8 ... (0s) Current status: DONE   \n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GPB7Kj8Bu4L4rdgP3y0N5D+Au4DPYDdzPAF7PokWevxCiQxDN81+zxub5V1fDu++mt+++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\nCM+ACJouAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7feb5c16b990>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What if we only focus on the 30 days previous to January 27th, 2011."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "query = \"\"\"\n",
      "  SELECT country, date, c AS count\n",
      "  FROM [gdelt-bq:sample_views.country_date_matconf_numarts] a \n",
      "  CROSS JOIN (SELECT i FROM [fh-bigquery:public_dump.numbers_255] WHERE i < 30) b\n",
      "  WHERE country='Egypt'\n",
      "  AND date+i*86400000000 = PARSE_UTC_USEC('2011-01-27')\n",
      "\"\"\"\n",
      "data_egypt=gbq.read_gbq(query)\n",
      "data_egypt.index=pd.to_datetime(data_egypt['date']*1000)\n",
      "x = data_egypt['count'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Waiting on bqjob_r2f64a01b0c420ec3_00000147cc356c4a_11 ... (0s) Current status: DONE   \n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mY5DCZFGj4aEqn182XVPxtNSvLxqyNVR6rvmH/cAWHaBaYmGLDshcy4IF0tKo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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7feb5c5fac90>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With that timeline defined, we can look for similar timelines across all years and countries.",
      "\n",
      "To make this work, we used the trick explained on http://stackoverflow.com/questions/24923101/computing-a-moving-maximum-in-bigquery/24943950#24943950."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "query = \"\"\"\n",
      "SELECT\n",
      "  STRFTIME_UTC_USEC(a.ending_at, \"%Y-%m-%d\") ending_at1,\n",
      "  STRFTIME_UTC_USEC(b.ending_at-30*86400000000, \"%Y-%m-%d\") starting_at2,\n",
      "  STRFTIME_UTC_USEC(b.ending_at, \"%Y-%m-%d\") ending_at2,\n",
      "  a.country, b.country, CORR(a.c, b.c) corr, COUNT(*) c\n",
      "FROM (\n",
      "  SELECT country, date+i*86400000000 ending_at, c, i\n",
      "  FROM [gdelt-bq:sample_views.country_date_matconf_numarts] a \n",
      "  CROSS JOIN (SELECT i FROM [fh-bigquery:public_dump.numbers_255] WHERE i < 30) b\n",
      ") b\n",
      "JOIN (\n",
      "  SELECT country, date+i*86400000000 ending_at, c, i\n",
      "  FROM [gdelt-bq:sample_views.country_date_matconf_numarts] a \n",
      "  CROSS JOIN (SELECT i FROM [fh-bigquery:public_dump.numbers_255] WHERE i < 30) b\n",
      "  WHERE country='Egypt'\n",
      "  AND date+i*86400000000 = PARSE_UTC_USEC('2011-01-27')\n",
      ") a\n",
      "ON a.i=b.i\n",
      "WHERE a.ending_at != b.ending_at\n",
      "GROUP EACH BY ending_at1, ending_at2, starting_at2, a.country, b.country\n",
      "HAVING (c = 30 AND ABS(corr) > 0.254)\n",
      "ORDER BY corr DESC\n",
      "\n",
      "LIMIT 10\n",
      "\"\"\"\n",
      "data=gbq.read_gbq(query)\n",
