notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# High school homicide analysis\n",
    "\n",
    "By [Iris Lee](http://www.twitter.com/@irisslee)\n",
    "\n",
    "The Los Angeles Times [Homicide Report](https://homicide.latimes.com) conducted an analysis of killings near public high schools and found that a small number of high schools are surrounded by extremely high number of homicides. Of the 262 schools included in the analysis, there are 15 schools which has been near more than 50 homicides in the last five years. That work resulted in the February 27th story [\"What it's like to go to school when dozens have been killed nearby\"](www.latimes.com/projects/la-me-edu-school-safety-deaths-nearby/).\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuration\n",
    "\n",
    "Set environment variables for all notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, warnings\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "alt.renderers.enable('notebook')\n",
    "pd.set_option('display.max_columns', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dir = os.path.join(base_dir, 'input')\n",
    "output_dir = os.path.join(base_dir, 'output')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "os.path.exists(input_dir) or os.mkdir(input_dir)\n",
    "os.path.exists(output_dir) or os.mkdir(output_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%store input_dir\n",
    "%store output_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data collection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download LA high schools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [California Departmen of Education](cde.ca.gov) provides list of schools every year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run src/download-la-highschools.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean LA high schools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Schools that moved locations in the last five years were excluded from the analysis. Excluded schools were indentified by comparing historic school location data from [National Center for Education Statistics](nces.ed.gov) to the most current list provided by CDE. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run src/clean-la-highschools.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run src/download-nces-schools.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run src/clean-nces-schools.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run src/find-changing-addresses.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run src/remove-changing-addresses.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run src/finalize-la-highschools.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download Homicides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieves homicide list from The Times' unpublished API. This step has been withheld from the notebook to maintain our site's security."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run src/download-homicides.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean homicides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creates a simplified homicides file that includes only the records needed for the analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run src/transform-homicides.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spatial join\n",
    "\n",
