{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# California fire zone analysis\n",
    "\n",
    "By [Ben Welsh](https://palewi.re/who-is-ben-welsh/)\n",
    "\n",
    "The Los Angeles Times conducted an analysis of California buildings within fire hazard zones for the Dec. 18, 2018, story [\"A million California buildings face wildfire risk. ‘Extraordinary steps’ are needed to protect them.\"](https://www.latimes.com/projects/la-me-california-buildings-in-fire-zones/)\n",
    "\n",
    "It found that at least 1.1 million structures, roughly 1 in 10 in the state, are within the highest risk zones. Here's how we did it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuration\n",
    "\n",
    "Set shared variables that will be used by all the notebooks in this repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import altair as alt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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",
    "%store input_dir\n",
    "%store output_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download building footprints\n",
    "\n",
    "Retrieve California building footprints mapped by Microsoft by running [download-buildings.ipynb](src/download-buildings.ipynb). Microsoft's list was compiled by a computer program that scours aerial and satellite photos. While it is among the most complete lists available, it is not comprehensive. According to a Microsoft expert who helped create the list, the database is believed to a slight undercount of the state's buildings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%run src/download-buildings.ipynb "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidy buildings\n",
    "\n",
    "Convert the Microsoft building footprints into centroid points by running [tidy-buildings.ipynb](src/tidy-buildings.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%run src/tidy-buildings.ipynb "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split buildings\n",
    "\n",
    "Break the building points into batches for processing by running [split-buildings.ipynb](src/split-buildings.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%run src/split-buildings.ipynb "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge fire zones\n",
    "\n",
    "Join the buildings to \"very severe\" fire zones by running [merge-fire-zones.ipynb](src/merge-fire-zones.ipynb). The maps were drawn by scientists at the California Department of Forestry and Fire Protection in 2007 using a computerized model that considers terrain, vegetation and the location of past fires. Our methodology was vetted by an expert there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%run src/merge-fire-zones.ipynb "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge neighborhoods\n",
    "\n",
    "Join the buildings to neighborhoods defined by The Times' [Mapping L.A.](http://maps.latimes.com) project by running [merge-neighborhoods.ipynb](src/merge-neighborhoods.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%run src/merge-neighborhoods.ipynb "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine merges\n",
    "\n",
    "Join all of the merges conducted above into a single file for analysis by running [combine-merges.ipynb](src/combine-merges.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%run src/combine-merges.ipynb "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First some configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "intcomma = lambda x: print(f\"{x:,.0f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent = lambda numerator, denominator: print(f\"{(numerator/denominator)*100:.1f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the prepared data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = pd.read_csv(os.path.join(output_dir, \"buildings-points-with-analysis.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many buildings are in the state?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10,988,525\n"
     ]
    }
   ],
   "source": [
    "intcomma(len(points))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many buildings are in a \"very severe\" hazard zone?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "points['in_firezone'] = ~pd.isnull(points.fire_zone_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_firezone = points[points.in_firezone]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1,151,181\n"
     ]
    }
   ],
   "source": [
    "intcomma(len(in_firezone))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What percentage is that?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.5%\n"
     ]
    }
   ],
   "source": [
    "percent(len(in_firezone), len(points))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What type of fire zone has more buildings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "zonetype_counts = in_firezone.fire_zone_type.value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "zonetype_counts.columns = [\"zonetype\", \"building_count\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "zonetype_counts['percent'] = zonetype_counts.building_count / zonetype_counts.building_count.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zonetype</th>\n",
       "      <th>building_count</th>\n",
       "      <th>percent</th>\n",
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       "  </thead>\n",
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       "      <td>481870</td>\n",
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      ],
      "text/plain": [
       "  zonetype  building_count   percent\n",
       "0      LRA          669311  0.581412\n",
       "1      SRA          481870  0.418588"
      ]
     },
     "execution_count": 17,
     "metadata": {},
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    }
   ],
   "source": [
    "zonetype_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(zonetype_counts).mark_bar().encode(\n",
    "    x=\"building_count:Q\",\n",
    "    y=\"zonetype:N\",\n",
    "    color=\"zonetype:N\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What Census defined places have the most buildings in fires zones?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crosstab_boolean(df, by_field, bool_field):\n",
    "    \"\"\"\n",
    "    Generate a crosstab that analyzes a boolean field.\n",
    "    \"\"\"\n",
    "    # Group on the index field and count frequencies of the boolean field. \n",
    "    counts = df.groupby([\n",
    "        by_field,\n",
    "        bool_field\n",
    "    ]).size().rename(\"building_count\").reset_index()\n",
    "    \n",
    "    # Flip the result into a crosstab\n",
    "    pivot = counts.pivot(\n",
    "        index=by_field,\n",
    "        columns=bool_field,\n",
    "        values=\"building_count\"\n",
    "    ).fillna(0).reset_index()\n",
    "    \n",
    "    # Calculate total\n",
    "    pivot['total'] = pivot[True] + pivot[False]\n",
    "    \n",
    "    # Calculate percentages\n",
    "    pivot[f'{bool_field}_percent'] = round((pivot[True] / pivot['total'])*100, 2)\n",
    "\n",
    "    # Clean up the column names\n",
    "    cleaned = pivot.rename(columns={\n",
    "        False: f\"not_{bool_field}\",\n",
    "        True: bool_field\n",
    "    })\n",
    "    \n",
    "    # Return the result\n",
    "    return cleaned.set_index(by_field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "place_pivot = crosstab_boolean(points, \"place_name\", \"in_firezone\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_places = place_pivot.sort_values(\"in_firezone\", ascending=False).head(20)"
   ]
  },
  {
   "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>in_firezone</th>\n",
       "      <th>not_in_firezone</th>\n",
       "      <th>in_firezone</th>\n",
       "      <th>total</th>\n",
       "      <th>in_firezone_percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>place_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Los Angeles city</th>\n",
       "      <td>666442.0</td>\n",
       "      <td>114355.0</td>\n",
       "      <td>780797.0</td>\n",
       "      <td>14.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Diego city</th>\n",
       "      <td>224153.0</td>\n",
       "      <td>88724.0</td>\n",
       "      <td>312877.0</td>\n",
       "      <td>28.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santa Clarita city</th>\n",
       "      <td>33712.0</td>\n",
       "      <td>18206.0</td>\n",
       "      <td>51918.0</td>\n",
       "      <td>35.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thousand Oaks city</th>\n",
       "      <td>20899.0</td>\n",
       "      <td>17062.0</td>\n",
       "      <td>37961.0</td>\n",
       "      <td>44.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rancho Palos Verdes city</th>\n",
       "      <td>356.0</td>\n",
       "      <td>13519.0</td>\n",
       "      <td>13875.0</td>\n",
       "      <td>97.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oakland city</th>\n",
