{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "777ee8d3-6725-43e0-a45f-5e7ead0aa26f",
   "metadata": {},
   "source": [
    "# Social Vulnerability Milan - Calculate Vulnerability"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45124731-302e-4184-9a4d-e57e2048a172",
   "metadata": {},
   "source": [
    "## Environment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b82a2d0-2ab4-4452-ae11-0cfb7ed631d5",
   "metadata": {},
   "source": [
    "### R Libraries\n",
    "The relvant R libraries are imported in to the kernal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6093e58a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: pacman\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Loaded Packages:\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'lubridate'</li><li>'forcats'</li><li>'stringr'</li><li>'dplyr'</li><li>'purrr'</li><li>'readr'</li><li>'tidyr'</li><li>'tibble'</li><li>'ggplot2'</li><li>'tidyverse'</li><li>'sf'</li><li>'pacman'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'lubridate'\n",
       "\\item 'forcats'\n",
       "\\item 'stringr'\n",
       "\\item 'dplyr'\n",
       "\\item 'purrr'\n",
       "\\item 'readr'\n",
       "\\item 'tidyr'\n",
       "\\item 'tibble'\n",
       "\\item 'ggplot2'\n",
       "\\item 'tidyverse'\n",
       "\\item 'sf'\n",
       "\\item 'pacman'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'lubridate'\n",
       "2. 'forcats'\n",
       "3. 'stringr'\n",
       "4. 'dplyr'\n",
       "5. 'purrr'\n",
       "6. 'readr'\n",
       "7. 'tidyr'\n",
       "8. 'tibble'\n",
       "9. 'ggplot2'\n",
       "10. 'tidyverse'\n",
       "11. 'sf'\n",
       "12. 'pacman'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"lubridate\" \"forcats\"   \"stringr\"   \"dplyr\"     \"purrr\"     \"readr\"    \n",
       " [7] \"tidyr\"     \"tibble\"    \"ggplot2\"   \"tidyverse\" \"sf\"        \"pacman\"   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load R libraries\n",
    "if(!require(\"pacman\"))\n",
    "    install.packages(\"pacman\")\n",
    "\n",
    "p_load(\"sf\", \"tidyverse\")\n",
    "\n",
    "print(\"Loaded Packages:\")\n",
    "p_loaded()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e566f14-5f10-4d08-a5ee-8c4dea1661d0",
   "metadata": {},
   "source": [
    "### Output directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "463b1703-15a6-4d3d-aa21-7096505d2ec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the output directory if it does not exist\n",
    "output_dir <- file.path(\"../..\",\"3_outputs\",\"Italy\",\"Milan\",\"2021\")\n",
    "if(!dir.exists(output_dir)){\n",
    "    dir.create(output_dir, recursive = TRUE)\n",
    "    print(paste0(output_dir, \" created\"))\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65901706-93e1-4751-9b51-ea78fac0c315",
   "metadata": {},
   "source": [
    "### Set the GUID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "417fd52a-4662-4d2d-9c69-32785c35d49c",
   "metadata": {},
   "outputs": [],
   "source": [
    "GUID <- \"SEZ2011\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4919bacb-e14d-455f-8550-aefaa84572fe",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "799416fe-aeea-43a2-9ea5-d7a39695d913",
   "metadata": {},
   "source": [
    "### Import the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38e1c16d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>SEZ2011</th><th scope=col>early_childhood_boy</th><th scope=col>early_childhood_girl</th><th scope=col>age_middle_to_oldest_old_male</th><th scope=col>age_middle_to_oldest_old_female</th><th scope=col>dependants</th><th scope=col>unemployment</th><th scope=col>no_higher_education</th><th scope=col>foreign_nationals</th><th scope=col>primary_school_age</th><th scope=col>one_person_households</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1.5146e+11</td><td>-1.230411</td><td>-1.064144</td><td> 2.1196292</td><td>-1.3062152</td><td>-0.7801407</td><td>-0.5060886</td><td>-0.6075338</td><td> 0.284861877</td><td>-1.458348</td><td> 0.9333249</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1.5146e+11</td><td>-1.230411</td><td>-1.064144</td><td> 0.2302043</td><td>-0.3189938</td><td> 0.1197864</td><td> 1.3419462</td><td> 0.8849880</td><td> 0.768744204</td><td>-1.458348</td><td>-0.7026211</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1.5146e+11</td><td>-1.230411</td><td>-1.064144</td><td> 3.8043177</td><td>-1.3062152</td><td>-2.2200240</td><td>-0.2425543</td><td>-1.6458099</td><td>-1.081394102</td><td>-1.458348</td><td> 3.2236494</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>1.5146e+11</td><td>-1.230411</td><td>-1.064144</td><td> 2.6982091</td><td>-1.3062152</td><td>-0.5183437</td><td>-1.2391934</td><td>-1.6458099</td><td>-0.004949997</td><td> 1.865648</td><td> 0.2789465</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1.5146e+11</td><td>-1.230411</td><td>-1.064144</td><td>19.6216703</td><td>-1.3062152</td><td>-2.2200240</td><td>-2.2981224</td><td>-1.6458099</td><td>-1.081394102</td><td>-1.458348</td><td> 3.2236494</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1.5146e+11</td><td>-1.230411</td><td>-1.064144</td><td>-1.0625601</td><td>-1.3062152</td><td>-2.2200240</td><td>-2.2981224</td><td>-1.6458099</td><td> 2.865567617</td><td>-1.458348</td><td> 0.9333249</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & SEZ2011 & early\\_childhood\\_boy & early\\_childhood\\_girl & age\\_middle\\_to\\_oldest\\_old\\_male & age\\_middle\\_to\\_oldest\\_old\\_female & dependants & unemployment & no\\_higher\\_education & foreign\\_nationals & primary\\_school\\_age & one\\_person\\_households\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 1.5146e+11 & -1.230411 & -1.064144 &  2.1196292 & -1.3062152 & -0.7801407 & -0.5060886 & -0.6075338 &  0.284861877 & -1.458348 &  0.9333249\\\\\n",
       "\t2 & 1.5146e+11 & -1.230411 & -1.064144 &  0.2302043 & -0.3189938 &  0.1197864 &  1.3419462 &  0.8849880 &  0.768744204 & -1.458348 & -0.7026211\\\\\n",
       "\t3 & 1.5146e+11 & -1.230411 & -1.064144 &  3.8043177 & -1.3062152 & -2.2200240 & -0.2425543 & -1.6458099 & -1.081394102 & -1.458348 &  3.2236494\\\\\n",
       "\t4 & 1.5146e+11 & -1.230411 & -1.064144 &  2.6982091 & -1.3062152 & -0.5183437 & -1.2391934 & -1.6458099 & -0.004949997 &  1.865648 &  0.2789465\\\\\n",
       "\t5 & 1.5146e+11 & -1.230411 & -1.064144 & 19.6216703 & -1.3062152 & -2.2200240 & -2.2981224 & -1.6458099 & -1.081394102 & -1.458348 &  3.2236494\\\\\n",
       "\t6 & 1.5146e+11 & -1.230411 & -1.064144 & -1.0625601 & -1.3062152 & -2.2200240 & -2.2981224 & -1.6458099 &  2.865567617 & -1.458348 &  0.9333249\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 11\n",
       "\n",
       "| <!--/--> | SEZ2011 &lt;dbl&gt; | early_childhood_boy &lt;dbl&gt; | early_childhood_girl &lt;dbl&gt; | age_middle_to_oldest_old_male &lt;dbl&gt; | age_middle_to_oldest_old_female &lt;dbl&gt; | dependants &lt;dbl&gt; | unemployment &lt;dbl&gt; | no_higher_education &lt;dbl&gt; | foreign_nationals &lt;dbl&gt; | primary_school_age &lt;dbl&gt; | one_person_households &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 1.5146e+11 | -1.230411 | -1.064144 |  2.1196292 | -1.3062152 | -0.7801407 | -0.5060886 | -0.6075338 |  0.284861877 | -1.458348 |  0.9333249 |\n",
       "| 2 | 1.5146e+11 | -1.230411 | -1.064144 |  0.2302043 | -0.3189938 |  0.1197864 |  1.3419462 |  0.8849880 |  0.768744204 | -1.458348 | -0.7026211 |\n",
       "| 3 | 1.5146e+11 | -1.230411 | -1.064144 |  3.8043177 | -1.3062152 | -2.2200240 | -0.2425543 | -1.6458099 | -1.081394102 | -1.458348 |  3.2236494 |\n",
       "| 4 | 1.5146e+11 | -1.230411 | -1.064144 |  2.6982091 | -1.3062152 | -0.5183437 | -1.2391934 | -1.6458099 | -0.004949997 |  1.865648 |  0.2789465 |\n",
       "| 5 | 1.5146e+11 | -1.230411 | -1.064144 | 19.6216703 | -1.3062152 | -2.2200240 | -2.2981224 | -1.6458099 | -1.081394102 | -1.458348 |  3.2236494 |\n",
       "| 6 | 1.5146e+11 | -1.230411 | -1.064144 | -1.0625601 | -1.3062152 | -2.2200240 | -2.2981224 | -1.6458099 |  2.865567617 | -1.458348 |  0.9333249 |\n",
       "\n"
      ],
      "text/plain": [
       "  SEZ2011    early_childhood_boy early_childhood_girl\n",
       "1 1.5146e+11 -1.230411           -1.064144           \n",
       "2 1.5146e+11 -1.230411           -1.064144           \n",
       "3 1.5146e+11 -1.230411           -1.064144           \n",
       "4 1.5146e+11 -1.230411           -1.064144           \n",
       "5 1.5146e+11 -1.230411           -1.064144           \n",
       "6 1.5146e+11 -1.230411           -1.064144           \n",
       "  age_middle_to_oldest_old_male age_middle_to_oldest_old_female dependants\n",
       "1  2.1196292                    -1.3062152                      -0.7801407\n",
       "2  0.2302043                    -0.3189938                       0.1197864\n",
       "3  3.8043177                    -1.3062152                      -2.2200240\n",
       "4  2.6982091                    -1.3062152                      -0.5183437\n",
       "5 19.6216703                    -1.3062152                      -2.2200240\n",
       "6 -1.0625601                    -1.3062152                      -2.2200240\n",
       "  unemployment no_higher_education foreign_nationals primary_school_age\n",
       "1 -0.5060886   -0.6075338           0.284861877      -1.458348         \n",
       "2  1.3419462    0.8849880           0.768744204      -1.458348         \n",
       "3 -0.2425543   -1.6458099          -1.081394102      -1.458348         \n",
       "4 -1.2391934   -1.6458099          -0.004949997       1.865648         \n",
       "5 -2.2981224   -1.6458099          -1.081394102      -1.458348         \n",
       "6 -2.2981224   -1.6458099           2.865567617      -1.458348         \n",
       "  one_person_households\n",
       "1  0.9333249           \n",
       "2 -0.7026211           \n",
       "3  3.2236494           \n",
       "4  0.2789465           \n",
       "5  3.2236494           \n",
       "6  0.9333249           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading layer `census_areas_TCD' from data source \n",
      "  `/Cities/2_pipeline/Italy/Milan/1b_Copernicus/2021/census_areas_TCD.geojson' \n",
      "  using driver `GeoJSON'\n",
      "Simple feature collection with 6079 features and 15 fields\n",
      "Geometry type: POLYGON\n",
      "Dimension:     XY\n",
      "Bounding box:  xmin: 1503202 ymin: 5025952 xmax: 1521750 ymax: 5042528\n",
      "Projected CRS: Monte Mario / Italy zone 1\n",
      "Reading layer `census_areas_IMD' from data source \n",
      "  `/Cities/2_pipeline/Italy/Milan/1b_Copernicus/2021/census_areas_IMD.geojson' \n",
      "  using driver `GeoJSON'\n",