      "data\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Waiting on bqjob_r5ccda16b63fbcd4a_00000147cc1a0174_11 ... (3s) Current status: RUNNING "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Waiting on bqjob_r5ccda16b63fbcd4a_00000147cc1a0174_11 ... (203s) Current status: DONE   \n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>ending_at1</th>\n",
        "      <th>starting_at2</th>\n",
        "      <th>ending_at2</th>\n",
        "      <th>a_country</th>\n",
        "      <th>b_country</th>\n",
        "      <th>corr</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 2011-01-27</td>\n",
        "      <td> 2013-10-19</td>\n",
        "      <td> 2013-11-18</td>\n",
        "      <td> Egypt</td>\n",
        "      <td>        Chad</td>\n",
        "      <td> 0.875190</td>\n",
        "      <td> 30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2011-01-27</td>\n",
        "      <td> 2011-05-15</td>\n",
        "      <td> 2011-06-14</td>\n",
        "      <td> Egypt</td>\n",
        "      <td>      Jordan</td>\n",
        "      <td> 0.862724</td>\n",
        "      <td> 30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2011-01-27</td>\n",
        "      <td> 2005-08-07</td>\n",
        "      <td> 2005-09-06</td>\n",
        "      <td> Egypt</td>\n",
        "      <td>      Turkey</td>\n",
        "      <td> 0.856022</td>\n",
        "      <td> 30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 2011-01-27</td>\n",
        "      <td> 2006-10-03</td>\n",
        "      <td> 2006-11-02</td>\n",
        "      <td> Egypt</td>\n",
        "      <td>       Italy</td>\n",
        "      <td> 0.847174</td>\n",
        "      <td> 30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 2011-01-27</td>\n",
        "      <td> 2006-05-29</td>\n",
        "      <td> 2006-06-28</td>\n",
        "      <td> Egypt</td>\n",
        "      <td>       Syria</td>\n",
        "      <td> 0.846571</td>\n",
        "      <td> 30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 2011-01-27</td>\n",
        "      <td> 2013-05-15</td>\n",
        "      <td> 2013-06-14</td>\n",
        "      <td> Egypt</td>\n",
        "      <td> South Korea</td>\n",
        "      <td> 0.842728</td>\n",
        "      <td> 30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 2011-01-27</td>\n",
        "      <td> 1995-06-11</td>\n",
        "      <td> 1995-07-11</td>\n",
        "      <td> Egypt</td>\n",
        "      <td>        Iraq</td>\n",
        "      <td> 0.842362</td>\n",
        "      <td> 30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> 2011-01-27</td>\n",
        "      <td> 2011-10-10</td>\n",
        "      <td> 2011-11-09</td>\n",
        "      <td> Egypt</td>\n",
        "      <td>    Portugal</td>\n",
        "      <td> 0.841235</td>\n",
        "      <td> 30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 2011-01-27</td>\n",
        "      <td> 2002-08-04</td>\n",
        "      <td> 2002-09-03</td>\n",