       "      <td>74663.0</td>\n",
       "      <td>12186.0</td>\n",
       "      <td>86849.0</td>\n",
       "      <td>14.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glendale city</th>\n",
       "      <td>27580.0</td>\n",
       "      <td>11870.0</td>\n",
       "      <td>39450.0</td>\n",
       "      <td>30.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paradise town</th>\n",
       "      <td>149.0</td>\n",
       "      <td>11703.0</td>\n",
       "      <td>11852.0</td>\n",
       "      <td>98.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Big Bear City CDP</th>\n",
       "      <td>1229.0</td>\n",
       "      <td>10908.0</td>\n",
       "      <td>12137.0</td>\n",
       "      <td>89.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Simi Valley city</th>\n",
       "      <td>26399.0</td>\n",
       "      <td>10432.0</td>\n",
       "      <td>36831.0</td>\n",
       "      <td>28.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lake Arrowhead CDP</th>\n",
       "      <td>129.0</td>\n",
       "      <td>9875.0</td>\n",
       "      <td>10004.0</td>\n",
       "      <td>98.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Truckee town</th>\n",
       "      <td>3083.0</td>\n",
       "      <td>8555.0</td>\n",
       "      <td>11638.0</td>\n",
       "      <td>73.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Murrieta city</th>\n",
       "      <td>22975.0</td>\n",
       "      <td>7946.0</td>\n",
       "      <td>30921.0</td>\n",
       "      <td>25.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Redding city</th>\n",
       "      <td>27432.0</td>\n",
       "      <td>7676.0</td>\n",
       "      <td>35108.0</td>\n",
       "      <td>21.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laguna Beach city</th>\n",
       "      <td>3047.0</td>\n",
       "      <td>7314.0</td>\n",
       "      <td>10361.0</td>\n",
       "      <td>70.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Big Bear Lake city</th>\n",
       "      <td>796.0</td>\n",
       "      <td>7273.0</td>\n",
       "      <td>8069.0</td>\n",
       "      <td>90.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>La Cañada Flintridge city</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7179.0</td>\n",
       "      <td>7179.0</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Lake Tahoe city</th>\n",
       "      <td>890.0</td>\n",
       "      <td>7057.0</td>\n",
       "      <td>7947.0</td>\n",
       "      <td>88.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calabasas city</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6964.0</td>\n",
       "      <td>6966.0</td>\n",
       "      <td>99.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lake Elsinore city</th>\n",
       "      <td>9887.0</td>\n",
       "      <td>6921.0</td>\n",
       "      <td>16808.0</td>\n",
       "      <td>41.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "in_firezone                 not_in_firezone  in_firezone     total  \\\n",
       "place_name                                                           \n",
       "Los Angeles city                   666442.0     114355.0  780797.0   \n",
       "San Diego city                     224153.0      88724.0  312877.0   \n",
       "Santa Clarita city                  33712.0      18206.0   51918.0   \n",
       "Thousand Oaks city                  20899.0      17062.0   37961.0   \n",
       "Rancho Palos Verdes city              356.0      13519.0   13875.0   \n",
       "Oakland city                        74663.0      12186.0   86849.0   \n",
       "Glendale city                       27580.0      11870.0   39450.0   \n",
       "Paradise town                         149.0      11703.0   11852.0   \n",
       "Big Bear City CDP                    1229.0      10908.0   12137.0   \n",
       "Simi Valley city                    26399.0      10432.0   36831.0   \n",
       "Lake Arrowhead CDP                    129.0       9875.0   10004.0   \n",
       "Truckee town                         3083.0       8555.0   11638.0   \n",
       "Murrieta city                       22975.0       7946.0   30921.0   \n",
       "Redding city                        27432.0       7676.0   35108.0   \n",
       "Laguna Beach city                    3047.0       7314.0   10361.0   \n",
       "Big Bear Lake city                    796.0       7273.0    8069.0   \n",
       "La Cañada Flintridge city              0.0       7179.0    7179.0   \n",
       "South Lake Tahoe city                 890.0       7057.0    7947.0   \n",
       "Calabasas city                          2.0       6964.0    6966.0   \n",
       "Lake Elsinore city                   9887.0       6921.0   16808.0   \n",
       "\n",
       "in_firezone                 in_firezone_percent  \n",
       "place_name                                       \n",
       "Los Angeles city                          14.65  \n",
       "San Diego city                            28.36  \n",
       "Santa Clarita city                        35.07  \n",
       "Thousand Oaks city                        44.95  \n",
       "Rancho Palos Verdes city                  97.43  \n",
       "Oakland city                              14.03  \n",
       "Glendale city                             30.09  \n",
       "Paradise town                             98.74  \n",
       "Big Bear City CDP                         89.87  \n",
       "Simi Valley city                          28.32  \n",
       "Lake Arrowhead CDP                        98.71  \n",
       "Truckee town                              73.51  \n",
       "Murrieta city                             25.70  \n",
       "Redding city                              21.86  \n",
       "Laguna Beach city                         70.59  \n",
       "Big Bear Lake city                        90.14  \n",
       "La Cañada Flintridge city               100.00  \n",
       "South Lake Tahoe city                     88.80  \n",
       "Calabasas city                            99.97  \n",
       "Lake Elsinore city                        41.18  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_places"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
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       "data": {
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       "datasets": {
        "data-ff8832ec819cb03c87a936663313c5ef": [
         {
          "in_firezone": 114355,
          "in_firezone_percent": 14.65,
          "not_in_firezone": 666442,
          "place_name": "Los Angeles city",
          "total": 780797
         },
         {
          "in_firezone": 88724,
          "in_firezone_percent": 28.36,
          "not_in_firezone": 224153,
          "place_name": "San Diego city",
          "total": 312877
         },
         {
          "in_firezone": 18206,
          "in_firezone_percent": 35.07,
          "not_in_firezone": 33712,
          "place_name": "Santa Clarita city",
          "total": 51918
         },
         {
          "in_firezone": 17062,
          "in_firezone_percent": 44.95,
          "not_in_firezone": 20899,
          "place_name": "Thousand Oaks city",
          "total": 37961
         },
         {
          "in_firezone": 13519,
          "in_firezone_percent": 97.43,
          "not_in_firezone": 356,
          "place_name": "Rancho Palos Verdes city",
          "total": 13875
         },
         {
          "in_firezone": 12186,
          "in_firezone_percent": 14.03,
          "not_in_firezone": 74663,
          "place_name": "Oakland city",
          "total": 86849
         },
         {
          "in_firezone": 11870,
          "in_firezone_percent": 30.09,
          "not_in_firezone": 27580,
          "place_name": "Glendale city",
          "total": 39450
         },
         {
          "in_firezone": 11703,
          "in_firezone_percent": 98.74,
          "not_in_firezone": 149,
          "place_name": "Paradise town",
          "total": 11852
         },
         {
          "in_firezone": 10908,
          "in_firezone_percent": 89.87,
          "not_in_firezone": 1229,
          "place_name": "Big Bear City CDP",
          "total": 12137
         },
         {
          "in_firezone": 10432,
          "in_firezone_percent": 28.32,
          "not_in_firezone": 26399,
          "place_name": "Simi Valley city",
          "total": 36831
         },
         {
          "in_firezone": 9875,
          "in_firezone_percent": 98.71,
          "not_in_firezone": 129,
          "place_name": "Lake Arrowhead CDP",
          "total": 10004
         },
         {
          "in_firezone": 8555,
          "in_firezone_percent": 73.51,
          "not_in_firezone": 3083,
          "place_name": "Truckee town",
          "total": 11638
         },
         {
          "in_firezone": 7946,
          "in_firezone_percent": 25.7,
          "not_in_firezone": 22975,