    "Identify homicides that occured inside school buffers through spatial join."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in GeoDataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "homicides = gpd.read_file(os.path.join(output_dir, 'homicides-five-years.shp'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "schools = gpd.read_file(os.path.join(output_dir, 'school-buffers.shp'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find all homicides that happened within one mile of a qualified LA County high school."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "homicides_near_hs = gpd.sjoin(homicides, schools, op='within')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove homicides that occured before the nearby school was open"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, convert school open date column and person death date colum to datetime type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "homicides_near_hs[['open_date', 'death_date']] = homicides_near_hs[['open_date', 'death_date']].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_homicides_near_hs = homicides_near_hs[homicides_near_hs.death_date >= homicides_near_hs.open_date]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There may be duplicate homicides if they are linked to more than one school"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4055"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(valid_homicides_near_hs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2215"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_homicides_near_hs.ID.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new dataframe that groups the schools with number of homicides nearby "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "homicides_by_school = valid_homicides_near_hs.groupby([\n",
    "    'cds_code', \n",
    "    'name'\n",
    "]).size().rename('homicide_count').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cds_code</th>\n",
       "      <th>name</th>\n",
       "      <th>homicide_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19642121930056</td>\n",
       "      <td>Cerritos High</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19642121930361</td>\n",
       "      <td>Artesia High</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19642121933159</td>\n",
       "      <td>Gahr (Richard) High</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19642460108407</td>\n",
       "      <td>Eastside High</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19642461930213</td>\n",
       "      <td>Antelope Valley High</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "         cds_code                  name  homicide_count\n",
       "0  19642121930056         Cerritos High               2\n",
       "1  19642121930361          Artesia High               7\n",
       "2  19642121933159   Gahr (Richard) High               8\n",
       "3  19642460108407         Eastside High               1\n",
       "4  19642461930213  Antelope Valley High              10"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "homicides_by_school.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Schools without homicides would have been left out from the join. Identify schools without homicides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many schools had no homicides nearby?