        "      <td> Egypt</td>\n",
        "      <td>       Sudan</td>\n",
        "      <td> 0.835436</td>\n",
        "      <td> 30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td> 2011-01-27</td>\n",
        "      <td> 2009-08-07</td>\n",
        "      <td> 2009-09-06</td>\n",
        "      <td> Egypt</td>\n",
        "      <td>     Germany</td>\n",
        "      <td> 0.832848</td>\n",
        "      <td> 30</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "   ending_at1 starting_at2  ending_at2 a_country    b_country      corr   c\n",
        "0  2011-01-27   2013-10-19  2013-11-18     Egypt         Chad  0.875190  30\n",
        "1  2011-01-27   2011-05-15  2011-06-14     Egypt       Jordan  0.862724  30\n",
        "2  2011-01-27   2005-08-07  2005-09-06     Egypt       Turkey  0.856022  30\n",
        "3  2011-01-27   2006-10-03  2006-11-02     Egypt        Italy  0.847174  30\n",
        "4  2011-01-27   2006-05-29  2006-06-28     Egypt        Syria  0.846571  30\n",
        "5  2011-01-27   2013-05-15  2013-06-14     Egypt  South Korea  0.842728  30\n",
        "6  2011-01-27   1995-06-11  1995-07-11     Egypt         Iraq  0.842362  30\n",
        "7  2011-01-27   2011-10-10  2011-11-09     Egypt     Portugal  0.841235  30\n",
        "8  2011-01-27   2002-08-04  2002-09-03     Egypt        Sudan  0.835436  30\n",
        "9  2011-01-27   2009-08-07  2009-09-06     Egypt      Germany  0.832848  30"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Those are the 10 periods that most closely resemble the timeline of material conflicts in Egypt before January 27th, 2011.\n",
      "\n",
      "Let's plot Chad 30 days before October 19th, 2013."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "query = \"\"\"\n",
      "  SELECT country, date, c AS count\n",
      "  FROM [gdelt-bq:sample_views.country_date_matconf_numarts] a \n",
      "  CROSS JOIN (SELECT i FROM [fh-bigquery:public_dump.numbers_255] WHERE i < 30) b\n",
      "  WHERE country='Chad'\n",
      "  AND date+i*86400000000 = PARSE_UTC_USEC('2013-11-18')\n",
      "\"\"\"\n",
      "data_chad=gbq.read_gbq(query)\n",
      "data_chad.index=pd.to_datetime(data_chad['date']*1000)\n",
      "data_chad['count'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Waiting on bqjob_r39c0b56b485a6249_00000147cc384b90_16 ... (0s) Current status: DONE   \n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7feb5c1ecbd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QMaIUZUsg6JgFQIsW6bUs1FiQvX+xujqYmAUE3zGvEPy/XmOLDlAtibBFBxRm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\nxZuFAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7feb5c1f6290>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In fact, both periods look remarkably similar (but not necesarily significant, more analysis is needed before reaching any