      "Simple feature collection with 6079 features and 15 fields\n",
      "Geometry type: POLYGON\n",
      "Dimension:     XY\n",
      "Bounding box:  xmin: 1503202 ymin: 5025952 xmax: 1521750 ymax: 5042528\n",
      "Projected CRS: Monte Mario / Italy zone 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 12 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>domain</th><th scope=col>indicator</th><th scope=col>sensitivity</th><th scope=col>prepare</th><th scope=col>respond</th><th scope=col>recover</th><th scope=col>adaptive_capacity</th><th scope=col>enhanced_exposure</th><th scope=col>weight</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>age                 </td><td>early_childhood_boy            </td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 1.0</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>age                 </td><td>early_childhood_girl           </td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 1.0</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>age                 </td><td>age_middle_to_oldest_old_male  </td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 1.0</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>age                 </td><td>age_middle_to_oldest_old_female</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td> 1.0</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>income              </td><td>dependants                     </td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td> 1.0</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>income              </td><td>unemployment                   </td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td> 1.0</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>info_access_use     </td><td>no_higher_education            </td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td> 0.5</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>local_knowledge     </td><td>foreign_nationals              </td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td><td> 0.5</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>social_network      </td><td>primary_school_age             </td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td>-1.0</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>social_network      </td><td>one_person_households          </td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td> 1.0</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>physical_environment</td><td>impervious                     </td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td> 1.0</td></tr>\n",
       "\t<tr><th scope=row>12</th><td>physical_environment</td><td>tree_cover_density             </td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td> 1.0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 12 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & domain & indicator & sensitivity & prepare & respond & recover & adaptive\\_capacity & enhanced\\_exposure & weight\\\\\n",
       "  & <chr> & <chr> & <int> & <int> & <int> & <int> & <int> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & age                  & early\\_childhood\\_boy             & 1 & 0 & 0 & 0 & 0 & 0 &  1.0\\\\\n",
       "\t2 & age                  & early\\_childhood\\_girl            & 1 & 0 & 0 & 0 & 0 & 0 &  1.0\\\\\n",
       "\t3 & age                  & age\\_middle\\_to\\_oldest\\_old\\_male   & 1 & 0 & 0 & 0 & 0 & 0 &  1.0\\\\\n",
       "\t4 & age                  & age\\_middle\\_to\\_oldest\\_old\\_female & 1 & 0 & 0 & 0 & 0 & 0 &  1.0\\\\\n",
       "\t5 & income               & dependants                      & 0 & 1 & 1 & 1 & 1 & 0 &  1.0\\\\\n",
       "\t6 & income               & unemployment                    & 0 & 1 & 1 & 1 & 1 & 0 &  1.0\\\\\n",
       "\t7 & info\\_access\\_use      & no\\_higher\\_education             & 0 & 1 & 1 & 1 & 1 & 0 &  0.5\\\\\n",
       "\t8 & local\\_knowledge      & foreign\\_nationals               & 0 & 1 & 1 & 0 & 1 & 0 &  0.5\\\\\n",
       "\t9 & social\\_network       & primary\\_school\\_age              & 0 & 0 & 1 & 1 & 1 & 0 & -1.0\\\\\n",
       "\t10 & social\\_network       & one\\_person\\_households           & 0 & 0 & 1 & 1 & 1 & 0 &  1.0\\\\\n",
       "\t11 & physical\\_environment & impervious                      & 0 & 0 & 0 & 0 & 0 & 1 &  1.0\\\\\n",
       "\t12 & physical\\_environment & tree\\_cover\\_density              & 0 & 0 & 0 & 0 & 0 & 1 &  1.0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 12 × 9\n",
       "\n",
       "| <!--/--> | domain &lt;chr&gt; | indicator &lt;chr&gt; | sensitivity &lt;int&gt; | prepare &lt;int&gt; | respond &lt;int&gt; | recover &lt;int&gt; | adaptive_capacity &lt;int&gt; | enhanced_exposure &lt;int&gt; | weight &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | age                  | early_childhood_boy             | 1 | 0 | 0 | 0 | 0 | 0 |  1.0 |\n",
       "| 2 | age                  | early_childhood_girl            | 1 | 0 | 0 | 0 | 0 | 0 |  1.0 |\n",
       "| 3 | age                  | age_middle_to_oldest_old_male   | 1 | 0 | 0 | 0 | 0 | 0 |  1.0 |\n",
       "| 4 | age                  | age_middle_to_oldest_old_female | 1 | 0 | 0 | 0 | 0 | 0 |  1.0 |\n",
       "| 5 | income               | dependants                      | 0 | 1 | 1 | 1 | 1 | 0 |  1.0 |\n",
       "| 6 | income               | unemployment                    | 0 | 1 | 1 | 1 | 1 | 0 |  1.0 |\n",
       "| 7 | info_access_use      | no_higher_education             | 0 | 1 | 1 | 1 | 1 | 0 |  0.5 |\n",
       "| 8 | local_knowledge      | foreign_nationals               | 0 | 1 | 1 | 0 | 1 | 0 |  0.5 |\n",
       "| 9 | social_network       | primary_school_age              | 0 | 0 | 1 | 1 | 1 | 0 | -1.0 |\n",
       "| 10 | social_network       | one_person_households           | 0 | 0 | 1 | 1 | 1 | 0 |  1.0 |\n",
       "| 11 | physical_environment | impervious                      | 0 | 0 | 0 | 0 | 0 | 1 |  1.0 |\n",
       "| 12 | physical_environment | tree_cover_density              | 0 | 0 | 0 | 0 | 0 | 1 |  1.0 |\n",
       "\n"
      ],
      "text/plain": [
       "   domain               indicator                       sensitivity prepare\n",
       "1  age                  early_childhood_boy             1           0      \n",
       "2  age                  early_childhood_girl            1           0      \n",
       "3  age                  age_middle_to_oldest_old_male   1           0      \n",
       "4  age                  age_middle_to_oldest_old_female 1           0      \n",
       "5  income               dependants                      0           1      \n",
       "6  income               unemployment                    0           1      \n",
       "7  info_access_use      no_higher_education             0           1      \n",
       "8  local_knowledge      foreign_nationals               0           1      \n",
       "9  social_network       primary_school_age              0           0      \n",
       "10 social_network       one_person_households           0           0      \n",
       "11 physical_environment impervious                      0           0      \n",
       "12 physical_environment tree_cover_density              0           0      \n",
       "   respond recover adaptive_capacity enhanced_exposure weight\n",
       "1  0       0       0                 0                  1.0  \n",
       "2  0       0       0                 0                  1.0  \n",
       "3  0       0       0                 0                  1.0  \n",
       "4  0       0       0                 0                  1.0  \n",
       "5  1       1       1                 0                  1.0  \n",
       "6  1       1       1                 0                  1.0  \n",
       "7  1       1       1                 0                  0.5  \n",
       "8  1       0       1                 0                  0.5  \n",
       "9  1       1       1                 0                 -1.0  \n",
       "10 1       1       1                 0                  1.0  \n",
       "11 0       0       0                 1                  1.0  \n",
       "12 0       0       0                 1                  1.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load census data\n",
    "census_indicator_data <- read.csv(\"../../2_pipeline/Italy/Milan/1a_CensusData/2021/censusDataZ.csv\")\n",
    "colnames(census_indicator_data)[colnames(census_indicator_data) == \"GUID\"] = \"SA_GUID__1\"\n",
    "head(census_indicator_data)\n",
    "\n",
    "# Load Coperncius data: tree cover density (TCD) and imperviousness density (IMP)\n",
    "tcd_indicator_data <- st_read(\"../../2_pipeline/Italy/Milan/1b_Copernicus/2021/census_areas_TCD.geojson\")\n",
    "imd_indicator_data <- st_read(\"../../2_pipeline/Italy/Milan/1b_Copernicus/2021/census_areas_IMD.geojson\")\n",
    "\n",
    "# Get the geospatial data from the TCS data (the IMD data also has same spatial data)\n",
    "oa <- subset(tcd_indicator_data, select = c(GUID, 'geometry'))\n",
    "\n",
    "# Load vulnerability mapping information from the config file\n",
    "## This mapping information is used to help guide the amalgamation of the data.\n",
    "## Weighting can be changed in this file, depending on the scenario.\n",
    "## Scenario 1 (best case scenario): Weighting values 1 or -1:\n",
    "##  where 1 means no change\n",
    "##  or -1 means all the indicator values are multiplied by -1, resulting in an inverse indicator.\n",
    "## Scenario 2: Weighting values 0.5 or -0.5:\n",
    "##  For domains with just a single indicators or where there is a lack of information related to missing indicators. \n",
    "##  For these domains the weights are halved using a weight of 0.5, or -0.5 for an inverse indicator.\n",
    "##  Therefore the influence of these indicators are reduced in half.\n",
    "## Other scenarios are supported by using other decimal numbers if decided for a particular dataset.\n",
    "indicator_mapping <- read.csv(\"config/vulnerabilityIndicatorMappings.csv\", header=TRUE, sep=\",\", stringsAsFactors = FALSE, fileEncoding=\"UTF-8-BOM\")\n",
    "\n",
    "# Print up to 100 rows of vulnerabiltiy mapping config file\n",
    "head(indicator_mapping,100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "733d3bae-f91b-4a66-b073-e1bdf149fde3",
   "metadata": {},
   "source": [
    "## Prepare Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cd692d7-9901-4c30-b8a2-825e0fdfc143",
   "metadata": {},
   "source": [