          "place_name": "Murrieta city",
          "total": 30921
         },
         {
          "in_firezone": 7676,
          "in_firezone_percent": 21.86,
          "not_in_firezone": 27432,
          "place_name": "Redding city",
          "total": 35108
         },
         {
          "in_firezone": 7314,
          "in_firezone_percent": 70.59,
          "not_in_firezone": 3047,
          "place_name": "Laguna Beach city",
          "total": 10361
         },
         {
          "in_firezone": 7273,
          "in_firezone_percent": 90.14,
          "not_in_firezone": 796,
          "place_name": "Big Bear Lake city",
          "total": 8069
         },
         {
          "in_firezone": 7179,
          "in_firezone_percent": 100,
          "not_in_firezone": 0,
          "place_name": "La Cañada Flintridge city",
          "total": 7179
         },
         {
          "in_firezone": 7057,
          "in_firezone_percent": 88.8,
          "not_in_firezone": 890,
          "place_name": "South Lake Tahoe city",
          "total": 7947
         },
         {
          "in_firezone": 6964,
          "in_firezone_percent": 99.97,
          "not_in_firezone": 2,
          "place_name": "Calabasas city",
          "total": 6966
         },
         {
          "in_firezone": 6921,
          "in_firezone_percent": 41.18,
          "not_in_firezone": 9887,
          "place_name": "Lake Elsinore city",
          "total": 16808
         }
        ]
       },
       "encoding": {
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         "type": "quantitative"
        },
        "y": {
         "field": "place_name",
         "sort": {
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          "op": "sum",
          "order": "descending"
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       },
       "mark": "bar"
      },
      "image/png": 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HFkXwJqBr2Qwq7Rdde9fVvSMyOMEMMkijGeSwDF4fBvnW/s0Zle75/kHtqQ5MvzKovKgBl37aL4cMwdmgkgsQbA/bo0yWzRmV9n7WR7ywH0d7W7TBdsTmjEouB5QBEjLI4MCYa17aHraHQaVdNPX3PdOAQSWXA8oACRlkcGDMNS9tD9vDoNKzMOyO22nAoJLLAWWAhAwyODDmmpe2h+1hUGkXTf19zzRgUMnlgDJAQgYZHBhzzUvbw/YwqPQsDLvjdhowqORyQBkgIYMMDoy55qXtYXsYVNpFU3/fMw0YVHI5oAyQkEEGB8Zc89L2sD0MKj0Lw+64nQYEKkNDQ+N6N5kDYy4naHvYHmWCUjsf083vM8zNDDJkB8dxDSjdnHDuK4cGDCoOSo0z0Y7Yc8Kg0to/e320Xx8GlRzxfcJIMeFLPzVumzE8Ra8naNsyOCDL0N4JtjVkFw+wPWwPg3x5aDOodNEJuSswqNgR2xGXd8RV+o4MsJS91GB7VKmB9j7ToDI+9piwoxpU2i+6Ko2fIShlkMGBMde8tD1sj2Z+0O/6qTI6DPBYBpVcDigDJGSQwYEx17y0PWwPg8oAg8J4X7pBJZcDygAJGWRwYMw1L20P28OgAjsD2wPzA0sDN4RSdgKOBtYa74Ae40u+q4HlGuRRSU6ybgM8B+jvw4BfjCC3ruscYMYYr+0KYF1gcWBJYMS3JTeOZVDJ5YAyQEIGGRwYc81L28P2MKi8qAEBwA+B1eOjxYDT+wBUBCkfALYFHgNeHxCyxwjg0C1QqWtvF2Be4Igy4GNQyeWAMkBCBhkcGHPNS9vD9jCojAwqvwP+ALwGuBn4HPAW4FjgCWA+4AsBCCcD747uLgM+BiwSwfvhCOSfBp4FjovMxxuB7wPfBq4CLgFeDSwBbBF9nRT/3g+s3SSjogzQRsDtBWNuAmwHbBrZlaWAhYA7gB0iU6SMyt3AMTHWfwL/BTwJ/J0XMk3FpjH2ju/vAnaMDM96wFnA3MDBccwaceIpMf6VzSaaQSWXA8oACRlkcGDMNS9tD9vDoDIyqPwpykECjeuB9YETgYOA3wQ4fD2goBmoCBYEHjpGwVugsHCUSmZGueRcYHngRmCzGOdw4HLgTQE40+P8HzQBFWVRFgBqBWO+PQBImRaVhI4C5omy1nuArwCzgfcDnwhgEbgIWiTX5sCZwOPRp84VqCnb9CCwG3BalI5UGtN11jMqgi1BjiBIfdah5SVzzaCSywFlgIQMMjgw5pqXtoftYVAZGVQUrOuB9uLIRlwYWZWnI4twZxxTBBVBxkcj0zIt9nHcE9kXZSwOjcD+EPC+6E+gskrAwf6RIdF36lcwo4yFYOk/Gox2W/QhCKo3ZXNUCpoCHAJMAgRbyn6sCQwB7wyIUhZI2Zq3AfuFDMokfSnkV5/KDF0ErNgwtuRpBJWtgZUCyiTzkTpneHh4Wq1W03XN0WbNlmgTtPmBb6UNa1DJFZRsD9ujDCSUXvBjOGFQb09ut0elDira36EMyW8joyLoEBhcGtmOVwF/iWAtMPgrMCs2vKoEsyBwHXB8QMOPgcmRUVk5Skp1UHlDAM3/APpO0NK4mfbLASpbBVhob43AZvco96j8o+9U+hEMCbxUwlG2Q/tZNo5MzoGAMjmClp8A34t+NJW0QfcWYLXIxCijJIg7IUBFUKQymM7Xpl+VvtSPMjMPtJqLzqjYCTbODQdGz4l+C4xjiLWlT/X6aL8+JvoD3zoFFWUJVEpRZkRQ8sUAD+1bUUZEmQ0BieDldcC3olyi8omg4s2ASjnaU6KsjDIbypJcGzCivS91UBHECBhUPlIpRZChbEWxyS67AspkqD+VgL4BnApoD4z+vTcyMvr781EOqt/1o1KP9pLoelTS0f4TyaC9LI8WBtoA2CsyPrpGbaCVzMqoCKJUEpMcZ0cWZVFg6kgr0aDSftGV9mRjOCGDE8wgg1SYQQ7L4PVhkG/t0AY1ozIGF+9TGzQgaBMgjXi7skHFjtiOuLwjrtLbZIAlg2MuP5HdHhM9o1Ll+p/IY10AaN+ONtiO2AwquRxQhqCUQYbsjrjduurm97aH12iz+ZRhXjij0s2V7r5aasCgYifojIozKp24yMyBsRP5u3VMBj1kB3lnVLo129zP8xowqBhUDCoGlU7cYYYAbRn6w18ZVDpZUT6mYw0YVPpj4Xds0C4cmCEYZP/F2AU1d9yF7eE16tJPx8vFB05EDRhU7ASdUXFGpRPflgGYLEN/+CtnVDpZUT6mYw0YVPpj4Xds0C4cmCEYOKOSa17aHrZHmayOQaULjthdvKgBgcrQ0NC4zisHxlxO0PawPcoEpSr9aYa5mUGG7OA4rgGlygnpsarRgEHFQcmlH5d+OvE2GQK0ZegPf2VQ6WRF+ZiONdCPpZ95n5hv4dOP+Lhe6NjVZif4gjoz6CGLHBl0kUEG2yMXIGS3h0Glq6HJnRlUcjmgDEEpgwzZHXGVnsP28Brtt1KcQaVKDzEAYxlU7ARd+nHppxNXlwGYLEN/+CuDSicrysd0rAGDSn8s/I4N2oUDMwQDZ1RyzUvbw/Yok9UxqHTBEbuLFzVgUMnlgDJAQgYZHBhzzUvbw/bIDirnAK8DHgLmBt4AHAT8qAsBfwqwMjBUsq/9gJ2Au4EaMC/wVeCsFv1sDSwH6Lyy7f1xnv6tt9cDlwLLAs+V6HA6cDVwcolz2h16BbAusDiwZLu3JTd2ZlDJ5YAyQEIGGRwYc81L28P26AdQORCYFYIqGF4V8NIuiLb7fiyg8oxeVRMDLA8IqJbqAagoi3UjsD7w1+h/d2AB4IB2F9jwfS9ApT7ELgFsR5SRyaCSywFlgIQMMjgw5pqXtoft0W+g8l7ge8AykQ0RxDwZv+i/HMH8/Mg4vBa4D9gZmB84AXgNsDDw33GOAuwDwCLAt4HvAh8C9gIeBv4F7AA8VlCUMiNFUFkT+H5kTaYCnwSeDZjYAti4kFHZF1gvMiEXA5J/K+C/4jr+HvIW7bI38LI4Vp8rKyIZlck5Js7T97qWpwBd//3AL4G/AV8A7gGUiTkqMipfij50HTr+8A7k2AiQLNL3XcCOIYuuR9kkZbwOjmPWiAs4BTgMuLLZRDOo5HJAGSAhgwwOjLnmpe1he/QDqLwZeBx4E/B7QMH+GkAQoH9vArYF1ga+FhkXZV6ejv9XgN08ykaCjLcCGwTEfDqyFYsCvwXeDtwMrALoWRn7APMUIEH6Uh+fiWCt0ovKUl8J2RTIFfQFDCcC5wVkqPSjYC4wUXZE7ez4W/0JOGaGnGfG9dZto3LXhcAKgABA1/9R4OfAd+K8dwNfjP+uA5YImLoFWAl4JI47CfgzcHzoS6Wrc4HdgG+MIId0IL2sDjwYx58GzADWArYrZFQuCdhSaUyZpjq0vGSuGVRyOaAMkJBBBgfGXPPS9rA9+gFU6qUfZR4UjLVfQxkOZVf2AJ4AXhFZAwX9MwBlOdSUtVAQ1XEXRXCvX7NKP6tG9kSf3Qm8B1AwV+BX0/4LQYkyJfXWmFEp6vCzwCYBBsr6HBlfClRuBSYX9qoIqpSZkIzqU3D0O0DZjmIGR10ISpSZUOZFwCOoEaAJRARkKhHNF9cq0BEcqBSl/SgCOLX9o4ykY6VTAY2aIE2ZkL+MIIcyTtLfig0T5vomoKI9OYKjOyLL8rwOhoeHp9VqNckwR5s1e1KzOZj2Mz/wrbemMai8qN8MusgggzSSQQ7L0B9zczzu+tEv8uIelaMjIKvsoEC7afzSVyZDm0uVbTg9gqe0WgcVlUoWi8yHsg3DkQ0obqYVqAgkboiykko/yqhoP0hxI2wrUNGm3z9GHyr9KNsgUFDWQqCiv7WvRBkV6VIZFcHHOpGFUbnmJ1HaUpaj2DaMLIqgQ3ClTI6ATFmQXwNLR5ZI116/foGLsie6RpWw9LnkEeBoXJVs1FQak563H0EOySsoWi0yTdrQLCBSOU0ZFWW0NJ6ySSqzXRblJ2WyVFpr2pxR6Y+F31s0mbP3DMHAgTHXvLQ9bI9mPqiVr8gAKgsGoAhUFLA/HpkQZRW09+RTLUDlHxFUFwJeGeChn/KNoKLy0ocBbVhVuUn/qTyk8k69tQIV7dH4KSAZVfrRHhKVQbTvpH7Xj84VmKiUomAuENoySinKrig7pD0xjzYYRn0LMH4Y5S19rYyNwE37TLT3Rn0pa1MEtQ9GJuWfIZfAQrCyZwCT+lVJR/tbNmsjh8pl2rsjnShbonOuDVCRHlXq2jUATFkUZWqKmaiXzDWDSi4HlAESMsjgwJhrXtoetkd2UKnyx5zH6p4GjgVObXe7skEllwPKAAkZZHBgzDUvbQ/bw6DSveDsnl7QwAWR5dLeoBGbQSWXA8oACRlkcGDMNS9tD9vDoNIumvr7nmnAoJLLAWWAhAwyODDmmpe2h+1hUOlZGHbH7TRgUMnlgDJAQgYZHBhzzUvbw/YwqLSLpv6+ZxowqORyQBkgIYMMDoy55qXtYXsYVHoWht1xOw0YVHI5oAyQkEEGB8Zc89L2sD0MKu2iqb/vmQYMKrkcUAZIyCCDA2OueWl72B4GlZ6FYXfcTgMClaGhofF4Ps+/RXNgzOUEbQ/bo0xQaudjuvl9hrmZQYbs4DiuAaWbE8595dCAQcVBqXEm2hF7ThhUWvtnr4/268OgkiO+Txgpspd+atS+M3P6VL1aoOctgwOyDO2dYM8nQmEA28P2MMiXhzaDSpVeagDGMqjYEdsRl3fEVbqGDLCUvdRge1SpgfY+06AyPvaYsKMaVNovuiqNnyEoZZDBgTHXvLQ9bI9mfjDTSwmr9NMeq2INGFRyOaAMkJBBBgfGXPPS9rA9DCoVB2cP96IGDCq5HFAGSMgggwNjrnlpe9geBpU85KDS2k7ANsBzgP4+DPjFCCIeDZwDzCgccwJwcrwccLRXdxnwMeDuNh1cAawLLA4s2e5tyY19GVRyOaAMkJBBBgfGXPPS9rA9DCqjDeXdP0+Q8gFgW+Ax4PUBIXuMAADjDSp1LewCzAscUUYtBpVcDigDJGSQwYEx17y0PWwPg0qZyNrbY28ANgJuLwyzCbAdsGlkV5YCFgLuAHYA6qCizMcxwBbAVyKj8gfguMjMvBH4PvBt4CrgEuDVwBJxzkPAocBqwH3AO4G1GzIqkm1v4EngLmBH4GpgPeAsYG7g4DhmjbiGU0LuK5upzqCSywFlgIQMMjgw5pqXtoftYVDpLXyU6V1ZlAWAWuGktwdgKNOiktBRwDyAoOY9ASWzgfcDnwiwqJd+HoiSzMwoy5wLLA/cCGwGXA8cDlwO3AJ8PUBJMtwGrFQAFY15M7A68CCwG3BalJzWCpiqZ1QEQTvHuSpL1aHlJbowqORyQBkgIYMMDoy55qXtYXsYVMqgRG+PFRy8L7Il9ZG0T0SloCnAIcAk4OHIYqwJDEX2Q9mRdwP3A3VQuSmyJAIIZUzU91sCVFYBHgf2jwyO4Gg5YJ8Y+OIYs75HZRHgImDFBhUIdhpBZeuAHGV9lGU5UucMDw9Pq9VqGm+ONmu2Liln8wPfqreLQSVXULI9bI8ykFClx/DtyVVq+8WxvhwwsVXsUVkMUBZk9yj3qPyj71T6UVZEmQqVYpS10H6WjSNTUgeVjwLXAccDgpofA5Pj3JWBJwqgov4OANYHXgHcCqxayKhoY6+yLioNKYNzEHBmQJFARTA1X2Ro5ge0GVfQtDmgzE7T5oyKnWDjxHBg9Jzot8BYZbjw+mi/PvzAt97OSOl3V0AZiaejBPQN4FRAe0z0772RpdDfn49yUP2uH5V4tCfkvbFHRdJOj4yJ+tO+k/8ArgUaQeV7AT0bBli8LTb2Fu/62QDYKzIxypZoA636EqiovxND/rMji7IoMHUklRlU2i+63k65OXvP4AQzyCCtZJDDMnh9GORbe0BnVKqMDhNzrGMDrH5lUOnMwA5KL+gpgx6yyJFBFxlksD1yAVt2ezij0lnMGfSjLsTuIIcAACAASURBVADujA22I+rCGZVcDihDUMogQ3ZHXKWDsT28RvutFGdQqdJDDMBYBhU7Qae2y6e2q3QNBhWvUYNKlSvOY6XTgEHFTtCgYlDpxDFlACbL0B/+yhmVTlaUj+lYAwaV/lj4HRu0CwdmCAYu/eSal7aH7VEmq2NQ6YIjdhcvasCgkssBZYCEDDI4MOaal7aH7WFQMTmMmwYMKrkcUAZIyCCDA2OueWl72B4GlXEL0x5YoDI0NDSumToHxlxO0PawPcoEpSq9aIa5mUGG7OA4rgGlygnpsarRgEHFQalxptkRe04YVFr7X6+P9uvDoFJN/B6YUVKXfmr874zhKXpScCUtgwOyDO2dYCWTIQaxPWwPg3x5aDOoVOmlBmAsg4odsR1xeUdcpWvIAEvZSw22R5UaaO8zDSrjY48JO6pBpf2iq9L4GYJSBhkcGHPNS9vD9mjmB/2unyqjwwCPZVDJ5YAyQEIGGRwYc81L28P2MKgMMCiM96UbVHI5oAyQkEEGB8Zc89L2sD0MKuMdreF9wDRgQeBJ4HbgS8ADwC3AcsAzoxRzQ+BjwE4tzt86+t9vlP1fAawLLA4sCYz4tuTGMQwquRxQBkjIIIMDY655aXvYHgaVUUboLp22PHAm8CHg5uhTd5q8Gdi9D0ClroZdgHmBI8roxaCSywFlgIQMMjgw5pqXtoftYVApE1m7f+zhASPHtOi6nlFZAtAxyri8DBAYPAWcD1wKvBa4D9gZWBE4Fvgn8HLgzsioTAU+CTwLLABsAWxcyKgoiyNgUvZG/Uq2YtsI2DtkuAvYEbgaWA84C5gbODiOWSNOPAU4DLiy2fUZVHI5oAyQkEEGB8Zc89L2sD0MKt2HjzI9ng4cD/wyyie/iJMFGKsWMioK+N8BZgLvBr4Y/10VJZenAf2/YOIE4EDgMmAvYJkAFUGG4EOAcyJwXkCPSks/DTnWBmrAucBuwJ9Cnnki47M68GB8dxowA1gL2K6QUbkkgOlu4BygDi0v0YtBJZcDygAJGWRwYMw1L20P28OgUgYrun/sUQEA+rfe5o99KosVQEXAoOyKgES3ic8XcHAGsGaceHF8dlFkSZR9WQeYEqDyWWAT4JGAlyPjPIGK+hfcXBefLRrZEYGI2iKA+lW2ptiubwIq2veyEnBHZFmeH2d4eHharVbbv1GFs2ZP6r5Wu9GjH/jWDS2W7sOgkiso2R62RxlIKL3gx3CCb08eg/JKnqrAryzKR4A/x7kfB44GVO6pl35+DnwD+DWwNLABoGyM/lNGQ60OKt8M6PgtoP0uApF9gT8Ck6P0IwA5ObIn+l79q0SjMo6aSkjKhtwWfwuOJMtqwGzgoNhbo+yNxt824EkZG4GWsjn3A5vHpuCmanFGxU6wcWI4MHpO9FtgLOnzx3S410f79eEHvo1pirU8WaWcrwELAa+I7MZXgJsKoKI7agQv2j/yGmAf4NYWoKI9KD8IoND/a+/K56K8ozuLVPrRxleVaP5e2KOyJ7B+ZEG0sVf7YHR+vQmOVEp6PLIl+v7aAJWVo5wkMDobUBZFWRnti2nZDCrtF11vplzzXjM4wQwySDsZ5LAMXh8G+dYe0BmVKqPDxBxLm3lPbXe7skHFjtiOuLwjrtJlZIAlg2MuP5HdHs6oVOkh+nesC+JOI22wHbEZVHI5oAxBKYMM2R1xu3XVze9tD6/RZvMpw7xwRqWbK919ufTTwRzIvPA7EL9rh2TQg0ElV3C2PWyPMrDkjErX3LE7kgacUcnlgDJAQgYZHBhzzUvbw/YwqJgZxk0DBpVcDigDJGSQwYEx17y0PWwPg8q4hWkPbFDJ5YAyQEIGGRwYc81L28P2MKiYF8ZNAwaVXA4oAyRkkMGBMde8tD1sD4PKuIVpDyxQGRoaGte9Tw6MuZyg7WF7lAlKVXrRDHMzgwzZwXFcA0qVE9JjVaMBg4qDUuNMsyP2nDCotPa/Xh/t14dBpZr4PTCjZC79zJj/xnnYf//nqjJGBgdkGdo7warmQ/ZfrVXqwbrINS+z28OgUvXqnODjGVRyOSCDiu3hDFfubEaGNWpQmeCB2Zc3pwYMKg6MDowOjJ34xQwB2jL0h79yRqWTFeVjOtaAQaU/Fn7HBu3CgRmCQfZfjF1Qc8dd2B5eo80mS4Z54Ufod7yMfeBYNGBQsRN0RsUZlU58SObA2In83Tomgx6yg7wzKmObbU8B1wI14GXADcCngcfH1u2/z74M+BiwJ3A+cM4o+90AuBH46yjP7/g0g4pBxaBiUOnEYWQI0JahP/yVQaWTFdX6mEeABQtfnxIw8d2xdfsSULl7jP2dChwIXD3GftqeblDpj4Xf1pBdPCBDMMj+i7GL6m7ble3hNdpskmSYFy79tF2+ozqgCCrKqAgIfhLZC4HBk8DiwJcjm6GsyP3AL4HzAh6Kx1wOHAqsBtwHvBNYG9gvAOh24AjgYWDeyN7o/48DXgu8Uu8FBH5VuBr1IbmuB7YEvgSsB+g23YuBbwIXAivHWDOBRUPuE4DvAZ8C/g9YFjgpPmuqMIOKnaAzKs6odOJNMwfGTuTv1jEZ9JAd5J1RGdtsU+nnj1H6UflHgV9QsRlwDXATsG0AwNeA64AlAJ23RZNjTgS+DmwELADcBqxUAJUVgFfHMWsAdwAfBpaM8tCrAMHO20Om+tXVMyrzBRytH1+cHX9rTMm5Q5SadgRWB54JKNL16L+Fov/lWqnNoGJQMagYVDpxqxkCtGXoD39lUOlkRbU+prH0Uz/yvcAewBPAK4B7AjbOBAQYas2O+Q0gCNgnjhH4TCmAyq+BacC60ecXAlCUDflHnLMUoD0p9xbEroPKKsDk6E9fC57uAgQwyuwIRlS2Whp4V2RS3g/ovL2jP2V1Jun/h4eHp9Vqtf0b1TNr9vNfp2t+4Nv4mCRDMMj+i7FKy9ge/RGcq5wT2deHQWVss6EVqChzsilwcwR4lUz2BU4H1oohmx3zbeAAQBkPAc6twKoFUFFGQxtiZwE7AYISAYkyKTpP5SeNo7KTsiH19lPg4ACSev+yvTIqh4WcKumo3KSsispSDwTwCJQEQkONoNJMdc6o2Ak2zgsHRs+JZr4iw7ywDP0xNw0qvQGVvYCPA3cCTwMLR3aiCCrNjlHJR5mLDQMU3gZ8oAAqKgV9C3gQmKew90WfaY+KxhFw6O9i2x3YBtg49rWsE+frrqJ69ub3wHeAY4HfAT8GjoqMjkFlFPPETvAFpWXQQxY5Mugigwy2Ry5AyG4Pg8ooApBPaa0BZ1RyOaAMQSmDDNkdcZU+xfbwGu23DJdBpUoPMQBjGVTsBF36ab3QM0BCBhkMjrn8RHZ7GFQGAB6qvESDSi4HlCEoZZAhuyOuco3aHl6jzqhUueI8VjoNGFTsBJ1RcUalE8eUAZgsQ3/4K2dUOllRPqZjDRhU+mPhd2zQLhyYIRg4o5JrXtoetkeZrI5BpQuO2F28qAGDSi4HlAESMsjgwJhrXtoetodBxeQwbhowqORyQBkgIYMMDoy55qXtYXsYVMYtTHtggcrQ0NC4ZuocGHM5QdvD9igTlKr0ohnmZgYZsoPjuAaUKiekx6pGAwYVB6XGmWZH7DlhUGntf70+2q8Pg0o18XtgRslW+qnV5tpm5vCWPxwPA2RwQJahvROscm7YHraHQb48tBlUqvRSAzCWQcWO2I64vCOu0jVkgKXspQbbo0oNtPeZBpXxsceEHdWg0n7RVWn8DEEpgwwOjLnmpe1he5QpBxpUqowaAzCWQSWXA8oACRlkcGDMNS9tD9vDoDIAQJD1Eg0quRxQBkjIIIMDY655aXvYHgaV1lF8XuAJ4GpA2aT5gWnAqcDHgHcC+3YAAbOAhYHHgVcAZwL7dHBep4fMDewJfBx4Bngu/r4cuAJYF1gcWBL4VQedjtTfBcASwKOA9HML8EXgLmA/YCfgbqAW338VOKvVmAaVXA4oAyRkkMGBMde8tD1sD4PKyKByH/CaOGRR4CZgkYCBDmL+84cIVHYJ4JkH+F0E98s67aDNcUPA24AdA1TeDpwLLAc8EudqfIHFER2MOVJ/pweQ1GXfHtgKWC8+FyhNjzGWB84BljKotNd6hgBtGXIFBNvD9mj0HBnmRHZwHLQ9KgrsRVBZJrIhKwCbAWsBe0RgXhOYDUwCNo9MQ32OFUHllcBvIvtxB/Al4EMBGOcDhwMrAwcCT0Ym5MvAXwF9fz/wS+CQwgS+DXg/oH/r7XVx7PUBEcpqKFNyMLA3sEYceApwGHBlh/0po6LMSR1UNCf+DqwU2ZQiqEgn3w9gahqpnVGxI7Yjbg2xGYJSBhmyB8b2P0O6d4Tt0d5nDiKoqPTz+wjyyhD8T4BJHVR+ASgDsUmUhm4OaFBJpAgqrwYei+yCoEDnK+NxPLB2lEqUBdkNUEbkmsjebBvffw24LsouTzVMe5WUBEAqtzQ2gYqAartCRuUSYOco0SjjUYeW+rkj9dcIKjpHpbEpcU2fiTKQyk8PAV8J/RlU2viqDA7IMrR3gt0LOe17sj1sD4N8eZAfRFApZlReBfwa2BVQGUgAcGPAg0BCbUZ83wgq9dKPshrfAu4EBDXKnAhA1NSnMh4PR6ZGkKQ9LfdEFkN7WxqhQufV4Uh91ttHgUuBi5qAytaRAVFGR/Ic2TAVRurvJ00yKtKRyjvSSzGjMke3w8PD02q12v6N027WbCWhcjQ/8O3W2uTJk8d1nWcIzpqNGeSwDAYVg0o1oKKswSqAAtxbGkoiOaJTaykaSz9y4BdHVkUZEoGKNtZqQ60yKvMFNGi/RitQ0WhfB7RX5WdRdtHxaspyKMMhINk0+lKZZtkYQ/tDNGZjU0bnrYCyGc8CqwLK9KhEpf0wOkeZGcmn0pI2Bat0ozKSylQPNHQ4Un/qt1j6+TywEfDhJntU2trXpR87Yjvi8o647cLq4gEZYMngmMtPZLdH2V9anwa+DTwNvCEC4meBE7q4jnrZVfGuH5VVXhYbY78QIFHfo3IQsDrwr4Ay3WWjPSX1VrzrRyURgYGyGspE6G6d9SOzoUyGMi+7xx4WZUikO90x9CmgFagIeg4ANox9LcpqqISkklW99KN9LydG1uPsyKIogzO1iQJH6q94149O1b6YzwH/NKiMbSpmCAiWIVdAsD1sD4N8eZAvCyoKYgKVvQJUtAFUezN0e+tEaW+KzaraNKqMhco474hbkTNf47GRDerkduWeXYczKnbEdsTlHXHPFmSTjjPAUvZf8LZHlRpo7zPLgoo2U24B6M4S3eKrZ4/oV73uSJko7eVxZ4tuWZZ+Tu6DjJGyIsrWaIPtuDaDSvtFV6WBMgSlDDI4MOaal7aH7dHMD7byFWVB5cdxx4oyKAqO2ouhfRkqe7hZAxhUcjmgDJCQQQYHxlzz0vawPXoJKtpwqrthPhj7Oy6MZ3hoL4ebNWBQKcyBDAHaMuQKCLaH7dEYJjLMiezgWDajouvRhtQFoixS17kejOZmDRhUDCovWQV2xA7OZX49V+lGM8zNDDJMNFDRczW0gVZ3yxTbaICnyvnosSrSgEs/Dkr+xdh6sWUIShlkyB4YK3KXzw9je7T3mWUBQ7ff6rkgf2x4N04n75up0vYea5w0YFBpv+iqNE0GJ5hBhiwBIYMuMshge+TyE9ntURZU9AwPPaej/uTVKn2ux+oDDQhUhoaGys6rrl6ZHXEuJ2h72B7NFniGeWEZ+mNulg0oepuv3o2jd8voBXv15rt+uhpq+7czg0p/LPwqZ1iGYJD9F6PtUaUGvEb7DRzLgoreg7NkPLW0+MK8PC93GZ/57lFDA1lKPzX47czpU949nobJEKAtg4NS4xrIMCcMjrnmZXZ7lAUV7VGR8xewuFkDL9GAQSWXA8oQlDLIkN0RV+lKbA+v0YmeUdEL8HTHzw+ApwoXe3WVC81j5dWAQcVO0L/gW6/PDJCQQQaDYy4/kd0eZTMqxXJPcTWW7SdvpLVkY9KAQSWXA8oQlDLIkN0Rj2nRlTzZ9vAanegZFb2AsFk7teRa8eETVAMGFTtBZ1ScUenEvWUAJsvQH/6qbCZELyL8ELAQoOzK3MBywBc6mZgT5Jj9gI3jWh4F9gCuAb4BnB/PmRnpUvUCx18A7y0cdADwDKB/6+3jwKYt3qN0QrwscbHQv2TqVrsCWBdYPDZOl3obs0GlPxZ+tyZLJ/1kCAaSM4MclsHrwyBfHuTLgsq5wPoNw9wdQa0Tn9Xvx6wCHAO8B3gWeBdwFLDaGC9Md1LpJY/LFvo5CzgU+HWTvnsJKvXhdonXJZR6mJ9BxY7Yjri8Ix6j/yh1egZYMjjm8hPZ7VEWVB4HdgK2AE6KjMp/ARuUWin9e7AyGJcB+0T2RHdB6UWNDwNHRzZlQUDPm/k/4C3AecCbgXcABwJXxnGrN6jh7HhGzSxA4yg7syJwGLBUZLHuAHYAmoHKlyLbpcyMzv1f4GZgDeABYGdAsgl+6m2jeKmknolzV8itjdF6K7ZASRmzg+MY9aN2Ssik63hJM6jkckAZglIGGbI74ipdou3hNdpsvmWYF61kKAsqCsifAfTclCUiiCkYKgAOSlNW5fNRHnkEUNlFQb0IKptH2eYjgN6P9H5gbUAwoUyFXkPQCCqfADYM/e4FPA18F9gmsjbzADdENkcP3Ts5gEalt58Cx8cYKskp87VbAOU9wLHApYDKSfpbTf3JdpLjwTj+NGAGsBawXSGjogf8CXSUPZPsdWgxqIww6zMv/CoXawY9GFRyBWfbw/YoA0tlQUWBc814jL7e96Nf738BVqjS8Y3jWMsHQNwSMqwU2QuVbAQPCuKCtpWBIeADscdEQV+f7R8ZkWagotu+9WoCHfe7OPdf8RJIgaEgUZkO6V/9FEHlT5Gtqb/aYNGAyGsjA6IMj/a/CIbqbRHgosjaFFWq1yQ0goqePKxrVUZHWZYjdcLw8PC0Wq0mWeZos2aP//P//MC3F0ySARIyyGBdODCWCYxVxhivj/ZzsyyoKJi+M36dbwKsE2UI/dIfhKb3HG0PfBTQRloBgTafKoirRDIWUJH+psfeF0X6T0ZWRhtqt4rSjx60p2zGtAZQ+XmUYwQyasp+SJbbAJWUZgM/BGYWjCTbC7i0v0bfHwScGfYUqGwLzAfo2TnzR8nrfkDZIpWSmjaXftovuioXSgYnmEEGg0queWl72B5lwLEsqKjvtwH6NV48V/sqBqHpmr8a4PBE4U6dMxpKP6PJqEh/2tPyZ+CD8T6lNwK69fveyGTob5WdlCEpZlRUftozNjor46GSjkpM2vCrLIoyINrnor+LTXuLVGbS3iNlS3SOsjACFV3DiVG6EuyoD4GZYK1lM6jkckAZICGDDA6Mueal7WF79BJUtNdBv9Yb22iAZxDAJsM1auOzSnMCrLE02V7QNOLtygaVXA4oAyRkkMGBMde8tD1sj16CivZJ6I6TCxt+navM4JZPAypH6d1MKlW1LNd0ILZunb4zNtiOeLhBJZcDygAJGWRwYMw1L20P26OXoHJV7J1QecLNGniJBgwquRxQBkjIIIMDY655aXvYHr0EFb2MUL/QtelSezTqTXe4uFkDGFRyOaAMkJBBBgfGXPPS9rA9egkqRTgpjqO7QtysAYNKYQ5kCNCWIVdAsD1sj8YwkWFOZAfHbm6C1R0jeuiZ2wBrwBkVO2I74tYOIENQyiBD9sBYpQu3Pdr7zG6Cip6I2s3+qpwrHqtLGjCotF90XVJ1R91kcIIZZHBgzDUvbQ/bo5kD69Yj9EdyjgaVjkLHxD5IoDI0NDSuwOrAmMsJ2h62R5mgVKWHzDA3M8iQHRy7GVAMKlWusKRjGVQclFz6cemnE/eUIUBbhv7wVwaVTlaUj+lYA+Ne+qnV7v3fz6zx+smTJ3dzbnd8/cUD7QRf0EYGPWSRI4MuMshge+QChOz26KYzd0ZlVOFsYp1kUMnlgDIEpQwyZHfEVXoB28NrtN9Kcd0EFb3gTu+qcRtgDRhU7ARd+nHppxMXmAGYLEN/+KuyoDIP8Glg1Xjnj/7/+01edtfJPPUxE1ADBpX+WPhVTr0MwcAZlVzz0vawPcpkdcqCyv8CO8RtyJOAewC9rE5v9HWzBsb/gW/eozLHLMwACRlkcGB0YCwTGKt05V4f7edmWVD5J7ATcCLwGmBr4BhgoSoNCxwI3Acc0cG4mwFrAXt0cGzjIWsCvwH+E7hyFOe3O2Ux4PSQr92xnXyvJwRfDSzXcPDcwJ7Ax4FngOfi78sBvXBwCeBRYF5AJbwvAncB+4W97wa0B0nf6y3MZ7USxhmV9ouuE0N265gMTjCDDAaVXPPS9rA9yoBjWVBRBmX3eALtayOQbQ8s1S3H2mE/VYHKtwOIFu/kzcEdyl48rCpQ0buY3gbsGKDyduDcABqBkoDkshBM9twKWC8+F9hMj++WB/Sm7Jb2NqjkckAZICGDDA6Mueal7WF79BJU9Gta/+kX+mPAAsBXIsMxijg96lNagcphEUSV4bkjylT1jMrBwBnAAcDFwHGAYOuVEYh/1SDNgsA1gIL6dcDaAS1TgM+FDpTRmQbcD/wS+BOwF/Aw8K8Y/yBgFnBqwIEC/eHACcAvAvr+EBmqm6Pvl7eQb2q8vfrZ0P0WsT/opJBdckjOxozKbcD7Af1bb68LuZVRKYKK4PXvwEqRTSmCijJM2pPU2P+/OzWo5HJAGSAhgwwOjLnmpe1he/QSVNT3R4EPAi8DLgR+FmWBUVPHKE5sBioqRW0DHAVo0+8NwHviv4/EHUkq/1wKfAFYMsofrwJUAhGQqLxRb9qLoyyESiYaT+AxDAhUBAzSgzIighiVTnSuQGMVYDawT8ih0tG2gN6FJEBSyerDwO+BDwXcLB1wcz2wPrBpC/kEQYKcp6L8dh7w5ijJKOuxBqA3XDeCxOMBZMXrq19nI6joc5WPdJ2CvM9EGUjloocCTCV702ZQyeWAMkBCBhkcGHPNS9vD9uglqCwSgXtvYDLw3/H3A6OAjbGc0gxUlIU4BNAmX2U0VLpQBkD7U/SyRO250N4LZTe+BawM/COEUCljA+DeglAqhSib8EgE+TcF7CiLobueBA0ClTMDEAQbymy8O/pYN4K89vH8McBlhQj+AqbNgf8pnK/TBDICJMFRM/m0x2STkGkZ4Mi4zpMjW6NMl2DnPxqUK4BSRuXOwucCLUHbT5pkVART0smuoYN66WeOboeHh6fVarX9Gw05a7ZMME7Nm2nnUHwGSMgggwOjA2OZwFil9/L6aD83y+5ROTs2lr4eUNA9PzabKmNRZWsGKspC6D/tr1Dp58YAiDqsaAPwzwNeBAPKpKgMpMzQvpE1EZiorRgZC2Uo6k3XqmyNSkWCCO37KO4xmS+yOPpOoKSMikpjKqtobIGdAr+yK++K8yVjcTNtHVQEI43y6e4qbehVPyr9zAAEKG+MjIqgR2Prs8aMimR9a4CTzhVoqewkG+rfYulHd3BtFFkffV4s/bS1sTMq7RddWyV28YAMTjCDDAaVXPPS9rA9yoBjWVDRnSHKECi4qSno6q4fBdUqm0BFY9czINovo1KF9oHoM2UWFMAVdFUaqd/1oyyIgrj2mCiroj0qC0cmRH/XmzIVN8W11T9TJkWA850WoKLjVNLRZmOVWvSfnjOjcok+F6wIbFTaUeZFG3QXbQEqynw0yqeNvT8FtHdGpR/dgXNJlIK+B7wa0N05givtLyk2lcIEZRsCTwZ87Bblp+JdPzpH+1ikH93hZVAZw6zOEKAtQ66AYHvYHo0uJcOcyA6OZUHl9oAUpfvrpZZ1AJU93KwBP0elMAcyOCDL4MDowNjaMXt99Mf6KAsq2lipu2Xq52lzpp6rcrxjtDUgDbj00x8Lv8rZmiEYZP/FaHtUqQGv0WbazrBOW8lQFlR0fe+Mu34EKSob9OJBaOMzaz3qmDVgULET9C94/4LvxJFkDoydyN+tYzLoITvIjwZUdCuuNqsKVLQXRHs+tEnVzRpwRsWln5esAjtiw2u//YKv0pV7fbRfH2VBRe/60S2+ja1sP1XOA49VoQacUWm/6Co0BxmcYAYZsv9iHLQ5YXvk8hPZ7VEWMPQgMz1QTA9S0221uotFD1rTA9DcrAFnVJxRcUZlBD+QAdoyyJA9MFbpym2P9tBWFlR