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "zero_homicides = schools[~schools.cds_code.isin(valid_homicides_near_hs.cds_code)][['cds_code','name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(zero_homicides)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add those schools into the ranking so it is complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "complete_homicides_by_school = pd.concat([homicides_by_school, zero_homicides]).fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure that worked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert len(complete_homicides_by_school) == len(schools)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the distribution of schools?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>homicide_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>count</td>\n",
       "      <td>295.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean</td>\n",
       "      <td>13.745763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>std</td>\n",
       "      <td>17.190404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25%</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50%</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75%</td>\n",
       "      <td>20.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>max</td>\n",
       "      <td>105.000000</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   index  homicide_count\n",
       "0  count      295.000000\n",
       "1   mean       13.745763\n",
       "2    std       17.190404\n",
       "3    min        0.000000\n",
       "4    25%        2.000000\n",
       "5    50%        6.000000\n",
       "6    75%       20.500000\n",
       "7    max      105.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_homicides_by_school.describe().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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pyUpgFpeABCTQYQJKsjJ4SrISmMUlIAEJdJiAkqwMnpKsBGZxCUhAAh0moCQrg6ckK4FZXAISkECHCSjJyuApyUpgFpeABCTQYQJKsjJ4SrISmMUlIAEJdJiAkqwMnpKsBGZxCUhAAh0moCQrg6ckK4FZXAISkECHCSjJyuApyUpgFpeABCTQYQJKsjJ4SrISmMUlIAEJdJiAkqwMnpKsBGZxCUhAAh0moCQrg6ckK4FZXAISkECHCSjJyuApyUpgFpeABCTQYQJKsjJ4SrISmMUlIAEJdJiAkqwMnpKsBGZxCUhAAh0moCQrg6ckK4FZXAISkECHCTQlyS2Bm4FdgDsCn24Tk6mpqf6lG7ZrpElKshGMViIBCUigEwSakOQxwCOBvwE+BUwAJwMvawsBJdmWSNgOCUhAAt0i0IQkvwe8CXgA8NvAa4ATgXu1BYWSbEskbIcEJCCBbhFoQpI3Ag8DPg58CHgj8Flg67agUJJtiYTtkIAEJNAtAk1I8rLS5V2BpwAvBm4FfrMtKJRkWyJhOyQgAQl0i0ATkty93H+MLF8CfAB4KTCQ56oTUZKrHgIbIAEJSKCTBEaR5LYL9Pj6thBRkm2JhO2QgAQk0C0Co0iyv0BXe21BoSTbEgnbIQEJSKBbBEaR5PMW6Gqecm3FpiRbEQYbIQEJSKBzBEaR5HBnb1/elUx2+UUgT7y2ZlOSrQmFDZGABCTQKQJNSPIhwEXA/UvPrwX2B77eFhJKsi2RsB0SkIAEukWgCUl+Evjl8mRr7kO+EvhhmVigFTSUZCvCYCMkIAEJdI5AE5L8GXAYcEHp/YHAO4CFnn5dLKy8b/kq4KfA1cBxZcfTgJ3LNHiZGu+q+SpUkotFbTkJSEACEhgm0IQk/x24HHgWkEzyLcBDgR0bQv0J4PnlXueFwF8CdyhiPhzYATgT2EdJNkTcaiQgAQlI4BcEmpDkeuDdwLrCNA/vJLPMZ01srwM+Uv5FmHmq9klpO3B2OcA1JavMSiSbbGaSTYTBOiQgAQmsPQJNSDLUskRWhlkjyGR7ecK1qS0ZalYU+TbwE2A/4LXAJUNDvFcAewEbpqenT+r3+1mF5DZbU0tlTR+xG1ttMbgeaKqL1iMBCUhAAm0lMDk5eZv3/pcyCcDDy7ytkWTuTV7ZUGczSXrkGAlntZFTgP8Ftin3J88px8m9yp3KnLFmkg3BtxoJSEACa51AE5nkAcD7ynBrBJshz2SVeS1k1C0pW4ZSs8pIprl7DnCfkkUeDBwJTAJnAXvPdzCHW0cNg/tLQAISWJsEmpBkssYMdw4/uJN3J/PwThNb7j8+F7iuDOc+o/x8OrB9eYr22PLw0JzHU5JNhME6JCABCaw9Ak1I8gbgUOD9Bd9TgQyDup7k2juf7LEEJCCBsSLQhCQ/U17JyPJYGW59RR6gAfZoCykzybZEwnZIQAIS6BaBJiSZh2py//Hepes/KE+g/ltbUCjJtkTCdkhAAhLoFoEmJJke/xKQmXHydOvngQzBtmZTkq0JhQ2RgAQk0CkCTUjyjkAmFLh7mSJuACBzuLZiU5KtCIONkIAEJNA5Ak1I8kPAvnP0fCnvWy4LQCW5LFitVAISkMDYE2hCkpkF50Tg7cDGIWKtWVNSSY79eWwHJSABCSwLgSYk+R4gc6q+eVla2EClSrIBiFYhAQlIYA0SGEWSg/lZ8z7kg4GvzXpg55Ft4akk2xIJ2yEBCUigWwRGkWTuRW5u+522oFCSbYmE7ZCABCTQLQKjSHK4p3myNe9HJqvMz99qEwYl2aZo2BYJSEAC3SHQhCSPAM4A7gw8Gvgk8HTgXW3BoCTbEgnbIQEJSKBbBJqQ5HeAT5UVObKE1ZuARwEPaAsKJdmWSNgOCUhAAt0i0IQkfwpk2arB0lh/AJxZ5nNtBQ0l2Yow2AgJSEACnSPQhCQjxyy6fB6wZZl951+A/dtCQ0m2JRK2QwISkEC3CDQhyUxsnnck9yld/3hZW/K7bUGhJNsSCdshAQlIoFsEmpDkoMcTZe7WW9uGQEm2LSK2RwISkEA3CIwiyTyk8zLgqvIk6/nA7wJXAll4+d/bgkBJtiUStkMCEpBAtwiMIsm3AH8EHA3cBTgZeCfwFOBz80x6vip0lOSqYPegEpCABDpPYBRJ/jfwNuCFwGXA98tiyy8CTvDp1s6fG3ZAAhKQwJonMIoks8pHssh/AL4HPBc4HTiuDMNmnclWbGaSrQiDjZCABCTQOQKjSPILQO5Lfht4PLA98EDgb4FvAo9tCw0l2ZZI2A4JSEAC3SIwiiT3LO9GJmPMkOtpwEeBXcsDPJFoKzYl2Yow2AgJSEACnSMwiiTT2R6wDhi89pFJBTK5eRZibs2mJFsTChsiAQlIoFMERpVkJzqrJDsRJhspAQlIoHUElGRlSK7fauIOnzx5/fWVu1lcAhKQgAQ6SGAUST4N+Bhw+7KW5M1t7b+ZZFsjY7skIAEJtJvAKJLMfccDy8M6WUcyEwi0clOSrQyLjZKABCTQegKjSPKasmZk5mxNFtmf1dut2tJ7JdmWSNgOCUhAAt0iMIokHwkcAGSGnXPKhALDvc+sO63YlGQrwmAjJCABCXSOwCiSHHT2iLLg8nXlA1cB6dxpYIMlIAEJSGAuAk1I8s7AmWVi8wy5ZjWQP2nTu5Jmkp78EpCABCSwFAJNSPIdwBOBs8rkAkcBfw8cuZQGLcc+SnI5qFqnBCQggfEn0IQkfwwcC0SW2Z4OvA64a1vwKcm2RMJ2SEACEugWgSYkmSWz/rqs/JHeZyHmZwH3bAsKJdmWSNgOCUhAAt0i0IQkXwG8GPhSGW7N/K2nlKdeW0FDSbYiDDZCAhKQQOcINCHJvCf5HOCg8q7kP5Z1JTe2hYaSbEskbIcEJCCBbhFoQpKt77GSbH2IbKAEJCCBVhJQkpVhcYLzSmAWl4AEJNBhAl2Q5PbA64FbgC2Bw4A8UZtFnncGMtx7DHDVfHEwk+zwGWrTJSABCawigSYk+ULgXCBzuS7H9lHgucCXyzR4/1FeL4ksDwd2KJMZ7KMklwO/dUpAAhJYuwSakOSG8spHRNn0lmW4PgtcDPwa8K/AS4CTgWuBs8sBI+hklXMu12Um2XRYrE8CEpDA2iDQhCQvBB4KvB+IMGcKulc2gPDeQDLHvYDPFClmDcvdgEuAC8oxrihlcvxNNiXZQCSsQgISkMAaJNCEJGcvkTXA2GuAZzLJ7wJ3KXXlNZM9i4yvLquP5Ff5eSfg1unp6ZP6/X4yzdtsl27YroHmwPQRu7HVFusaqctKJCABCUig/QQmJydv47Nauc23buSNDXX9i8AzgcuAU8sw67eAg8v8sJNl3ti95zuemWRDkbAaCUhAAmuMQBOZZKafew2wK/Aw4O3A84AfNMRyF+C1wE3Aj4A/Kk+6ng7kyddty9yxlyvJhohbjQQkIAEJ/IJAE5LMDDv3Bx4IZEzzK0DuET6hLYzNJNsSCdshAQlIoFsEmpDkT4ADgfOAOwF5FSPi3KYtKJRkWyJhOyQgAQl0i0ATksxDM3nK9NnA44ATgQcBD2kLCiXZlkjYDglIQALdItCEJJ8MvAfYunQ9M+M8tWSTraChJFsRBhshAQlIoHMEmpBkOp2Hdx4DbAF8GvjPNpFQkm2Khm2RgAQk0B0CTUgyT5fmadY98p4i8Iky12pTr4CMTFNJjozQCiQgAQmsSQJNSDLT0f1+WXQ5EPPKxjvLvKqtgKokWxEGGyEBCUigcwSakGSebj26vB8ZAH8MTJcnXVsBREm2Igw2QgISkEDnCDQhyY8UQb679P7Y8pTrE9tCQ0m2JRK2QwISkEC3CIwiyUw0nu1Xyusenwd+qazWkfckn9IWFEqyLZGwHRKQgAS6RWAUSX5uga4+qi0olGRbImE7JCABCXSLwCiS7ExPlWRnQmVDJSABCbSKQBOSzNJVLy8P6gyvIJIJz1uxKclWhMFGSEACEugcgSYk+U3gZuDrQwsuB8QBbaGhJNsSCdshAQlIoFsEmpDk98pCyNe2tetKsq2RsV0SkIAE2k2gCUk+F8j8re8raz4OevzWtnRdSbYlErZDAhKQQLcINCHJj5b3Imf3fPj+5KpSUZKrit+DS0ACEugsgSYkmRl3ngF8AegPkfhWW6goybZEwnZIQAIS6BaBJiT5d2VZrMzX2spNSbYyLDZKAhKQQOsJNCHJa4BJ4IbylOug03dqS++VZFsiYTskIAEJdItAE5J8wzxd/rO2oFCSbYmE7ZCABCTQLQJNSLL1PVaSrQ+RDZSABCTQSgJNSPL6eXqWxZhbsSnJVoTBRkhAAhLoHIEmJHl66XVe+bh9eWfyfOBP20JDSbYlErZDAhKQQLcINCHJ2T3eF3glsHtbUCjJtkTCdkhAAhLoFoEmJPm4oS7fDlgPHAxs3RYUSrItkbAdEpCABLpFoAlJDk8gMOj964C/aAsKJdmWSNgOCUhAAt0i0IQkHzvU5Qjzh8CVbcKgJNsUDdsiAQlIoDsERpHkQpMFbGgLBiXZlkjYDglIQALdIjCKJOcaZh3uvROcd+tcsLUSkIAEJDCLwCiSfPasuu4PHFNeA7kC2KUttM0k2xIJ2yEBCUigWwRGkeSgp1uWh3ReDGwETgbOAG5tC4qmJbntDTPn0vvFO6EjbbfcNPHUD5+2/scjVeLOEpCABCSwbARGlWRe/8jcrQ8GzgWOA763bK1dYsWNS/LGmf8CRp5RaGbjxD0vfvX67y+xW+4mAQlIQALLTGAUSWaJrLwTmewxovzYrLZeuMxtX3T1SnLRqCwoAQlIQAJDBEaR5Jp9cGdbM0n/iCQgAQmsCQKjSHLXBQhd3haCZpJtiYTtkIAEJNAtAqNIsjM9VZKdCZUNlYAEJNAqAkqyMhzXbzVxB4dbK6FZXAISkEBHCSjJysApyUpgFpeABCTQYQJdkuTLgXsBzyi8TwN2BibKJAZXzRcHh1s7fIbadAlIQAKrSKArknwScALw1SLJJwCHAYcDOwBnAvsoyVU8kzy0BCQggTEk0AVJPgiYKiI8qEgyWeW1wNklJteUrPLmuWJkJjmGZ65dkoAEJLACBNouycxqcx7wh2Uu2CzmnOHW04FLgAsKo8wVuxcw58ojSnIFziQPIQEJSGAMCbRdkgcCJwHXAXcG7l6yyrsCVwPnlJjk550yX+z09PRJ/X4/88feZrt0w3aNhG/6iN146bu+xE23ZKKh0bZXHLord9x6i9EqcW8JSEACElhWApOTk7dZ1ao1S1zN6nXmiR1kkvuWn48EJoGzgL3no2Qmuaznj5VLQAISGFsCbc8kh8EPSzKfZ8h1+zLR+LHAvDP8KMmxPX/tmAQkIIFlJdAlSS4ZhJJcMjp3lIAEJLCmCSjJyvA7mUAlMItLQAIS6DABJVkZPCVZCcziEpCABDpMQElWBk9JVgKzuAQkIIEOE1CSlcFTkpXALC4BCUigwwSUZGXwlGQlMItLQAIS6DABJVkZPCVZCcziEpCABDpMQElWBk9JVgKzuAQkIIEOE1CSlcFTkpXALC4BCUigwwSUZGXwlGQlMItLQAIS6DABJVkZPCVZCcziEpCABDpMQElWBk9JVgKzuAQkIIEOE1CSlcFTkpXALC4BCUigwwSUZGXwlGQlMItLQAIS6DABJVkZPCVZCcziEpCABDpMQElWBk9JVgKzuAQkIIEOE1CSlcFTkpXALC4BCUigwwSUZGXwlGQlMItLQAIS6DABJVkZPCVZCcziEpCABDpMQElWBk9JVgKzuAQkIIEOE1CSlcFTkpXALC4BCUigwwSUZGXwlGQlMItLQAIS6DABJVkZPCVZCcziEpCABDpMQElWBk9JVgKzuAQkIIEOE1CSlcFTkpXALC4BCUigwwSUZGXwlGQlMItLQAIS6DABJVkZvKYl2ZvY+MBejy0rm7FJ8R/dtO7z/3za+htGrcf9JSABCUjg/wgoycqzoXlJzlzW63GPyq/v94EAABOnSURBVGZsUvwWJnb48NT6a0atx/0lIAEJSEBJLvkcUJJLRueOEpCABDpHwEyyMmRKshKYxSUgAQl0mICSrAyekqwEZnEJSEACHSagJCuDpyQrgVlcAhKQQIcJKMnK4CnJSmAWl4AEJNBhAkqyMnhKshKYxSUgAQl0mICSrAyekqwEZnEJSEACHSagJCuDpyQrgVlcAhKQQIcJKMnK4CnJSmAWl4AEJNBhAkqyMnhKshKYxSUgAQl0mICSrAxemyW5BRtf2ad3n8oubVK8t3HjkRe++tCrR63H/SUgAQl0nUAXJLkf8ALgeiATeB8B/Bw4DdgZmACOAa6aLxhTU1P9Szds10isWi3J/saL6fV2GLWjMxsnHnHxq9dfPmo97i8BCUig6wS6IMlM2r07cB3wcuCHwNeAw4DDgUjhTGAfJakku/4HafslIIF2EeiCJLcBflawvR74MnA/4Frg7PJ5RJqs8ua58JpJ1p10ZpJ1vCwtAQmML4EuSHJA/1DgaGBP4FTgEuCC8ssrgL2ADUrS4dbx/XO1ZxKQwEoT6IokTwSeABw4NOyaB0vOKcDy807ArdPT0yf1+/2TZ4Ns6p7k9BG78dJ3fYmbbtk4cqxeceiunPr+K/npDbeMXNeL1j+MN3/o3/mfn9w0cl3P/72HcJ9f2XrkeqxAAhKQwDgQmJyc7A334zb/04IOvhjYETgSGNhkX+Dg8tkkcBaw93xtdbi1LooOt9bxsrQEJDC+BNqeSd4D+A7w1SFBvh14A3A6sD2wLXAsMO/TmEqy7gRWknW8LC0BCYwvgbZLshHySrIOo5Ks42VpCUhgfAkoycrYrpX3JCcmZvaegXtV4tmk+Eyv/6YPTR3yrVHrcX8JSEACq0FASVZSXzOSXLfxLOg9ohLPpsX7E3teOL3+0pHrsQIJSEACq0BASVZCV5KVwJRkJTCLS0ACbSKgJCujoSQrgSnJSmAWl4AE2kRASVZGQ0lWAlOSlcAsLgEJtImAkqyMhpKsBKYkK4FZXAISaBMBJVkZDSVZCUxJVgKzuAQk0CYCSrIyGkqyEpiSrARmcQlIoE0ElGRlNJRkJTAlWQnM4hKQQJsIKMnKaCjJSmBKshKYxSUggTYRUJKV0VCSlcCUZCUwi0tAAm0ioCQro6EkK4EpyUpgFpeABNpEQElWRkNJVgJTkpXALC4BCbSJgJKsjIaSrASmJCuBWVwCEmgTASVZGQ0lWQmsYUnuf/x7XwrsVtmKOYr3T71o+pDPjl6PNUhAAuNMQElWRldJVgJrWJJPPP69F9Njv8pWbFp8pn/Qhacect7I9ViBBCQw1gSUZGV4lWQlMCVZCcziEpBAmwgoycpoKMlKYEqyEpjFJSCBNhFQkpXRUJKVwJRkJTCLS0ACbSKgJCujoSQrgSnJSmAWl4AE2kRASVZGQ0lWAlOSlcAsLgEJtImAkqyMhpKsBNZiSfYneDj09qns0SbFe73eKfTZpU9//5Hrmumf7lO3o1J0fwk0R0BJVrJUkpXAWi3J3voeHFTZo02Lz3BUf6K/R4/eUaPW1e9z9EXTB//VqPW4vwQk0AwBJVnJUUlWAutP7DnT6993gv59KvfcNGO7deK8/rqZNzb1nmR/QkmOGhP3l8C4E1CSlRFWkpXA+hN79nsbT+nR27Nyz02KzzCz30R/4mglOSpJ95eABBZLQEkullQppyQrgSnJKmAOt1bhsrAElp2AkqxErCQrgSnJKmBKsgqXhSWw7ASUZCViJVkJTElWAVOSVbgsLIFlJ6AkKxEryUpgSrIKmJKswmVhCSw7ASVZiVhJVgJTklXAlGQVLgtLYNkJKMlKxEqyEpiSrAIWSTLR/3Z/pveYqh3nKNzr9z5Ejy379EefMIHexy+aftqHR22T+0ugawSUZGXElGQlMCVZBSyS7PX620Pv6Kod5yzce2G/P7Ntr9d74ah19fv9Uy6aPuRFo9bj/hLoGgElWRkxJVkJTElWAVOSVbgsLIFlJ6AkKxEryUpgSrIKmJKswmVhCSw7ASVZiVhJVgJTklXAlGQVLgtLYNkJKMlKxEqyEpiSrALWZknS7/1Pr9cf/b5kj7fM0PvGRL//qio4cxXu8e4Lpw45ZuR6rEAC8xBQkpWnhpKsBKYkq4C1WZK93sT10D+lqkNzFu6f0e/3run1OGPUuvr0z7po6pA/GrUe95fAfASUZOW5oSQrgSnJKmBKsgoXSrKOl6XrCSjJSmZKshKYkqwCpiSrcCnJOlyWXgKBLkvyNGBnYALIPYmr5uv/1NRU/9IN2y0Bz6a7KMlKjEqyCpiSrMLVuCSf/IL33GvjOh5Z14pNS89s7P3gg6ce/LlR6xnsv+9x597ldltt3GPU+iZY97+3/tLNl03ctO6xo9bV28jPLjz1kH8atZ7l2n//E9/z5CbqPuMZv/6BycnJ3nBdt/mfJg6yDHU8ATgMOBzYATgTmHdWESVZF4GZjROPmFi38SzoPaJuzzlKK8kqhEqyClfjktz/hPce1INz61ox13nPBy+cPvh3R66nVPDE48/dg97Mp0evr3/ZzMZ1R02sm7ls5Lr6/asvnD7kQSPXswwVPPbkc7fd9saZnzZR9eufuTtdlOTLgWuBswuEa0pWefNcUJRk3amiJOt4McNR/Yn+Hj16R1XuuUlxJVlHsOl7kkqygr+SrIC18kVPBy4BLiiHvgLYC9gwPT19Ur/fP3m4SVtsscXMLbfckmFZNwlIQAISkMCiCey4444bDjjggDsP79CF4dZkklcD55SG5+edgFvnyyRPOOGE1vUrGa7tWvS5irwWzyol28qrzW1rKzPb1a5zv3UymQPPvsDBwJHAJHAWsPd8GD3B2nWC1bXm/0obxzpybeWlJOviKK/28eqCJEMtQ67bA9sCxwKXK8n6k8nMe3RmbZVRW9vll