    "### Combine data into a single indicator dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c750b2c-34c9-4a77-88bb-8dd9c6c9bc5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>SEZ2011</th><th scope=col>early_childhood_boy</th><th scope=col>early_childhood_girl</th><th scope=col>age_middle_to_oldest_old_male</th><th scope=col>age_middle_to_oldest_old_female</th><th scope=col>dependants</th><th scope=col>unemployment</th><th scope=col>no_higher_education</th><th scope=col>foreign_nationals</th><th scope=col>primary_school_age</th><th scope=col>one_person_households</th><th scope=col>tree_cover_density</th><th scope=col>impervious</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>151460000001</td><td>-1.230411</td><td>-1.064144</td><td> 2.1196292</td><td>-1.3062152</td><td>-0.7801407</td><td>-0.5060886</td><td>-0.6075338</td><td> 0.284861877</td><td>-1.458348</td><td> 0.9333249</td><td>0.6144226</td><td>1.409195</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>151460000002</td><td>-1.230411</td><td>-1.064144</td><td> 0.2302043</td><td>-0.3189938</td><td> 0.1197864</td><td> 1.3419462</td><td> 0.8849880</td><td> 0.768744204</td><td>-1.458348</td><td>-0.7026211</td><td>0.6144226</td><td>1.469237</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>151460000003</td><td>-1.230411</td><td>-1.064144</td><td> 3.8043177</td><td>-1.3062152</td><td>-2.2200240</td><td>-0.2425543</td><td>-1.6458099</td><td>-1.081394102</td><td>-1.458348</td><td> 3.2236494</td><td>0.6144226</td><td>1.057469</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>151460000004</td><td>-1.230411</td><td>-1.064144</td><td> 2.6982091</td><td>-1.3062152</td><td>-0.5183437</td><td>-1.2391934</td><td>-1.6458099</td><td>-0.004949997</td><td> 1.865648</td><td> 0.2789465</td><td>0.6144226</td><td>1.226182</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>151460000005</td><td>-1.230411</td><td>-1.064144</td><td>19.6216703</td><td>-1.3062152</td><td>-2.2200240</td><td>-2.2981224</td><td>-1.6458099</td><td>-1.081394102</td><td>-1.458348</td><td> 3.2236494</td><td>0.6144226</td><td>1.319901</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>151460000006</td><td> 0.000000</td><td> 0.000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.000000000</td><td> 0.000000</td><td> 0.0000000</td><td>0.6144226</td><td>1.484096</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 13\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & SEZ2011 & early\\_childhood\\_boy & early\\_childhood\\_girl & age\\_middle\\_to\\_oldest\\_old\\_male & age\\_middle\\_to\\_oldest\\_old\\_female & dependants & unemployment & no\\_higher\\_education & foreign\\_nationals & primary\\_school\\_age & one\\_person\\_households & tree\\_cover\\_density & impervious\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 151460000001 & -1.230411 & -1.064144 &  2.1196292 & -1.3062152 & -0.7801407 & -0.5060886 & -0.6075338 &  0.284861877 & -1.458348 &  0.9333249 & 0.6144226 & 1.409195\\\\\n",
       "\t2 & 151460000002 & -1.230411 & -1.064144 &  0.2302043 & -0.3189938 &  0.1197864 &  1.3419462 &  0.8849880 &  0.768744204 & -1.458348 & -0.7026211 & 0.6144226 & 1.469237\\\\\n",
       "\t3 & 151460000003 & -1.230411 & -1.064144 &  3.8043177 & -1.3062152 & -2.2200240 & -0.2425543 & -1.6458099 & -1.081394102 & -1.458348 &  3.2236494 & 0.6144226 & 1.057469\\\\\n",
       "\t4 & 151460000004 & -1.230411 & -1.064144 &  2.6982091 & -1.3062152 & -0.5183437 & -1.2391934 & -1.6458099 & -0.004949997 &  1.865648 &  0.2789465 & 0.6144226 & 1.226182\\\\\n",
       "\t5 & 151460000005 & -1.230411 & -1.064144 & 19.6216703 & -1.3062152 & -2.2200240 & -2.2981224 & -1.6458099 & -1.081394102 & -1.458348 &  3.2236494 & 0.6144226 & 1.319901\\\\\n",
       "\t6 & 151460000006 &  0.000000 &  0.000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.000000000 &  0.000000 &  0.0000000 & 0.6144226 & 1.484096\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 13\n",
       "\n",
       "| <!--/--> | SEZ2011 &lt;chr&gt; | early_childhood_boy &lt;dbl&gt; | early_childhood_girl &lt;dbl&gt; | age_middle_to_oldest_old_male &lt;dbl&gt; | age_middle_to_oldest_old_female &lt;dbl&gt; | dependants &lt;dbl&gt; | unemployment &lt;dbl&gt; | no_higher_education &lt;dbl&gt; | foreign_nationals &lt;dbl&gt; | primary_school_age &lt;dbl&gt; | one_person_households &lt;dbl&gt; | tree_cover_density &lt;dbl&gt; | impervious &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 151460000001 | -1.230411 | -1.064144 |  2.1196292 | -1.3062152 | -0.7801407 | -0.5060886 | -0.6075338 |  0.284861877 | -1.458348 |  0.9333249 | 0.6144226 | 1.409195 |\n",
       "| 2 | 151460000002 | -1.230411 | -1.064144 |  0.2302043 | -0.3189938 |  0.1197864 |  1.3419462 |  0.8849880 |  0.768744204 | -1.458348 | -0.7026211 | 0.6144226 | 1.469237 |\n",
       "| 3 | 151460000003 | -1.230411 | -1.064144 |  3.8043177 | -1.3062152 | -2.2200240 | -0.2425543 | -1.6458099 | -1.081394102 | -1.458348 |  3.2236494 | 0.6144226 | 1.057469 |\n",
       "| 4 | 151460000004 | -1.230411 | -1.064144 |  2.6982091 | -1.3062152 | -0.5183437 | -1.2391934 | -1.6458099 | -0.004949997 |  1.865648 |  0.2789465 | 0.6144226 | 1.226182 |\n",
       "| 5 | 151460000005 | -1.230411 | -1.064144 | 19.6216703 | -1.3062152 | -2.2200240 | -2.2981224 | -1.6458099 | -1.081394102 | -1.458348 |  3.2236494 | 0.6144226 | 1.319901 |\n",
       "| 6 | 151460000006 |  0.000000 |  0.000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.000000000 |  0.000000 |  0.0000000 | 0.6144226 | 1.484096 |\n",
       "\n"
      ],
      "text/plain": [
       "  SEZ2011      early_childhood_boy early_childhood_girl\n",
       "1 151460000001 -1.230411           -1.064144           \n",
       "2 151460000002 -1.230411           -1.064144           \n",
       "3 151460000003 -1.230411           -1.064144           \n",
       "4 151460000004 -1.230411           -1.064144           \n",
       "5 151460000005 -1.230411           -1.064144           \n",
       "6 151460000006  0.000000            0.000000           \n",
       "  age_middle_to_oldest_old_male age_middle_to_oldest_old_female dependants\n",
       "1  2.1196292                    -1.3062152                      -0.7801407\n",
       "2  0.2302043                    -0.3189938                       0.1197864\n",
       "3  3.8043177                    -1.3062152                      -2.2200240\n",
       "4  2.6982091                    -1.3062152                      -0.5183437\n",
       "5 19.6216703                    -1.3062152                      -2.2200240\n",
       "6  0.0000000                     0.0000000                       0.0000000\n",
       "  unemployment no_higher_education foreign_nationals primary_school_age\n",
       "1 -0.5060886   -0.6075338           0.284861877      -1.458348         \n",
       "2  1.3419462    0.8849880           0.768744204      -1.458348         \n",
       "3 -0.2425543   -1.6458099          -1.081394102      -1.458348         \n",
       "4 -1.2391934   -1.6458099          -0.004949997       1.865648         \n",
       "5 -2.2981224   -1.6458099          -1.081394102      -1.458348         \n",
       "6  0.0000000    0.0000000           0.000000000       0.000000         \n",
       "  one_person_households tree_cover_density impervious\n",
       "1  0.9333249            0.6144226          1.409195  \n",
       "2 -0.7026211            0.6144226          1.469237  \n",
       "3  3.2236494            0.6144226          1.057469  \n",
       "4  0.2789465            0.6144226          1.226182  \n",
       "5  3.2236494            0.6144226          1.319901  \n",
       "6  0.0000000            0.6144226          1.484096  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# combine census data with copernicus TCD and IMD data (without geospatial data to advoid duplication)\n",
    "indicator_data <- merge(tcd_indicator_data, st_drop_geometry(census_indicator_data), by.x = GUID, by.y = GUID, all.x = TRUE)\n",
    "indicator_data <- merge(imd_indicator_data, st_drop_geometry(indicator_data), by=GUID)\n",
    "\n",
    "# drop the geometry\n",
    "indicator_data <- st_drop_geometry(indicator_data)\n",
    "\n",
    "# change the small area id column data type (GUID) to character string\n",
    "indicator_data[GUID] <- lapply(indicator_data[GUID], as.character)\n",
    "\n",
    "# trim the columns\n",
    "indicator_data <- subset(indicator_data, select=c(names(census_indicator_data), 'tree_cover_density', 'impervious'))\n",
    "\n",
    "# Set missing data fields to zero (0)\n",
    "# Note: In Italian census the NA or empty data fields are areas with no population (e.g. parks, schools, etc.)\n",
    "indicator_data[is.na(indicator_data)] <- 0\n",
    "\n",
    "# Print the first part of the indicators, which are now collated into one table\n",
    "head(indicator_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c8b119a-3fe2-4c3c-8ecb-07a061bcf66c",
   "metadata": {},
   "source": [
    "### Weight the indicator datas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "300e4b16-0c6d-4094-afca-88f6b747eb1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>SEZ2011</th><th scope=col>early_childhood_boy</th><th scope=col>early_childhood_girl</th><th scope=col>age_middle_to_oldest_old_male</th><th scope=col>age_middle_to_oldest_old_female</th><th scope=col>dependants</th><th scope=col>unemployment</th><th scope=col>no_higher_education</th><th scope=col>foreign_nationals</th><th scope=col>primary_school_age</th><th scope=col>one_person_households</th><th scope=col>tree_cover_density</th><th scope=col>impervious</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>151460000001</td><td>-1.230411</td><td>-1.064144</td><td> 2.1196292</td><td>-1.3062152</td><td>-0.7801407</td><td>-0.5060886</td><td>-0.6075338</td><td> 0.284861877</td><td>-1.458348</td><td> 0.9333249</td><td>0.6144226</td><td>1.409195</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>151460000002</td><td>-1.230411</td><td>-1.064144</td><td> 0.2302043</td><td>-0.3189938</td><td> 0.1197864</td><td> 1.3419462</td><td> 0.8849880</td><td> 0.768744204</td><td>-1.458348</td><td>-0.7026211</td><td>0.6144226</td><td>1.469237</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>151460000003</td><td>-1.230411</td><td>-1.064144</td><td> 3.8043177</td><td>-1.3062152</td><td>-2.2200240</td><td>-0.2425543</td><td>-1.6458099</td><td>-1.081394102</td><td>-1.458348</td><td> 3.2236494</td><td>0.6144226</td><td>1.057469</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>151460000004</td><td>-1.230411</td><td>-1.064144</td><td> 2.6982091</td><td>-1.3062152</td><td>-0.5183437</td><td>-1.2391934</td><td>-1.6458099</td><td>-0.004949997</td><td> 1.865648</td><td> 0.2789465</td><td>0.6144226</td><td>1.226182</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>151460000005</td><td>-1.230411</td><td>-1.064144</td><td>19.6216703</td><td>-1.3062152</td><td>-2.2200240</td><td>-2.2981224</td><td>-1.6458099</td><td>-1.081394102</td><td>-1.458348</td><td> 3.2236494</td><td>0.6144226</td><td>1.319901</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>151460000006</td><td> 0.000000</td><td> 0.000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.000000000</td><td> 0.000000</td><td> 0.0000000</td><td>0.6144226</td><td>1.484096</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 13\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & SEZ2011 & early\\_childhood\\_boy & early\\_childhood\\_girl & age\\_middle\\_to\\_oldest\\_old\\_male & age\\_middle\\_to\\_oldest\\_old\\_female & dependants & unemployment & no\\_higher\\_education & foreign\\_nationals & primary\\_school\\_age & one\\_person\\_households & tree\\_cover\\_density & impervious\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 151460000001 & -1.230411 & -1.064144 &  2.1196292 & -1.3062152 & -0.7801407 & -0.5060886 & -0.6075338 &  0.284861877 & -1.458348 &  0.9333249 & 0.6144226 & 1.409195\\\\\n",