0y61ucdXD0XSrsh4h/7e4zbdK23qspBpwRqX9oqvSdBmcYAYZHBhzzUvbw/Zo5ge7tZn214De0Kvna+iZHn+J55Qos+JmDTyfURkaGioLwF3VnANjLidoe9geZYJSV51Bm84yzM0MMmQHx7IBRY+R/1g8CE2Pq9dj2fXSPW+mrXJ1JR7LoOKg1Dg97Yg9JwwqrZ2210f79dEpqOhpqCM1vQ/HzRp4YY/Kg5P0AsdKWm2u2qEzp0/9enEwL/z2C78S48QgtoftYVAxqHTic8Za+mn21t3iuJ0CTyey+pg+1kDle1RqtQNnDE/9ikGl+aTJAAkZZJB2MshhGQxtjSs1w5zIvj46BQy97Xekdlgfx1aL3kUNGFTsiO2Ic/96dmD0Gu23DFenoFK8Lj3sTW/p1Vt4rwEqS/N3MZ66qx5pwKBiJ2hQMah04l4yAJNl6A9/VRZUNgZ+FM9O0RU+AGwFnNvJxPQxE18DBpX+WPhVzsQMwSB7atv2qFIDXqMTPaNyG3B13PWjtyfvFbcnTx6faZZm1HmAy0OaZYD7gYcC4PYdpZTXA2sB3diorOfe3Aj8dZSydHyaQcVO0BkVZ1Q6cRgZANYy9Ie/KptR+QfwPuDmuDwFwO8Bi3cyMQfkmJOBHwIzxni93QSVU4EDAzLHKNbIpxtU+mPh93QSNHSeIRg4o5JrXtoetkeZrE5ZUDkqMijfBBYA/hu4Nh6rr3EvrtIBJh2rEVR+D9wBPAnMjJc46oF5enCeslN6qeNGwN5xzF3AjvGdMipTgA/Hv+8KnT8M8FqMgwAAIABJREFU6HUGO4UOjounA78SmA78qqAbve1aoCLw2RL4ErAe8FzYS7a8MPYdrR0yLhrweUKA6KeA/wOWBU6Kz5qq36CSywFlgIQMMjgw5pqXtoft0UtQ8W3K7emoEVRUctG7kK4Ctm4CKm+PDNXqwIPAbsBpkZHRe5VWjfMEFoIFgcc/4+nA2iOkstOSwJ7Aq6IEpT6LtqpnVOaLzMr6cRlnx996Dsm2wA7xQD+BkuR5BhAUbRb/aSO1SlyCK4NKm7mQIUBbhlwBwfawPRrdRoY5kR0cy2ZU1mnjm51RgWagIth4rAFUXhHw8m7gImDFBt0qA6LsigBFGRe9pkDQo2PVlNH6HfCGyIaoLKe2VLyL6d5Cf3VQWQXQfiJldNS+FmMIYJTxEZB8F1gaUPZGmRQ9fVjnKeOjdjswSf8zPDw8rVar7d84J2bNfv7rapqfozKinjM4wQwyZHfE1SyWF0axPV7UdgZdZJAhy7wY6wPfqlxH/T5WM1DR7dxPBAgIOrYHVGYRFCwP3AKsFuUcvZLgTEBlF5V+jgWuAFSiuSmO00ZdZUV0i/hKkUk5AHgZoM272o+ibEi96b1MBwMCEh2ncwWpyqjoGTjac6SSzn2RVTkv7ujSHiSVniT/UCOoNDOUSz92go3zwo7Yc6KZr8gwLyxDf8zNshmVfoeIKuQfCVReDZwRgPHngBWVcgQEuoNKb6fWfpZdYu+PQGVe4Epgk8iW6DhlP1TaEfBor8q3Yo/KwgEc+rvYdge2AXR7+acBZcZUMroM2CcO1F6a7wQYKVPzY0B7kgwqo5w1doL+BZ8R2jLMy+y/4Ee55Ed1mu3RHpYMKqOaWj6plQacUWm/6KqcPRmcYAYZHBhzzUvbw/Yok2UzqFQZNQZgLINKLgeUARIyyODAmGte2h62h0FlAIAg6yUaVHI5oAyQkEEGB8Zc89L2sD0MKlmj+ADIZVDJ5YAyQEIGGRwYc81L28P2MKgMABBkvUSDSi4HlAESMsjgwJhrXtoetodBJWsUHwC5DCq5HFAGSMgggwNjrnlpe9geBpUBAIKsl/g8qDw46V9VyVebq3bozOlT9WTdfzcHxly6sD1sjzJBqSrfYViaU9MZ1qkf+Fbl7B/gsQQqQ0ND43o3WYYFZyeYKzjbHraHYWnkwJTBbxpUBhgeqrx0g0qugJDZ+VQ5Lw0queal7WF7lAHHcf3lW7Wj8ni910BlpZ+5+PqM6VMOLTPZe3/1/ZNKrVIXGWDJgdGBMauv8PpoPzcNKlV67AEYq7LNtLW5vjJjeEu90+glzQu//cKvciraHrZH1nWaYW5mkCE7yBtUqvTYAzCWQcVBqXGa2xF7ThhUWjt/r4/268OgMgDwUOUlGlTaL7oq7ZHBCWaQIfsvxkGbE7ZHLj+R3R4GlSo9xACMZVDJ5YAyQEIGGbI74ipdg+3hNdpvGS6DSm89xDrARcCWwE9jKOn8NmAGsMsohr8CWBd4tMW5GwA3An8dRd86pd7/4sCSwK/K9GNQsRN06Sd3mt+g4jVqUCkT1Sb+sQKVbwJ/BjaNy3038APgl6MElXZaOxXQJtOr2x3Y5ntB1LzAEWX6MajYCRpUDCqd+IwMwGQZ+sNfOaPSyYoa/TEClZ2AZYH3AXpi67HAA8BrAlRuAZYDngGmFwDjc8DcAQrTgPsDbrYF1gJeBxwDPAm8LPp6AyBQuT6yOPsDSwELAXcAOzRcykbA3tHHXcCOMf56wFkx/sFxzBpx7inAYcCVzdRiUOmPhT/6KV3+zAzBQFJnkMMyeH0Y5MuDvEGlvN8tc0YdVG4ABCQq/5wDfBtQZkVZi1agMhX4KLAYcB2wBPBUQIhA5fvAd4CZ0dcXgS0CVJRRuR3YBjgKmAeQDO8B7okL0Gc3A6sDDwK7AadFSUr9b1fIqFwC7AzcHfLXoeUlujCo2BHbEZd3xGWcyliPzQBLBsdcfiK7PQwqY131I59fB5WvRhbiW8DbI7vRDFSGgauiy1WBvQJUzgTqcKBsiUDiDwE5TwOy43zAhgVQUbnpEGAS8DCgLMmawJ3R/yKxf2bFhkuo918Ela2BlUJuZXmO1DnDw8PTarWasjZztFmzNWSPm5+j0pGCMwSlDDJkd8QdGbNLB9keuSDB9mhvD4NKlxZ/i27qoDIFuBS4F/gC8J+FjIrAQCWYv0W24nvR18rAUIDK6QEn+qoOEj8CvgH8Glga0Cba4yJro3LNMrEvZqso/WiDrWBH46jJ9srmrAbMBg4CBEQnxFgqMQl+DgfmBy6L8tPmUbpqesnOqLRfdL2dcnP2nsEJZpDBoJJrXtoetkczP+h3/VQZHV4cqwgqewCbxF4VgUs9o6LMxZejVKPSzs87BJXXA0fH3hbtd9kH+C2we5R8PhV7WARHyoK8Efg8cHlBFYIbZW0ej2yJSlHXBqgIlE4EdgXOjizKooBKUi2bQSWXA8oACRlkcGDMNS9tD9vDoDI+UDLRR9UmYG3UHfF2ZYNKLgeUARIyyODAmGte2h62h0FloiND9dd3QextUfZnxGZQyeWAMkBCBhkcGHPNS9vD9jCotIum/r5nGjCo5HJAGSAhgwwOjLnmpe1hexhUehaG3XE7DRhUcjmgDJCQQQYHxlzz0vawPQwq7aKpv++ZBgwquRxQBkjIIIMDY655aXvYHgaVnoVhd9xOAwaVXA4oAyRkkMGBMde8tD1sD4NKu2jq73umgedB5cFJelVAb9tcfH3G9CmHlpnsvRXopb1nCNCWIVdAsD1sj0ZPkWFOZAdHP/Ct6ug1wccTqAwNDY3rvPLCdzDICrAZ5mYGGbIHxirdtO3R3l+Na0CpcjJ4rGo0YFBpv+iqscQLo2RwghlksC5yzUvbw/Yo82PCoFJl1BiAsaoo/dTmqp0wc/pUvUSxaXNgzOUEbQ/bo0xQqtJNZpibGWTIDo4GlSpXxQCMVcVm2hocO3P6FL0OwKDSZk5lcIIZZMjuiKt0DbaHwbHfwNGgUqWHGICxDCp2go3T3IHRc6LfAmOVrtrro/36MKhUOSMHYCyDSvtFV+U0yOAEM8jgjEqueWl72B5l4NWgUmXUGICxDCq5HFAGSMgggwNjrnlpe9geBpU8QHAO8DrgIWBu4A3AQcCPOhTxMuBjwN2F4zeMz3YCrgDWBR7tsL9ODqv3uTiwZLu3JTd2aFDJ5YAyQEIGGRwYc81L28P2MKh0Eo6rOUagciAwK4ZT4L8q4KUTCdqBSid9jPaYXYB5gSPKdGBQyeWAMkBCBhkcGHPNS9vD9jColImsvT22EVTeC3wPWAZYCjgGeBJ4GSAwuAPQ01ZXA+4D3gmsDSwKHAv8E3g5cCegjMr1wFrAb4BLgFcDSwBbRBZnOrAmMBuYBGwO3FK45I2AvUOGu4AdgauB9YCzIgt0cByzRpx3CnAYcGUz1RlUcjmgDJCQQQYHxlzz0vawPQwqvYWPMr0LVN4MPA68Cfg9sC9wDfBz4DvATODdwBeBQ4CvAwKIBYDbgJWAEyIzowzLXgE6RVBRv5sFuBwOXA78DRgCNgHmB24G3l8AlXnis9WBBwE9l+Q0YEbAz3aFjIogaOcoQema6tDyEl0YVHI5oAyQkEEGB8Zc89L2sD0MKmVQorfHFjMqWwWMCBYeA24KaHga0Kbm+YCfAMsB+4RYFwNTgN/F58q+rBOfNYLKKgFE+wO3B2Qou/K16EsAsmsBVBYBLgJWbFBBPUtTBJWtA5iU8dFemyN1zvDw8LRarabx5mizZit507vm56h0rtsMkJBBBgdGB8YygbHzFTb2I70+2s9N3/Uz9nk2Ug+NpZ+jA0hUYjkD+Abwa2BpYIMouxwArA+8Qk9AB1YFvhsZld8GbAhmGkFlZeAJoA4qN0b2RhkVQZAyKirp1Es/sr3+X2UmlYa0yffMyN6onLRtnKcMjTIyyubcH+WjB1pdtDMq7Rddb6fcnL1ncIIZZDCo5JqXtoftUQYcDSq9jRqNoLIgcF3sBfk/QODyDPCayKIIRLRnRHf2CAbeBnwg9rD8IIDi2di/0g5UtBdG8KHSjt5mrIyL7hD6a+GSBUcqJak0pWyJ9slcG6Ufgc+JAUZnRxZFe2WmjqQyg0ouB5QBEjLI4MCYa17aHraHQaW38NEvvWtPjDIo34/MiADpHQElo7kGbeY9td3tygaVXA4oAyRkkMGBMde8tD1sD4PKaMLwxDtHdwcJUrQXRZmzk6OsM5orvSDuNNK+lRGbQSWXA8oACRlkcGDMNS9tD9vDoNIumvr7nmnAoJLLAWWAhAwyODDmmpe2h+1hUOlZGHbH7TRgUMnlgDJAQgYZHBhzzUvbw/YwqLSLpv6+ZxowqORyQBkgIYMMDoy55qXtYXsYVHoWht1xOw0YVHI5oAyQkEEGB8Zc89L2sD0MKu2iqb/vmQaeB5UHJ+l26J612ly1E2ZOn6on6TZtDoy5nKDtYXuUCUo9cxxNOs4wNzPIkB0c/RyVKlfFAIwlUBkaGhrXeeWF78DowNja2Xh9eH302/oY14AyAHF74C7RoGIn2DjpHRg9J/otMFbpuL0+2q8Pg0qVM3IAxup16afGXJ+bObzlD0dSpRd++4Vf5VS0PWwPg4ozXJ34nFa+wqDSifZ8TMca6PVm2ueofeqX06fq9QAtmwOjA6MDo9dHJ04rg6/IIIN0lUEOg0ons9bHjFkDBhVDgks/uSEhQ0DKHhjH7AhLdGB7tPeZzqiUmFA+tL0GDCrtF117LXbviAxOMIMMDoy55qXtYXuUyXoaVLoXE9wTYFDJ5YAyQEIGGRwYc81L28P2MKi0RoZ5gfuA1yShCsnzBHB1vDhwfmBavKV4rCIuBpwOrFWyoyuAdYHFgSXbvS25sW+DSi4HlAESMsjgwJhrXtoetodBpb9ApQhOiwI3xRuPnysJGI2HjxZU6v3sAgikjigjh0EllwPKAAkZZHBgzDUvbQ/bw6BSHlRWBg4EnoxMwpeBy1XJANYEZgOTgM0jQ7EcsB+gDIiyIfr7KuAS4NXAEsAWwNzAcZEteSPwfeDbBfEaMzzLAGcCKwAvj3NfC7wyZPkVMBX4JPAssECM8wCwD7AeMB/wM+CnwO+AP0QG6Wbgcw2q2QjYO677LmDHuB71c1bIf3Acs0acewpwGHBlMzUbVHI5oAyQkEEGB8Zc89L2sD0MKuVBRVBxTWQztgXWBvSsjiFgkwASBfr3jwAqNwKbAdcDhwfo6ByVUGZGGeVcYPkGUFHp5/cBBfrufwJKvhDn7Am8Kvp7O7BX9P8UcCJwHnADcAzwXuBlwNcjEyJZlgYeDrnWBwQkavMAkm914EFAj6Q/DZgR17hdIaMiANsZuBs4B6hDy0s0bVDJ5YAyQEIGGRwYc81L28P2MKiUBxUF+D1iv8grgHuAyyIz8rXoTgF81wZQ0bHKpCijIlBZBXgc2B+4HbgQODQC/kPA+4C3jJBREZD8OsbZClCm5x9x/FLABsDHA54eAZSBOTLkVt+Cm3pT6UfZmTpUXAzsANwSBywCXASs2KAuwY32tRRBZWtgJeCOACqNyfDw8LRaraZrnaPNmq3kU2+an6NSTq8ZICGDDA6MDoxlAmO5VTa2o70+2s/NQbvrp9Vm2uuATSPDoFLIslGi2TegQOUUZR9UEhE8qGSyfWRevlsAFX2nDEkdVFYD1PfxUUL6MTB5BFCRPQQUyqooeyJwOSCyJJLl2Ci5qA+VfgRPJwcsKaMiWFG56YyAFn1X30zbCCoaS9AiGVXaOijA5oQ4R5klXbeyQypxCdzuj/KXSk1NmzMq7Rfd2NxaubMzOMEMMhhUcs1L28P2KAOOgwgq9bts6nrSZlHtH1Gm4k7gaWDhgBEFb5VG9DZgZUt0N4zKJAIBgcKfA1beGRmVRlBRf9rnouyK+tVx/wGobKNWvOunFkAyKyBDkPAtQHtUJM9JAU/ae7Jg9KHzVZYZjpLQBwJUBCiCmOJdP42govGVoVEpSVkgZUu0gfbaABVdi0pLyiKdHZkbbfbVHpmWzaCSywFlgIQMMjgw5pqXtoftYVAp96Oz1dFvigyKNsAKGpQZeUcE9e6M0F+9KJtzarvblQ0quRxQBkjIIIMDY655aXvYHgaV7gCA7roRpGgvhzJPylKoLDKI7YLINmnfyojNoJLLAWWAhAwyODDmmpe2h+1hUGkXTf19zzRgUMnlgDJAQgYZHBhzzUvbw/YwqPQsDLvjdhowqORyQBkgIYMMDoy55qXtYXsYVNpFU3/fMw0YVHI5oAyQkEEGB8Zc89L2sD0MKj0Lw+64nQYMKrkcUAZIyCCDA2OueWl72B4GlXbR1N/3TAPPg8qDk3Q7d09ajbk+N3N4Sz01uGVzYMzlBG0P26NMUOqJ42jRaYa5mUGG7OA4aM9RqXINDORYApWhoaFxnVde+A6MDowG+U4ccAZfkUEGg0ons8XHTBgNGFQMCY2T2Y7Yc8LgaHDsJMi18hXj+su3E8F9TH9poPeln9qGM4enXurST2fzIgMkZJAh+y/GzqzZnaNsD4Njv4GjQaU7a9+9hAZ6vpm2Ntf7fjm8pV7c2LLZEdsR95sjrtKBeH14ffTb+jCoVOkhBmAsg4qdoEs/uSHaoOI1alAZgGDsS2ytAYOKnaBBxaDSiY/MAEyWoT/8lTMqnawoH9OxBgwq/bHwOzZoFw7MEAx0GRnksAxeHwb58iA/qKDyAeBn8l3xwsEasC3wZ+AKYF3g0TY+el7gCeDq6GN+YFq8YbgL7h3JuAvwsQ4604sTzwFW7+DYdofUr39xYMl2b0tu7MygYkdsR1zeEbdblN38PgMsGRxz+Yns9hhkUClCwEeAzwEblnAIApX7gNfEOYsCN8Xblp8r0U+rQ8cLVOrySD+6xiPKXItBJZcDyhCUMsiQ3RGXWWNjPdb28BptNocyzAvfnjynZRohYBvgP4GdgOuBtYClgG8C9wNPAU8DWxe6aQSVZYAzgRXi3GOAJ4GXRWbkDuCw+G4hQH/vAEwJSJo7MjIXxhitQGUq8EngWWABYAtA5yqjsgbwHeAW4EDgS8CHgGeA84HDGyboRsDeIeddwI6RIVoPOCv6PTiOUd9qp8R1XNlsshtU7ASdUXFGpROYyhwYO5G/W8dk0EN2kB/kjMpPgZuB+YBlgY2BSwqg8gtgH+D3wOeBtZuAiko/+l6gsDzwP8B04OcBDDOBdwNfBD4DCIiOAuYBbgDeE2UmwcdHGyZ+K1ARWAg4BE8nAucBghuByJ+idHUksBJwfMit0ta5wG5xjIaSDLp+lYsejO9OA2YEqG1XyKhILzsDdxeAqOk6NagYVAwqBpVOgniGAG0Z+sNfDTKoFEs/bwX0ELFJwOURqLVfRVkVBXkF813bZFReBej5HjpOgKCshrIw0rFgSOWlQ2KMhwFlLdYMkFkV2KtDUPkssAnwCKAsjqBE2ZR/AL8BLgAOAgQ/yqpcF/2qNKXsiEBETftaLgJWbBi3nlEqgooySQIfZYEEZRqT4eHhabVabf9GpzRrttTYm/acn6NSSrF2xP3hiEsZdYwHZ5gT2X/Bj1HFpU63PdqvUYPKCzpaGPhLQMRlASqnAvsCf4hsgkpDI5V+pMuLI6uiDMw3AlyWBjYA/glsCmwFqPRzY5Rq1O/KwFAHoPI64I/A5Cj9CDpOBs4OWFGGRpthtTFYTaUmAZGaMiICmtvib8krmFoNmB1wo9LVCXH96kOApeyNNgpLLyqDbQ480GolOqPSftGV8mJjPDiDE8wggwNjrnlpe9gezVyb96jMqZXiXT/a66GArJKMSin1jMKbgaOBhyJQ3wN8qtBN8a4fZV20F2UW8AVAcKJztTdEm21VQhIgCH7ujazEG6OkpGNbgUr9zqT6sIKHA4AFo/QjGVSWkdz1u35UovpWQJBkWT/GU5lHWSRdb70JoJTJeTyyJfr+2gAVyaR+lSESCCmLoqyMMjUtm0EllwPKAAkZZHBgzDUvbQ/bw6Ayxl+hcfp/xd6POyMbodt1v9qdrvuyl2MDtH5lUOnMfhkCtGXIFRBsD9uj0XtkmBPZwXFQSz+dRBrdESMw0S3Iut1Yd8QoGzKITfteBGzatzJic0bFjtiOuPUSyRCUMsiQPTC283Pd/N72aO8zDSrdnHHuC4NK+0VX5TTJ4AQzyODAmGte2h62RzM/6D0qVUaHAR7LoJLLAWWAhAwyODDmmpe2h+1hUBlgUBjvSzeo5HJAGSAhgwwOjLnmpe1hexhUxjtaD/D4BpVcDigDJGSQwYEx17y0PWwPg8oAg8J4X/rzoPLgpH/1So4atQ1nDk/Vw/laNgfGXE7Q9rA9ygSlXvkOyzCyZjOsU+9RqXL2D/BYApWhoaFx3aSdYcH5F2Ou4Gx72B4GFYPKAIdmX3pRAwaVXAEhA7RlkMGgkmte2h62RxlwHNdfvg7xE08DPS79PDhjeIrevzRic2DM5QRtD9ujTFBqt767+X2GuZlBhuzgaFDp5qx3Xz19jkqtxgMzh6fofUcGlXZKiO8zOMEMMmR3xB2asyuH2R4Gx34DR4NKV5a+O6lroJd3/RhUys+zDEEpgwwGlVzB2fawPcrAkkGlvO/3GSNowKCSywFlgIQMMjgw5pqXtoftMeig8gZgD2Bh4Drgf00W1WnAoJLLAWWAhAwyODDmmpe2h+2RDVTmjRf7vWYU4VLnHAqsGC8GvAf4bAcvB9wb2BVYGnhiFOMWT9kQ+BiwUwf9nBqyFp8jsinwFeBkYEHgncC+Lfp6C7Ak0PiG4m8A5wPnFM6bH7gaWK4DucoccgWwLqC3RTeTZcS+DCq5HFAGSMgggwNjrnlpe9geEwlUFJh/BpwYF/VpYEtggzaR91rgQuAa4HsNx25f6K+TAF4WVI4D9LbhYjshQKXx88bxdwEEdkd0IFivQKU+dBlZ/i2uQSWXA8oACRlkcGDMNS9tD9ujX0BlZeBA4Mn49f5l4PKC8LoN9RJgUuEz7alRSecBYCrwSeBZYAFgi/h8HeBLwBDww8hgFHVyPbBC4YPDAI21EHAHsENkRY4F/gm8HLgzMirNji32rYzKSKCiDNFagPpRhkRPWH1tZJz2jEzK3MDOwDdDHunn/sim/AY4KQbUZ2tHRkXXrmvWZ/MF3AmO9NmHgGdivMMbJsdGgLJPGuMuYMfI0qwHnAVIloPjmDXi3FNC/iubTTSDSi4HlAESMsjgwJhrXtoetke/gIqCqzIeNwHbRtBVgK43BeGDogzR7JoUYBV4n4oMyXmAgqjg5CfATGAWsHsAkEoviwW4KMj+Hfg8sA1wFDAPcAPwnuhPEHUZsBewTIBPs2NVjqo3gcp/AA/HB5Lh60A9o1IElauitPI0oP8XNGxWyKjcGDCm744OUFGZRxmX6YDA4QeAgE97cfTvowEp5wIq4Rwfeq0B+mw34E8hm673ZmB14MH47jRgRsDUdgVZBIyyzd0hRx1aXmIXg0ouB5QBEjLI4MCYa17aHrZHv4DKe2PTq/aQvAJQwFc2o96WAC4G3lr4TBkVBVCVcz4DbAI8EiBxZMDJrcDvY0+L+tA+DgFGvRUzKsqWHBJZG8GFMglrAspcCAqUaVCGZgrwxRbHKttSBJVOMypnxFg6V9ep6/pwA6isCjxWABXtdxFwCTqU7dC1fAT4dgHotJF4dkCLYEsQo7ZoZEcEImqLABdF9qg4Z9Snsj5FUNkaWCkyPBpXumZ4eHharVbbv3HCzZpdTII1m46j+8y3J5fXWwZIyCCDA6MDY5nAWH6ljf4Mr4/2c7OK25NbbaZVAFXg1a96ZUeWBbR/pNi0R0VZku/Gh9pI+wlgK+CPwOQo/Sj4KoArY/HmyILolFcCt0WpR2UctSKoaHz9p/5U+lEWQ9kCZSIU5H8bm3IFLSrVNDv2bwWBy5R+Tg8gKILKxlG6UaZIsihLIpCrZ1TeHiDz/+2dC7htY7nHf22KSlK2U0napZSK3E5RVFIu3bRP2Q+RnNp0PJFLtJdy2shtSXG2FCF1OhVytMOOiqRU4nAkklK6Sh1sFRu5rPP8eefTMI25xhhzzTnXO9f8f8/T07bmuLzf/739x/t+Y3yHx2+as45RZWqDIDXCS7JeGS0akS8NVUSEp/DQkO5vBDYKYqPq1TlR/RFRUZVLbSTJovUwqi6ptbR9tNhKPdMVlWqn6z6kNT8zQxDMIIOJSi67tD6sjybEcVBERclWlY3W0GLR1YG5sf5D7Q+tPVH7ozj01H9CvL2jv2sNid6+0RqVM+MtGrV+RIbUnhDh0P9a7Q2do7UmqnrobRu9ydIaIhhawyFy8eeoUEgmtYO0XkNtFVUmtAbmVuDQDscW19VMlaisEW0nvbEkgtBOVNTeUjVp5WjDiFSp0iHypnOEi17PVlXn84DWvWwVcxMh1AJZzac1tChZra27A1v9roXIIiq6txYx67rnRxVFVRmtDeo4TFRyBaAMJCGDDE6MuezS+rA+shGVyfKaf+sNAgdGW0okRBWVk0pecZ7qnUT4RMTaX51+xHVNVHIFoAwkIYMMToy57NL6sD5MVKaakofvfBEVvd3zF+DXwF6xRqdXM9Fr1apKad3KpMNEJVcAykASMsjgxJjLLq0P68NEpSqb+ve+IWCikisAZSAJGWRwYsxll9aH9WGi0rc07AtXIWCikisAZSAJGWRwYsxll9aH9WGiUpVN/XvfEDBRyRWAMpCEDDI4MeayS+vD+jBR6Vsa9oWrEDBRyRWAMpCEDDI4MeayS+vD+jBRqcqm/r1vCDxEVJbOKW7K2Mt7LT1vfAdtdzDpcGLMFQStD+ujSVKq8u9e/p7BNjPIkJ04DuI7Kr20K18rOQIiKmNjY9NqV3Z8J0Ynxs6Bwv5h/xg2/5jWhJI851q8LhAwUXEQbDcbJ0bbxLAlxi5CX9en2D+q/cNEpWvz8ollCPSz9TMBxywZ30FfCHbrpwqE+D1DEMxM/U75AAAaqElEQVQgQ/bSdk119uQw66M6MfYE6JoXsT6q9WGiUtOYfFg9BPq7mHbiiCXjO364ShI7frXjV2HYy9+tD+vDFZXOHmX/qPYPE5VeRmRfCxOVaqcbpJlkCIIZZHBFJZddWh/WRxPyaqIyyKwxAvcyUckVgDKQhAwyODHmskvrw/owURkBQpB1iiYquQJQBpKQQQYnxlx2aX1YHzOdqBwEvCEmeRewP/Djhon7icBbgS8CJwKLgQs6XON1wJ5xfNVtZsd1Nq46sPD7ywDtTKzxUuAnsaHgEcDZbdfZAVgfGGtw/aaHXg5sATwDWLNqt+T2i5uo5ApAGUhCBhmcGHPZpfVhfcxkorIBcAKwOfAA8ArgeGCjhtn4JcBhQT6mm6gURb8FeB5wZ4f5DIKotG4tcrY8cFwTbE1UcgWgDCQhgwxOjLns0vqwPmYyUXk6cBlwIPAt4FZgZeCvkeBVmbgHWAHYC1gGnA5sFqDoXFVSFgJvAvQGicjO6oDW66gi8j7gqgKInSoqOwI7BWFShWYeMCsqKqqSfBa4MQjR3sAbgftD7mM7JP8iUSm7/lZR3bk9ZD0ZOC2u/cHAQV+FnR9zr7rvtsAC4F7gZmA34GpgS+DcmM+RcYzmpHEGcAxwRdkcTFRyBaAMJCGDDE6MuezS+rA+ZjJR0dxUVRGZUHtClQe1gpRUzwfULvkesCnwUWDXDkRFhKRYUfkdcDiwPfB6YPcaREUJXoTj78CpwDeBi4KIXAeohbIIWA/4TMg0AXwD2A/QMe2jSFTKri8y9W5AhGU14PvAi4GfBy53BIlbDjin4r46RuepTbU0ZFKr6Txgk8CuVVG5BNgDkHxqkbVIy6MmYKKSKwBlIAkZZHBizGWX1of1MZOJyjrAfVGp0DxFAlRZWRu4Jqoq+l2Vjd9HQi1WVH4EbBfViCJRUVL/OvCaqEbsXIOovBd4c5Cl5wYpURL/Y5ClC4M4qTKie2ntiYYIhqoUIgSTEZWy66sisyGg6omG5qg22BcKVSMROBEtzWmy+4qsXQys2ybEtSVERXgI698GtiJgjI+PL5yYmDi4fRKX3jGnzAan/LeJCX9HpSmIGUhCBhmcGJ0YmyTGpn42lePtH9W2OWyvJyvpvyfIhhbSKumrcqEk+pWooqjKoIrKx6LN8wPghcCTgF/EsU8NsiDSUlyjUpeorBrtobWi9SPSIUKkqo7IisiD5NolVKBWidopGqpM6JibJiEqal2p/dR+fZGw4mJaERUdc338XS0wtcXUivrvaNF0uq90r9aU1veoEqNqlMjNKUFUJLvkUNVoxWi53RZVJ7WeSocrKtVON5Wg1vTcDEEwgwwmKrns0vqwPpoQx2EjKpL3I8A7Yi2KKgz6pPrXoqqihbVabyFS8v6oYmjditpFqgZo512tUbkbuDTIxbMKb/10IioiQb8sAKskrvuuFK0ftUjUHlELSCRE7RSRpZOiqqP1MmrXqNKjdosWqmoxcPtotX60tubMkuv/poSorBHrbT4Q89Lc1B76C3BAxX23juqMzhE+kkuVKbV+RIg0n32CgKmKImIosthxmKjkCkAZSEIGGZwYc9ml9WF9zGSi0vSB0sf3DgERvrOqXlc2UckVgDKQhAwyODHmskvrw/owUeldcvaVHkZA623UZtLi5EmHiUquAJSBJGSQwYkxl11aH9aHiUpVNvXvfUPARCVXAMpAEjLI4MSYyy6tD+vDRKVvadgXrkLARCVXAMpAEjLI4MSYyy6tD+vDRKUqm/r3viFgopIrAGUgCRlkcGLMZZfWh/VhotK3NOwLVyFgopIrAGUgCRlkcGLMZZfWh/VholKVTf173xB4iKgsnaPP+Pd8TMAxS8Z30Gvhkw4nxlxB0PqwPpokpSr/7uXvGWwzgwzZieOwfUellzbqa/UBARGVsbGxabUrO74ToxNjZ+e2f9g/hs0/pjWh9CFP+pLTjICJioNguwk6Mdomhi0xDjKM2j+q/cNEZZAWOQL36mvr5zGztlhy1Lwrq2C041c7fhWGvfzd+rA+TFRc4aoTUzrFChOVOuj5mNoI9HMxLQ/Oevl5R8/THkqTDidGJ0YnRifGqjih3zPEigwyZMfCRKWONfuY2giYqJgkuPWTmyQ4MdpHh43Im6jUTsE+sA4CJioOgiYqJip1YkUGwmQZhiNemajU8SgfUxsBE5XhcPzaCu3BgRmSQfbSdg9grn0J68M+6opKbXcZ6gNnAQcAc4H7gQfjv3/UYVZrAKcDm3X4fQdgfWAsASpaA7IF8AxgzardktvlNVFxEHRFxRWVOnEsA2GyDMMRr1xRqeNRjz5GhOIFwG5BVF4MfAN4IXBnySWHiai0xN8TWB44rglEJirD4fhNdDrVYzMkA1dUctml9WF9NKnqmKh0F4VvAl4L6P9bY1XgtqiMHAbcG1WJfYE/FCoqOwI7AQ8ATwTmAVsB7wf+BDwlyMFioOzYvwNfBO4DVgE+BKgKcgzwbODJwG+B+cA7gHeFLJJhj7bpbgssiN9vDuJ1NbAlcC6gytGRcczL4twz4l5XlEFnopIrAGUgCRlkcGLMZZfWh/VhotId+Why1t3AE4CJkpNEPH4M3ADsAmwKHF4gKiIGxwIiHKcC3wREGN8JvBFYOc5XxUYkp/3Y3wAfDRIiUvI04Lo4/3hgOeB6YHPg88AJwBJge+AcQLJr6LifAxsDS4H9gLOB84BNgF0LFZVLguTcAlwAtEjLo6ZvopIrAGUgCRlkcGLMZZfWh/VhotKEcnR3rBK8Kiq/L5y+HfADYB1gf+Ae4PFRJTm4QFTeC7w5WkTPBRZF+0j/PiKud1kQizeUHKtqyl5RiVFr5hDgIuBoYA7w16iIvDwqNgcBGwA/BPYGlsU9ZgMXA+u2QXBtCVHZGVgvKjWqskhmxsfHF05MTGhujxiX3iEx+jD8HZXGoGYgCRlkcGJ0YmySGBs72hROsH9U26ZbP90ZmNaoPB/YPVo4GwJfBV4SZOVtUa1Q9WRtYGEQFZGZq4C14jxVL7TIVm0ckRJVYERudMyrgP8pOfangO53cqyJOS1aMbqnWj2qsvwsqh4iRarIqCX1ZeBzsZZGs5bubwQ2Au4IkqSKyylBVCTLCnH+ioDIk66jysztnWBzRaXa6bozue7OyhAEM8hgopLLLq0P66MJcTRR6S7+q22iXXy3ifUdevNHrRMRiw/G20CqtoiAaM2JFt2KkIh8nAmsFK0fVUTUVlE7R1UStXBU6TgRuLDDsWrlqKqi1pPWuXwa0HqRs4A/x7qS1YH3RYVFcmn9iSo8WrdyV2HKW4e8agdpXYsW0F4TREVvIak1tQ9wflRRVot1Mx1RM1HJFYAykIQMMjgx5rJL68P6MFHpjnz4rMkR+FSQoW9PdpiJSq4AlIEkZJDBiTGXXVof1oeJiilHrxFQdUcVIi2wnXSYqOQKQBlIQgYZnBhz2aX1YX2YqFRlU//eNwRMVHIFoAwkIYMMToy57NL6sD5MVPqWhn3hKgRMVHIFoAwkIYMMToy57NL6sD5MVKqyqX/vGwImKrkCUAaSkEEGJ8Zcdml9WB8mKn1Lw75wFQImKrkCUAaSkEEGJ8Zcdml9WB8mKlXZ1L/3DYGHiMrSOX/rxw0mHjNriyVHzbuy6tpOjLmCoPVhfTRJSlX+3cvfM9hmBhmyE0d/R6WXVu9rIaIyNjY2rXZlx3didGLsHIzsH/aPYfOPaU0ozuszDwETFQfBdqt2YrRNDFtiHGRktn9U+4eJyiAtcgTu1c/Wz/L3rrDm4uPm6nP/kw47frXjV2HYy9+tD+vDRMUVrjoxpVOsMFGpg56PqY1APxfTTvDAU5cctZN2ejZRqQIhfs9AEjLIIDgyyGEZTNpccWxO2kxUagZ8H1YPARMVB2IH4uaBuJ539eaoDGTJxDFXnMiuDxOV3vi+rxIImKjkCkAZklIGGbIH4kEGEOvDPlpmbxnswq2fQUaCEb6XiYqDoCsqrqjUCYGZE2Md+Xt1TAYcshP5Ua+oLA/cCqxSw+hmAxcAG9c4tnjIecDzgDsLfzweuAg4Hdisw/UuB7YA7mp4v6ke3rrvM4A1gUl3S26/mYmKiYqJiolKnSCUIUFbhuGIVyYqgyEqRwGXtjnvGhVEpY6v9/OYPQERueOa3MREZTgcv4lOp3pshmSQ/Ylxqhg3Od/6sI+W2UsGu3Drp9yTO1VU1gcOA+4FVFnYVy8NREXlZcBngRvjmL2BNwL3A98Cjm27lSoqVUTl48BawErAl4FTgWuBTYBjgCcAy4B1QpargA8DWwIPAt8JWU4EngmsGjLtUiHbtsCCmOfNwG7A1XHdc4FZwJFxjOatcUbIdEUZpCYqDoKuqLiiUoc4ZU6MdeTv1TEZcMhO5F1RKa+ozAN+DNwAKNlvCvx7EJHrALVHFgHrAZ+J3yeAbwD7ATqmNURUngMUPyu/a7SC1Pp5VZCgbYDfAm8JMlAkKvr3J4HtAJGL04KYbBU3OT/+e+eQWWSpSrblgJ9HK0uv/EruswHJK4IkGVsVlUuAPYBbCmSt1E9NVExUTFRMVOok8QwJ2jIMR7wyUSknKiIP+wP3AI8H/gSMAX8EvgdcCBwB7BgE4Seh7tWiAqFkXyQqVRUVkQ+1Wp4FfAH4WFtF5WuAyMhrgkCIOKgCc1Dc5BBAFZENgHOAr9eQTWtuLgbWbQsqLYJUJCoiQCI+IlKqsoikMT4+vnBiYuLg9qB06R1z6sSpxsf4OyqNIfO3QwqQOSk9DEYGHLLIkQGLDDJk14eJSjlREfF4W1Qc1BpZO9ofWky7eVRUVGnRUGtGLRgNVR10zE0NiIpIiqo1HwQeFxWRFwFqrbRaP4vjui2iohbPoYAqKtKhSIzkeDvQOlakZTLZdJ7aVxsB+tqriJdIzilxX81vhWhlrQhcBtwGbA/c3illuqIyHE8ozSlP92c4ENsmyqwng11YhuGwTROVh6smWpfRGlo8ujowF/g9cB/wFOCdhbd+1Ao6CdC6jb2CMKjSoFaKKiMPtBGV9rd+VPFQy6j11s848MogAmo5faStotJOVFTtUDVFxEUtHJGIAwERmNaxEuGACtm2DoJ0d1RLJPs1QVS0TkdrZfYJIqQqiipGqiJ1HCYqw+H43dOO5mdmSAbZnxibo9r9GdaHfXTYiOOoE5XuvX30zvwUcFbV68omKg6C7a7hxGibGLbEOMjwbv+o9g8TlUFa5PDeS2tyVF1SJWfSYaJS7XRVGPby9wxBMIMMrqjkskvrw/poQl5NVHqZFXwtTFRyBaAMJCGDDE6MuezS+rA+TFRMGKYNAROVXAEoA0nIIIMTYy67tD6sDxOVaUvTvrGJSq4AlIEkZJDBiTGXXVof1oeJivnCtCFgopIrAGUgCRlkcGLMZZfWh/VhojJtado3foioLJ1T/Apvz0BZ/t4V1lx83Fx982XS4cSYKwhaH9ZHk6RU5d+9/D2DbWaQITtx9GLaXlq9r/XQYtqxsbFptSs7vhOjE2PnYGT/sH8Mm39Ma0JxXp95CCxatOi+ZcuWaY8gDyNgBIyAETACtRGYPXv2PfPnz9e2NY8YJiq1IfSBdRDIUFHJIIOwyiCHZfiH1RqLh7HIgEMWOTJgkUGG7PowUamTfX1MbQQyOF0GGbI7fm2F9uBA68NkqcyMMtiFZRgO2zRR6UEg9iWGw9gHrScHQT/Bt9ucbcKxIqNNZH+wMlEZdPaa4fdzIHYgdiDu7OT2D/uH/aO5f5iozHDiMOjpjY+PL1ywYMEhg75v8X4ZZJA8GeSwDP+wDGPxMBYZcMgiRwYsMsiQXR8mKtOZUX1vI2AEjIARMAJGYFIETFRsIEbACBgBI2AEjEBaBExU0qrGghkBI2AEjIARMAImKraBfiCwPnAssAy4FljQj5sM6JrPBT4N3AM8EXgfcANQNccnA18A5GM69z3AX4GdgfnAA8CJwFdiHsLrRcAsYG/gpwOaXze3ORRYPeah86tk74RV2XlVuHYjbz/O2Rb4AHAf8EtgL2DlDjov3r9szk1tpR/z6faa/xRzvgtYDtgF+MsI+seBwIQ+UxNAduvnZecNwj6eDnwHeGHIvwJwGvCksOuPA+dMQa9NYsCjbNFEpVv39HmTIfD9CFgK4P8FnBJOMIyofQn4T+AC4LXAnsC/AFVzPAK4EfhsJPQ1gOOA7wIbBSH5IfAaYJMgMAryzwdOintlxOvNwBhwfcxrqxqyl2H1uA7nVeGaARMFbwX1LYJ87hdB/YASnR9cELgTVk1sRWQ301gIiKQcA+wPPBY4coT8Qw8v8vENgVODqKzSpZ93Ok/+VjeWdGMfbwI+CqwJrBrGtTughzTdWwT8J8Czu9SrfKBJDDBRyeThM1QWBSolsefF/N4V//73IZ3visDfgQeBucDbgH+tMUcRkncCvwkHF2FTUNffdL6Gnlj091fHU/nn4+8KSqqu6L6ZxtoRiEWktg+iouqKCGkn2TvZg54+289bD7hmCGxn69Cj9KPgLiJ+eiSndp1vXlBgJ6wubGArF2UyCOCtwD8DH45kp2rKf4yQf6wGyG71gCGioYqKHmi68fNO57XiRp1Y0o19yJ4vB/4XmBP2pW1QVCG7F1C1RURD1Zaq2F4W9zSvsvPKYkBp3HNFJZnXzwBx5KyXAC+Nuaj68CpgnyGf27rAWcAbgNtqzPHqeOJeCjwV+DagJws9hau1o7Eo/q6qijD7avxdyVqYVe4UPUBMV4r5KwBLtzsEUVGVaDLZO9mDRG8/T9UalZez285OwNFRBr87grhI7NklOlfJuzU6YdWqztSxlcUD1HmdW70S+CLwq3gCV3VQrc6qGDDT/OPfCkRF5K0bP+90Xitu9Ns+fl0gKi3dy3+/DnwCEKHuRq+t+Nbu12UxoDTumajUcUUf0wQBPUFfB+jpW0PtDJUQiyXwJtfLcKxK9lpbsGM88deZo54slNB+F0/dah/piVp/03oVDZWM9Xc9cfwiev36u/69DnB/hskXCKee7BQsnwJobYKeHlUqnkz2TlhpLU77eSKDImnZbWcb4L1RYRM8JwB6khUZb9e5gnRrSP9lWInE1rUVkZpMQ0/ah0cyk5/sC7ylRgyYaf5RJCry5278vNN5rbhRJ5ZMxT7aicpzgnyLlH852npVsb1Mr6/vYA9lMaA07pmoZHL5mSOLgpcWhd0UiVhtgW5KkhkQ0ZPRxwA97f+xIFDVHNWn/1m0RN4NyOnVx9cTiUrl8j2VW/UEsWlUKNQSWisIjNpBWcfrChUVlY1VXZlM9jKsVFouO68K1wyYqNwvObXWSOszLgWkY7U523VebHl2wqqJrXSzBqGfmOlpW+sbtN5KZXu1wV4R+EwWA5rMeRj8o0hUtPi1Gz/vdJ4W6taNJVOxjyJReVpUe3cDfjDFuCcfaBIDHmWvJir9dOHRvfYGkdz1RoQcTE9Zwzq0iEwL5m6PCei/lZTL5qg3Yb4FvDjaPVpcp6cG+ZlaJurfK3jvCihRnxxlc11abQGt61GLRU/mKo1nHUWi0kn2ecCWUXnoZA9lcx4W2xHJUnLS21tLojSuFl+Zzq+KtU0i7mVz7nReJ1vJZBcviDUpesNPfqLFl1rrMGr+USQq0k8TP1cL+IqoqJadNyj7KBKV4+NBQutiWkMEVLFND27F2F4n7jWJASYqmTzcssw4BLT4TGVwBetRH6o6qMowPupARMtPbTIl81Ee9o9y7Wvdm9Y6XTykxtF3vbqiMqSWYbFTIqAqyWzglpTSDVYorWHRYuBsby4NFoWH7/asWKs0HffOdE/7R7k2ht0++q5XE5VMbmxZjIARMAJGwAgYgUcgYKJigzACRsAIGAEjYATSImCiklY1FswIGAEjYASMgBEwUbENGAEjYASMgBEwAmkRMFFJqxoLZgSMgBEwAkbACJio2AaMgBEwAkbACBiBtAiYqKRVjQUzAkZgAAicF5uubdzlvfStGO1+rFg6P/Yq0leMdV0PI2AEeoCAiUoPQPQljIARGFoEtC+NvqiqvUy6Gfp8vvY+0t4/2vVaX6z9Wmwf0c31fI4RMAJtCJio2CSMgBEYZQSKFZULgrRo40VtJngZsH1sfVCG0edifx/99ssgKfoUuioq2lDyfOBMYNv425OAj0cFR5vHidzoY1nabK449Bl+VWe0DYH20XlmfGJ9z9jcTV8+/lBswaDtGPSBQW2Cp20X9El77bejT5ZfD+icH42ygj334UfARGX4degZGAEj0D0C7URFO9jODcJyRpCJz3S4vDaQVPXkb8AeQTraico3Y2PO7wVx0E7L+tsnYqdo7aStjS81REo2DFLzB+BKYDHwJUA7V2vTOu0uuzegTf30t68CurY2B3wHoH2FRFy0AaaI0CaxIeaD3UPkM43A9CJgojK9+PvuRsAITC8C7UTl6cD6QVTuBA6K/Zs6Samqi7YK2AbQOpd2orJdrFt5O/CVIDUiDaqkaMM3bfKm8f7Y3G8BcDRwCPCR2NxSFZ43AefGbtuvDKKilpPurXvq/3cE/i/2jdHWBdoQ83GxI7eIj4cRGEoETFSGUm0W2ggYgR4h0E5UVokqxIqR8LVF/WGT3KuKqLwa+G6QCFVGXg5cHntCaUdukZbXR5tI62S0y7bGwVExaZERtZPOATYHNgui8njgnmhRiVTp3JuBFtlRBUa/39sjrHwZIzAtCJioTAvsvqkRMAJJEOg3URGx0ILbNYBfxdtAx8fiXbWAVDVRu0Zj3yAVt0aFRITmrDhWbZ6VgJcA+3QgKq+LNSyPjTUuhwLPjoqKWz9JDM5iNEfARKU5Zj7DCBiBmYPAoIiKEFNVZDzIw/ejArJprDMpIqrFr1pbsnMQGS2m1d+0DuYGQItptUalvaIioqLFtCfHWpebgN2BH84cdXkmo4iAicooat1zNgJGwAgYASMwJAiYqAyJoiymETAC04aAvo+iN3zax17AJ6dNKt/YCIwIAiYqI6JoT9MIGAEjYASMwDAiYKIyjFqzzEbACBgBI2AERgQBE5URUbSnaQSMgBEwAkZgGBEwURlGrVlmI2AEjIARMAIjgoCJyogo2tM0AkbACBgBIzCMCJioDKPWLLMRMAJGwAgYgRFB4P8B8rn/2+kDDFkAAAAASUVORK5CYII=",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(top_places.reset_index()).mark_bar().encode(\n",
    "    x=\"in_firezone:Q\",\n",
    "    y=alt.Y(\n",
    "        \"place_name:N\",\n",
    "        sort=alt.EncodingSortField(field=\"in_firezone\", op=\"sum\", order=\"descending\")\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many cities have more than 90% of the buildings in the \"very severe\" zones?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "very_high_places = place_pivot[place_pivot.in_firezone_percent >= 90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "174\n"
     ]
    }
   ],
   "source": [
    "intcomma(len(very_high_places))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are Paradise, Malibu and Topanga in this group?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>in_firezone</th>\n",
       "      <th>place_name</th>\n",
       "      <th>not_in_firezone</th>\n",
       "      <th>in_firezone</th>\n",
       "      <th>total</th>\n",
       "      <th>in_firezone_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Malibu city</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5963.0</td>\n",
       "      <td>5967.0</td>\n",
       "      <td>99.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Paradise town</td>\n",
       "      <td>149.0</td>\n",
       "      <td>11703.0</td>\n",
       "      <td>11852.0</td>\n",
       "      <td>98.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Topanga CDP</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3574.0</td>\n",
       "      <td>3574.0</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "in_firezone     place_name  not_in_firezone  in_firezone    total  \\\n",
       "102            Malibu city              4.0       5963.0   5967.0   \n",
       "118          Paradise town            149.0      11703.0  11852.0   \n",
       "160            Topanga CDP              0.0       3574.0   3574.0   \n",
       "\n",
       "in_firezone  in_firezone_percent  \n",
       "102                        99.93  \n",
       "118                        98.74  \n",
       "160                       100.00  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "very_high_places.reset_index()[\n",
    "    very_high_places.reset_index().place_name.isin([\n",
    "        'Paradise town',\n",
    "        'Malibu city',\n",
    "        'Topanga CDP'\n",
    "    ])\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What Los Angeles neighborhoods have the most buildings \"very severe\" zones?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "hood_pivot = crosstab_boolean(points, \"neighborhood\", \"in_firezone\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_hoods = hood_pivot.sort_values(\"in_firezone\", ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>in_firezone</th>\n",
       "      <th>not_in_firezone</th>\n",
       "      <th>in_firezone</th>\n",
       "      <th>total</th>\n",
       "      <th>in_firezone_percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pacific Palisades</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9303.0</td>\n",
       "      <td>9303.0</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hollywood Hills</th>\n",
       "      <td>226.0</td>\n",
       "      <td>6096.0</td>\n",
       "      <td>6322.0</td>\n",
       "      <td>96.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hollywood Hills West</th>\n",
       "      <td>65.0</td>\n",
       "      <td>5826.0</td>\n",
       "      <td>5891.0</td>\n",
       "      <td>98.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Silver Lake</th>\n",
       "      <td>2794.0</td>\n",
       "      <td>5114.0</td>\n",
       "      <td>7908.0</td>\n",
       "      <td>64.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shadow Hills</th>\n",
       "      <td>915.0</td>\n",
       "      <td>5049.0</td>\n",
       "      <td>5964.0</td>\n",
       "      <td>84.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beverly Crest</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4915.0</td>\n",
       "      <td>4921.0</td>\n",
       "      <td>99.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagle Rock</th>\n",
       "      <td>5183.0</td>\n",
       "      <td>4836.0</td>\n",
       "      <td>10019.0</td>\n",
       "      <td>48.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brentwood</th>\n",
       "      <td>3583.0</td>\n",
       "      <td>4813.0</td>\n",
       "      <td>8396.0</td>\n",
       "      <td>57.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sherman Oaks</th>\n",
       "      <td>11855.0</td>\n",
       "      <td>4497.0</td>\n",
       "      <td>16352.0</td>\n",
       "      <td>27.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Highland Park</th>\n",
       "      <td>7153.0</td>\n",
       "      <td>4343.0</td>\n",
       "      <td>11496.0</td>\n",
       "      <td>37.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mount Washington</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4342.0</td>\n",
       "      <td>4342.0</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Studio City</th>\n",
       "      <td>5617.0</td>\n",
       "      <td>4337.0</td>\n",
       "      <td>9954.0</td>\n",
       "      <td>43.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Encino</th>\n",
       "      <td>8424.0</td>\n",
       "      <td>4277.0</td>\n",
       "      <td>12701.0</td>\n",
       "      <td>33.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>El Sereno</th>\n",
       "      <td>6046.0</td>\n",
       "      <td>4172.0</td>\n",
       "      <td>10218.0</td>\n",
       "      <td>40.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Feliz</th>\n",
       "      <td>3030.0</td>\n",
       "      <td>4015.0</td>\n",
       "      <td>7045.0</td>\n",
       "      <td>56.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montecito Heights</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3612.0</td>\n",
       "      <td>3627.0</td>\n",
       "      <td>99.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tujunga</th>\n",
       "      <td>4397.0</td>\n",