conclusions):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_egypt['count'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7feb5cd93890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mY5DCZFGj4aEqn182XVPxtNSvLxqyNVR6rvmH/cAWHaBaYmGLDshcy4IF0tKo\n7cPE3BrTCIEwM6e8aH8NRakZ2JY5BRrTSJqdO6GgQEaaDXJ022SYNQsuvlh+UIqi5C533AF168Kd\nd2b3uhrT8IE5c6RHZtgGA6BHD+khumJF2EoURQkSv9Jt/USNhodEPr9su6YSacnPhxNOyI6LKpf8\nw35hiw5QLbGwRQdkruWrr/xzT2lMI8uE3T8jGp3NT1Fym23b4PvvJRBuE2HENJYBm4BdwE7gCKAx\nMBZo52w/F9jo7D8IuMzZ/3rgLae8JzASqAdMAm6IcS3fYhpdusi4T0VFvpwuYxYvlvTblSvDTQGu\naaxbBwMHym+hTp2w1Si5zJdfwvnnS2sj29gW0zBAMdADMRgAtwFvAwcA7zjrAF2Afs57b+BJIh9k\nOHA50Ml59Q5KcFkZfPcddO0a1BVSp0MH2HPPcH5QNZnx4+F//4Pnnw9biZLr+Oma8pOw3FPRFqwP\n4M5JNwro6yyfAYxGWiTLgEXAkUBLoCHgjsL0rOeYtInn85s5U4LPfuRKZ6rFSzZSb3PJP+wH48bB\n2WeXcM89UF4etprg6mTlSvj4Yzu0pIotOiAzLX4HwatzTMMAU4FZwBVOWXNgjbO8xlkHaAUs9xy7\nHNgvRvkKpzwQsjVIYaqcdJKOQ5VNVq+Gzz+HK66A/faD0aPDVhQcDzwA55wD27eHraTmYmPmVFi0\ndN6bAaVAL2BD1D4/Ou+PA3/wlD8D/B6JZ3hvl72ACTGuZfygTx9jxo3z5VS+sm6dMQ0bGrN9e9hK\nagZPPGHMhRfK8tSpxhxwgDHl5eFqCoJdu4xp1cqYgw825t//DltNzaVzZ2PmzAnn2sjDfUyy6HD5\nhVXO+zrgFSSusQZoAaxGjMpaZ58VQBvPsa2RFsYKZ9lbHrPXQv/+/SksLASgoKCAoqIiiouLgUhz\nLdG6jGxbzGOPJbd/Ntfnzi2hRQvR16uXbN+1C3r0KGbjRpg6tYTNm6GwUNZnzZLP88gjxdSrF77+\n6rb+9NMlnHsuQDEnnAB16pQwZAjcfbcd+vxar127mMaN4fLLSxg8GC69tJj8fHv01YT17dthyZIS\nVq2Crl2Dv15JSQkjR44E+OV+aQv1kVgEwF7AR8ApwAPArU75bcB9znIXpDWyB9AeWEwkHjIDiW/k\nIdlTsQLhKVnXadOm7Vb27bfGNG9uTEWFTyY8Ay2xuO02Y1q2NKZtW2MaNTKmVi1jCgqMKSw0pqjI\nmOOPN+YOT4e8AAAej0lEQVTMM4259FJjbrrJmKOPNmbwYP91ZIMwtaxcKfW6bVtEx5Qpxhx0kDyZ\nh0UQdXL99cbcdZf85o8+2pgxY8LTkg626DAmfS1z5hhz4IHhacGilkZzpHXhXvsFJIV2FjAOyYZa\nhqTcAsxzyucB5cBAIh9mIJJyuydiNKYEIdjtn2FrWusdd0C/fjLEyT77QMOGUCtBpGr5ckkbPu88\nOPDA7Oms7owfD6efLkM6uJxyitT3+PHi/88FKiokO+ydd+Q3P2gQ/PWvcO659v4HchHb5tDwkus/\nA8dops+f/yw34zvu8EmRBTz6KLzyCkybpjeCZDn2WLjlFvjd7yqXT5woN9bS0sTGurrw4YfSD+XL\nL2XdGHnIuO8+OPXUcLXVJAYPlrq/665wrm9bP41qha2ZU5lw7bWwZQuMGlX1voqM8TV3rvTCj+a0\n06ST32uvZV9XEIwbhxO3EfLy4LbbYOjQ8DTVRGzOnFKj4cENDLmUl8Nnn8Hhh4evxU/y8+Gpp+DW\nW+GHH8LTkSphaRk/Hvr0ibimvDry8mQE0rvukifDbONnnbiuqWhX2znnSL+NDz7InpZMsEUHpK8l\nCPeUX/WiRiMBc+dCmzaw995hK/Gfnj3hggvgL38JW4n9RD99R9OnjxiMN97InqYg+OgjaNZs92lF\na9eWB4x77w1HV01jxw6ZWjqb07umQq57tDOKaTz9tPSKdTLRco7Nm+Vp5rnnwMnCU6JYvhwOOUTm\nUdljj/j7jR8P998viRPVNU503XXQokXs+N327bD//hLDsWX8tVxl3jw480z4+uvwNGhMI01sG9nW\nbxo2hMcfhwEDtOdvPMaPhzPOSGwwQP7kP/0Eb76ZHV1+s2tXbNeUS926cNNNEhBXgsXmzClQo1GJ\naJ9fmNO7Zssv27cvHHSQPCWHqSMZwtASyzUVS0etWvC3v8Hf/57d2IZfdfLRR9C8ORxwQPx9BgyQ\nVNyFC4PVkim26ID0tAQ1xavGNAJm82bxK3bvHraS4Hn8cXjsMfjmm7CV2MX334uL4MQTk9v/7LNh\n40a5sVY3qorbgLRMBw6UcamU4LA5cwo0phGXadOkU9NHH/msyFKGDYMJEyKduhSpk6++gmeeSf6Y\nF16Af/0L3n+/+tTjrl3QurVo7tQp8b7r18s+c+bIoI2K/3TrJnHGMGNHGtNIgzBdU2Fw3XXylPzc\nc2ErsYdx41Lv6d2vH6xZA++9F4ymIPjwQwmAV2UwAJo0gf794aGHApdVI9m5ExYtsjdzCtRoVMLr\n8wu7U1+2/bK1a0u22C23yNNkWDoSkU0t334rvvsTTkhNR+3akn2UrZ68ftRJMq4pLzffLBmF3t+J\nX1r8wBYdkLqWxYul1bfnnuFriYcajTjkeuZULA47TJ6Ub7klbCXh87//SUZUOlO6XnCBGJ2qOsPZ\nwK5dqY+d1bo1nHWWxMGU+EyfHj9pIB62xzNqAmmNBvn998Y0bZr9kW1toKzMmNatjSkpCVtJuBxx\nhDFvvZX+8c88Y8zJJ/unJyimTTOmR4/Uj/vmG/mPbNrku