       "\t2 & 151460000002 & -1.230411 & -1.064144 &  0.2302043 & -0.3189938 &  0.1197864 &  1.3419462 &  0.8849880 &  0.768744204 & -1.458348 & -0.7026211 & 0.6144226 & 1.469237\\\\\n",
       "\t3 & 151460000003 & -1.230411 & -1.064144 &  3.8043177 & -1.3062152 & -2.2200240 & -0.2425543 & -1.6458099 & -1.081394102 & -1.458348 &  3.2236494 & 0.6144226 & 1.057469\\\\\n",
       "\t4 & 151460000004 & -1.230411 & -1.064144 &  2.6982091 & -1.3062152 & -0.5183437 & -1.2391934 & -1.6458099 & -0.004949997 &  1.865648 &  0.2789465 & 0.6144226 & 1.226182\\\\\n",
       "\t5 & 151460000005 & -1.230411 & -1.064144 & 19.6216703 & -1.3062152 & -2.2200240 & -2.2981224 & -1.6458099 & -1.081394102 & -1.458348 &  3.2236494 & 0.6144226 & 1.319901\\\\\n",
       "\t6 & 151460000006 &  0.000000 &  0.000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.000000000 &  0.000000 &  0.0000000 & 0.6144226 & 1.484096\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 13\n",
       "\n",
       "| <!--/--> | SEZ2011 &lt;chr&gt; | early_childhood_boy &lt;dbl&gt; | early_childhood_girl &lt;dbl&gt; | age_middle_to_oldest_old_male &lt;dbl&gt; | age_middle_to_oldest_old_female &lt;dbl&gt; | dependants &lt;dbl&gt; | unemployment &lt;dbl&gt; | no_higher_education &lt;dbl&gt; | foreign_nationals &lt;dbl&gt; | primary_school_age &lt;dbl&gt; | one_person_households &lt;dbl&gt; | tree_cover_density &lt;dbl&gt; | impervious &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 151460000001 | -1.230411 | -1.064144 |  2.1196292 | -1.3062152 | -0.7801407 | -0.5060886 | -0.6075338 |  0.284861877 | -1.458348 |  0.9333249 | 0.6144226 | 1.409195 |\n",
       "| 2 | 151460000002 | -1.230411 | -1.064144 |  0.2302043 | -0.3189938 |  0.1197864 |  1.3419462 |  0.8849880 |  0.768744204 | -1.458348 | -0.7026211 | 0.6144226 | 1.469237 |\n",
       "| 3 | 151460000003 | -1.230411 | -1.064144 |  3.8043177 | -1.3062152 | -2.2200240 | -0.2425543 | -1.6458099 | -1.081394102 | -1.458348 |  3.2236494 | 0.6144226 | 1.057469 |\n",
       "| 4 | 151460000004 | -1.230411 | -1.064144 |  2.6982091 | -1.3062152 | -0.5183437 | -1.2391934 | -1.6458099 | -0.004949997 |  1.865648 |  0.2789465 | 0.6144226 | 1.226182 |\n",
       "| 5 | 151460000005 | -1.230411 | -1.064144 | 19.6216703 | -1.3062152 | -2.2200240 | -2.2981224 | -1.6458099 | -1.081394102 | -1.458348 |  3.2236494 | 0.6144226 | 1.319901 |\n",
       "| 6 | 151460000006 |  0.000000 |  0.000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.000000000 |  0.000000 |  0.0000000 | 0.6144226 | 1.484096 |\n",
       "\n"
      ],
      "text/plain": [
       "  SEZ2011      early_childhood_boy early_childhood_girl\n",
       "1 151460000001 -1.230411           -1.064144           \n",
       "2 151460000002 -1.230411           -1.064144           \n",
       "3 151460000003 -1.230411           -1.064144           \n",
       "4 151460000004 -1.230411           -1.064144           \n",
       "5 151460000005 -1.230411           -1.064144           \n",
       "6 151460000006  0.000000            0.000000           \n",
       "  age_middle_to_oldest_old_male age_middle_to_oldest_old_female dependants\n",
       "1  2.1196292                    -1.3062152                      -0.7801407\n",
       "2  0.2302043                    -0.3189938                       0.1197864\n",
       "3  3.8043177                    -1.3062152                      -2.2200240\n",
       "4  2.6982091                    -1.3062152                      -0.5183437\n",
       "5 19.6216703                    -1.3062152                      -2.2200240\n",
       "6  0.0000000                     0.0000000                       0.0000000\n",
       "  unemployment no_higher_education foreign_nationals primary_school_age\n",
       "1 -0.5060886   -0.6075338           0.284861877      -1.458348         \n",
       "2  1.3419462    0.8849880           0.768744204      -1.458348         \n",
       "3 -0.2425543   -1.6458099          -1.081394102      -1.458348         \n",
       "4 -1.2391934   -1.6458099          -0.004949997       1.865648         \n",
       "5 -2.2981224   -1.6458099          -1.081394102      -1.458348         \n",
       "6  0.0000000    0.0000000           0.000000000       0.000000         \n",
       "  one_person_households tree_cover_density impervious\n",
       "1  0.9333249            0.6144226          1.409195  \n",
       "2 -0.7026211            0.6144226          1.469237  \n",
       "3  3.2236494            0.6144226          1.057469  \n",
       "4  0.2789465            0.6144226          1.226182  \n",
       "5  3.2236494            0.6144226          1.319901  \n",
       "6  0.0000000            0.6144226          1.484096  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>SEZ2011</th><th scope=col>early_childhood_boy</th><th scope=col>early_childhood_girl</th><th scope=col>age_middle_to_oldest_old_male</th><th scope=col>age_middle_to_oldest_old_female</th><th scope=col>dependants</th><th scope=col>unemployment</th><th scope=col>no_higher_education</th><th scope=col>foreign_nationals</th><th scope=col>primary_school_age</th><th scope=col>one_person_households</th><th scope=col>tree_cover_density</th><th scope=col>impervious</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>151460000001</td><td>-1.230411</td><td>-1.064144</td><td> 2.1196292</td><td>-1.3062152</td><td>-0.7801407</td><td>-0.5060886</td><td>-0.3037669</td><td> 0.142430939</td><td> 1.458348</td><td> 0.9333249</td><td>0.6144226</td><td>1.409195</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>151460000002</td><td>-1.230411</td><td>-1.064144</td><td> 0.2302043</td><td>-0.3189938</td><td> 0.1197864</td><td> 1.3419462</td><td> 0.4424940</td><td> 0.384372102</td><td> 1.458348</td><td>-0.7026211</td><td>0.6144226</td><td>1.469237</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>151460000003</td><td>-1.230411</td><td>-1.064144</td><td> 3.8043177</td><td>-1.3062152</td><td>-2.2200240</td><td>-0.2425543</td><td>-0.8229049</td><td>-0.540697051</td><td> 1.458348</td><td> 3.2236494</td><td>0.6144226</td><td>1.057469</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>151460000004</td><td>-1.230411</td><td>-1.064144</td><td> 2.6982091</td><td>-1.3062152</td><td>-0.5183437</td><td>-1.2391934</td><td>-0.8229049</td><td>-0.002474999</td><td>-1.865648</td><td> 0.2789465</td><td>0.6144226</td><td>1.226182</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>151460000005</td><td>-1.230411</td><td>-1.064144</td><td>19.6216703</td><td>-1.3062152</td><td>-2.2200240</td><td>-2.2981224</td><td>-0.8229049</td><td>-0.540697051</td><td> 1.458348</td><td> 3.2236494</td><td>0.6144226</td><td>1.319901</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>151460000006</td><td> 0.000000</td><td> 0.000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.000000000</td><td> 0.000000</td><td> 0.0000000</td><td>0.6144226</td><td>1.484096</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 13\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & SEZ2011 & early\\_childhood\\_boy & early\\_childhood\\_girl & age\\_middle\\_to\\_oldest\\_old\\_male & age\\_middle\\_to\\_oldest\\_old\\_female & dependants & unemployment & no\\_higher\\_education & foreign\\_nationals & primary\\_school\\_age & one\\_person\\_households & tree\\_cover\\_density & impervious\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 151460000001 & -1.230411 & -1.064144 &  2.1196292 & -1.3062152 & -0.7801407 & -0.5060886 & -0.3037669 &  0.142430939 &  1.458348 &  0.9333249 & 0.6144226 & 1.409195\\\\\n",
       "\t2 & 151460000002 & -1.230411 & -1.064144 &  0.2302043 & -0.3189938 &  0.1197864 &  1.3419462 &  0.4424940 &  0.384372102 &  1.458348 & -0.7026211 & 0.6144226 & 1.469237\\\\\n",
       "\t3 & 151460000003 & -1.230411 & -1.064144 &  3.8043177 & -1.3062152 & -2.2200240 & -0.2425543 & -0.8229049 & -0.540697051 &  1.458348 &  3.2236494 & 0.6144226 & 1.057469\\\\\n",
       "\t4 & 151460000004 & -1.230411 & -1.064144 &  2.6982091 & -1.3062152 & -0.5183437 & -1.2391934 & -0.8229049 & -0.002474999 & -1.865648 &  0.2789465 & 0.6144226 & 1.226182\\\\\n",
       "\t5 & 151460000005 & -1.230411 & -1.064144 & 19.6216703 & -1.3062152 & -2.2200240 & -2.2981224 & -0.8229049 & -0.540697051 &  1.458348 &  3.2236494 & 0.6144226 & 1.319901\\\\\n",
       "\t6 & 151460000006 &  0.000000 &  0.000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.000000000 &  0.000000 &  0.0000000 & 0.6144226 & 1.484096\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 13\n",
       "\n",
       "| <!--/--> | SEZ2011 &lt;chr&gt; | early_childhood_boy &lt;dbl&gt; | early_childhood_girl &lt;dbl&gt; | age_middle_to_oldest_old_male &lt;dbl&gt; | age_middle_to_oldest_old_female &lt;dbl&gt; | dependants &lt;dbl&gt; | unemployment &lt;dbl&gt; | no_higher_education &lt;dbl&gt; | foreign_nationals &lt;dbl&gt; | primary_school_age &lt;dbl&gt; | one_person_households &lt;dbl&gt; | tree_cover_density &lt;dbl&gt; | impervious &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 151460000001 | -1.230411 | -1.064144 |  2.1196292 | -1.3062152 | -0.7801407 | -0.5060886 | -0.3037669 |  0.142430939 |  1.458348 |  0.9333249 | 0.6144226 | 1.409195 |\n",
       "| 2 | 151460000002 | -1.230411 | -1.064144 |  0.2302043 | -0.3189938 |  0.1197864 |  1.3419462 |  0.4424940 |  0.384372102 |  1.458348 | -0.7026211 | 0.6144226 | 1.469237 |\n",
       "| 3 | 151460000003 | -1.230411 | -1.064144 |  3.8043177 | -1.3062152 | -2.2200240 | -0.2425543 | -0.8229049 | -0.540697051 |  1.458348 |  3.2236494 | 0.6144226 | 1.057469 |\n",
       "| 4 | 151460000004 | -1.230411 | -1.064144 |  2.6982091 | -1.3062152 | -0.5183437 | -1.2391934 | -0.8229049 | -0.002474999 | -1.865648 |  0.2789465 | 0.6144226 | 1.226182 |\n",
