{ "metadata": { "name": "Getting_Started_with_GDELT" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting started with GDELT\n", "### David Masad \n", "####david.masad[at]gmail[dot]com / [@badnetworker](https://twitter.com/badnetworker)\n", "**[Department of Computational Social Science](http://css.gmu.edu), George Mason University**\n", "\n", "\n", "\n", "If you're reading this, chances are you're already excited about Global Data on Events, Location and Tone, better known as [GDELT](http://eventdata.psu.edu/data.dir/GDELT.html). If you aren't, you should be. [Lots](http://ideas.foreignpolicy.com/posts/2013/04/10/what_can_we_learn_from_the_last_200_million_things_that_happened_in_the_world) [has](http://dartthrowingchimp.wordpress.com/2013/04/10/the-future-of-political-science-just-showed-up/) [been](http://badhessian.org/gdelt-and-social-movements/) [written](http://www.guardian.co.uk/news/datablog/2013/apr/12/gdelt-global-database-events-location) about how revolutionary this dataset might be, and I won't try to add to it here. \n", "\n", "Instead, let's dive right in! In this tutorial, I'll go through extracting some basic time series from GDELT.\n", "\n", "To follow along, go download the data from the [GDELT website](http://eventdata.psu.edu/data.dir/GDELT.html) and unzip it. The data is about 4.6 GB uncompressed in a series of text files, one per year" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(First, some code to style the IPython notebook and make it more readable. I've adapted the CSS styling from the excellent [Probabilistic Programming and Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers))_" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import HTML\n", "styles = open(\"Style.css\").read()\n", "HTML(styles)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "" ], "output_type": "pyout", "prompt_number": 1, "text": [ "" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're going to need only a few libraries to start with: Matplotlib for visualization, datetime for handling date objects, and Pandas for handling, aggregating and reshaping some of the data. Pandas provides great functionality to easily plot time series, so we'll use it for that too. We'll also import *defaultdict* while we're at it, since it's often useful for data collection." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import datetime as dt\n", "from collections import defaultdict\n", "\n", "import matplotlib.pyplot as plt\n", "import pandas\n", "\n", "matplotlib.rcParams['figure.figsize'] = [8,4] # Set default figure size" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Set this variable to the directory where the GDELT data files are\n", "PATH = \"GDELT.1979-2012.reduced/\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Peeking at the data:\n", "!head -n 5 GDELT.1979-2012.reduced/2010.reduced.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Day\tActor1Code\tActor2Code\tEventCode\tQuadCategory\tGoldsteinScale\tActor1Geo_Lat\tActor1Geo_Long\tActor2Geo_Lat\tActor2Geo_Long\tActionGeo_Lat\tActionGeo_Long\r\n", "20100101\tAFG\tAFGCOP\t173\t4\t-5.0\t34.9669\t69.265\t34.9669\t69.265\t25\t45\r\n", "20100101\tAFG\tAFGCVL\t080\t1\t5.0\t31\t64\t31\t64\t31\t64\r\n", "20100101\tAFG\tAFGCVL\t190\t4\t-10.0\t35.3472\t70.1485\t35.3472\t70.1485\t35.3472\t70.1485\r\n", "20100101\tAFG\tAFGGOV\t043\t2\t2.8\t31\t64\t31\t64\t31\t64\r\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Just how much data are we talking about?\n", "\n", "It's important to know how big our dataset is. It's also important to know if the data available over time is biased -- does GDELT have more events for recent years than for distant ones? If so, is that because more has happened recently, or because the data collection has gotten better?\n", "\n", "The [paper introducing GDELT](http://eventdata.psu.edu/papers.dir/ISA.2013.GDELT.pdf) (warning: large PDF) goes over this, but it'll be good practice to replicate some basic diagnostics.\n", "\n", "So let's start with a simple count of just how many events -- all events -- the dataset has per month (which is a common typical unit of temporal aggregation). To do that, we'll open each file, figure out which month each event (meaning each row) occured in, and add them up." ] }, { "cell_type": "code", "collapsed": false, "input": [ "monthly_data = defaultdict(int) # We'll use this to store the counts\n", "count = 0 # While we're at it, let's count how many records there are, total.\n", "\n", "for year in range(1979, 2013):\n", " #print year # Uncomment this line to see the program's progress.\n", " f = open(PATH + str(year) + \".reduced.txt\")\n", " next(f) # Skip the header row.