379OdfWWK7VdnVFkos+09ZqIBcNaFZBedWRk1cdLyUpr3oCdXss99/k2EkyD/Mcf/zxL6vDvPylbVcdY3mNB6/0wliORyzXahzHTpJ1p6OlJSABCUhAAvMTUJKeHRKQgAQkIIF5CChJTw0JSEACEpDAGpPkrkDme/058BXg+FU6AzLpwfOBJ5Xjt6FdJwCPB/pApr7K/dtfLu+h5qLpRiDLEf1khZkdDWT+xbRhGvhIS9oVDOtKe/4WeGfhsuj5hJeJ43rgtcB/l/pfAHy8nPeLmud4mdqVp9BfD9wCbFmmlPzxKrcr7fjsUH/vV+L43NK+ZwAbgb8B3rdMXOar9u7lb+9n5TzL9Jv/24J23au8bpf30W8AngVct8rtmv19Ot/3VqYxbSym45pJfqbM9Zrp7PKl9lbgkyt88r8YOAT4D+B3yrFXu12ZezHT+z26tCczGf15eQ810/3lHdScXPcBbjOT0TKz+1XgQ8CvAbcHvgw8EMh6hqvZrkG3Ty3v5v5VOZ+q5hNeJnZTwD8DHxiqvw3t+igQ+SSGB5Tz/6418y8vE69BtfcF/r7M/5zvv0+V8y6zdIVnJgNfyQvEk4AI8jXA84AtgDe1oF25IEws3w38AfAbQNq6Wrzm+j6d6/shrws22sZxlGROsq+W9yrzh/H08vNLlvmPb3b1vw98rZz8kWQb2pV4R0LJsPPzvwEHFTn+IfBt4P5FBHuuMK/blVmUHgCcD+xWTvbVbtfTygxPaV/imYuuqvmEl4ljvsASx22ALwL5EnlpzTzHy9CunFvJ2C4u4vlXIH93ueBa9PzLy9Cu4SqTKb4D+Mciypxfmagk22CkYCVXu8iFxO7Ai4BXlCwyf5er3a7Pl4uc/yoLS4RbLn5Wq12zv08Tr8hw9vdDRN5oG8dRkncqc73uUk78A8tcr5mEYKW3HctECJFkm9qVq+Y3FCllmDOTM/xWGU65Sxm2y9DwSm+5oMkXarLd/He12/XQcvUcUUaMA0nOO5/wCgJL1pEvru+UkZJ8sWaVnDnnOV6hdt27ZI4ZFsuoSeL4sXLBs5rtGnT/IUWEv14+iKBy3mf5vWxnlHM/meZKbY8B3gV8o4yePArIv9Vu15uBXORk1aV8d/5puXW0mu0a/j5NfOb6fsj3RqNtHEdJJmO7Ehgs65Ix/gzdreTw4eAPbDiobWnX1iUbStb4F8BMuSI7tHzB5X5NrrRHXoNuid8yydhyby1X1n8JrGa7BsOsuSeTDDeZ27OB366ZT3iJHBbaLffZBivh5F7uU4DvrnK7kkmmDbnQypZRioxIbFjldg1YvrpktLn3mC1L7uX8yj34bLndkHN/JW/N5GIi53my7wyXHweknavdrl8pFw13KDySyeVvcjXbNVuSySRnfz/kYrbRNo6jJHOy58TLzdtvlpM+V7QrOYQylyTb0K7E+8PlPtYbh76FX1WypHDKElARwkoOT+cKf3DPIc1KrDJ0+MRVbtewqF45lElWzSe8kO2W8Ps8SJSLnIyW/AiIzL9VBLDoeY6XcNzF7JKh32cCWcMw7cowa9q22u1K27OC0O+VNuX/8+BHMtwMd+Zv41/KqNNK3pOMHDPMmvuheeAqz0/s14J2RYpfL/eWjyjfCa9b5XbNluRc31u5t9toTMdVkllAOFdjecIuQ2S5OluNbXZQV7tdTy1DO3nid7DlwZ38MbwNyDBszomM6ecJu5XccjWdL6u04RPl6joZyWq3a8BgWJL5bNHzCS8TxFxAZCQgWVruG2XIMOf7arcr4s5TtzcVgSdLa0O7cl7ngiIPEeXJ7sGWi+lIICMYbyl/H8sUsjmrfXB5GnhwfzlPn+cCY7XblYfo8gR3zq/8+5PygNFqtmv29+l83w+NtnFcJbmSJ7nHkoAEJCCBMSWgJMc0sHZLAhKQgARGJ6AkR2doDRKQgAQkMKYElOSYBtZuSUACEpDA6ASU5OgMrUECEpCABMaUgJIc08DaLQlIQAISGJ2AkhydoTVIQAISkMCYElCSYxpYuyUBCUhAAqMTUJKjM7SGtUcg89rmhe/MLJMZUrJlxqDMnHJP4PsNILkQuAfwyM3UlflbM2lGpg67fla5zJm6FbBHA21Zrioyp/EHy9R1ly7XQaxXAqMQUJKj0HPftUpgJSSZ+Vizwsd7NgM5K6VkMvFMMZhZbYY3JblWz0773SgBJdkoTitbIwQGksy0cFlvL1tWSsji3oNMMosiJ7PMyhhfAP6sTLyf1VeSQWWFhfw384Vm/s4szp2/x0yRlvl1hzPJLICb1Rj2LlOEZV7bt5f1BweZZNqQKfxS5+eALOabdQqTSWbqs2S8mRYxy8ilLVkKKdMAZrWHncqUbVmjMmtmDm+ZJu2FZcq2tC1ZciaQzgoM89Wb9UjDJfVncvi0Ncsspf6s9HIV8PCydmlW3Ei7M81epmbLNImZljBT7f1dWew3K+j8oKwvuhoLFayR09puzkVASXpeSKCewECSc+0ZSWYR6UgwAogsssZdJtOOjCK1LDsUWST7y+8zIXPmOI0sv1eW+hmWZNY+jOCyAkkWwH0SMFkWFh9IMpLNpPRZNDtzk0Y8WdsxkswyWpFbJn/+47IUUyaxzzHS3iwFlgXCty8T3A9npZFkJpJOHy4APl3amXbMV+9ziuwzHJyl6iK+iDP/zYXEYKLsrGKS4daPAJkbN/8y92vWNM3k6JlrOEPOkXJEm/ZlwnQ3CawYASW5Yqg90BgRGEgyk4kn68sWyWRZtkgnk0En28sEzNeVLCmiy9BohJSVVrJkWUSVdQST2WXI9B+A+xYhDiSZbCwZVpZ3ykT9WXIty5ttnJVJRsg5XoZgs0Vg2S9rJv5PyegipUwgn2W2ItnILFli1qSMqLLocMQ6O5OMJO9csthkxZnwOv2dr96I/vFlubWsaBFeWeklfCLJXDBkpY3BPcmUzfBw2hoRZ3mtiDoZcfYPn+yz0guBj9Epa1eWSkBJLpWc+61lAgvdk0zGl8xrIJZkfhFgvuSzZFRWKcgQ4nZlObfBA0ARXT5L/QNJZoHgDJsmM4wssl+EkweHIrlBJvn+ssJFVm/IluHUiCZrOkZakVOys8jmxrJKR4SZtuVfRJVsL5L90lBwB5lk1orMfhFXHhLKSjHz1Zs2pU/5b1bXyBBploFK9vyCIuocYiDJDLVeVNbEDINHl+NktZqsUJPfp+9ZpcNNAitKQEmuKG4PNiYEFpJkZJN7jeeVB28izG2Bh5YhzxpJ5unWCCQZZZbDijCSjWboMWv+DSQZiWS4MuslJtP8QBFNhluzCHky0AzFZlHaDGcmk0yZu5X7frnfeUbJdjOkOtjmk+TjNlNvss20IXVm2axcNKTvEWuGYCPOYUlG6GGUJ4SzHFIuFHJhkHu+GSKOnDPsutLLt43J6Wo3RiGgJEeh575rlcBCksz9v4gwQ655cCcSyBBssqI8uFMrydSRB2wyXPvjcu/xHbOGW28tZZ5S7odmODbZ3+DBnWRhyRKzEPmzyiK/yTozzJv7gBlCzZDuy2YFdXOSzIM7c9UbeaefGdbNcHSy53wW0c8lycgxWXFkGKF+tLQhQ88ZCj63XBis1fPNfq8iASW5ivA9tAQkMC+BR5X7nkcD+5Z7puKSwIoTUJIrjtwDSkACiyCQe6yRYzLmZOFuElgVAkpyVbB7UAlIQAIS6AIBJdmFKNlGCUhAAhJYFQJKclWwe1AJSEACEugCASXZhSjZRglIQAISWBUCSnJVsHtQCUhAAhLoAgEl2YUo2UYJSEACElgVAv8PlPosEFBg8vkAAAAASUVORK5CYII="