       "      <td>3489.0</td>\n",
       "      <td>7886.0</td>\n",
       "      <td>44.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chatsworth</th>\n",
       "      <td>9164.0</td>\n",
       "      <td>3399.0</td>\n",
       "      <td>12563.0</td>\n",
       "      <td>27.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bel-Air</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porter Ranch</th>\n",
       "      <td>5144.0</td>\n",
       "      <td>3286.0</td>\n",
       "      <td>8430.0</td>\n",
       "      <td>38.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "in_firezone           not_in_firezone  in_firezone    total  \\\n",
       "neighborhood                                                  \n",
       "Pacific Palisades                 0.0       9303.0   9303.0   \n",
       "Hollywood Hills                 226.0       6096.0   6322.0   \n",
       "Hollywood Hills West             65.0       5826.0   5891.0   \n",
       "Silver Lake                    2794.0       5114.0   7908.0   \n",
       "Shadow Hills                    915.0       5049.0   5964.0   \n",
       "Beverly Crest                     6.0       4915.0   4921.0   \n",
       "Eagle Rock                     5183.0       4836.0  10019.0   \n",
       "Brentwood                      3583.0       4813.0   8396.0   \n",
       "Sherman Oaks                  11855.0       4497.0  16352.0   \n",
       "Highland Park                  7153.0       4343.0  11496.0   \n",
       "Mount Washington                  0.0       4342.0   4342.0   \n",
       "Studio City                    5617.0       4337.0   9954.0   \n",
       "Encino                         8424.0       4277.0  12701.0   \n",
       "El Sereno                      6046.0       4172.0  10218.0   \n",
       "Los Feliz                      3030.0       4015.0   7045.0   \n",
       "Montecito Heights                15.0       3612.0   3627.0   \n",
       "Tujunga                        4397.0       3489.0   7886.0   \n",
       "Chatsworth                     9164.0       3399.0  12563.0   \n",
       "Bel-Air                           0.0       3364.0   3364.0   \n",
       "Porter Ranch                   5144.0       3286.0   8430.0   \n",
       "\n",
       "in_firezone           in_firezone_percent  \n",
       "neighborhood                               \n",
       "Pacific Palisades                  100.00  \n",
       "Hollywood Hills                     96.43  \n",
       "Hollywood Hills West                98.90  \n",
       "Silver Lake                         64.67  \n",
       "Shadow Hills                        84.66  \n",
       "Beverly Crest                       99.88  \n",
       "Eagle Rock                          48.27  \n",
       "Brentwood                           57.32  \n",
       "Sherman Oaks                        27.50  \n",
       "Highland Park                       37.78  \n",
       "Mount Washington                   100.00  \n",
       "Studio City                         43.57  \n",
       "Encino                              33.67  \n",
       "El Sereno                           40.83  \n",
       "Los Feliz                           56.99  \n",
       "Montecito Heights                   99.59  \n",
       "Tujunga                             44.24  \n",
       "Chatsworth                          27.06  \n",
       "Bel-Air                            100.00  \n",
       "Porter Ranch                        38.98  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_hoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-2db9fde95f2aff870c5ea918725caba8"
       },
       "datasets": {
        "data-2db9fde95f2aff870c5ea918725caba8": [
         {
          "in_firezone": 9303,
          "in_firezone_percent": 100,
          "neighborhood": "Pacific Palisades",
          "not_in_firezone": 0,
          "total": 9303
         },
         {
          "in_firezone": 6096,
          "in_firezone_percent": 96.43,
          "neighborhood": "Hollywood Hills",
          "not_in_firezone": 226,
          "total": 6322
         },
         {
          "in_firezone": 5826,
          "in_firezone_percent": 98.9,
          "neighborhood": "Hollywood Hills West",
          "not_in_firezone": 65,
          "total": 5891
         },
         {
          "in_firezone": 5114,
          "in_firezone_percent": 64.67,
          "neighborhood": "Silver Lake",
          "not_in_firezone": 2794,
          "total": 7908
         },
         {
          "in_firezone": 5049,
          "in_firezone_percent": 84.66,
          "neighborhood": "Shadow Hills",
          "not_in_firezone": 915,
          "total": 5964
         },
         {
          "in_firezone": 4915,
          "in_firezone_percent": 99.88,
          "neighborhood": "Beverly Crest",
          "not_in_firezone": 6,
          "total": 4921
         },
         {
          "in_firezone": 4836,
          "in_firezone_percent": 48.27,
          "neighborhood": "Eagle Rock",
          "not_in_firezone": 5183,
          "total": 10019
         },
         {
          "in_firezone": 4813,
          "in_firezone_percent": 57.32,
          "neighborhood": "Brentwood",
          "not_in_firezone": 3583,
          "total": 8396
         },
         {
          "in_firezone": 4497,
          "in_firezone_percent": 27.5,
          "neighborhood": "Sherman Oaks",
          "not_in_firezone": 11855,
          "total": 16352
         },
         {
          "in_firezone": 4343,
          "in_firezone_percent": 37.78,
          "neighborhood": "Highland Park",
          "not_in_firezone": 7153,
          "total": 11496
         },
         {
          "in_firezone": 4342,
          "in_firezone_percent": 100,
          "neighborhood": "Mount Washington",
          "not_in_firezone": 0,
          "total": 4342
         },
         {
          "in_firezone": 4337,
          "in_firezone_percent": 43.57,
          "neighborhood": "Studio City",
          "not_in_firezone": 5617,
          "total": 9954
         },
         {
          "in_firezone": 4277,
          "in_firezone_percent": 33.67,
          "neighborhood": "Encino",
          "not_in_firezone": 8424,
          "total": 12701
         },
         {
          "in_firezone": 4172,
          "in_firezone_percent": 40.83,
          "neighborhood": "El Sereno",
          "not_in_firezone": 6046,
          "total": 10218
         },
         {
          "in_firezone": 4015,
          "in_firezone_percent": 56.99,
          "neighborhood": "Los Feliz",
          "not_in_firezone": 3030,
          "total": 7045
         },
         {
          "in_firezone": 3612,
          "in_firezone_percent": 99.59,
          "neighborhood": "Montecito Heights",
          "not_in_firezone": 15,
          "total": 3627
         },
         {
          "in_firezone": 3489,
          "in_firezone_percent": 44.24,
          "neighborhood": "Tujunga",
          "not_in_firezone": 4397,
          "total": 7886
         },
         {
          "in_firezone": 3399,
          "in_firezone_percent": 27.06,
          "neighborhood": "Chatsworth",
          "not_in_firezone": 9164,
          "total": 12563
         },
         {
          "in_firezone": 3364,
          "in_firezone_percent": 100,
          "neighborhood": "Bel-Air",
          "not_in_firezone": 0,
          "total": 3364
         },
         {
          "in_firezone": 3286,
          "in_firezone_percent": 38.98,
          "neighborhood": "Porter Ranch",
          "not_in_firezone": 5144,
          "total": 8430
         }
        ]
       },
       "encoding": {
        "x": {
         "field": "in_firezone",
         "type": "quantitative"
        },
        "y": {
         "field": "neighborhood",
         "sort": {
          "field": "in_firezone",
          "op": "sum",
          "order": "descending"
         },
         "type": "nominal"
        }
       },
       "mark": "bar"
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(top_hoods.reset_index()).mark_bar().encode(\n",
    "    x=\"in_firezone:Q\",\n",
    "    y=alt.Y(\n",
    "        \"neighborhood:N\",\n",
    "        sort=alt.EncodingSortField(field=\"in_firezone\", op=\"sum\", order=\"descending\")\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many neighborhoods have 1,000 or more?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hood_pivot[hood_pivot.in_firezone >= 1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge grids\n",
    "\n",
    "Group our building points into grids that segment that state by running [merge-grids.ipynb](src/merge-grids.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%run src/merge-grids.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze grids\n",
    "\n",
    "Calculate the number of at-risk buildings in each grid segment by running [analyze-grids.ipynb](src/analyze-grids.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%run src/analyze-grids.ipynb"
   ]
  }
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