\n", " for raw_row in f:\n", " try:\n", " row = raw_row.split(\"\\t\")\n", " # Get the date, which is in YYYYMMDD format:\n", " date_str = row[0]\n", " year = int(date_str[:4])\n", " month = int(date_str[4:6])\n", " date = dt.datetime(year, month, 1)\n", " monthly_data[date] += 1\n", " count += 1\n", " except:\n", " pass # Skip error-generating rows for now. \n", "\n", "print \"Total rows processed:\", count\n", "print \"Total months:\", len(monthly_data)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Total rows processed: 67927691\n", "Total months: 402\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we just turn this dictionary into a Pandas series, and plot it. Pandas will automatically recognize that we're dealing with a time series, because it's useful like that." ] }, { "cell_type": "code", "collapsed": false, "input": [ "monthly_events = pandas.Series(monthly_data)\n", "monthly_events.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 6, "text": [ "" ] }, { "output_type": "display_data", "png": 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wNEYWpaVGQc/K0kJ/zTVa7C9fti/2PXsap91Zll24ACxYoEMAbNkTERE5oZISoE0bbZGn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\nxKZ7X0RUNXbjuzl3HHtylLNl8cc/6gS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Xhat/JkTqloVl1vnAgQNl8uTJ1kJoq6V8LqrL\nwtU/F19//bV4eHjIwIEDJSwsTMLCwmT79u1y/vx5mTJlioSGhkpUVJRdA2DZsmUSEhIiYWFhsnfv\n3gr7dNXPRUNm4aqfC5e7EQ4RERHVjWvMLCAiIiKHsdgTERG5ORZ7IiIiN8diT0RE5OZY7ImIiNwc\niz0REZGb+//Ey3yfb1xdqQAAAABJRU5ErkJggg==\n" } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we might expect, the number of events in the dataset isn't uniform, and goes up rapidly in the later years." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cooperation and Conflict\n", "\n", "One important and useful feature of GDELT is the QuadCategory classification of each event. Per the documentation, each event has one of the following quad categories:\n", "\n", " 1. Material Cooperation\n", " 2. Verbal Cooperation\n", " 3. Verbal Conflict\n", " 4. Material Conflict\n", "\n", "Let's repeat the analysis above, but now examine material cooperation and conflict. Very (very very) roughly, is the world becoming more cooperative, or more violent?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "material_coop = defaultdict(int) \n", "material_conf = defaultdict(int)\n", "\n", "for year in range(1979, 2013):\n", " f = open(PATH + str(year) + \".reduced.txt\")\n", " next(f) # Skip the header row.\n", " for raw_row in f:\n", " try:\n", " row = raw_row.split(\"\\t\")\n", " # Check the quadcat, and skip if not relevant:\n", " if row[4] not in ['1', '4']:\n", " continue\n", " \n", " # Get the date, which is in YYYYMMDD format:\n", " date_str = row[0]\n", " year = int(date_str[:4])\n", " month = int(date_str[4:6])\n", " date = dt.datetime(year, month, 1)\n", " if row[4] == '1':\n", " material_coop[date] += 1\n", " elif row[4] == '4':\n", " material_conf[date] += 1\n", "\n", " except:\n", " pass # Skip error-generating rows for now. " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# Convert both into time series: \n", "monthly_coop = pandas.Series(material_coop)\n", "monthly_conf = pandas.Series(material_conf)\n", "\n", "# Join the time series together into a DataFrame\n", "trends = pandas.DataFrame({\"Material_Cooperation\": monthly_coop,\n", " \"Material_Conflict\": monthly_conf})\n", "trends.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 8, "text": [ "" ] }, { "output_type": "display_data", "png": 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1ykrf/dDrXO1n6v37q1uytg7q6f3TKa3thUx94sSJfltEbtu2jczMTEAN1lu2\nbAFg69atzJ07F5PJRFJSEikpKeTk5FBUVITD4WDcuHEAzJ8/Xzun5bXmzJnDzp07AdixYwdTp07F\narVitVq54YYb/L5cCCGEEOeT3Q6jRnUc1D3l97IydSU5j44ydfDOVW9Zfv+Pfv/R6Uy9yyvKlZSU\nEBcXB0BcXBwlJSUAFBYWkpGRoR2XmJiI3W7HZDKRmJiotSckJGC3q/Pt7HY7AwYMUB/EaCQyMpLS\n0lIKCwt9zvFcqy3Lli3Tfp48eTKTJ0/u6lsSQgghOlRfDzExnQvqdXVqYI6N9b6u9akHGP0OalCv\nrlbPDQ5WKPi6gJz8HA4ePsiyk8s6fMYeLROr0+nQ6XQ9uUSPtQzqQgghxA/B5VK3UrVaAwd1l8t/\nSlvfvt7X61319Avv126mbjRCUJCa5RuC6zANMbHsN8t4/7/f5/b5t7N8+fJ2n7PLo9/j4uK0AW1F\nRUX0bX7ihIQE8vPzteMKCgpITEwkISGBgoICv3bPOadPnwbA5XJRWVlJTEyM37Xy8/N9MnchhBDi\nh7RtGzT3DgPQ0ABmszo6vcX4cB+tM/Vz51pl6q46+ob1paqhqt17WyxQUgKYq7AEWYgJiaG0tpR7\n3r+nw+fuclCfOXMma9euBWDt2rXMnj1ba9+4cSNOp5O8vDyOHTvGuHHjiI+PJyIigpycHBRFYf36\n9cyaNcvvWps2bWLKlCkATJ06lU8++YSKigrKy8v59NNPmTZtWlcfVQghhOiWoiJYvx6OHVN/r69X\nM/CwsPYydYXiuHXogxooKVEz7uBg7+v1rnpiQmOobaxt994WizrKvsnkIMIcgdloxmw080X+Fx0+\nd7vl97lz57Jr1y5KS0sZMGAAf/jDH3j88ceZN28eNpuNIUOGsH79egBSU1PJysoiPT0do9HImjVr\ntNL8m2++SVZWFrW1tcyYMYPp06cDsGjRIhYvXkxaWhqxsbG88847AERHR/Pss88yadIkAJ577jms\nVmuHb0YIIYToDQ0NYDLBc8/B66+rv3uCevNQMv9zXE6+Tcnk3r//lfqG90nu75s31zXWERMSQ01j\nDYqiaDFyz+k9DIkaQj9LP0AN6keOQJPRgcWsDp+PCYmh2hmgRNBCu0F9w4YNbbZ7Rq+3tmTJEpYs\nWeLXnpqaSk5Ojl+7yWRi9erVbV7rtttu47bbbmvv8YQQQogfhNMJv/gFfPQRfP459OvXcabe6Haj\nbzJz4MzyvjTgAAAgAElEQVReiCggNnagz+v1rnosZgsGnYEGdwPBRjWNf+DjB1g0dhG/vPKXgBrU\n6+ubg3qQGtT7hPahn6UfX/Jlu88tK8oJIYQQrTidEB8P//f/wn/+Z+fK740uN3rFRGRwJJjqfAbJ\ngdqnHmwMJiwoTCvBn6k5w4HiAxwvP64d55nb7jJ4M/WHr36Y34z7TYfPLUFdCCGEaMUzMO6qq9T5\n6Z0K6m43OgyEmkIJDq/1GSTnanLRpDRh0psIM4VR41QvsvPETkJNoRwv8w/qjTpvpn7ryFsZ2Xdk\nh88tQV0IIcRlKdDGLKBm6kFB6gIwtbVqUDebOwrqLnSKGtRNYb5Bvd5VT7AxGJ1OR6gplJpG9SKf\nnviUO9PubDNTd+q8mTpA37BWqX8bJKgLIYS47OTlqVl4IA0N/kG9M+V3T6YeFKqW37cc3sI/8/+p\nBXXAp/y+r2gfd6XdxYnyE9rS6p6g3oA3Uwe1X70jEtSFEEJcdsrLA49iBzVTN5vVQN7Q4FnhrRMD\n5TCqQb05U3/9X6/zWd5n1DXWEWIMAfApv5+qOEVaXBphpjCKq9U1YLSgrvhm6kZ9x+vFSVAXQghx\n2XE61UAdiCdT1+nUxWQqKjoT1F1apm4MUYP6geIDlNWV+WTqnvJ7ZX0lriYXUcFRDIkeopXgLRZ1\nOl11o2+m3hkS1IUQQlx2Ogrqnkwd1BJ8WVknFp9xu9E3B/VhP6klPvkcdoedsroy6lx1hJiaM/Xm\n8vvpytMMsg5Cp9MxJGqINljOYlHvWdVQJUFdCCGE6Ehjo7qUa6DBcp6BctD5oO4pv4eYQpj181pK\njd+gQ+eXqXvK76cqTzEochAAAyMHUlClLqlusajVAUeDb/m9M3q0oYsQQghxKXI61bXaGxu9wbsl\nT/kdupape8rvtY21HCg+wKj4UWqm3qJP3VN+dzgdDIxUF6iJCo6ipEbt5NeCulPK70IIIUSHnE71\n70Al+LbK72azGuibmtQvA601Nrm08nttYy3flHzDdUnX+WfqzeX3lpl6VEgUZXVlgLf83p1MXYK6\nEEKIy05HQT1Qpq7TBc7WXW43ep2BUGModa46SmpKsMXZ/PvUPeX3ilM+mXp5fTkAycnqdDuHU93Q\npSskqAshhLjseIJ6XV3g19saKAcdBPXmKW21jbVU1Fcw2DqYsroyqhqqCA8KV69nCqXW5R0oBxAd\nEk15nRrUBw2CVauaM3UpvwshhBDt626mDoGDeuvye0V9BXHhcRj1Rv595t8kWZPU85sz9dOVp72Z\neog3U/dwOKX8LoQQQnTI0yfe2T718vJOZuo6b1CvrK/EGmwlOiSafUX7SLYmq+cHhVHVUMWZmjP0\nC1e3W40K9vape0imLoQQQnRCZwbKdTVTdze5MTRPafNk6pHmSDWoF+4jOUoN6qGmUE6UnyAmNAaT\nwQT4lt9B3QCmwd1AqCm0S+9LgroQQojLTkd96q3L701NncjUm7yZenldOQoKwcZgokOiqWyo1IJ6\nmCmMo6VHSYxI1M4NDwqnwd1Ao1stIVQ7qwkPCken03XpfUlQF0IIcdnp6pQ28P4eHKxu8NKaq8mF\noTmoF1UXEWmORKfTER0SjUlv0oJ4WFAYJTUlJFgStHN1Oh3WYCt7Tu/hL3v/ogX1rpKgLoQQ4rLT\nOqiPHw933glVVervrTN1UIP5+0feB2Mdbrf/NVv2qRc6CrEGWwG1tJ5kTcKgN6jXay6pJ0Qk+Jwf\nFRzFxu82suXIFmqcNYSZwrr8viSoCyGEuOy0LL+7XPDNN5CfD1u3el9vnakHB8MDHz+AI+ybtoN6\nkxuDzjulrWVQ95TeAS1Yt8zUPcd9fvpzapw11DTWEBYkQV0IIYToUMvR76dPQ1wc3HADHDqktrce\nKAdgMrsoqCqgyeQIENRdWqYOEBkcCUBsaCwp0SnacZ5g3bJPHdRpbd+d/Y6axhpqnDVdHiQHsva7\nEEKIy5DTqWbetbVw7BgMHQqpqfDWW+rrDQ3+mboDO27FTZOxCpfL/5pqpm7Q1nj3ZOr3X3m/NgAO\nWpTfLf7ld4AaZw21jbVSfhdCCCE6w+kEq1UN6kePeoP6wYPqSHeXC4xGKK0t5f3q/wNAWdMpANym\nqjYzdXeL8jtApFnN1MODwokKidKO08rvEf7ldx06NVOX8rsQQgjROZ6gXlenZupXXAEpKWop3uFQ\nS+86HWz/fjv/qFoDwLnG5qBuaLv87vZk6ibfTL01T7D2y9RDohgRO0LtUz/fA+VWrVrFhAkTSE9P\nZ+nSpQA4HA5mz56NzWbj5ptvprq6Wjv+pZdewmazMXbsWPbs2aO1Hzp0iPHjx2Oz2Xj00Ue19sbG\nRu6++25sNhtTpkyhuLi4u48qhBBC+HA6ITLSt/weFKRupvLtt97S+2cnP6O+SY1lJQ3NQd3Ydqbu\nUtQ+db1OT7AxOGBQN+qN7Fqwy28J2H7h/ZgwYIKWqXenT71bQb2srIynn36aTz/9lK+//pqjR4+y\nY8cOnnzySSZMmEBubi4ZGRmsWLECgIMHD7J69Wr27dvH5s2bWbBgAUrzzvSZmZmsXLmS3Nxc9u/f\nz/bt2wH1S4PZbCY3N5d7771X++IghBBC9FTLoO4pv4Nagj9wwDtI7rO8z6hzNwf1+lMMtg7GZWin\n/N5i2pqn/N6Wawdd69d2b/q9rPzZSlxNLqoaqs5f+T0kJARFUaisrKSuro7a2lqsVivbtm0jMzMT\nUIP1li1bANi6dStz587FZDKRlJRESkoKOTk5FBUV4XA4GDduHADz58/Xzml5rTlz5rBz587uPKoQ\nQgjhp7FRLb9XVoLdDoMHq+0DBsDJk2qmnleeR11j85JzBieFtadIi0vDpQ9cfjfq1PHnoabQgJl6\nIEa9kWBjMGGmMM7Wnu1W+b1bo99DQkJ49dVXSUpKwmw285vf/Ibx48dTUlJCXFwcAHFxcZSUlABQ\nWFhIRkaGdn5iYiJ2ux2TyURiondIf0JCAna7HQC73c6AAQPUhzQaiYyMpKysjOjoaJ9nWbZsmfbz\n5MmTmTx5cnfekhBCiMuI0wn9+sHOnZCY6M3Mo6LUzD0oCLYd2cb0lOlsObSNyqBq7NWnmJD0cw7r\nT7Yd1BUXxhaZeleDukdYUBhnas4wPGY42dnZZGdnd/rcbgX1s2fPcv/993Pw4EGioqK49dZb+eCD\nD3yO0el0XV6ztjtaBnUhhBCiMzzl9+PHYfp0b3t0NBQVqZn6xu828sSkJ/j0+51UBlVTXFPEsJhh\nNOpzAwR1N2adGtRDjCHaPPWuCjOpQX1s/FgmT/RNVpcvX97uud0qv3/11VdkZGSQkpJCTEwMt956\nK7t37yYuLk4b0FZUVETfvn0BNQPPz8/Xzi8oKCAxMZGEhAQKCgr82j3nnD59GgCXy0VlZaVfli6E\nEEJ0h2f0O6gj3z2io6G4GLDm8X3Z90wZPIVwcziG0CpqGmtIsCTQ2F75Xa/mys9MeYb0fundejZP\npn7e+tQnTpzI3r17KSsro6GhgY8//pipU6cyc+ZM1q5dC8DatWuZPXs2ADNnzmTjxo04nU7y8vI4\nduwY48aNIz4+noiICHJyclAUhfXr1zNr1iztHM+1Nm3axJQpU7rzqEIIIYQfT6YO3kFy4M3UG+L/\nwfXJ12MymIgIDmfZ84WEB4VjDbbi1AUYKKd4B8r9bOjPuhWUQc3Uz9acxz71iIgIHnvsMW6++WZq\na2uZPn061113HePGjWPevHnYbDaGDBnC+vXrAUhNTSUrK4v09HSMRiNr1qzRSvNvvvkmWVlZ1NbW\nMmPGDKY310EWLVrE4sWLSUtLIzY2lnfeeac7jyqEEEL4aZmptw7q5eUQZa71WTwmYbgda4mVCHNE\nO0HdpQX1nvBk6ud1mdgFCxawYMECnzaLxaKNXm9tyZIlLFmyxK89NTWVnJwcv3aTycTq1au7+3hC\nCCFEQI2N3ky9Zfk9qnnhN31QA2ajOlk9PCgcu8NOpDkSi9lCoy5A+V1RF5/pqTBTGA3uBllRTggh\nhOgMp1PNykNDYeBAb7tn6JbO1ECQQR0SrwX14EgizBE00Ham3qS4MRl6vqWKJ5jL2u9CCCFEJzid\nEBurriZnaJFce0ryelMDZoOaqYeZwrBXqZl6iDGEJl0jzhYbtHj0Wvm9OZhLpi6EEEJ0gmdr1f79\nfdsNhubAbvQGdU+mbg22otPpCMJCrcvhd80mxa3NU+8JTzA/b8vECiGEEJcypxNMprZfi44GnbFV\nn3qVXZt3HkwEdU1Vfuc14Z3S1hNapi7ldyGEEKJjnky9LVFR+GXqJTUl2mj4YF3bQb3linI9IeV3\nIYQQogsaGwMH9bYydfDujx6st1DfFKD8bpDyuxBCCHFetZepR0eDovcdKAfe/dGDdRHUK22X33tr\noFyQIahbpXwJ6kIIIS4riqIG9crGc22+Hh0NiqGNTL25T92kN9OoNPid15tT2rrTnw4S1IUQQlxm\n3G6gz2Emr5uotTUpTdrPMTGAwenTpw7e8rtBZ8TV5PK7bhO916fe3SVmJagLIYS4rDidYApzkF/p\n3Wjs6tVXs+f0HgB+/WtITAqcqRt1RtxN/qvPNNF7ferd6U8HCepCCCEuM04nGIMbqGmswdHgoMZZ\nQ05BDs9/8TwAcXGAocEvU/f0qRv0BlxKG5l6L5XfY0JiiA7p3q6kEtSFEEJcVhobwRhcD0BxdTHf\nnf2OK2KuYPep3eSV5wHQ4PZm6p5SeMvye5uZuq53yu+2OBs77trRrXN7/pVCCCGEuIQ4nWA0qwPd\niqqLOFp6lIzEDBrcDew5vYfBUYNpcPln6p7yu0FvwBkoUzf2PKjrdDoizBHdOleCuhBCiMuK0wmG\n5ky9yFFEbkkutjgbFfUVfF/+PeCbqVuCLBj1Rm1EulFnpE7xz9QV3Jh6oU+9J6T8LoQQ4rLidKob\ntoCaqX9T8g22OBsp0Sl8X9Yc1Ftk6hazha/u+QqdTgeombq7rUyd3ulT7wkJ6kIIIS4rTicYzGqm\nbnfYtUx9aPRQjpUeA3wzdYAx/cZoPxv1gUa/uy54pi7ldyGEEJcVpxP0QQ0EG4P56NhH9AvvR9+w\nvujQtZmpt2YMkKkrut6Z0tYTkqkLIYS4rDQ2gj6oniRrEgfPHuSGITcA0Ce0D27FTVldmV+m3pJR\nb8QtfepCCCHEhefpUx9sHQzA9YOvB9RR555+9fYy9UB96orORZBR+tSFEEKI88bpBJ2pnuSoZIIM\nQUxOmqy95ulXby9TNxmMNAUYKCfldyGEEOI8qq4GQ1ADCZYEjj1wDIvZor0WFx5HcXUx7iY3Jr2p\nzfONeiNNBCi