       "| 5 | 151460000005 | -1.230411 | -1.064144 | 19.6216703 | -1.3062152 | -2.2200240 | -2.2981224 | -0.8229049 | -0.540697051 |  1.458348 |  3.2236494 | 0.6144226 | 1.319901 |\n",
       "| 6 | 151460000006 |  0.000000 |  0.000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.000000000 |  0.000000 |  0.0000000 | 0.6144226 | 1.484096 |\n",
       "\n"
      ],
      "text/plain": [
       "  SEZ2011      early_childhood_boy early_childhood_girl\n",
       "1 151460000001 -1.230411           -1.064144           \n",
       "2 151460000002 -1.230411           -1.064144           \n",
       "3 151460000003 -1.230411           -1.064144           \n",
       "4 151460000004 -1.230411           -1.064144           \n",
       "5 151460000005 -1.230411           -1.064144           \n",
       "6 151460000006  0.000000            0.000000           \n",
       "  age_middle_to_oldest_old_male age_middle_to_oldest_old_female dependants\n",
       "1  2.1196292                    -1.3062152                      -0.7801407\n",
       "2  0.2302043                    -0.3189938                       0.1197864\n",
       "3  3.8043177                    -1.3062152                      -2.2200240\n",
       "4  2.6982091                    -1.3062152                      -0.5183437\n",
       "5 19.6216703                    -1.3062152                      -2.2200240\n",
       "6  0.0000000                     0.0000000                       0.0000000\n",
       "  unemployment no_higher_education foreign_nationals primary_school_age\n",
       "1 -0.5060886   -0.3037669           0.142430939       1.458348         \n",
       "2  1.3419462    0.4424940           0.384372102       1.458348         \n",
       "3 -0.2425543   -0.8229049          -0.540697051       1.458348         \n",
       "4 -1.2391934   -0.8229049          -0.002474999      -1.865648         \n",
       "5 -2.2981224   -0.8229049          -0.540697051       1.458348         \n",
       "6  0.0000000    0.0000000           0.000000000       0.000000         \n",
       "  one_person_households tree_cover_density impervious\n",
       "1  0.9333249            0.6144226          1.409195  \n",
       "2 -0.7026211            0.6144226          1.469237  \n",
       "3  3.2236494            0.6144226          1.057469  \n",
       "4  0.2789465            0.6144226          1.226182  \n",
       "5  3.2236494            0.6144226          1.319901  \n",
       "6  0.0000000            0.6144226          1.484096  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the indicator weighting, previously loaded from the config file\n",
    "indicator_weighting <- indicator_mapping %>% select('indicator', 'weight')\n",
    "indicator_weighting <- indicator_weighting %>% spread(key = 'indicator', value = 'weight')\n",
    "\n",
    "# Get the column names and weights\n",
    "names <- names(indicator_weighting)\n",
    "weights <- indicator_weighting[, names]\n",
    "\n",
    "# Copy and rename the dataset\n",
    "indicator_data_weighted <- indicator_data\n",
    "head(indicator_data_weighted) \n",
    "\n",
    "# Multiply the indicators by the config file weighting\n",
    "indicator_data_weighted[, names] <- sweep(indicator_data_weighted[, names], 2, unlist(weights[, names]), \"*\")\n",
    "head(indicator_data_weighted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4305b36c-46c6-4d56-a328-9cf9b0cf485b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PY/AfDPLbnteuqt6flfscxjL17e2/4j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\nJEnSHsMmdpIkSZKU/C8Q3iMkf6oynAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “'early_childhood_boy' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jW0qWmgho+OOuvQCQM3y6VNB/9+Z9Mx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\nMftYF0TkAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “'early_childhood_girl' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ps5DJ/Zbtd9P94Pw9IG041Lkpta6vz+\nJOOn0s1yp5u3X9uJTtu4m/2DmdZRP9q70/0BGjaeWi9uuA6YrUXxjQsAsDv7B3Tj4jiCxJD7\no5RvNtekdC3aesj6NSl3uE7Kof0vd/E6z0pnF1p+I+5dMFfDtq6HrZ75blTW56j8HTCqBrV/\nwAImIDGstmXi8PZFKechfyelC8/zWqb70+x+MehcvSGddd15Tso5yczesK3rYatnvhuV9Tkq\nfweMqkHtH7DAOcWOYbQsyWcz9cWbu5L8Rcq58wDAwmD/gH65OE6xY8g9lORVKTeBOyfJoSl3\n1v5ZkhuTXJly0SgAsHDYP6DvBCSG3f+tAwDAOPsH9M0w3hgPAACgEQISAABAJSABAABUAhIA\nAEAlIAEAAFQCEgAAQCUgAQAAVAISAABAJSABAABUAhIAAEAlIAEAAFQCEgAAQCUgAQAAVAIS\nAABAJSABAABUAhIAAEAlIAEAAFQCEgAAQCUgAQAAVAISAABAJSABAABUAhIAAEAlIAEAAFQC\nEgAAQCUgAQAAVAISAABAJSABAABUAhIAAEAlIAEAAFQCEgAAQCUgAQAAVAISAABAJSABAABU\nAhIAAEAlIAEAAFQCEgAAQCUgAQAAVAISAABAJSABAABUAhIAAEAlIAEAAFQCEgAAQCUgAQAA\nVAISAABAJSABAABUAhIAAEAlIAEAAFQCEgAAQCUgAQAAVAISAABAJSABAABUAhIAAEAlIAEA\nAFQCEgAAQCUgAQAAVAISAABAtbjpAlgwnpXkKS2/fyPJAw3VAgAAkxKQGJTPLF269FlLlizJ\ntm3b8thjj12UZH3TRQEAQCun2DEoi9/+9rfnyiuvzBFHHJEI5wAADCEBCQAAoBKQAAAAKgEJ\nAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoB\nCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAq\nAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAA\nKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAA\nACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAA\nAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQ\nAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKAS\nkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACg\nEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAA\noBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkA\nAKASkAAAACoBCQAAoBKQAAAAKgEJAACgWtx0AQP2hCTPTHJgkmVJtifZlOTGJNsarAsAABgC\nCyUgnZnkoiQnJ9lzkvGPJNmQ5JIkXxtgXQAAwBBZCAHp3UkuTfJwkmuTfD/Jlvr70iSrkxyb\n5PQkL0tyfpKPNFIpAADQqFEPSE9L8t4k1yU5N8ndM0z7qSQfSnJ1yql3AADAAjLqnTSsTTml\n7rxMH46S5LYkb0q5Nunlfa4LAAAYQqMekFamXF90+yynvynJziSr+lYRAAAwtEY9IG1KsleS\no2Y5/XEp6+TOvlUEAAAMrVEPSFendOV9RZIjZ5j2eUk+nmRrks/3uS4AAGAIjXonDZuTXJDk\nwym9192YiV7sdqT0YrcqydFJDkvp2e71Se5polgAAKBZox6QkuTyJN9NcmFKV96vnmSau1JC\n1PokNw+sMgAAYKgshICUJNcneUN9vCrJgSm91T2UEo62NFQXAAAwRBZKQBr3hCSHZCIgbU/p\nxOHBJNsarAsAABgCCyUgnZnkoiQnp9wXqd0jSTYkuSTJ1wZYFwAAMEQWQkB6d5JLUzpguDYT\nnTQ8nNJJw+okx6Zcn/SyJOcn+UgjlQIAAI0a9YD0tCTvTXJdknOT3D3DtJ9K8qGU7sE39b06\nAABgqIx6QFqbckrdeZk+HCXJbUnelOSGJC9P90eRjsnc1u/yJF/q8jUBAIAujHpAWplyfdHt\ns5z+piQ7U3q668bTk3wrk1/vNJ29kjza5WsDAAAd2qPpAvpsU0roOGqW0x+Xsk7u7PJ1b0kJ\nn4tmOZxc5xv19gAAgKE26jvkV6d05X1FkiNnmPZ5ST6eZGuSz/e5LgAAYAiN+il2m5NckOTD\nKb3X3ZiJXux2pPRityrJ0UkOS+nZ7vVJ7mmiWAAAoFmjHpCS5PIk301yYUpX3q+eZJq7UkLU\n+iQ3D6wyAABgqCyEgJQk1yd5Q328KsmBSZYleSglHG1pqC4AAGCILJSA1GpzHZISlp6RZE1K\n997bmyoKAABo3qh30pCUI0XvSbkn0riDk/x1ytGjr6V0yX1/kj9IsvegCwQAAIbDQjiC9Nkk\nL0vyH5JckxKArku5V9H1KeFoWZIXJnl7kqck+eVGKgUAABo16gHplJRw9P8m+U/1udelhKPf\nSPL+lmmXpHTocG6SE5J8Y2BVAgAAQ2HUT7EbS7Irye/Vn0np0vuelNDUakdKT3dJ8vyBVAcA\nAAyVUQ9IeyXZmRJ+xm1PufZo1yTTb07yWMopdwAAwAIz6gHp20n2TPLmluf+JuUUu5WTTP/K\nOv2N/S8NAAAYNqMekP4mpZe6/57Sk91TkmxI8ukkH09ySJ3ugCTvTPKxJLck+eLAKwUAABo3\n6p007Ew5KvSJJL9dh5+knGJ3TJIfpZx+t6ROf1uSs5M8POhCAQCA5o16QEpKhwynJXlJkn+V\n0kPdoSnXGj1cx38vyV+lHEF6qJEqAQCAxi2EgDTu2joAAABMatSvQQIAAJg1AQkAAKASkAAA\nACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAA\nAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQ\nAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKAS\nkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACg\nEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAA\noBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkA\nAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJ\nAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoB\nCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAq\nAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAA\nKgEJAACgEpAAAAAqAQkAAKBa3HQBjLTfS3JEfby6yUIAAGA2BCT66d8ef/zx+61evTpf+MIX\nmq4FAABm5BQ7+urss8/Ou971ruyxh7caAADDz14rAABAJSAtLBcluaVluLTZcgAAYLi4Bmlh\nedaxxx572Ctf+cpcc801+frXv/7spgsCAIBh4gjSArN69eqceuqpWbNmTdOlAADA0BGQAAAA\nKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoFpo90F6QpJnJjkwybIk25NsSnJjkm0N1gUA\nAAyBhRKQzkxyUZKTk+w5yfhHkmxIckmSrw2wLgAAYIgshID07iSXJnk4ybVJvp9kS/19aZLV\nSY5NcnqSlyU5P8lHGqkUAABo1KgHpKcleW+S65Kcm+TuGab9VJIPJbk65dQ7AABgAZlLJw1v\nTnLZLJZ3e8opbcNgbcopdedl+nCUJLcleVPKtUkv73NdAADAEJpLQDosyS/MMM0+KR0gHNFx\nRb21MuX6ottnOf1NSXYmWdW3igAAgKE1m1Ps/r7+fGqS/Vp+b7co5TS1pUnu6760ntiUZK8k\nR6VcezST41JC4539LAoAABhOswlIX0hyQpKfT7J3SocGU/lpkj9N8vHuS+uJq1O68r4iyRuS\n/GCaaZ+X5GNJtib5fP9LAwAAhs1sAtJ76s+Lk/xSpg9Iw2ZzkguSfDjlCNKNmejFbkfK0a5V\nSY5OOYXw4SSvT3JPE8WOiP+ccrQxSfZtshAAAJirufRi98cpvbzNN5cn+W6SC1O68n71JNPc\nlRKi1ie5eWCVjZ49krzzpJNOyv7775+rrrqq6XoAAGBO5hKQ7qzD6pQjLstTrjuazA8y/els\ng3Z9yil2STlidGBKb3UPpYSjLQ3VNZLOOeecHH300fn8552pCADA/DLX+yB9IOVIzEy93/1u\nyil5w+YJSQ7JREDantKJw4NJtjVYFwAAMATmEpBOTPIfknwvyV8luTelS+zJTNXTXVPOTHJR\nkpNT7ovU7pEkG5JckuRrA6wLAAAYInMNSBtTerR7uD/l9MW7k1yaUvO1meik4eGUThpWp3Q8\ncXqSlyU5P8lHGqkUAABo1FwC0rKUcDGfwtHTkrw3yXVJzk1y9wzTfirJh1K6B9/U9+oAAICh\nMpeA9K0k/zalY4Zd/Smn59amnFJ3XqYPR0lyW5I3JbkhycvT/VGkpyZZMstpD+ryteaVnTt3\nJsnKlK7Vk3Jk8pHGCgIAgGouAelLKSFpfZL/mPlxJGllyo737bOc/qaU66pWdfm6T0/yTx3M\nN1WvgCNl48aNSfJrdUjKvbbWNVYQAABUcwlIL0zyoyRvTfLGJN/J1DdU/Ys6NG1TSi91R6Wc\nHjiT41J66Luzy9e9JeUI0tJZTn9ckk9n/hyZ68rOnTtzzjnn5Oyzz84HP/jBfPvb33ZDWQAA\nhsJcAtIvpnTxnSQrUjo1mMo/ZTgC0tUpXXlfkXIfpOnuzfS8JB9LsjVJL27g85M5TLu6B6/X\npFVJ9s3M3b//i+XLl+fJT35yli1b1r+qAABgjuYSkP4gyZ8keWwW0/60s3J6bnOSC5J8OOUI\n0o2Z6MVuR8oRnlUpN749LOW0wddn6iNjPN6KJHdk7vfUAgCAoTOXndp76zDfXJ7kuylHv05P\n8upJprkrJUStT3LzwCobDUuTLP7ABz6Q1atX581vfnPT9QAAQMfmEpAOrsNM9kw5onBLRxX1\nx/Upp9gl5YjRgSndlj+UEo62NFTXyDjggANy0EELqjM+AABG0FwC0lsy+57GfjfJxXOuZjA2\n16HVL6d0tf2Hgy8HAAAYFnMJSH+X5JIpxh2Q5MRM3Jj12i7rGrQzkhwbAQkAABa0uQSk6+ow\nnV9Nucbn9zuuqLfOrsNMTkmyf8p1SEnyuToAAAALSK97HvsvSd6W5KVJvtjjZXfiuCS/Mofp\nx6e9IwISAAAsOLO+b80c/Dil2+xh8LkkN6V0xvD7SZ6cZL9Jhj9NufHt+O/vb6JYAACgWb0O\nSE9M8twkD/R4uZ26PskxKd13vz3lOqrnJvnntmFHyv2dxn9/qIliAQCAZs3lFLuX1WEyi5Ks\nTHJayrU8X+myrl56OMnvJPlkkv+Rch3VR5L8WpL7G6wLAAAYMnMJSL+Q0gnDdH6a5J1Jvt9x\nRf3z/ZTOGC5I8r4kZ6b8PZ9ssigAAGB4zCUg/XGSq6YYtyvJz5LcmuSRbovqo50pXXn/ZZL/\nluQTSV7faEUAAMDQmEtAurMOo+COlO6//1WS/5pkdZJvNVoRAADQuE66+V6d5KjSQA0AACAA\nSURBVI0pN4Y9sD63KclXk1yR0snBfPHpJBuS/PsMT8cSAABAQ+YakM5M8r+SLJ9k3OuS/FaS\nVyb5hy7rGqR/TvKeposAAACaN5duvlekHCF6MKXL7OckWVWHY5JcmGTPJH+eZFlvywQAAOi/\nuRxBOj3lPkfH5/HX69yd5Lsp9xn6RpK1KTdpBQAAmDfmcgTpsJRrjabrzOCbSW5P8sxuigIA\nAGjCXALSY0n2meUyd3ZWDgAAQHPmEpC+n3Id0i9PM83pSZ6a4bxRLAAAwLTmcg3ShiS3pHTU\n8MdJrku5L9KiJAclOS3JW5PcnOR/97ZMAACA/ptLQHok5eaqf5nkV+vQ7oYkv1SnBQAAmFfm\neh+kHyQ5KskZSZ6f5MlJdqV03vDlJH+d5NFeFggAADAocwlIi1LC0CNJrqzDuCUpwUjnDAAA\nwLw1204aTky5v9EBU4x/R5K/TfL0XhQFAADQhNkEpGNSOmQYS3LKFNM8McnJdboDe1MaAADA\nYM0mIP3PJHsneV2Sz04xzW8meVOSNUk+1JvSAAAABmumgPSclCNHH0ryyRmm/bMklyd5VUpQ\nAgAAmFdmCkjPrT+vmOXyPpJkz5Qe7gAAAOaVmXqxe3L9eessl3dL/XlwZ+UwTxyQ5G9STr3c\ns+FaAACgZ2YKSOM3fF06y+XtW39u66wc5okDkhz1tre9Ldu3b89HP/rRpusBAICemOkUu9vq\nz1+Y5fJeVH/+uKNqmFfWrl2bF73oRU2XAQAAPTNTQPpSkoeT/HqSvWaYdkWS30jyQJJru64M\nAABgwGYKSPcn+aMkJyT5dJL9p5juGUk2JDksyR8m2d6rAgEAAAZlpmuQkuTdSY5P8sokpyW5\nKsl3kvwsycokz0tyesrF+huSXNyPQgEAAPptNgFpe5IXJ3lPkguSnFOHVluS/H6SDyR5rJcF\nAgAADMpsAlIycR3Se5KcnOTnU3qs25LSBfhXIhgBAADz3GwD0rgHk1xTB+javffemyQvTbnW\nLUn+MsnVjRUEAMCCNteABD21ZcuWrFmz5ujDDjvs6Jtuuil33XXXkghIAAA0ZKZe7KDvTjrp\npKxbty7HHnts06UAALDACUgAAACVgAQAAFAJSAAAAJWABAAAUAlIAAAAlYAEAABQCUgAAACV\ngAQAAFAJSAAAAJWABAAAUAlIAAAAlYAEAABQCUgAAACVgAQAAFAJSAAAAJWABAAAUAlIAAAA\nlYAEAABQCUgAAACVgAQAAFAJSAAAAJWABAAAUAlIAAAAlYAEAABQCUgAAACVgAQAAFAJSAAA\nAJWABAAAUAlIAAAAlYAEAABQCUgAAACVgAQAAFAJSAAAANXipguAcffee2+SPD/JH9Wnvpjk\ns40VBADAgiMgMTTuvPPOrF69+vAjjjji8FtvvTUbN248KAISAAAD5BQ7hsqxxx6bdevW5aST\nTmq6FAAAFiABCQAAoHKKHbN1aJKrkuydZEmzpQAAQH84gsRsHZzkqHe+852HvfKVr3xq08UA\nAEA/CEjMyVlnnZUTTzyx6TIAAKAvBCQAAIBKQAIAAKgEJAAAgEpAAgAAqAQkAACASkACAACo\nBCQAAIBKQAIAAKgEJAAAgEpAAgAAqAQkAACASkACAACoBCQAAIBKQAIAAKgEJAAAgEpAAgAA\nqAQkAACASkACAACoBCQAAIBKQAIAAKgEJAAAgGpx0wUw9J6QZM8ky5suBAAA+k1AYjq/mOS6\nposAAIBBcYod03niPvvsk8suuyznnHNO07UAAEDfCUhMa88998zhhx+eVatWNV0KAAD0nYAE\nAABQCUgAAACVgAQAAFAJSAAAAJWABAAAUAlIAAAAlYAEAABQCUgAAACVgAQAAFAJSAAAAJWA\nBAAAUAlIAAAAlYAEAABQCUgAAACVgAQAAFAJSAAAANXipguAydx3331J8qwk769PfSvJpxsr\nCACABUFAYiht3Lgx+++//9Of/exn//rGjRtz6623/n0EJAAA+swpdgytww8/POvWrctLXvKS\npksBAGCBEJAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAACqhXaj2CckeWaS\nA5MsS7I9yaYkNybZ1mBdAADAEFgoAenMJBclOTnJnpOMfyTJhiSXJPnaAOsCAACGyEIISO9O\ncmmSh5Ncm+T7SbbU35cmWZ3k2CSnJ3lZkvOTfKSRSgEAgEaNekB6WpL3JrkuyblJ7p5h2k8l\n+VCSq1NOvQMAABaQUe+kYW3KKXXnZfpwlCS3JXlTyrVJL+9zXQAAwBAa9YC0MuX6ottnOf1N\nSXYmWdW3igAAgKE16gFpU5K9khw1y+mPS1knd/atIgAAYGiNekC6OqUr7yuSHDnDtM9L8vEk\nW5N8vs91AQAAQ2jUO2nYnOSCJB9O6b3uxkz0YrcjpRe7VUmOTnJYSs92r09yTxPFAgAAzRr1\ngJQklyf5bpILU7ryfvUk09yVEqLWJ7l5YJUBAABDZSEEpCS5Pskb6uNVSQ5M6a3uoZRwtKWh\nugAAgCGyUALSuCckOSQTAWl7SicODybZ1mBdAADAEFgoAenMJBclOTnlvkjtHkmyIcklSb42\nwLoAAIAhshAC0ruTXJrSAcO1meik4eGUThpWJzk25fqklyU5P8lHGqkUAABo1KgHpKcleW+S\n65Kcm+TuGab9VJIPpXQPvqnv1QEAAENl1APS2pRT6s7L9OEoSW5L8qYkNyR5ebo/irRvkiWz\nnHZ5l68FAAD0wKgHpJUp1xfdPsvpb0qyM6Wnu248PaW78LneiHdRl68LAAB0YdQD0qaUXuqO\nSrn2aCbHpYSaO7t83VtSrmua7RGko1OOWO3q8nUBAIAujHpAujqlK+8rUu6D9INppn1eko8l\n2Zrk8z147e/NYdqlPXg9AACgS6MekDYnuSDJh1OOIN2YiV7sdqQEk1UpR3AOS+nZ7vVJ7mmi\nWAAAoFmjHpCS5PIk301yYUpX3q+eZJq7UkLU+pRrhwAAgAVoIQSkJLk+5RS7pBwxOjDJsiQP\npYSjLQ3VBQAADJGFEpBaba7DZBYlOSTJP9eBIbBjx44kWZHktPrUT1K6YwcAgJ6aazfU89He\nSS5J6TThtiSfSPLsKaZdWqd5x2BKYzZuvPHG7Lnnns9avnz5hqVLl25I8pmmawIAYDQthID0\nsSS/mRKKViY5J8m3Um4Kyzywc+fOHHHEEbnyyivz9re/PVmYRz4BABiAUQ9IxyR5TZK/Trnu\naEWSI5N8J8lHk5zbXGkAAMCwGfWANFZ/XpCJjhhuSPLClHskXZ7klMGXBQAADKNRD0gHJNmV\n5Mdtzz+ccqrdDUn+IuUeSAAAwAI36gHpxyk90x09ybifJTk7yc6Uo0mrB1gXAAAwhEY9IH0p\nybYk/yPJ0yYZf3uSs1KONH01yYkDqwwAABg6ox6Q7kry2ynXIt2a5KRJpvlmklNTbhz7t4Mr\nDQAAGDajHpCS5D+n9GR3bZJ7p5jmeyk93v1JkscGVBcAADBkFsr9ZD6TmW8uek+St9QBAABY\ngBbCESQAAIBZEZAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKAS\nkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAqsVNFwBz\nsWnTpiRZk+Sb9akfJHlzYwUBADBSBCTmlS1btmTFihXLXvva14796Ec/yoYNGw5ruiYAAEaH\nU+xod3CS9yV5f5I3NVzLpJYvX55zzz03L3jBC5ouBQCAEeMIEu3WLl269Dee/exn54477si2\nbduargcAAAbGESTaLTrggAOyfv36vPjFL266FgAAGCgBCQAAoBKQAAAAKgEJAACgEpAAAAAq\nAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAA\nKgGJeevHP/5xkuyXZFcd7k6yqMmaAACY3wQk5q1t27Zl7733zvr163PBBRckyQHxngYAoAuL\nmy4AurF48eKMjY1lyZIlTZcCAMAI8G07AABAJSABAABUAhIAAEAlIAEAAFQCEgAAQCUgAQAA\nVAISAABAJSABAABUAhIAAEAlIAEAAFQCEgAAQCUgAQAAVIubLoChcF6SjzRdBAAANM0RJJJk\n9SGHHJL169dnbGys6VoAAKAxAhJJkn333TdjY2NZuXJl06UAAEBjBCRG0UFJtiXZVYdt9TkA\nAJiWgMQoemKSvdetW5d169Ylyd71OQAAmJZOGhhZxxxzTNMlAAAwzziCBAAAUDmCxEh4+OGH\nxx9elORJDZYCAMA8JiAxEu64444kyXHHHfe+Bx54ILfcckvDFQEAMB85xY6Rsn79+rzlLW9p\nugwAAOYpAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAA\noBKQAAAAKgEJAACgEpAAAAAqAQkAAKASkAAAACoBCQAAoBKQAAAAKgEJAACgEpAAAAAqAQkA\nAKBa3HQBMGBXJFlVHz+W5FeT3NRcOQAADBMBiYVkjyRveMlLXpIDDzwwn/nMZ7Jjx46jIiAB\nAFA5xY4F5xWveEXOP//8LF26tOlSAAAYMgISAABAJSABAABUAhIAAEAlIAEAAFQCEgAAQKWb\nb0barl27xh/+SpItDZYCAMA8ICAx0rZu3ZokOfTQQ9+1ZMmS3HzzzQ1XBADAMBOQGGnjR5DW\nrVuXNWvW5LTTTmu4IgAAhplrkAAAACoBiQXroYceSpJ1STYk+eskY40WBABA45xit0A9+uij\nSbIiJRQ8pdlqmvHoo4/mlFNOOXrNmjW56qqrsnXr1j9P8q2m6wIAoDkC0gJ1ww03JMkLknyz\n4VIatXbt2pxyyin58pe//C8dOgAAsHA5xW6B2rlzZ0466aRcd911OeGEE5ouBwAAhoKABAAA\nUAlIAAAAlYAEAABQCUgAAACVgAQAAFAJSJDkgQceSJJ3ptw09pokL2qyHgAAmiEgQZLt27fn\nxBNPfNa555572v777//SJCc1XRMAAIMnIEH1ghe8IOeff35WrVrVdCkAADREQAIAAKgEJGhz\nxx13JMn7kuxKsjPJOxotCACAgRGQoM0jjzySM844I+vXr8+RRx65KMlTm64JAIDBEJBgEgcd\ndFDGxsayYsWKpksBAGCABCSYxq233pokF6acbtc+/LfmKgMAoB8EJJjGjh07cuqpp2b9+vV5\n0pOelOOPPz7r16/PqaeemiRrmq4PAIDeEpBgBqtWrcrY2FiWLl2aJz3pSRkbG9MVOADAiBKQ\nAAAAKgEJAACgEpAAAAAqAQk68MMf/jBJzspEj3afbLQgAAB6QkCCDmzbti1HHnlk1q9fnzPO\nOCNJDm66JgAAure46QJgvlqxYkXGxsZy8803tz793CSXJtmz/n5jkn836NoAAOiMgARdqqfb\nHZ/kviRLly9fvs9ZZ52VjRs35itf+cpYBCQAgHnDKXbQpQcffDCHHHLI4vXr1+83Nja2z4oV\nK3L++edn7dq1TZcGAMAcCUjQA/vuu2/GxsaycuXKpksBAKALAhIAAEAlIAEAAFQCEgAAQCUg\nAQAAVLr5huHyr5Osro8fS/I/k9zfXDkAAAuLgATDY48kf7JmzZpFe++9d2655ZY89thjtyT5\nbNOFAQAsFE6xg8F4ZpItKTeTvS/JxiT7TjLdogsvvDCXXXZZ9tlnnyRZNLgSYeT8QSY+c/cl\nubjRagCYFxbiEaRFKTumy5JsT/Jgs+WwQByY5Enr16/P5s2b88EPfnC/lPfhbN9/r0hyZH28\nK8mnkvxolvMemuS1mQhbP0jyV7OcF+azQ0899dT9zjrrrHzyk5/MN7/5zUOaLghgQE5OckrL\n7xuSXN9QLfPOQglIq5P8P0nOSNnJ3Kdl3NYk301yZZI/SvLTgVfHgnHcccflpptuGv/100l2\nJPlZyrVHP5tm1g+uWrXq8BUrVuT222/PQw89tEeS98/yZV+3bNmySw8++OA88MAD2bx5880R\nkFggVq1albGxsVx77bVNlwIwSL++3377veKAAw7IXXfdlZ/+9KfPTHJe00XNFwvhFLu1SW5O\n8jtJnpXkpiTXpOwgXpPktiQnJPlAne6EZspk1GzcuDFJnphyas9V48/ff3/pc+FVr3rVC1/1\nqledluSXkjx1hsUteuMb35jLLrsshx12WDL5qXf/f3t3HiZJWR9w/Du7s7PM7Oyy4Aqycuzi\nAR5RdIEIJhEIPoJ4H6AG7yuRKInEKPEaJR5RYwwSJHjghY8xRFaD94kHYhC8gOUQUJFDBFF2\nl9nZmd3OH7+36J6ampmqmemumZ7v53n6qe6qmupfv11d8/7eeuutXYENLY97ZX+7//77c9ZZ\nZ3HiiSdO9redMMT47k6nz9F2D6D5mR82R9ssYwlwUMt736+D7y1J0lR6HvvYx3LWWWdx+OGH\n1x3LgtPtZ5BWA58G/gCcCHwRGCtYbxfgmcB7iQviD8Cud5qlLVu20N/f3/PWt751t+9///ts\n3Lhx3PLnPve5NBoNzj//fIAXE9cozca7gJe1vP4s8PRZbnMqxwPr0/MDgZuIM7KTdQHc7+CD\nD97thBNO4IILLuDCCy9cz+ytBjYxPul7FPDDOdj2dI4BvtDyejvQD+zswHtLkqQ26fYE6Thg\nN6Jr3cVTrLcN+ARwK3FW6VjgvLZHp67X29vLhg0bsrNJE2zevBmAdevWvbqvr49rrrnmnmUj\nIyMAbya6h943m3/HHXcAvAg4Ks0fAW4HHnzUUUdx8sknc84557Bx48a+/PvddtttpL/5Wpq1\nCXgV0Q31g0RjQS/wAOCqtM6BwLVE48I24KXEb+UDa9eu3X1wcJBrr72WPfbYg+m6AK5Zs4YN\nGzZwySWXTFluFfQBPWeccQZ77703T3nKUwCWz9XGk78AXk/zjPvFwBuB5YODg5x77rlceeWV\nnHrqqX3M/OzcaURiB5FgvQ34zixiliRJM9RDtPYCvIXuG+HnVOJzTagoTmIp0Qr8espf31Fk\nPdGCXTYB7QVWEnGOzuJ9p/Oh3t7eF/f393P33XfT09NDf38/w8PDNBoNBgYGGB4eZseOHQwO\nDjIyMsLo6CiDg4OMjo6ybds2Vq5cydjYGMPDwwwODtJoNNi6dSsDAwMsXbqUzZs3j3ve399P\nb28vmzdvZvny5fT19bFlyxb6+vrueb5s2TKWL1/O1q1bWbJkCcbXfN6qNb5Go1H4Bff29t4T\n09jY2ChxXdMuS5cu7c/iGxsbdxJ1J/BHmvtgGZuJZGl1f39/T1H5NRqNYSKZGiDu5zQCDPT2\n9i4viK8PWEactV1GJDhbiN/jQHq/JcAgcY1gT4o1e746V2ZZfCuJgVjG0t+OEL+vFWm6PRff\nLunzbUsxLAXuTvP7Wz7/jvTey3p6egYHBwfv+U6Je1a1xgewiuaZtVXps+3MxbcqvV+mqPwm\ni2825Vc2vtmUX53xDfb19S3Lfr87d+4cSTHNl/jme/kZn/EZ38KN757jX/qf+2HgJWgqQ0TD\ndNcnSCcBZwB7AreVWH9vYvjlk4AzZ/G+S4hW57IJUg8xytm5s3jPMvYCHpKe70r8oH9L/KB2\nBW4mfrx7Ar8m4t+HuE6rh7jG4hfp7x9AnFXIP78fcD2xX60nynMM2De91wiwlqiUb03vtS29\n3j1t4/fGZ3zGZ3zGZ3zGZ3zGNyfxAVwB3IKmMkRKkCAKukH3JUcQI9Y1iMRjurNIK4iR7HYC\nD2xzXJIkSZLmjyFSXtTt1yBdSZwJegXwGGLkuiuIi+G308zmHwY8CVgDvIMYzU6SJEnSItTN\nZ5AgTl2+ijgV2ZjicQ1xLxpJkiRJi8sQi+QMEsQHPR14P/BQotvdHkT/0G3EaFw/pzlilyRJ\nkqRFajEkSJkGkQj9vO5AJEmSJM1PS6ZfRZIkSZIWBxMkSZIkSUpMkCRJkiQpMUGSJEmSpMQE\nSZIkSZISEyRJkiRJSkyQJEmSJCkxQZIkSZKkxARJkiRJkhITJEmSJElKTJAkSZIkKTFBkiRJ\nkqTEBEmSJEmSEhMkSZIkSUpMkCRJkiQpMUGSJEmSpKS37gC0KN0ErK07CEmSJN3jj8DquoOY\nD0yQVIdbgPOBc+oORF3tm8BpwLfqDkRda3fgq8DxwPU1x6Lu9UjgbOBQYGfNsah7PRV4Tt1B\nzBcmSKrDduIs0qV1B6KutgO4Dvcztc8eaXoFcGWdgairrUjTSzFBUvs8EhirO4j5wmuQJEmS\nJCkxQZIkSZKkxARJkiRJkhITJEmSJElKTJAkSZIkKTFBkiRJkqTEBEmSJEmSEhMkSZIkSUpM\nkCRJkiQpMUFSHbanh9RO7mdqt1GggfuZ2ms7zX1Nahf/Z+Y00mOo5ji0eOwF9NcdhLrefsDS\nuoNQ19u/7gDU9XqA9XUHoa63DNin7iBqNkTKi3prDkSL0y11B6BF4Vd1B6BF4fq6A1DXawA3\n1B2Eut4ocGPdQcwXdrGTJEmSpMQESZIkSZISEyRJkiRJSkyQJEmSJCkxQZIkSZKkxARJkiRJ\nkhITJEmSJElKTJAkSZIkKTFBkiRJkqTEBEmSJEmSEhMkSZIkSUpMkCRJkiQpMUGSJEmSpMQE\nSZIkSZKS3roD0KKzBtgfGAE2AdvrDUddpA84fIrldwI/7VAs6j7r0uPnwB1TrDcAHAAsBa4F\n/tjuwNRV1jH1fnYosY8VaQAXtiUqdZNdgfsBw8ANwLYp1l3UdbZGegzVHIe62xrgf4AdNPe5\nO4FX1hmUusr9ae5bRY+v1xeaFrAe4jg1TOxHT5hkvSXA24GtNPe57cDZwC7tD1MLXNn97DdM\nfowba3+YWsD2Bc5j/D4zAryXiUn3Yq2zDZE+r2eQ1Ak9wPlE6/6/AZ8nWjD+ETgd2AKcU1t0\n6har0/QTwKcKlt/ewVjUHfYijk1/CVwBPHyKdU8DTgX+FziTqHicCLwUWA48v62RaiGrsp+t\nJs6Ev65g2c65D01dYhXwFeABREL0FWAFcDLw90RC9Ly0rnW2xDNIarcnEvvYv+bmryBaw24i\nuqNIs3E0sZ/9Xd2BqGu8n+iC8iiiQjpZy/4aopvKJUy8tncjUXF9cPvC1AJXdj/rTcs2di40\ndYm/priu30/Uw0aJOhks7jrbECkvcpAGdcJT0/Ts3PytREv/WuCwjkakbpSdQfpDrVGom3wZ\nOAi4eJr1Hk+cJfoQE1vxzyZaZJ8259GpW5TdzzzGaaauAl4PfDA3fxi4jEi+90jzrLPhIA3q\njIOIU7JXFyz7Ucs63+tYROpGrZWHfYluKquAK4Ef1xWUFrQvlFzvoDS9tGDZj3LrSHll97PW\nY9xq4GCie96NwEUssgvoVcm306PIfkTyc0t6bZ0NEyR1xt40f3h5N6fpPh2KRd1r1zT9B6Kr\nSmsXgIuAE4juAdJc2ztNby5Y9jvi4nmPcZqt7Bh3BPDLltcQx7a/Ar7T2ZC0wJ0IPAx4H83R\n7Kyz4X2Q1BkDTD6M5HCarphkuVRW1ro6ADyTGJr0UOBc4mLTC+jeftOqVzYCVNFxrpHme4zT\nbGXHuLXAa4nr2h4CvJHoHvUFYH09oWkBOoroRncZ0f0uY50NzyCpM8aYfF/L5ts1QLP1DuDf\nie4n2XC3NxAtZPcCjgGOJRIlaS5l+9tUxzmPcZqt7wD3JrpDDbfMv5K4yP6dwEnEWXRpKi8C\nziKSo+OAu1uWWWfDM0jqjDuA3SZZtnua/r5Dsah73U0M5V10L5Dz0vQRnQtHi0h2Q8+i49wA\ncR8kj3GarVHiGDdcsMxjnMpYArwH+DDwOeBIJt6Q2DobJkjqjKuBPRnfXzrzoDTd1LlwtAiN\n1h2Aulp2MfMBBcsOTFOPcWonj3Eq4z+BU4ibWh9PcbJtnQ0TJHXG14lhbo8tWPZEosX/mx2N\nSN3o3cCXiNb6vMPTtOsP6qrF19P08QXLnpimX+lQLOpeLyD2o0cWLPMYp+m8HXgJ8BrimqPG\nJOtZZ0u8UazabQ1wF3AdzdGeIPrANohTvdJsvYfYn84E+lrmP5loXb2ZuCmeNBNT3cATYqTE\nEWKEscxBxLHvarzmV+VMtZ89IS27mPi/mnkQcb3lDuxip2KPJu7Rdk6JdRdznW2IZl5kgqSO\neAZxUd8wcaHp5cR+9xOaI/NIs7EC+AGxX/0WuBC4Nr3+Pc0WVqmsC4nK6MXAr4l96aqWeUMt\n6z4QuJWohFxCJExjwJ0Ut/hLmSr72elp+Vbg+8T/0O1EcvS3HYtYC81GYr+5guZ+lX8c17L+\nYq2zDZHyIlu01CnnEaf+X0b00/8d0Rf2g0w+nKRUxVbgz4ghvo8mWr4uBz5CtHjdVl9oWqBG\naHZDuT49WrVe93EN8FDgFcAhRBeVdwAfoPj+SFKmyn72KuC/gGcB64hRO78GfIw43klFfkkk\n4lPZ0fLcOhueQZIkSZK0uA2R8iIHaZAkSZKkxARJkiRJkhITJEmSJElKTJAkSZIkKTFBkiRJ\nkqTEBEmSJEmSEhMkSZIkSUpMkCRJkiQpMUGSJEmSpMQESZIkSZISEyRJkiRJSkyQJEmSJCkx\nQZIkSZKkxARJkiRJkhITJEmSJElKTJAkSZIkKTFBkiRJkqTEBEmSJEmSEhMkSZIkSUpMkCRJ\nkiQpMUGSJEmSpMQESZIkSZISEyRJkiRJSkyQJEmSJCkxQZIkSZKkxARJkiRJkhITJEmSJElK\nTJAkSZIkKTFBkiRJkqTEBEmSJEmSEhMkSZIkSUpMkCRJkiQpMUGSNJkjgHvVHUSNDgQawFkl\n1n1DWveYGaybvc+HZhBjt6hSBlXKeqHo1s8/m317XzwGSaqJCZKkyXwL2FB3EDW6DTgVOL/u\nQObQc4Az6w5igbCs6nU8HoMk1aS37gAkzWub6w6gRr8H3ll3EHPsCcD+NKy2gwAACqBJREFU\ndQexQFhW9dqSpov5GCSpJiZIkqZSVDlZA+xDdJ25Hrhrir8/EFgFXAP8AdgDeDBwOXB7bt0V\naf1e4AbiDM5MDQCHpu38CtgdeCBwJ3B1y3q7AgekWG5Inym/jZtT/Jke4EHASuBaIpGaTJV1\ni8xVmawkWuIPA4aJrkt3Aj/NrbeKKKels3y/vJlut2r5lS2vZUQXrnunbV4PjKVlZcuqik59\n/unMxe+iVZVjQd5031XZBGlPoowm83PgjpIxTbVf5O0GPCDFdx2wfZL1pvvu89/Jgen9v5