     },
     "metadata": {
      "jupyter-vega3": "#39a7ee38-5a31-4536-b7a5-076b4d4de871"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.Chart(complete_homicides_by_school, title=\"School homicide counts\").mark_bar().encode(\n",
    "    x=alt.X('homicide_count:Q', bin=alt.Bin(step=5), axis=alt.Axis(title=\"Homicides nearby\")),\n",
    "    y=alt.Y('count()', axis=alt.Axis(title='Number of Schools'))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many schools have a homicide nearby?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "262"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(complete_homicides_by_school[complete_homicides_by_school.homicide_count > 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What percent schools had homicides nearby?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'89%'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"{0:.0%}\".format(\n",
    "    len(complete_homicides_by_school[complete_homicides_by_school.homicide_count > 0]) / \n",
    "    float(len(complete_homicides_by_school)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What percent of homicides occured near schools?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'69%'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"{0:.0%}\".format(valid_homicides_near_hs.ID.nunique() / float(len(homicides)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many schools 50 or more homicides nearby?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "over_50 = homicides_by_school[homicides_by_school.homicide_count >= 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(over_50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cds_code</th>\n",
       "      <th>name</th>\n",
       "      <th>homicide_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>19647330126573</td>\n",
       "      <td>Academy for Multilingual Arts and Science at M...</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>19647330108894</td>\n",
       "      <td>Alliance Judy Ivie Burton Technology Academy High</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>19647331933118</td>\n",
       "      <td>John C. Fremont Senior High</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>19647330126508</td>\n",
       "      <td>Augustus F. Hawkins High C Responsible Indigen...</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>19647251936947</td>\n",
       "      <td>Polytechnic High</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>19734370115725</td>\n",
       "      <td>Lifeline Education Charter</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>19647330118588</td>\n",
       "      <td>Alain Leroy Locke College Preparatory Academy</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>19647330111492</td>\n",
       "      <td>Alliance Patti And Peter Neuwirth Leadership A...</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>19647330124479</td>\n",
       "      <td>Dr. Maya Angelou Community High</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>19647330106849</td>\n",
       "      <td>Animo Pat Brown</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>19647330111641</td>\n",