/98I89Z6QoC6EEOKSd/w45OR07li7HUIi6jEbzQyMHOjzmjXYytnaswQZgrQpbK21\nN1BOyu9CCCFED731FrzwQueOLSiAkHB19HtrkeZISmpKApbeAYwGI01tDJRDd+GntHU7qNfU1JCZ\nmcmYMWNITU0lJycHh8PB7Nmzsdls3HzzzVRXV2vHv/TSS9hsNsaOHcuePXu09kOHDjF+/HhsNhuP\nPvqo1t7Y2Mjdd9+NzWZjypQpFBcXd/dRhRBC/MgdPQonTvi333gjFBb6thUUgDmsvs2BcJHmSM7U\nnAk4SA7UTL2JtjP1S7b8/stf/pJJkyaxf/9+cnNzGT58OE8++SQTJkwgNzeXjIwMVqxYAcDBgwdZ\nvXo1+/btY/PmzSxYsABFUQDIzMxk5cqV5Obmsn//frZv3w7AqlWrMJvN5Obmcu+997J06dJeeLtC\nCCF+jI4eVUvwrf3jH2oQb6mgAEwhbWfq1mArZ2vOtpupmwyB+9SDLsWgXllZye7du1m4cCEARqOR\nyMhItm3bRmZmJqAG6y1btgCwdetW5s6di8lkIikpiZSUFHJycigqKsLhcDBu3DgA5s+fr53T8lpz\n5sxh586dPXunQgghfpQURQ3qNTVQXu5tr6pSB8VVVvoeX1AARnN9m4E7MlgtvwcZggLer60+dUUB\n9Bd+Slu37p6Xl0dsbCwLFixg7969XHXVVbz44ouUlJQQFxcHQFxcHCUlJQAUFhaSkZGhnZ+YmIjd\nbsdkMpGYmKi1JyQkYLfbAbDb7QwYMEB9yOYvDWVlZURH++4xu2zZMu3nyZMnM3ny5O68JSGEEJeo\nM2fAZILBg9USfHq62t4cTqio8B6rKGpQTwkKnKmXVJeQHJUc8H4mgxGlVabe1ATo3b2y9WpL2dnZ\nZGdnd/r4bgV1l8vF119/zWOPPcarr77K4sWLee+993yO0el0AUcO9qaWQV0IIcTl5+hRuOIK6NdP\nLcF7grqnL71lpl5WBsHB0KgE7lNvb446gNHg36fudgN6N4ZeDuqtk9Xly5e3e3y3yu+JiYnExMRw\n0003ERISwty5c9m+fTvx8fHagLaioiL69u0LqBl4fn6+dn5BQQGJiYkkJCRQ0KKzw9PuOef06dOA\n+iWisrLSL0sXQgghPEF9yBDffvW2MvWCAkhMVDdsaXP0e/Oe6e0NlGurT93tBp3ejVF/CU5pi4+P\n1/rFm5qa+PDDD5kyZQo33XQTa9euBWDt2rXMnj0bgJkzZ7Jx40acTid5eXkcO3aMcePGER8fT0RE\nBDk5OSiKwvr165k1a5Z2judamzZtYsqUKb3xfoUQQvzInDihBvTkZO8I+MZGNajrdL6Zuieo17sC\n9Kmbm4N6uwPlAmXqLgy6S3SXtrVr1zJ//nzOnTtHWloazz33HE1NTcybNw+bzcaQIUNYv349AKmp\nqWRlZZGeno7RaGTNmjVaaf7NN98kKyuL2tpaZsyYwfTp0wFYtGgRixcvJi0tjdjYWN55551eeLtC\nCCEuJW63+ico8Lg1ysth5EgYNQpefhmOHIHJk+Gmm9R+9paZ+nffqW0Frvo2M3Wz0UywMbj9TN1o\nPG/l967qdlC/4oor+PLLL/3aPaPXW1uyZAlLlizxa/fMcW/NZDKxevXq7j6eEEKIH4GXX4Zvv4XX\nXw98TGUlREbC+PHqiPff/Q6Ki2HzZrjqKm+mriiwdi289hr8/UDb5XdQs/WOMvXWA+U85fcLnanL\ninJCCCEuWgcOwP/8D7j813rReIK6Xg933gnvvw+33QalpZCa6g3qX3yhXueaa5rL7wGycWuwteM+\ndV3b5fdLsk9dCCGEOB8OH4b6emixEKkfT1AHyMpSA/tDD6m/p6Z6y+8ffwy33qr2swcaKAfqYLn2\nMvUgo/+UNrcb0F348rsEdSGEEBclRYFDh2DRIjVbD6SqCiIi1J+HDYO334YxY2DxYt9MvaICYmPV\nnwMNlIOOM/V2p7RJ+V0IIYTwV1ysLiqTmQk7dvi+dvXVcOqU+nPLTN1Dr1f7zvv08WbqDoc3+De4\nut+nHmQ0oujayNQv1SltQgghxA/t0CEYPlwd1X7unO8a7t98A/v2qT+3FdQ9IiO9mbrDARYLKIpC\ng7sh4FKwkcGRHfSpG1Ba9am7XIDOJeV3IYQQoi2HD8OIEWrWfd114NkCpKZG/ZObq5boPcG6LRER\nanm+qcl7nNPtJMgQhF7Xdgi0Blu73KdeXY2U34UQQohAjh+HlBT15+uv9wb1s2fVv7/9Vg3uZrNa\npm+L0QihoWrQrapSg3p7I98Brux/JaPiRgV8va1Mvby8eUrbpTpPXQghhPghlZerA90A/uM/1D5y\nUIN6eLga1NsrvXtYrepxnky9vZHvALeNvK3d67XVp15RAYpMaRNCCCHa1jJgR0SoQRnUXdkyMiA/\nH4qKOg7qkZFq0PUMlGtv5HtnmIxqpv7WW+riONA8GE934cvvkqkLIYS4KFVWqlk2qBl2dbX685kz\nkJCgbuLyxRfeEe2BWK3eoG6xQGk7I987I8hoBJ2L77+HvDy1raICFC58+V0ydSGEEBeligpvFh4e\n7s3Uz55V55uPHAk5OR1n6tHR6upynqBeH2Dd984yGQygd1Nb690JrrxCQdE1XfBMXYK6EEKIi1LL\n8ntoqLqynNutZup9+6rT3ToT1Pv2hdOn1UFzJpPap97eQLmOmAxG0LuoqWkZ1N3o0GublV0oEtSF\nEEJclCoqvOV3vV4N7DU13kx9+HD4/vuOy+9xcb7H9TRTN+qN6Joz9cJCta28wo2eC5ulgwR1IYQQ\nFyFF8R/ZbrGoJXRPpj5ihNremUz9+HHvXPYGV0OPBsoZ9AYtU6+qUvv6yysv/CA5kKAuhBDiIlRf\nDwaDOgfdIzxcDaBnzqiZ+tCh6uYsXQ3q5fXlRJo7OKkdRr0R9G5qatTf7XaoqHJhuMDT2UCCuhBC\niItQy0FyHp5M/exZNVAHB8PgwZ0L6idOeIP6qYpTDLIO6vazGXTeTB08QV0ydSGEEKJNLaezebQs\nv3t2WxsxonNBvbGxRVCvPMWgyO4HdU+fek2Neu/CQqiscmO8wNPZQIK6EEKIC2DfPvjf/w38eluZ\neni4utiMZ+lXgBdegJtv9j//XO05AP528G9ExjQA3oFyJytO9iiot+xTHzLEm6mbDFJ+F0IIcRl6\n911Yty7w620t/2qxqGX0vn29bVdc0bwMbH0lhQ51KHqRo4jkF5NpcDWQuSWTkqaD6HS+mXqSNanb\nz96yT33IEHVlu+paF0aDZOpCCCEuQydO+G6l2lpb5ffwcPU8T+m9pad2P8Xvd/4egMPnDuNwOvif\nw/9DTWMNVc4y+vTxbrvaW33qtbXqtrDZ2RAWfuFXkwMJ6kIIIS6AvDw1ww0k0EC51pk6qIF608FN\nnKw4CcCxsmMAvLZX3QGmtK6Uvn3hW8v/z5LtS3ArbqKCo7r97Ea9uqFLTQ3ceKPaJRBhdclAOSGE\nEJenvDw1U1eUtl8PlKkfP+4f1PcX76e0rlQL6kdLjzIwciC7Tu1Ch47SWjWo15lP8ureVxkUOahH\nK795+tTr6tR++ltvhbBoBxHmDlbBOQ8kqAshhDivKiuhoUFdJa6iou1jAmXqdrt/+f2Dox+wYPQC\nCh2FuJpcHCs7xp1pdwKQ3j+dsroydQS8oRxFUXpUeofmPvXm/dSDg+Gee2DYqHKiQrqf/feWbgd1\nt9vNmDFjuOmmmwBwOBzMnj0bm83GzTffTLVnOx3gpZdewmazMXbsWPbs2aO1Hzp0iPHjx2Oz2Xj0\n0Ue19sbGRu6++25sNhtTpkyhuLi4u48phBDiIpOXB8nJMGBA4H71QFPaFMU/Uy90FDI8Zjh9w/pS\n6CjkWOkxfj7i50SHRHND8g2U1pXyhz9ARFw59195PzcOvbFHz2/QGbT91M1mda/3u39VgTXY2sGZ\nP7xuB/UXX3yR1NRUrYTx5JNPMmHCBHJzc8nIyGDFihUAHDx4kNWrV7Nv3z42b97MggULUJrrLZmZ\nmaxcuZLc3Fz279/P9u3bAVi1ahVms5nc3Fzuvfdeli5d2tP3KYQQ4iJx4oS6aIwnqDc1we7dvscE\nmtIG/pl6aV0pMaExDLIO4njZcfIq8kiNTcX+oJ2h0UMpqyvjiiug2l3O3J/M5ZdX/rJHz6+OfndB\nn0McrToAQHldeY/66XtLt4J6QUEBH330Effcc48WoLdt20ZmZiagBustW7YAsHXrVubOnYvJZCIp\nKYmUlBRycnIoKirC4XAwbtw4AObPn6+d0/Jac+bMYefOnT17l0IIIS4ankw9MVEdLHfiBEyfrgZ3\nj0BT2sA/Uy+rKyM6JJpBkYP4x6l/EBsaS4gphGBjMNEh0ZTWlWrH9Ubg1ev0oFNg5Lus+24VABX1\nF0em3q2Z8r/97W/505/+RFVVldZWUlJCXFwcAHFxcZSUlABQWFhIRkaGdlxiYiJ2ux2TyURiYqLW\nnpCQgL15Dzu73c6AAQPUBzQaiYyMpKysjOjoaL9nWbZsmfbz5MmTmTx5cnfekhBCiPPk+HF1JbjS\nUjVTLyuD2lo4dUrN4CHwQDnwD+qltaXEhMSQZE1i5VcruWXELdprMaExlNWVAWo2HR3iH0e6SqfT\nQZMBXbCDkpoi9dr15T9IUM/OziY7O7vTx3c5qH/wwQf07duXMWPGBLyRTqc7b3vKtgzqQgghLn5H\njsDs2WqWvmePGtQBvvvOG9QDDZSDtsvvnky9vL6ch69+WHstJiSG0tpSFEWhor6i1waz6RQjhlCH\ntuBNRX0FQ6KG9Mq1W2qdrC5fvrzd47sc1P/5z3+ybds2PvroI+rr66mqqmLevHnExcVRXFxMfHw8\nRUVF9G3+KpWQkEB+i8mIBQUFJCYmkpCQQEGLERKeds85p0+fpn///rhcLiorK9vM0oUQQlx6jhyB\nYcPU9diLi6G8XG0/eBBmzFB/DjRQDvyDelldGTGhMUxOmsyT1z3JkGhvcPWU32saazAZTAQZgnrl\nPegUA4bgaoqqf9hMvau63Kf+9NNPk5+fT15eHhs3buSnP/0p69evZ+bMmaxduxaAtWvXMnv2bABm\nzpzJxo0bcTqd5OXlcezYMcaNG0d8fDwRERHk5OSgKArr169n1qxZ2jmea23atIkpU6b01vsVQghx\nATkcahAfMEANzmfPqpl6aKiaqXsEytQjItQR557xXPWuelxNLsJMYQzrM4zfT/y9zznRIdGU15X3\nWn+6h04xogt2UOQo6vUqQE/0ePV5T5n98ccfZ968edhsNoYMGcL69esBSE1NJSsri/T0dIxGI2vW\nrNHOefPNN8nKyqK2tpYZM2Ywffp0ABYtWsTixYtJS0sjNjaWd955p6ePKYQQ4iJw5Ii6D7per/aN\nnzmjBvWMDDVTB3XAXE2NdwMWj4QE+OQT9eeMNzJYOn4pk5ImER0SHbDL12QwERYUxqmKU70adHUY\nwOygsamR0rpSyusujky9R0F90qRJTJo0CQCLxaKNXm9tyZIlLFmyxK89NTWVnJwcv3aTycTq1at7\n8mhCCCEuQkeOwPDh6s8tM/VrroHnn1cDusMBYWFq4H/n23fYW7iXP93wJwx6A+PHg9Pt5EDxAZZs\nX3WRm7AAACAASURBVMLL/9/LxITEtHvP6JBovi/7vtczdYIcgDpPvqK+4tKd0iaEEEJ0x+HDan86\neAN3fr46QM5iUfcmr6hQ+9Mr6it4cMeD7Dm9hwc/eVC7xunK0yRYErjxihvZemRrhyPaY0JiOF5+\nvNczdSXIQZAhiCJH0aXbpy6EEEJ016FD3kwd1Gz9yBGIioKUFPj+e+8c9Ve+foWfDf0ZH//iY9Ye\nWKtNTTtRfoLkqGTS+qax6+QuYkLbz9QTIxL5uvDrXs/Um4wOBlsHezP1i6BPXYK6EEKI8+arr+DK\nK72/x8bCsWMQHa32tR875h0kd/DsQa5Luo6Y0BhuGnYTaw+oA6g9Qd0WZ8PusHeYqU8dMpW/n/h7\nr8xR99BjoMnoIDkqmVOVp3C6nYSZwnrt+t1/LiGEEOIH5FL3PsFuh7o6GNJiOnffvurmLtHRvpm6\n1ar2Vfe39AdgwagFvHvwXUAN6oOtg0nrmwbQYZ/6jUNvpElp6vVM3W2sJiU6hYNnD2INtp639Vna\nI0FdCCHED8bpVEetu1zw5ZfqKPeWsc8z5zwqyj9TtzvsJFgSABgSPURb6MWTqceFxxEbGtthBj7I\nOoif9P1Jr5bH9c3jzCcNmsT277dfFP3pIEFdCCEuaU1NsGNH9893u72Z9A+hvFydtma3wxdfwFVX\n+b7uWfK1dZ9660w9NjSWc7XnAG9QB7DF2TrM1AFWXLeCnw7+aa+9Lx0GAK4bfB2zhs+6KEa+Qy/M\nUxdCCHHhnDwJN96oBk/Pimud8eWXapZ8771qH/ezz/4wz+dZLe7kScjJgdYre8fGQkiI+iclRV0X\nvrwcgiOrUBSFCLM6WT3UFEqT0kRtY61afo9S15N96qdPMTByYIfPMWv4rF58V95MPcQYwovTX+Tb\nkm979frdJZm6EEKcZ3V10L9/72TIZ8+q2fbnn3ftvN/+Vp1aVl4Of/ubuk/5D6GiQv07Lw9yc2H0\naN/XY2PVLB3ULyWRkfDNN6CLULN0Tz+1TqcjNjSWE+UnaGxq1LLz8Ynj6Wfp98M8fDv0zZm6Zye4\nSUmTzvsztEWCuhBCnGd5eVBUpO5Q1lNnz6p/79rVtfOKi9XNVD7/XP2SceRIz5+lLZ5M/fPPITgY\nYlpVyvv2VQfJeUyZAh99BK5QOwkRCT7H9gntwzfF35BgSbjgg9L0GDEqIRf8OVqToC6EEOfZiRO+\nf/fE2bMwcCB0YXdOFEUN6mlpatl7xgx4//2eP0tbPJn6Rx/ByJH+r48eDfPmeX+fMUP9kuE0F2qD\n5Dxiw2I5UHLAL9hfCHoMmAi50I/hR4K6EEKcZ8ePq3/3VlCfORP+/W9vAO1IVRWYTOqKbqAu0fqv\nf/X8WdpSUaGOai8s9A3qXxZ8yfbvt9O/Pzz0kLd92jQwGKDOaNcGyXm0zNQvND1GTDoJ6kIIcdk7\ncUItOfdGUD9zRt3x7PrroeX2G4oSuJ+8uBji472/Jyaqo9N/COXlMGaM+nPLoL71yFZezHnR73ir\nFe67D9yh/pl6n9A+HCg+QGJE4g/zsF2g1xkI0oVe6MfwI0FdCCHOsxMn1L7j3srUY2Phjjtg40a1\n7csvYexYuP9++OADeOwx33OKiyEuzvt7QsIPF9QrKtQyv17vG9QLqgr4Z/4/cTe5/c55+WWoNRQR\nHx7v0x4bGsvZ2rMXRaZu0BkJ0kumLoQQl70TJ9TMOi+v59fyBPUZM9R54CNGqAH+/vth+3a1v/rl\nl9VFYDxKSnwz9YQEtTz+Q4yALy9XB8MtWACjRnnb8yvzcTQ4yC3JbfO8c7XniA2L9WnrE/r/2jvv\nsKiup49/gaWDShOkhCKCSLWgaCyIJnZsMWKiMYmJMZYk5qcx1mjsUaOxRE2ssUQN6quxYMcSFZWI\nBZSOdJDe2V123j8mLCI2FBXwfJ6HR/bee849d/bI3JkzZ8aYx1sL1tTtbNRgbiIsdYFAIHijUShY\nmT+Lpa5QsFK+d6/iWFERIJPx9rDffqtQ6rq6wJkzwM6dvGY/ejSwezewYQMr+nPnKvp42P2uo8OR\n6VlZNfusQEXFtY0bK9dHT8xLRBebLjgff/6R7TKLMqsklTHRYSVfGyx1PV0J9LWEpS4QCARvFPfu\nsRIt59w5Vm52dqygLS2B995jS/lhsrJYSfv4AIWFfOzbb1lh//gjsHhxhVIH2OXesiUHmgFAu3bA\n4MFsxc+axQFxZWVVlTrw8lzw2dkV+9DLISIk5iXi/Rbv43Li5Ue2yyjKqFJ9rTZZ6hJVCbTVhVIX\nCASCN4K8PP53zBh2O589y9vG3n+frVYVFeDwYeD4cVZ6c+ZU7SM1lcuUNm5cEZ1+5gywZw9w+jQr\n/cTECqX+OAYPrlDm//zzapV6uaX+IBlFGdDV0IWTiROS8pMgK5Nh9+3dyvNEhMziR1jquiZQVVGt\nstb+OlBTUYOOunC/CwQCQb2HiK3mbdtYif76KzB0KPDJJxy41qcPX+ftDbRoASxYAPz1V2WLHqhQ\nvu7unGUtNZWj3Zcv54xwb78NSCSAnt6Tx9O8OadoHTkS2Lfv1VvqDyv1hLwEWDWwgpmeGVILUhGe\nGY5he4chvTAdAFAoK4SailoVS9iqgRWGtBgCierrz3AuUZVAWyIsdYFAIKj3xMbyevm4cRUu8KAg\ntszbtq16vYkJMHw4MH9+5ePlytfNjdfQL1xgRT56NDBzJv9uYlK56tmTGDiQrfxbtx6t1G/cAL7+\nml37NVXkJSenqvs9MS8Rlg0slUo9KS8JBMLB8IMAeD293NX+IPqa+tj13q6aGdgLoqYqLHWBQCB4\nrWRm8k9NUlRUNd3rmTO8Tm5gAPTrx8esrVnBP45Zsziw7eYDweAPW+pnzwKdO1ec9/Hhfp8VZ2dg\n/HiOjH84u5uFBbBmDUfGnz4NTJ784tHwCgVXXGvYsPLxhNwEWDW0QkPNhiiVlyIyKxIGWgbYf3c/\nALDrXefplddeJ2JNXSAQCF4zM2eyMpszB/D3r15bIuDzz1mJP8jPP7MbvbSUt419+y270t99Fzh1\niq3qZ8HYGPjhh8rZ1cqVuosLZ4z7809+WSinXTt+gXhWVFSAadP4R0ur8jl7e8DWFti0Cdi/Hzh5\nkiPvS0uf3OeOHRyN/ygKCjgNrbp6xbGAqABsvbEVlvqWUFFRgZmeGYJTgjHcbTjO3TuHYlnxIyPf\naxtqKmrC/S4QCASvk+BgdokXFQETJvB6NxEwdiynJ31S/vTUVN4edv6hHVhHj7KresUKVuI7dvC/\nPj6sKB9Wnk/i88+BO3eAixcr7mlmxpZu48bAhx9y1PyDPKgwX4Ru3fjFQUeHPQxBQVwu9a+/Ht/m\n9m1eNnjci8WdO1ULuEw8NhG+jr74tOWnAAAzPTNcS74GJ2MnOBk7ITgl+JGR77UNiapEuN8FAoHg\ndSGXsxL65hveCrZ2LfDFF0BoKK91Dx/OSVvKq50dOwYsWVLR/sYN/vfkyYpjWVm8Pn3oEF+7YQMw\nZQpvM3tY+T4LGhrA9OkVa+sPBrRt2PDoCPmaQkWFrepydHR4fX3TpopjpaUcqFfO5s2cmW7//qr9\nZWRwcOCCBcCd+3cAAEl5SbhfeB/TOk1Tlks10zND2P0wWDawRHur9riYcPGRke+1DTXVemSpJyQk\noGvXrnB2doa3tze2bNkCAMjPz8eAAQPg5uaGgQMHoqCgQNlm5cqVcHNzQ6tWrXDhwgXl8Tt37qBd\nu3Zwc3PD9OnTlcdlMhlGjRoFNzc3dOvWDakPh4UKBAJBNbh7l/eE6+vz5/79gfx8jiTv1YtdzXPn\nAsuW8fktW/j3sv+ymN64AXh5VVbq/v68xu3iwkFo+/YBgwZVjfauDiNGsJUcG1tZqXfr9uz9Hos6\nhuis6OcfxH+UF4opL8u6ahXQuzevle/ZA/zxB2/PO3CAjwG8BFFSwhH/3bsDvu8VwGWtC4plxTgV\newo+tj5QValQPWZ6ZlCQAhYNLNDBsgMr9ccEytUm2jRpAzdTt9c9jKrQc5CSkkLXr18nIqL79++T\nqakphYWF0eTJk2nx4sVERLRo0SKaMmUKERGFhoaSu7s7SaVSio2NpaZNm5JCoSAiIk9PTwoKCiIi\nol69etHRo0eJiGjNmjX05ZdfEhHRrl27aOjQoVXG8ZzDFwgEbyBbtxL5+VU+9u23