tb\nby6Pj5JKaKTHUM1xSJpfGsC6ltf7Al8HdtI8buwEPkZUqFr9CbCpZb1h4I3A36TXx7Wsuwvw\nH8BIy/qN9F6t71/F+rSNtwNvStseS/N+AKwG/inFNZrmX0RUTDJF1yA9JPe5RoH3pc+Wvy6k\n7LpF12nMdZkcnNtOtq3MIPE9jubW+eYM36/qdovKoEpZVymvk4Bbc+vdDLwwLZ+urKro1Ocv\nay5+F1D+WDCbffspTDwGFTmRid9X6+MJ0/x9Zrr9IrMC+DjNcmsQyUp+varf/WnAB9Pzy1uW\nt+P4KKnYEM3fmAmSpEIPZfxZ5p8RraSvJCpvDwf+hTh+fLJlvT6ihXMH8DqiAnA0cCVwCRMr\nd58hKhFvIFqC7we8nGiN/gXRwlpV1qp9NXAu0dq7HDgjzf8u8CWi9bmXqBQ2gHe1bCOfIC0j\nWvF3AKekOB9NXCdxXe5zVVm3qBI512WylKj8biO+g9VERS/zpRTDO4nW7gOA1xCVwF8A/RXf\nr+p282VQpfygfHk9Jv3tV4HDiS50f5FeN9J7TFdW8/HzlzUXvwsofyyYzb49yMRjUJFB4P65\nxxFEknc7sNc0fw/l9ovM59K89xBnGY8mksudRFKXKfvdZ0nrN4AfA08CHtWynXYcHyUVG8IE\nSVIFg8DrgVcULNsE3E1z0JcnEceUM3LrrScqVa2Vuw00Kxt5r0zL8i2zZeyd/vbOFHtmXZq/\nnfEVp+VExeV7LfPyCdJxudeZfpotz8fMYN18JbJdZQJR6b84N+/wtM3zCtZ/W1r2ghm8V5Xt\n5sugSvlVKa9sBLgjcuutBv4Z+NOWeUVlVUWnPn8Vc/G7qHIs6OS+nVlCJJIN4Mkl/6bsfnFI\nWu+c3Hr3Icrpa+l1le8++052APvl1u1EeUlqGiLlRV6DJKmMLcQ/9h6i3/1exJkigK1ExW2Q\naNXMKhNfzm3jBqJbyLEt87LnY8Czcutn2/9zJlZIyrqU5rUMEF1mIK4puqVl/ghxzcFuU2zr\nsDT9am7+MPAV4HkzXDev3WWSd3SafrZg2eeJswiPAT7awe1WKb8q5XVjen0ScU3Rnen1H4hK\n8lzq1Oefidn8LqocC/I6sW+/jkh0PkCc7Smj7H7xuDS9IPf3txJnGUfS65l895cR1yC16vSx\nQFJigiSprGcC76XZ4jmcpruk5Vmr8do0vZGJfsr4BCkbJey1U7zvfWYSbPLb3Ovtk8zPli2d\nYlv3TdObCpb9ehbr5rW7TPLWpen1BcuyCts+Hd5ulfKrUl6fAp4GPIM4u/BDotX/fOJi/rm0\nLk3b/flnYra/i7LHgrx279uHAG8huvOeklt2GhF3qxcS3ePK7hdZ/L8peO+Rlufr0rTKd1+0\nzU4fCyQl3gdJUhmHAJ8m+sIfSbReriBaivMXsGctm6MF27k793pZmj6OaHkuepTtJlOkUXH+\nVLJYiz7XjlmsO9n7tKtMJnu/olG4sviXd3i7MynrMuU1mp4fSXT7ui9Rof4ZsJGZX2tVpFOf\nfyZm87uocizIa+e+PUgkOjuAZxNJW6u7iDM9rY/suym7X2TxTzayXWYm3/3WKbbTqWOBpMQz\nSJLKeDbRoHIK8O3csjW511nXnZUF28m3dmZDfa8hrvmYz7LhhvMj9sHEMqiybl6nyyQbOvpe\nBct2T9OywyTP1XarlN9MyuvbNPfjA2ieXXgd8OaS25hOpz5/p1U5FuS1c98+gxig4WQiscl7\nd3pM5dtMvV9M9Z22mqvf1EI6PkpdxTNIksrIrkHIdxlZDzwiNy9b58G5+T3AY3PzfpSmxzLR\nfYi+/POlIefaNH1owbLDcq+rrJvX6TK5NE0PLVh2SJr+uMPbrVJ+VcprFVGJbnU1MVT0GONH\nK5utTn3+TqtyLMhr1759AvB84IvA6TP4+7L7xWVp+igmOpvmwDRz9ZtaSMdHqes4ip2k6WT3\nXnl5y7zdiGGBL0/L1qf52chL/8f44ZFPIbqRtI7ANQj8jmgdPaRl3WXECFANiisZ08mujfhk\nwbLsHiJ5vwGuanmdH8XuQen1Jsa3DB9P814nx8xg3fxIX+0qE4iLzq/LzVtFtHjfzPgRzAaJ\na8a2E0MLV1Vlu/kyqFJ+VcrrG0Sr/HrGy+599PGWeUVlVUWnPn8Vc/G7qHIs6MS+vR/xXd1K\n3Ih6JsruF7sSAzj8lvEjzj2d8ceKKt/9VN9JO48FkiYawmG+JVWwlujDP0LcP+UTRAXgTcCr\niWPIRUQrLsB/p3nXE5WLC4lW8exeKa2Vu8cR1yZtIy6K/iTwy7TeaTOMtx0JEkQrcYOoIH2O\nqBTeTnTdaTC+pbfsukX3imlHmUBz+OPvEfdXyTyJ+G7vIL6vjxKVu53EzX1nqux2i8qgSlmX\nLa9DgT8S16d8ldiXv5ZivI3oVpWZrKyq6NTnL2sufhdVjgWd2Lc/kf72Z2lb+cfTSmyjyn7x\nVCLB2ULc6+ii9P5XM360v7Lf/VTfCbTvWCBpoiFSXrSUZmJ0IRP7E0sSxDURnyEuyL4vcSbo\nrUTF52fE9UYriNbR7MLm24gW0AGicvdSogvOkcSwtDekbV9H/NMfISpfA8QNOl9N3Il+JpYT\nlZ4fpvdudSTRvSU/hPJhxAhT56fXA8AjiQrQD9K8L6R1dkmPnwIvJspnH6KLz40V183e5wfp\nvaA9ZQJRFtk1EJuAb6bnVxPfbw8xAtcg8B2iIpcf0riKststKoMqZV22vG4iLuS/i2jl351I\nQD5OjGh2c8u6k5XVfPz8Zc3F76LKseAXBZ9rrvft7Iz1MPGd5h+bgJ9Ms40q+8VVNO9vdG/i\nDM9HgJfQvHYMyn/3U30n0L5jgaSJjqDlfmieQZLUDssK5n2MON4cULBMkiSpLkOkvMhBGiTN\ntVXE9QCXEq2dmQOJEaGuI25IKUmSNO84+omkuXYXcCZxH5FriG4jK4kuPBAXd1e9D9FjGN+/\nfyqbiYuuu12ny2Sxfwfz8fPPx5gkqSvYxU5SOxwJfIC4luIC4J1MHEq3rIuJ4XbLPC6fVdQL\nR6fLZLF/B/Px88/HmCRpoRrCUewkSZIkCfAaJEmSJEmayARJkiRJkhITJEmSJElKTJAkSZIk\nKTFBkiRJkqTEBEmSJEmSEhMkSZIkSUpMkCRJkiQpMUGSJEmSpMQESZIkSZISEyRJkiRJSkyQ\nJEmSJCkxQZIkSZKkxARJkiRJkhITJEmSJElKTJAkSZIkKTFBkiRJkqTEBEmSJEmSEhMkSZIk\nSUpMkCRJkiQpMUGSJEmSpMQESZIkSZISEyRJkiRJSnpbnj8aeG1dgUiSJElSTR6dPekBGjUG\nIkmSJEnzhl3sJEmSJCn5fxUiP9/wXjqcAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “'age_middle_to_oldest_old_male' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Q9OPP/Kcx1UfbLhA/uRDec6uFHDivFf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\nlE7V5VDkAPAAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “'age_middle_to_oldest_old_female' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UbDoBIAXyUgAfuDp+25CcCy/u84AeyR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\nAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “'dependants' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G6KMNnP/7mUD+AnpJzLsFtK8PxlyjV5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\nSZKS/w/nZYtEVnDmhAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “'unemployment' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6uha+BLG/b6/EHDyeSTtz2wXb9AfbHh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\nFP1/AqV6K2xfWTEAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “'no_higher_education' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9g15GlNffjf3zT87eMWtHt51z2R+lA9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\nAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “'foreign_nationals' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eu6UmNdvGqF2320+uGZ/1c6MP+O7bpe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\nwl1TH33EAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “'primary_school_age' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lyzoer8efNuaejO8Ifjcu/dcGL9Fxu+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\nRK5CYII=",
      "text/plain": [
       "Plot with title “'one_person_households' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Zs+g9WW+WbTH+e1fHCFZU5cpe1mP4vd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\nhT9agP46BiJJkiRJ44Zd7CRJkiQp+f9o2EZJS86kHgAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “'tree_cover_density' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PFNRwo+2nQE4BWtLSgeym81fSP/75uO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\nRU5ErkJggg==",
      "text/plain": [
       "Plot with title “'impervious' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram visualisation of weighted indicators\n",
    "indicator_columns <- colnames(indicator_data_weighted)[-1]\n",
    "for( current_indicator_column in indicator_columns ) {\n",
    "    indicator_filtered <- indicator_data_weighted[,current_indicator_column] \n",
    "    indicator_filtered[indicator_filtered == \"NaN\"] <- 0\n",
    "\n",
    "    title <- paste(\"'\", current_indicator_column, \"' domain histogram\", sep = \"\")\n",
    "    x_label <- paste(\"'\", current_indicator_column, \"' z-score\", sep = \"\")\n",
    "    y_label <- paste(\"Count\", sep = \"\")\n",
    "    hist(indicator_filtered, breaks=\"FD\", col=\"grey\", labels = FALSE, main=title, xlab=x_label, ylab=y_label)\n",
    "    box(\"figure\", lwd = 4)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c6d6526-ffd5-4cf5-abaa-df78e9cee3ba",
   "metadata": {},
   "source": [
    "## Process social vulnerability scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b30ed1a-947e-4893-915a-68dc9de52f99",
   "metadata": {},
   "source": [
    "### Calculate domain scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a8f2459-ee44-4862-a0ed-a829b8a76824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>SEZ2011</th><th scope=col>age</th><th scope=col>income</th><th scope=col>info_access_use</th><th scope=col>local_knowledge</th><th scope=col>social_network</th><th scope=col>physical_environment</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>151460000001</td><td>-0.37028523</td><td>-0.6431147</td><td>-0.3037669</td><td> 0.142430939</td><td> 1.1958366</td><td>1.0118090</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>151460000002</td><td>-0.59583610</td><td> 0.7308663</td><td> 0.4424940</td><td> 0.384372102</td><td> 0.3778636</td><td>1.0418299</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>151460000003</td><td> 0.05088688</td><td>-1.2312892</td><td>-0.8229049</td><td>-0.540697051</td><td> 2.3409988</td><td>0.8359458</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>151460000004</td><td>-0.22564026</td><td>-0.8787686</td><td>-0.8229049</td><td>-0.002474999</td><td>-0.7933508</td><td>0.9203024</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>151460000005</td><td> 4.00522505</td><td>-2.2590732</td><td>-0.8229049</td><td>-0.540697051</td><td> 2.3409988</td><td>0.9671620</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>151460000006</td><td> 0.00000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.000000000</td><td> 0.0000000</td><td>1.0492594</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & SEZ2011 & age & income & info\\_access\\_use & local\\_knowledge & social\\_network & physical\\_environment\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 151460000001 & -0.37028523 & -0.6431147 & -0.3037669 &  0.142430939 &  1.1958366 & 1.0118090\\\\\n",
       "\t2 & 151460000002 & -0.59583610 &  0.7308663 &  0.4424940 &  0.384372102 &  0.3778636 & 1.0418299\\\\\n",
       "\t3 & 151460000003 &  0.05088688 & -1.2312892 & -0.8229049 & -0.540697051 &  2.3409988 & 0.8359458\\\\\n",
       "\t4 & 151460000004 & -0.22564026 & -0.8787686 & -0.8229049 & -0.002474999 & -0.7933508 & 0.9203024\\\\\n",
       "\t5 & 151460000005 &  4.00522505 & -2.2590732 & -0.8229049 & -0.540697051 &  2.3409988 & 0.9671620\\\\\n",
       "\t6 & 151460000006 &  0.00000000 &  0.0000000 &  0.0000000 &  0.000000000 &  0.0000000 & 1.0492594\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 7\n",
       "\n",
       "| <!--/--> | SEZ2011 &lt;chr&gt; | age &lt;dbl&gt; | income &lt;dbl&gt; | info_access_use &lt;dbl&gt; | local_knowledge &lt;dbl&gt; | social_network &lt;dbl&gt; | physical_environment &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 1 | 151460000001 | -0.37028523 | -0.6431147 | -0.3037669 |  0.142430939 |  1.1958366 | 1.0118090 |\n",
       "| 2 | 151460000002 | -0.59583610 |  0.7308663 |  0.4424940 |  0.384372102 |  0.3778636 | 1.0418299 |\n",
       "| 3 | 151460000003 |  0.05088688 | -1.2312892 | -0.8229049 | -0.540697051 |  2.3409988 | 0.8359458 |\n",
       "| 4 | 151460000004 | -0.22564026 | -0.8787686 | -0.8229049 | -0.002474999 | -0.7933508 | 0.9203024 |\n",
       "| 5 | 151460000005 |  4.00522505 | -2.2590732 | -0.8229049 | -0.540697051 |  2.3409988 | 0.9671620 |\n",
       "| 6 | 151460000006 |  0.00000000 |  0.0000000 |  0.0000000 |  0.000000000 |  0.0000000 | 1.0492594 |\n",
       "\n"
      ],
      "text/plain": [
       "  SEZ2011      age         income     info_access_use local_knowledge\n",
       "1 151460000001 -0.37028523 -0.6431147 -0.3037669       0.142430939   \n",
       "2 151460000002 -0.59583610  0.7308663  0.4424940       0.384372102   \n",
       "3 151460000003  0.05088688 -1.2312892 -0.8229049      -0.540697051   \n",
       "4 151460000004 -0.22564026 -0.8787686 -0.8229049      -0.002474999   \n",
       "5 151460000005  4.00522505 -2.2590732 -0.8229049      -0.540697051   \n",
       "6 151460000006  0.00000000  0.0000000  0.0000000       0.000000000   \n",
       "  social_network physical_environment\n",
       "1  1.1958366     1.0118090           \n",
       "2  0.3778636     1.0418299           \n",