       "      <td>Alliance Ouchi-O'Donovan 6-12 Complex</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>19647330100750</td>\n",
       "      <td>Wallis Annenberg High</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>19647330121285</td>\n",
       "      <td>Alliance Cindy and Bill Simon Technology Acade...</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>19647331939305</td>\n",
       "      <td>George Washington Preparatory High</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>19734371931963</td>\n",
       "      <td>Compton High</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cds_code                                               name  \\\n",
       "127  19647330126573  Academy for Multilingual Arts and Science at M...   \n",
       "78   19647330108894  Alliance Judy Ivie Burton Technology Academy High   \n",
       "161  19647331933118                        John C. Fremont Senior High   \n",
       "124  19647330126508  Augustus F. Hawkins High C Responsible Indigen...   \n",
       "58   19647251936947                                   Polytechnic High   \n",
       "240  19734370115725                         Lifeline Education Charter   \n",
       "101  19647330118588      Alain Leroy Locke College Preparatory Academy   \n",
       "84   19647330111492  Alliance Patti And Peter Neuwirth Leadership A...   \n",
       "117  19647330124479                    Dr. Maya Angelou Community High   \n",
       "73   19647330106849                                    Animo Pat Brown   \n",
       "89   19647330111641              Alliance Ouchi-O'Donovan 6-12 Complex   \n",
       "63   19647330100750                              Wallis Annenberg High   \n",
       "110  19647330121285  Alliance Cindy and Bill Simon Technology Acade...   \n",
       "187  19647331939305                 George Washington Preparatory High   \n",
       "243  19734371931963                                       Compton High   \n",
       "\n",
       "     homicide_count  \n",
       "127             105  \n",
       "78               85  \n",
       "161              81  \n",
       "124              72  \n",
       "58               63  \n",
       "240              63  \n",
       "101              62  \n",
       "84               59  \n",
       "117              59  \n",
       "73               55  \n",
       "89               54  \n",
       "63               53  \n",
       "110              51  \n",
       "187              50  \n",
       "243              50  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "over_50.sort_values('homicide_count', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What percentage of total homicides occured around the \"over 50\" schools?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "over_50_schools_homicides = valid_homicides_near_hs[valid_homicides_near_hs.cds_code.isin(over_50.cds_code.tolist())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20%'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"{0:.0%}\".format(over_50_schools_homicides.ID.nunique()/float(len(homicides)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write out the school ranking as a shapefile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_homicides_near_hs[['open_date', 'death_date']] = valid_homicides_near_hs[['open_date', 'death_date']].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "valid_homicides_near_hs = gpd.read_file(os.path.join(output_dir, 'homicides-near-hs.shp'))"
   ]
  }
 ],
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