6ScYPFiY5o1kzpKZS6eu+4yZtCg4HQl\nCwlGudWWRgxc11R1CWT6SaNGMqDhVVfV3L4by5bBkiVw/PHpn+OiiyQb7ZNPfJMVCKm6plw6dRLX\n3dNPp37sqlWRARFzkW3bpOV2xx3w1lvizrvnnuRSsatDS0ONhgfX52eDaypMv+yZZ8pN4cEHq7d/\nOF1eekncL/HmhE9Gxx57yOi3d9/tr7Z0tMQjHdeUl9tug4cfjjxcVKVl7ly47DK5KZ5wgqwHQdi/\n2euvh44d5b2srIQZM2RU6YsvrvpBLEij4Ve9hDFzn/XMmCF/+JpKXh78859w6KGSVdOmDaxbB2vX\nyive8g8/yM2yUSMZr8v7HqusqEjiKLYxbpw/PZ/794d//EOGojn66MzP5zfvvy/fbYcO6R3fo4cM\nsTJqFFx5Zex9jJE07v/7P/jiC7jmGskOmjxZHk5mzpS5YHKFUaOkXmfOjHgqWrWSsosukj4/r7wi\nY3xFU14uMRDb57nJdQeM455Lnl275Ef83Xcyj0ZN5sknZaC6pk1h330jr2bNKq+7ryZNJGWwrAw2\nbar6feJEGQTv0kvD/qQRliyBX/1KRnWN19JIhVdekRvqAw+IEbHJ5Xn11VBYKN9xunzwgXx/CxZU\nrq8dO2DMGEnNLS+HP/1JEgTq1Yvsc/310oH2tddyYy6SOXOkBTVtGnTtuvv2igrp+zV2rPSJim5R\nfPONzFmyeHF29CYiUT+NXCflANCXXxpzwAG+x5WUGCxYINPSDh1qT9LBffcZc9VV/p5zzhxjunY1\n5rzzjNm40d9zp8vOncbsu68EbDPlmGOMefFFWd6wQepwv/2MOfFEYyZNiv/d7thhTK9exgwZkrmG\nsNm40ZhOnYx57rmq9x01SoLkU6ZULn/lFWN+97tg9KUKGghPjpKSEms69YXtl3UJUkfnztLjfswY\neerctcsfLaWl8mT/00+pa0omMJxqnXTtKskVjRuLS87P4Hi638/770PbtjJybabcfrsEes8+u4T9\n95dYxRtvwNSp8uQcr3VVp47U97//7e+w8tn+7xgjsZoTT4QLL6xay8UXSyzpkkukNe/y1VfBjDmV\nSEs6qNGIIuxOfTUN1987d67MW75tW/rn2rFDpsk8+WQ556GHwuzZyR+/aJHM0nfsselriMeee8IT\nT8jQJH37yk22KiMZJOn0do/HqafKQId16khWVCpDYLRoIYkHl12Wep8GWxg2TNzZjzyS/DG9eskD\n0+OPywNTeXn1yJyqCaTcLOve3ZhPPw2gvackZNs2Y845x5ji4vRcOLNmGdOtmzTvly+XstGjxQ0w\ndKgx5eVVn2PoUGMGDkz92qny/ffGHHecfFZXazbZuVPqZcmS7F87HsOHG3PwwcZs3hy2ktT44ANx\n8y1dmt7xGzYYc9JJxvTubcyBBxozc6av8tKGBO6pXCelitq82Zj69eUGpmSf8nJjrr1WDPeKFckd\ns22bMbffLjfB557b3X/+7bdyc+7Vy5hlyxKfq6hIOrtlg/JyY+6+25jmzY157bXsXNNl6lRjDj88\nu9esiooKYy691Jhzz7UnvlUVa9ZIR9g33sjsPDt2GDNggDG1ahmzZYs/2jIFNRrJ8cgj08yRRwb0\nLaTItGzdvaog2zoqKoy5915j2rUzZv78xFpmzDCmSxdj+vY1ZuXK+OcsLzfm/vvFsDz/fOx9vv5a\nbuDJtEj8rJOPPpJkgGuuMeann1I/Ph0tV15pzAMPpH6tILR4+flnYw47zJgHHwxXRzKUlxtzwgnG\n3HGHP1oqKvxJSvBDizEaCE+a+fM1nhE2eXnSaWzIEJmCdvr03ffZtk3SRE8/XSY+evllaNky/jnz\n82U8rTfflFjCBRfIiL5eXnpJ5sPIz/fz01TN0UfD559Lf5cjjpBgaJCUl0t9+RXP8JN69SRA/NBD\n8O67YatJzJ13yvvf/+7P+fLy/ElKUDInJUv8+98b88ILKZpvJTAmTpSxeyZMiJR9/LExnTvLd7V6\ndern3LpVnurbtq08vtYhhxj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       "text": [
        "<matplotlib.figure.Figure at 0x7feb5cb6a990>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To look further into how to read these results to understand history, look at:\n\n",
      "* Kalev Leetaru's [GDELT correlations post](http://blog.gdeltproject.org/towards-psychohistory-uncovering-the-patterns-of-world-history-with-google-bigquery/).\n",
      "* Andrew Gelman's [counterpoint](http://andrewgelman.com/2014/08/15/psychohistory-hype-paradox/).\n",
      "* Anthony A. Boyles's [counterpoint](http://aaboyles.com/2014/08/access-event-data-in-google-bigquery-from-r/).\n",
      "\n",
      "\n",
      "By [Felipe Hoffa](https://plus.google.com/+FelipeHoffa/)\n",
      "([@felipehoffa](http://twitter.com/felipehoffa), [/r/bigquery](http://reddit.com/r/bigquery))"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}