XPZk717+XFhI\nZGxMFBFBZGJCZGVFdOYMn/PzI9q4kahhQ6LERKKxY4msrSvOx8cTTZ5cM2OdOJFoyhQiIyOi9PTq\nt3f91ZWmn5r+1OsyCjOoVF76xGtWrSJyciLKzCTq04dIIiEaNIjI1ZXozz/5mhYtiC5dIpJKiXx8\niNq2JbKwILp5kyg6K5owGxSWHkYj9o2gdVfXVer/hzM/EGaD0grS6F7OPWq8pDFNODKBVlxaUf0H\nfwN4mt57LkvdzMwMHh4eAABjY2N4enoiKSkJBw8exMiRIwEAI0eOxP/9VzLowIEDGDZsGNTV1WFj\nYwN7e3sEBQUhJSUF+fn5aPvfHo+PPvpI2ebBvgYPHoxTp069yLuLQCB4w/nnn4pqYeW89x4XGvHx\n4c86OsBXX1VY22PHciDcvXtsqbduze57b2/Oq377Nv8OcKW0n36qmbGOH88WcF5e1TXpp5GYl4hb\n6bcQGBf4xOvOxJ6Bw2oHzDs3D1nFWYjLiXvsWDp25G1uFy5wsOG5c1wf3c+PrxkwgF3ws2axC9/O\njuMJXF2h3HsenR2NiwkX0cm6U6X+zfTMoK6qDmMdY1g1sIKuui4CogJq/Zp6beWFd/BHRUUhNDQU\nXl5eSEtLg+l/pX9MTU2RlpYGAEhOToaXl5eyjaWlJZKSkqCurg5Ly4oSehYWFkj6L/tBUlISrKys\neJASCRo2bIisrCwYGhpWuv/s2bOVv3t7e8O7/H+YQCB448nK4rXuhAQOPntghQ8Au9NDQiq7tb//\nnjO/denCketxcUCbNhzJ7eTEiio3Fxgy5OlJX54XOztW6rNn80vH09h2YxumnpqKpe8uRYG0AP0c\n+uF07GkUSguhq6ELuUJeKWELEWH0odGY3mk6fvrnJwQlBcFAy+Cxe8BnzAAcHDg6fsYMftkxfsA7\nPnAg78XPz6/YA19czOfKlfqttFtIzk+Gg5FDpb7N9Mxgrm+udMn/9M5PGPLXkFq/pv6qCAwMROCT\nIjgf4oWUekFBAfz8/LB8+XLoPTS7VVRUoPKsGRFegAeVukAgEJSTksJbvnr3Zgt7yRKgSZPK16io\nsJJ+EHV14MQJPtegAbBuHa8lx8dzIBvA6/AvioIUldaWH8bXt2KP+5O4nnId3538Dit7rsS009OQ\nkp+CNb3XIKs4C/8k/IPDkYcRnhGOgOEByjbhmeEokZdgotdEHIk8gsuJl9HcuPlj7/HWWzweQ0N+\nyTB+aLm7dWveRTB4MO93BypeeMqV+qHIQ3A0dqySDc7ZxBk97HsoPw92GozpnabDw8zj6Q//BvCw\nsTrnKdGSz63UZTIZBg8ejOHDh6N///4A2DpPTU2FmZkZUlJS0LhxYwBsgSckJCjbJiYmwtLSEhYW\nFkh8IGtD+fHyNvHx8TA3N4dcLkdubm4VK10gEAgeRi5n1/iyZcCoUbz3u7o8nCxFXR1o2rRmxgcA\neaV5cFjlgKMfHkXLJi0fe92T7KL43HhcTbqKwHuBGNN6DIY4D8FAp4E4d+8cOlh1QKGsEP139Yej\nkSOis6OVVjsA/B3+N/o69IWKigrW9V2HtII0DNg94Ilj3rChIhjuUePcvLlqQhuAlbqjkSMuJVzC\ncLfhVc43M2qG9X3XP9CXCub5zHviWASP57nW1IkIo0aNgrOzM7755hvlcV9fX2zduhUAsHXrVgwY\nMEB5fNeuXZBKpYiNjUVkZCTatm0LMzMzNGjQAEFBQSAibNu2TfmC8GBf/v7+6Nat2ws9qEAgqD/I\n5ewSv36df38w89mSJZzApWXLqu722sKVpCuQlkkx1H8oimRFuHP/DvJL8x95rayscmaXa8nXcCP1\nBmYHzsZQ/6HYErIFIz04/kiiKoGPrQ+0JFoY6zkWOVNycG30NbQxb4OD4Qcx68ws5JXmYXfobvRz\nYDeAvaE9vwRIC1EgLahy/3IaNHhy9H23blVTzwKs1L0svUAguDZ2rXqBoGZ5nui78+fPk4qKCrm7\nu5OHhwd5eHjQ0aNHKS8vj/r370+urq40YMAAys/PV7ZZsWIFubi4kIeHB507d055PDQ0lNq2bUsu\nLi70/fffK49LpVL65JNPyMXFhbp27UopKSnVjgIUCAT1k7//JtLVJfL0JGralGjNGj4eH88R4zEx\nr3d8RETHoo5RobTwkefmnp1Lk45PoiF7htD4I+PJaLERjT08tsp1JbISMlpsRNeSrimPDdo9iEyX\nmJLBIgM6HnWcpp6c+tSxLDi3gHTm65D5MnMyXGxIfXb0IalcWukap9VOdDP1ZjWf8ukM8x9Ga6+u\nJcwGHYk4UuP9v2k8Te/Vaa0olLpA8GYyaBDR2rVE3boRjR/PW8tkMqL164k++uj5+lx7dS2djTtb\nI+PLL80n7Xna9Hvw748832dHH/IP9ae47DjSmqdFow6MIuOfjCkiI6LSddFZ0aQ6R5Vcf3UlqVxK\nCoWCzJaa0bjD42jWmVnPPJ7babfJa4MX5RTn0B8hf1CJrKTKNb139KYDdw9U70GfgW5bu9HxqOPk\nvMaZUvKrGmeC6vE0vScyygkEgjpFTAynJR02jBPBrFrFRU327eMkKS1aPLl9Ul4SIjMjkVOSg7/D\n/wbALuIpJ6fgw30fwj+Mk8JnFWchJjvmucb4d/jf0JJoYdftXShTlGH8kfGYeWYmAF6+vJx4GV6W\nXrBuZI0Ln1zA6t6r8WnLT7H1xtZK/cTnxqO9ZXsAQHBKMOJy4qACFazqtQpzvJ89vZxzY2dcGnUJ\nDbUaYoT7CGhKNKtcY9vI9rmf90mkF6ajsW5j3B57u1bUQa/vCKUuEAheOvfucX3xP/7gzwpFxTq4\nQlG5jvfmzZyRbMkS4OBBLhbyYBnQ774D/ve/ysFsQ4ZwvvXwcMDR8fHjOBVzCi3Xt0TnLZ0xeM9g\nDNozCHE5cdh3Zx96N+uNVb1WYcnFJSAi+Pn7YdjeYdV6TlmZDDPPzMTKKyuxqPsiBKcE451t7+BW\n+i2sv7YeW0K24H3/92GkYwSLBhwm3tq8NbQkWuhi3QWXEi9V6i8hNwHWjazR3qo9riZdxcWEi+hg\n1eGl7CyyM7BDbE4sSuQlWHxhMUJSQ2qk33KlLng1vP5K8wKBoF5TWgr06cOpWOfM4UjyHTtYyf/4\nI+cWnzAB2LaNa4N/+imnGp0/H9DU5MhqXV2uUFZcDFy9ytc+SOvW/DKQl1eh1O8X3kfPHT0R8GEA\nTHRNAACL/lmEFT1XIL80HwcjDuLrdl9j5pmZiM6KxuQOk9HPoR/+d/x/6L2zNzKKMpBRlIFryddg\nqG3ISi87FtaNrKGqoooxh8YgKZ/fRrQl2tg+aDv2hu3Fvjv70Ny4Ofxc/FAsK4ZEVYLPWn2G7Te3\n4+dLP+PzVp9jQ78NVeTkZemFq0lXUaYog5qqGgC21K0aWMHe0B7n7p2Dnoae0nKvaewN7bH1xlZM\nODoBIakhWHppKfYP3Y+Ob3VUXrM3bC8uJ13GkneWPKGnChSkQGZx7U/5Wq94JYsAL4k6PnyB4I1g\n3Tqinj2JFAqiy5eJtLWJnJ05oC0qin9ftoxTslpYEA0YwNf068cBb6GhRP7+nLbV0bEiNemDFBZy\nG01NotL/sp5+e+xb0pmvQ3PPziUiouzibNJfoE8FpQXKdukF6dR1S1fq/2d/KpIWERHRv8n/0m/X\nfqPE3ESaEziHtOZpkf4CfbqZepPUf1Qnl19daNaZWWSzwobWXV1Hv137jQbsGkC9d/Qmj3UeL7Qu\n7bjKkW6k3lB+/uLvL2jNlTUUkhJCtitsyfgnY4rMjHzu/p+ErExG3534jux+saOMwgzafXs3tVjT\ngr4J+IZupd0iIqIOGzuQ7nxdpQzL030/iuDkYGr3ezsyXGz4Usb7pvI0vVentaJQ6gJB7UYqJbKx\nIbp4seLY+vVEt28TrVhBpK/PSl2h4Hzq//sfUVERkbk50b59lfs6cIDzrysURHfv36VtN7ZRsaxY\ned7Njcjenn8/GX2SDBcb0rGoY9RkaRMqlhXTjps7qN/OftUaf3ZxNh2NPErD/IeR+TJzmnR8Eh2O\nOEzeW7wpIDJAeV2xrJhmnJ5BHTd1JHmZvNpyKmfk/pG08PxCZR+9tveig3cPkqxMRtrztGngroHP\n3fezUq6oFQoFfXfiOxpzaAw5rHKgs3FnyWypGXXb2o123txJYelh1PSXppRekE7xOfFUpiir1M/o\nv0eTn78fLTy/8KWP+U3iaXpP5b+L6iQqKiqow8MXCOo9gYHApEnAtWuPPh8fz2vmDyd2ycrimt6P\nWjpOyE2A61pXNDVsihYmLfDHgD+goqKCUaOAtDTgh/VX0ffPvtg1eBe62nbFsL3DUCIvQURmBL5/\n+3uMcB9R7ecITg5Gx80dETUhSrkW/jIIjAvEmENjoCXRwtEPj+Ld7e9i+8DtcDdzxxeHvsBnLT+D\np4XnS7v/45h0fBK2hGzBhLYTYG9ojwUXFkBHXQc5JTlwNnHG8ejjWPruUhhqG+JI5BF4WXph5pmZ\nCPkiBFYNrV75eOszT9N7QqkLBIJHsnEj18OuTn7z8eM56G3NGsDcnEuZ5ufXXKETAJgdOBuZxZlY\n3H0xem7viXu597DknSXQin4fyclAoLEfvCy98I0XJ8YqlhXjw30fopttN4z1HPvcQWY5JTlopPUC\nNVWfESLCgvMLsP3WdiTmJeLeN/dgqF17smkqSIGtIVtxMfEifujyA5qvbo5JHSbhl6BfoC3Rxo9d\nf8Taa2thoGWAkx+dfHqHgmohlLpAIKg20dFcZat9e67GFRcH5ORw3WwjIy7q8TCFhZz3+6OPeMvZ\n5ctcveurryrnMC9TlEFFRUWZ9zyrOAunYk5hiPOQR47lcuJlbLy+ER6mHhjVahSarWqGIx8cgaup\nK4gIQUlBGOo/FD91/wlvv/U23Na6IfbrWDTUavjI/uoKnxz4BH+F/oX8qfmvpI7G81KefnbdtXXw\nNPdEa/PWkJXJUCIvgb6m/useXr1DKHWBQPBESkqAmzd5r/d/RRaxZg0QFMQFPI4eZYVuY8Png4OB\nxMTKrvHZs7lyWXg4cPgwR7BnZwOnT/N2NgMDvi4mOwY9tvdAYl4ifun5C0a3Ho2lF5di8onJWNFj\nBca1HVep4EdeaR5cfnXBmDZjsDt0N9IK0tDTvie2DNhS6RkO3D2AFUEr0MW6C+4X3cea3mteqsxe\nBQXSAgREBeC9Fu+97qEIahFCqQsEbzgREVwLfMijDWGMGwf4+7NV/n//x8f69QM+/JDd77/9BnTq\nVJHUxdER2LmTt5GlpHDlMhsbQEuLXwbef5+3sQ0axMr/xg1uVyAtgOtaV3zX4Tt0su4En60+CB8f\njne2vYPPWn2GzSGbcTv9NnTUdbBz0E6kF6Zj2aVlaGPeBr/1+w3Zxdm4nHgZPe17VrFcS+QlaLKs\nCTTVNJ9aJEUgqMsIpS4QvOG8+y4QGso1xR+uza1QAJaWrMy7dwdSU4GyMnajx8ayq/1hvvsO0Nbm\nWuM2NlwF7coVfjFQV6+w4EtLgfR0wMqK14m/OfYNsouz8cdAzkAz5tAY3Ey7iejsaCR9mwSJqgS5\nJbkITgmG75++cDBywI9df0RP+55VynU+ihH7RyA0PRT/fvHvC0pMIKi9PE3vieQzAkE9hYgTu8TG\ncvKWf//lymYKBa97h4ayJd6gAdC2Lf8cO8bpVocOfbRCBziJzLRpgJcXZ3qbPZst9PJa4+VoagKS\nRinovHkoMoszoaaihlMfnVKeX9lrJVZfWQ0tiZZSaTfUaggfWx/cHnsbFvoWUFdTf+bn/aHLD8gq\nzqqumASCeoVQ6gJBHebAAf7ZtKnquWnTOMht507gr7/4ujZtODNbZCSXJR02DCivnjxgALvcW7QA\nzp17/D09PXkN/t9/gQ8+4BeBXr0efe3hyMPQ09DDou6L4GXppQyOAwANNQ182/7bR7azaWTzjBKo\nwN7QvtptBIL6hnC/CwTPiVwOSF7ja/Hx48Dw4TyO4GDA1rZiTGVl7FYPDOQ18KAgwM+PU6x6egJb\ntgBdugABAazE33qLU7CGhXH61qcFW7u48L/ffstjkKgrKinscob8NQR9m/VV1vsWCAQvxtP0nijo\nInhu0tOBmTNZgbxMFi4EvviiogBIbWH0aGDZssef372bI8JfBlevslW9fz9by9u3s1vd3h74+2+2\ntJs0qciD3q4dB7a5uwNdu7JCB4CePYES3QhEZkZCW5uveZbdU56e7L53dQXUJGVwW+uGY1HHKl0j\nV8hxKuYU3m36bg0/vUAgeBzCUq+DhIXxViOzR1QxTE5mZTJoEG9RehakUkBNjX/KKS0FJk9mxe3r\ny1aeqmqFYs3LA7y9Obp57lxgzJiKtgoFZ/Zq0ADQ0eHtUOVbmp7GvXuVx52WxpakiQnQty8wahTg\n5PRsfdUkMhkHgT2IvT1QUMBr1tranB1t2jRg6VJ2TfftC3z8MY9/3Tpes962rbKcAZZpdbchDx0K\ndO7MketBQcCIEcCePUC3bjxOMzN2rU+ZUtEmMRGYOpXHoqtbfm9C+43tEZ0djVMfnYKbqRsAjiZf\n8s8STOowCdrq2so+AuMCIVfIEXmsO8aN4+f/J+UEhu0dBkNtQ7iZukFBChjpGOFm2k1Iy6S4/sX1\n6j2cQCB4LCL6vZ4RE8NWUvfuwJdfAuvXAx07Am5uwIULbDl26sRpOffuZUXyJGQy7gtga9jfn124\n5uasrPz8gJUrgd69OXXnli2szPT12aobPRrw8eG129atefvS55+zclZV5WuvXeOXjB492LV7OcHF\n9QAAFTZJREFU7x7QqBGf19JiN3FODq8L//QTpxX19uZ13z//ZCtz4kRWlv7+rKzatOEx+/ryy8Oz\nUlbGCvThKPCysqrKtpyAAM6UdudOhWLPzuaXj7ff5u9jzhzg++9ZDvHx/GKzfTsnXrGx4bFPm8b3\nnjev4nv5919ey54xg2X5KK5fB375hWVjbMxucnNzThBjbMwvBY6OLFsDA2DgQJZn//4crCZXyFEo\nLVQmYyEirL22FnKFnNe7LyzCjM4zMO/cPNwYcwNaEi0M3z8cB8MPYqLXRNg0skFcThyCkoIQdj8M\npfJS7PEOxfhPTHD1ejFG7B+BrjZdcfv+bVjqW8Jc3xyFskK4NnZFc+PmMNUzffYvSCAQPBGh1GsR\npaXArFkcZPR//8fKzcHh8dcTsZLMyGDFWVgIdOjAbteff2bF9NVXQFQUu0IdHTkS2c6OI5i//BJY\nvpzds+X97dzJLwKdO/Mf/lu32Jpu3pwVzOef89andevYAjQzY2u5ZUtesz18mAOubt1iJaOqyp8/\n/pifr0kTVsDjxgEhITyu3r1ZOf/zD6/9WluzW1pFBSgq4r3OhoY8hkWLuN/UVFaGPj6suDU1+RlS\nUoBVqzjDWX4+u6F//pkV/YPWbl4esHo1rxU7OPAzdevGUd8REVyru18/4OxZtv7T0/nlw8GBrU8N\njYpo7o4d2TuyYgWP+9NPgUuX2EOxbRv34+ICnDgBXLhAaNRIBTE5kchAODwaeaOJkR7Si1IQmR6P\nC3vd8Oc2bdy6xR4SS0tW6IsXczBbhw6V50BEBO8fb9OGvQERERWW+LEHvN1z5/Lc2r2b94kDnNY0\nICoAiy4sgkwhw40xNyBRlWDnrZ2Yc3YOutp0xanYU/ihyw8Y7jYcQ/4aAk01Tehr6uNG6g1s7r8Z\nrX5rhdZNWsPH1gdOxk7o69AX009PR7G8GJPaTofnJjdYN7LGmZFnalUqU4GgviKU+ksiOZldwg+7\nZB9HYiIrnnI3dHAwK5KvvmLFNGUKW4v6+myl3rrF5+LjOfe2ujor5datORnIqlWs+BYsePw9r19n\n63jiRL4fEVt306bxPc3N+adXr6rW7sMu4fh4VryPywOel8cKvjp5wmuC4GDgk0/Yqjc355eKdu14\nvHl5LO+ICHY3h4ezEl2wgJcLzM253R9/8AvIqVP8khESwpZ2z57sOt/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} ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both seem to have roughly the same shape as the total counts, with material conflict slightly but persistently remaining more likely than material cooperation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Israeli-Palestinian Conflict\n", "\n", "The Israeli-Palestinian conflict gets a lot of media attention, so we would expect it to be well-represented in the dataset. It's generally considered to be fairly important, with effects spilling over far from where it is actually taking place. It is also one of the case studies that Leetaru and Schrodt use to compare GDELT against a similar dataset in their paper.\n", "\n", "All GDELT events have a source and a target actor. These are coded down to an impressive level of specificity, often down to whether a political party is a member of the government or the opposition when the event occurs. For a first pass, however, only the highest level of the actors will suffice. These will be **ISR** for Israel, and all Israeli actors; and either **PSE** or **PAL** for all Palestinian actors. We'll grab only those events which involve Israel-coded actors acting on Palestinian-coded actors, or vice versa.\n", "\n", "Incidentally: learn from my mistakes, and [RTFM](http://xkcd.com/293/). My first pass of this analysis was way off because I didn't read the GDELT documentation closely enough, and thought that the actor prefix for Palestine was PAL. In fact, almost all of the events are coded as PSE, the UN code for the Palestinian Occupied Territories. RTFM." ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = []\n", "for year in range(1979, 2013):\n", " f = open(PATH + str(year) + \".reduced.txt\")\n", " for raw_row in f:\n", " row = raw_row.split(\"\\t\")\n", " actor1 = row[1][:3]\n", " actor2 = row[2][:3]\n", " both = actor1 + actor2\n", " if \"ISR\" in both and (\"PAL\" in both or \"PSE\" in both):\n", " year = int(row[0][:4])\n", " month = int(row[0][4:6])\n", " day = int(row[0][6:])\n", " quad_cat = row[4]\n", " data.append([year, month, day, actor1, actor2, quad_cat])\n", "print \"Israeli-Palestinian Conflict Records:\", len(data)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Israeli-Palestinian Conflict Records: 528698\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we can turn this data into a Pandas DataFrame; essentially a big table we can manipulate." ] }, { "cell_type": "code", "collapsed": false, "input": [ "ilpalcon = pandas.DataFrame(data, \n", " columns=[\"Year\", \"Month\", \"Day\", \"Actor1\", \"Actor2\", \"QuadCat\"])\n", "ilpalcon.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YearMonthDayActor1Actor2QuadCat
0 1979 1 3 ISR PSE 2
1 1979 1 3 PSE ISR 2
2 1979 1 4 ISR PSE 3
3 1979 1 4 PSE ISR 3
4 1979 1 5 ISR PSE 3
\n", "
" ], "output_type": "pyout", "prompt_number": 10, "text": [ " Year Month Day Actor1 Actor2 QuadCat\n", "0 1979 1 3 ISR PSE 2\n", "1 1979 1 3 PSE ISR 2\n", "2 1979 1 4 ISR PSE 3\n", "3 1979 1 4 PSE ISR 3\n", "4 1979 1 5 ISR PSE 3" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas provides some powerful table manipulation tools; I'm partial to pivot tables, possibly due to several years of using Excel heavily for work. Let's pivot the data so that we count the number of events by QuadCat for each month." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pivot = pandas.pivot_table(ilpalcon, values=\"Day\", rows=[\"Year\", \"Month\"], cols=\"QuadCat\", aggfunc=len)\n", "pivot = pivot.fillna(0) # Replace any missing data with zeros\n", "pivot = pivot.reset_index() # Make Year and Month regular columns\n", "pivot.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
QuadCatYearMonth1234
0 1979 1 1 16 8 13
1 1979 2 0 14 5 5
2 1979 3 17 47 13 15
3 1979 4 2 18 10 56
4 1979 5 14 55 26 40
\n", "
" ], "output_type": "pyout", "prompt_number": 11, "text": [ "QuadCat Year Month 1 2 3 4\n", "0 1979 1 1 16 8 13\n", "1 1979 2 0 14 5 5\n", "2 1979 3 17 47 13 15\n", "3 1979 4 2 18 10 56\n", "4 1979 5 14 55 26 40" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have a nice table of monthly event counts, we need to index it by date. It would also be nice to rename the columns to the QuadCat description. To create a date from the Year and Month, we need to create a function that generates a datetime object from them, and apply it to each row." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# date-generating function:\n", "get_date = lambda x: dt.datetime(year=int(x[0]), month=int(x[1]), day=1)\n", "\n", "pivot[\"date\"] = pivot.apply(get_date, axis=1) # Apply row-wise\n", "pivot = pivot.set_index(\"date\") # Set the new date column as the index\n", "\n", "# Now we no longer need the Year and Month columns, so let's drop them:\n", "pivot = pivot[[\"1\", \"2\", \"3\", \"4\"]]\n", "# Rename the QuadCat columns\n", "pivot = pivot.rename(columns={\"1\": \"Material Cooperation\",\n", " \"2\": \"Verbal Cooperation\",\n", " \"3\": \"Verbal Conflict\",\n", " \"4\": \"Material Conflict\"})" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "pivot.plot(figsize=(8,4))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 13, "text": [ "" ] }, { "output_type": "display_data", "png": 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Pj4Ex41hfhelemDhxIvz8/PDJJ5/g3Llz+O9//yut0VxSUoJFixbBx8cHPj4+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\ng4ODg4PjXwon0hwcHBwcHP9SO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} ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interestingly, it looks like Verbal Cooperation is the most common form of interaction, even when violence (Material Conflict) spikes. We can also clearly see the peace process of the 90s, where Verbal Cooperation events are significantly greater than all others, and the spike in Material Conflict when the Second Intifada breaks out.\n", "\n", "Finally, let's see what a general 'peace index' might look like, measuring the difference in volume between cooperation and conflict events." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pivot[\"Peace_Index\"] = (pivot[\"Material Cooperation\"] \n", " + pivot[\"Verbal Cooperation\"] \n", " - pivot[\"Verbal Conflict\"]\n", " - pivot[\"Material Conflict\"])\n", "pivot[\"Peace_Index\"].plot(figsize=(8,4))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 14, "text": [ "" ] }, { "output_type": "display_data", "png": 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FRUV49dVXAQArV67EnDlzkJiYiLy8POTn52NdPFdiPwmgYrMEZQcdwWxx6BDF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} ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is barely scratching the surface of what we can do with the GDELT data. Hopefully it'll help you get started interacting with the data, so that you can do real work with it. Happy analyzing!" ] } ], "metadata": {} } ] }