       "3  2.3409988     0.8359458           \n",
       "4 -0.7933508     0.9203024           \n",
       "5  2.3409988     0.9671620           \n",
       "6  0.0000000     1.0492594           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the domains and their associated indicator ID\n",
    "domain_indicators <- indicator_mapping %>% select('domain', 'indicator')\n",
    "\n",
    "# Get a vector/array of the unique domain names\n",
    "unique_domains <- unique(domain_indicators$domain)\n",
    "\n",
    "# Initialise the domain score dataset with the GUID\n",
    "domain_scores <- indicator_data_weighted %>% select(all_of(GUID))\n",
    "\n",
    "# Loop through each domain\n",
    "for (current_domain in unique_domains) {\n",
    "    # Identify which indicators are used within this domain (current_domain)\n",
    "    current_domain_info <- domain_indicators %>% filter(domain == current_domain)\n",
    "\n",
    "    # Count the number of indicators in this domain\n",
    "    domain_indicator_count <- length(current_domain_info$indicator)\n",
    "\n",
    "    # Get a vector/array of the indicators used by this domain, and add the GUID column name\n",
    "    current_domain_indicators <- current_domain_info$indicator\n",
    "    current_domain_indicators <- (c(GUID, current_domain_indicators))\n",
    "\n",
    "    # filter the dataset to only use the indicators in the domain\n",
    "    current_domain_data <- indicator_data_weighted[current_domain_indicators]\n",
    "\n",
    "    # Calculate the internal weight distribution for the indicators within this domain,\n",
    "    # using an equal weight distribution across this domain\n",
    "    internal_domain_weight <- 1.0 / domain_indicator_count\n",
    "\n",
    "    # Internally weight the data for this domain\n",
    "    current_domain_data_weighted <- current_domain_data %>% mutate_if(is.numeric, function(x) {x*internal_domain_weight})\n",
    "\n",
    "    # Sum each data row to get the total score for the domain\n",
    "    current_domain_data_weighted[, current_domain] <- rowSums(current_domain_data_weighted[2:(domain_indicator_count+1)], na.rm = TRUE)\n",
    "\n",
    "    # Add the current domain score to the overall results\n",
    "    domain_indicator_score <- current_domain_data_weighted %>% select(all_of(GUID), all_of(current_domain))\n",
    "    domain_scores <- merge(domain_scores, domain_indicator_score, by=GUID)\n",
    "}\n",
    "\n",
    "# Print the first part of the domain z-scores, which are now collated into one table\n",
    "head(domain_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "85066c36-35e5-4aac-b579-111ec7917a37",
   "metadata": {},
   "outputs": [
    {
     "data": {
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6vjqkOWGXeTht3rLqq+2jDzBAAAzIfj\n256LZjogVd2t+nbbn+f3qy9V/9wwU3TexLavVLebTpkAAMCUHN+YCeahzfenq9tUv9Swq93h\nbT9R7CXVWdX7qpOqt1WXTadMAABg2uYhIFX9pHrNuAAAACxpXgJSDZ3pjqpu3/YZpIurs6vP\nVR+ptk6rOAAAYPrmISDtU72kelZ1jRXGnV/9fvUH7ficSQAAwAyah4D0lob23Z+p3lGdVp1b\nXdrQre7Q6ojq8Q0B6bDqmVOpFAAAmLpZ7mJ3r4bn9kfVHjsYu3f1+nH8HXZzXcyfG1UPWrTc\nfJoFAQDwr45vTrrY3afhib6wHe82d3nDuZCe2nCs0hfW8bjXqP5jtWWV47c0zFw9bR2Pyeb2\nP7Zs2fJL++23X1WXXnppW7duPbk6drplAQAwadYD0r7VFQ0ngF2N86ptDQ0d1uOg6jENjSBW\nY/+G8y/9xzSKmFV7HXPMMf3Gb/xGVa997Wt705vetNeUawIAYJFZD0hnNDzHY6qTVzH+0dWe\nDSeQXY+zqvutYfyR1anrfEwAAGCd9px2AbvZe6tvV2+sjqsOWWbcTRp2r/vL6mvj7QAAgDkz\n6zNIP6keVZ1YvWpcftDQxW5rwy54h1TXGcefXj2yocMdAAAwZ2Y9IFV9urpN9UsNu9od3vYT\nxV7SsDvc+6qTqrdVl02nTAAAYNrmISDVMJP0mnEBAABY0qwfg3RE9ZTqBtMuBAAA2PxmPSA9\nqjqh+lJDC+1Zf74AAMA6zEtgeE/1Z9UnqvtOuRYAAGCTmpeA9NTqoQ0ncP1Y9X+re0+1IgAA\nYNOZl4BUwyzS4dVvV0dVH68+Uz2vut30ygIAADaLeQpINZzf6L9Xh1XHNzRv+MOGY5QuqP6x\nOrl63JTqAwAApmjeAtKC86oXVjetHtbQ/vus6u7VQ6rbT680AABgWublPEjLuax697jUcPLY\nG1UXTq0iAABgauY9IC12SfW1aRcBAABMx6zvYvfV6n3VFdMuBAAA2PxmPSC9sTqmYVc6AACA\nFc16QAIAAFg1AQkAAGAkIAEAAIwEJAAAgJGABAAAMBKQAAAARgISAADASEACAAAYCUgAAAAj\nAQkAAGAkIAEAAIwEJAAAgJGABAAAMBKQAAAARgISAADASEACAAAYCUgAAAAjAQkAAGAkIAEA\nAIwEJAAAgJGABAAAMBKQAAAARgISAADASEACAAAYCUgAAAAjAQkAAGAkIAEAAIwEJAAAgJGA\nBAAAMBKQAAAARgISAADASEACAAAYCUgAAAAjAQkAAGAkIAEAAIwEJAAAgJGABAAAMBKQAAAA\nRgISAADASEACAAAYCUgAAAAjAQkAAGAkIAEAAIwEJAAAgJGABAAAMBKQAAAARgISAADASEAC\nAAAYCUgAAACjvaddwAZ6QPXQ6vbV9av9qours6vPVe+qPjG16gAAgKmbh4B0s+rt1T0m1m2t\nLq32re5ePbz67eq91ZOqH2xwjQAAwCYw67vYbalOro6oXl4dWR3YEIyuPV4eXD2wen11dHVS\ns/+6AAAAS5j1GaQHV4dXT67esMyY86oPjctnq1dUR1WnbEB9AADAJjLrMyWHV1dUb17l+NdU\nV1Z32W0VAQAAm9asB6QrGp7jllWO31Lt0RCSAACAOTPrAenTDYHnuFWOf954qZsdAADMoVk/\nBumj1anVy6p7Ve+sTqvObehkt291SHWn6onVMdX7x9sAAABzZtYD0rbqEdVrq8eNy0pjT6ie\nnV3sAABgLs16QKr6YfWY6tYNM0SHt/1EsZdU51Sfr95dnTmlGgEAgE1gHgLSgjPGBQAAYEnz\nFJAeUD20un3bZ5Aurs6uPle9K80ZAABgrs1DQLpZ9fbqHhPrtlaXNjRpuHv18Oq3q/dWT6p+\nsME1AgAAm8Cst/neUp1cHVG9vDqyOrAhGF17vDy4emD1+uro6qRm/3UBAACWMOszSA9uaMrw\n5OoNy4w5r/rQuHy2ekV1VHXKBtQHAABsIrMekA6vrqjevMrxr6n+pLpL6wtI1xvvZ7Wv73XX\n8VgAAMAuMusB6YqG3eW2VJevYvyWao/Wfx6kS6t/qfZaw3gAAGDKZj0gfboh8BxX/dEqxj9v\nvFxvN7sLGpo+rNaRDc0hAACAKZr1gPTR6tTqZdW9qndWp1XnNnSy27c6pLpT9cSGE8m+f7wN\nAAAwZ2Y9IG2rHlG9tnrcuKw09oTq2a1/FzsAAOBqaNYDUtUPq8dUt26YITq87SeKvaQ6p/p8\n9e7qzCnVCAAAbALzEJAWnDEuAAAAS5qXgHSdhuONvjux7sjql6tbNHSR+2zDrnjf2vDqAACA\nTWHPaRewAY5tCD2Pnlj32w2NGJ7ZcDLZh1e/W32xeshGFwgAAGwOsx6QDq7e2nAc0hfGdUdU\nL66+XD2yulF1y4awdGn11w0zTgAAwJyZ9V3sHlbtX/3b6p/GdY9sOGnsQxtO5rrgL6rvVCc1\nzCK9eePKBAAANoNZn0G6YUMY+qeJdQc3NGv4lyXGv7+6orr5bq8MAADYdGY9IJ3bMEt2i4l1\nX6sOWGb8QdVe1Y92c10AAMAmNOsB6d0N5zr6y4aZo6q3VNesHrVo7DWr/9Vwkti/26gCAQCA\nzWPWj0E6p3pW9ZqGmaO3V5+o/rR6U0MDh9OrGzecTPbQ6n+M6wAAgDkz6wGp6vUN4ejF1dOr\nZ0xs+3cT17/T0MnuLzasMgAAYFOZh4BU9eGGTnY3rO5W3ay6VkMDh+9Xn29o5LBtWgUCAADT\nNy8BacFZ4wIAAPBTZr1JAwAAwKoJSAAAACMBCQAAYCQgAQAAjAQkAACAkYAEAAAwEpAAAABG\nAhIAAMBIQAIAABgJSAAAACMBCQAAYCQgAQAAjAQkAACAkYAEAAAwEpAAAABGAhIAAMBIQAIA\nABgJSAAAACMBCQAAYCQgAQAAjAQkAACAkYAEAAAwEpAAAABGAhIAAMBIQAIAABgJSAAAACMB\nCQAAYCQgAQAAjAQkAACAkYAEAAAwEpAAAABGAhIAAMBIQAIAABgJSAAAACMBCQAAYCQgAQAA\njAQkAACAkYAEAAAwEpAAAABGAhIAAMBIQAIAABgJSAAAACMBCQAAYCQgAQAAjAQkAACAkYAE\nAAAwEpAAAABGAhIAAMBo72kXMAV7VPtX+1UXVz+ebjkAAMBmMS8zSIdWL6w+WV1UXVidO16/\noPpY9fzq2tMqEAAAmL55mEF6cPWO6oCG2aKvNISjS6t9G8LTPar7Vs+tHt4QpAAAgDkz6wHp\nOtVbqvOrJ1UnV5cvMW6/6nHVH1d/U902u94BAMDcmfVd7I6tDqp+oXpXS4ejqkuqN1RPrG5U\nPWRDqgMAADaVWQ9IN60uq/5hleNPqbZVt9ptFQEAAJvWrAekC6ot1fVXOf4GDa/JBbutIgAA\nYNOa9YD0ofHy5dU+Oxi7f/Wq6srq73ZnUQAAwOY0600avlj9WXVcdf/qpOq0hi52Wxu62B1S\n3al6RPVvqpdWp0+jWAAAYLpmPSBVPbuhtffzq2euMO6M6nnV/96IogAAgM1nHgLSldUrqj+t\n7lAd3nBM0n4N3evOqT5ffXlaBQIAAJvDPASkBVc2BKEvNBxvtF91cc53BAAAjGa9ScOCQ6sX\nVp+sLqoubDgO6aKGjnUfa9gF79rTKhAAAJi+eZhBenD1juqAhtmirzSEo0sbmjQcWt2jum/1\n3OrhDUEKAACYM7MekK5TvaU6v3pSdXJ1+RLj9qseV/1x9TfVbbPrHQAAzJ1Z38Xu2Oqg6heq\nd7V0OKqhWcMbqidWN6oesiHVAQAAm8qszyDdtLqs+odVjj+l2lbdap2Pe7PqA9Veqxy/3zof\nDwAA2AVmPSBdUG1paOv9vVWMv0HDrNoF63zc71T/udW/vretXrTOxwQAANZp1gPSh8bLl1dP\nrbauMHb/6lUN7cD/bp2Pe3nDsUyrdWQCEgAATN2sB6QvVn9WHVfdvzqpOq2hi93Whi52h1R3\nqh5R/ZvqpdXp0ygWAACYrlkPSFXPbmjt/fzqmSuMO6N6XvW/N6IoAABg85mHgHRl9YrqT6s7\nVIc3HJO0X0P3unOqz1dfnlaBAADA5jAPAWnBlQ1B6POL1u/Z0LkOAACYc7N+HqSbVver9li0\nfkv1O9XXGxoqXFz9fXX0RhYHAABsLrMekP599dGGZgyT3t7QNe6m1VerHzU0cXhP9fSNLBAA\nANg8Zj0gLeXo6pENwemm1W2qQ6t7V9+s/md18NSqAwAApmYeA9KDG45HemJ11sT6f6z+Q8P5\nkB48hboAAIApm8eAdO3qzOrbS2z7aEN4uvlGFgQAAGwO8xiQvlFdZ5lt+zY0dLhww6oBAAA2\njXkMSG+vrlk9cIltTx0vnRMJAADm0LycB+nChk5154/LJdVLq3uN2/es/rh6dvWV6kNTqBEA\nAJiyWQ9In6ne2rBL3cJy44bnfc2JcdsaWoKfVT06J44FAIC5NOsB6V3jspTFz/3R1ceqS3dr\nRQAAwKY16wFpJZcv+vuDU6kCAADYNOaxSQMAAMCSBCQAAICRgAQAADASkAAAAEYCEgAAwEhA\nAgAAGAlIAAAAIwEJAABgJCABAACMBCQAAICRgAQAADASkAAAAEYCEgAAwEhAAgAAGAlIAAAA\nIwEJAABgJCABAACMBCQAAICRgAQAADASkAAAAEYCEgAAwEhAAgAAGAlIAAAAIwEJAABgJCAB\nAACMBCQAAICRgAQAADASkAAAAEYCEgAAwEhAAgAAGAlIAAAAIwEJAABgJCABAACMBCQAAICR\ngAQAADASkAAAAEYCEgAAwEhAAgAAGAlIAAAAIwEJAABgJCABAACMBCQAAICRgAQAADASkAAA\nAEYCEgAAwEhAAgAAGAlIAAAAIwEJAABgtPe0C9hg165uV12/2q+6uDq7+nL1kynWBQAAbALz\nEpCOrV5Q3bfaa4ntl1UfqF5S/b8NrAsAANhE5iEg/Wb10urS6oPVadW549/7VodWR1RHV8dU\nz6heP5VKAQCAqZr1gHRY9eLqlOoJ1fd2MPZt1auq9zTsegcAAMyRWW/S8OCGXeqe2srhqOpf\nql9uODbpIbu5LgAAYBOa9YB0cMPxRd9a5fivVNuqQ3ZbRQAAwKY16wHp7GpLdftVjr9rw2ty\n1m6rCAAA2LRmPSC9p6GV9xurw3cw9l7Vm6oLq3fv5roAAIBNaNabNHy3Oq56bUP3ui+3vYvd\n1oYudodUd6pu0dDZ7onV96dRLAAAMF2zHpCqTqg+Vz23oZX3zy8x5pyGEPWH1ekbVhkAALCp\nzENAqvpM9Uvj9UOq6zd0q7ukIRydO6W6AACATWReAtKCa1c3a3tAurihicOPq59MsS4AAGAT\nmJeAdGz1guq+DedFWuyy6gPVS6r/t4F1AQAAm8g8BKTfrF7a0IDhg21v0nBpQ5OGQ6sjGo5P\nOqZ6RvX6qVQKAABM1awHpMOqF1enVE+ovreDsW+rXtXQHvzs3V4dAACwqcx6QHpwwy51T23l\ncFT1L9UvV1+qHtL6Z5Fu2HCc02rHAgAAUzbrAenghuOLvrXK8V+ptjV0uluPW1Zf3Ynb7bHO\nxwUAANZh1gPS2Q1d6m7fcOzRjty12rM6a52P+7XqpuNjr8Zdq7dXV67zcQEAgHWY9YD0noZW\n3m9sOA/SF1cYe6/qr6oLq3fvgsc+cw1jD90FjwcAAKzTrAek71bHVa9tmEH6ctu72G1t6GJ3\nSHWn6hYNne2eWH1/GsUCAADTNesBqeqE6nPVcxtaef/8EmPOaQhRf1idvmGVAQAAm8o8BKSq\nzzTsYlfDjNH1GzrMXdIQjs6dUl0AAMAmMi8BadJ3x2XSYxpabb9y48sBAAA2iz2nXcAm8dDq\n3027CAAAYLpmfQbpEeOyI/errttwHFLVu8YFAACYI7MekO5aPW0N4xfGfjsBCQAA5s6s72L3\nruorDc0YXl7doDpoieUN1Wcn/v79aRQLAABM16wHpM9Ud25o3/3s6iPVXarzFy1bqysm/r5k\nGsUCAADTNesBqYaTv/7XhmD0/eqU6nUNM0UAAAD/ah4C0oLTGpox/Gr1uOpL1S9OtSIAAGBT\nmaeAVLWt4VxHh1efqN5SnVhdb5pFAQAAm8NaAtKTqz9fxf19qzp2pyvaGN9uaE7MOccAACAA\nSURBVP/9C9U9W10rcAAAYMatJSDdorr3DsZcs7p+ddudrmhjvb36meq/NXSyAwAA5thqzoP0\nD+PljRsaG/zDMuP2qA6r9q1+uP7SNsz51e9NuwgAAGD6VhOQTq7uUd26ukZ1xApjL2iYiXnT\n+ksDAADYWKsJSAuzK8dXj2rlgAQAAHC1tZqAtODV1dt2VyEAAADTtpaAdNa4HFrdqTqg4bij\npXxxXAAAAK421hKQqv6gem477n73woZd8gAAAK421hKQ7lk9v/p8dVL1g4YTry5luU53AAAA\nm9ZaA9KZDR3tLt095QAAAEzPWk4Uu191WsIRAAAwo9YSkD5d3a7lGzMAAABcra0lIP19Q0j6\nw2rf3VINAADAFK3lGKSfrb5RPb16UvXZ6vvLjP0/4wIAAHC1sZaA9ICGFt9VB1ZHrzD2qwlI\nAADA1cxaAtKfVn9ZXbGKsRfsXDkAAADTs5aA9INxAdbp8ssvr7p2dbeJ1edU35lKQQAAVGsL\nSDcdlx3Zq/p29bWdqgjmwJe+9KWq+1Sfmlj9heqOUykIAIBqbQHp31f/bZVjX1gdv+ZqYE5s\n27atu9zlLh1//PFVvf/97+9Vr3rVPtOtCgCAtQSkj1QvWWbb9ap7VodVL64+uM66YObtvffe\nHXDAAVXtu6/O+QAAm8FaAtIp47KSX69+vnr5TlcEAAAwJWs5Uexq/EnDbNLP7eL7BQAA2O12\ndUCq+mZ1p91wvwAAALvVrg5I16nuUv1oF98vAADAbreWY5COGZel7FEdXD2oum71sXXWBQAA\nsOHWEpDu3dCEYSUXVL9RnbbTFQEAAEzJWgLSq6u/XWbbldVF1dery9ZbFAAAwDSsJSCdNS4A\nAAAzaS0BacGh1ZMaTgx7/XHd2dWp1Rur83dNaQAAABtrrQHp2OrN1QFLbHt89TvVI6t/XGdd\nAAAAG24tbb4PbJgh+nH17OqO1SHjcufqudVe1Tuq/XZtmQAAALvfWmaQjm44z9Hdq08v2va9\n6nPVR6pPVg+u3rUrCgQAANgoa5lBukXDsUaLw9GkT1Xfqm63nqIAAACmYS0B6Yrqmqu8z207\nVw4AAMD0rCUgndZwHNJjVhhzdHXjnCgWAAC4GlrLMUgfqL7W0Kjh1dUpDedF2qO6YfWg6unV\n6dXf7doyAQAAdr+1BKTLqkdU/7f69XFZ7EvVo8axAAAAVytrPQ/SF6vbVw+tjqxuUF3Z0Lzh\no9X7qst3ZYEAAAAbZS0BaY+GMHRZdeK4LNinIRhpzgAAAFxtrbZJwz0bzm90vWW2/6fqw9Ut\nd0VRAAAA07CagHTnhoYMd6vut8yY61T3Hcddf9eUBgAAsLFWE5BeV12jenz1N8uM+a3ql6ub\nVK/aNaUBAABsrB0FpDs2zBy9qnrrDsb+dXVC9eiGoAQAAHC1sqOAdJfx8o2rvL/XV3s1dLiD\neXaDhh8XFpaDp1sOAACrsaMudjcYL7++yvv72nh5050rB662jq5uNvH386pbT6kWAAB20o4C\n0sIJX/dd5f3tP17+ZOfKgautEw466KBD99tvv6rOOeecnv70p/eEJzyh6l8vAQDY3HYUkP5l\nvLx39c5V3N9R4+U3d7YguJra81d/9Vc76qijqjr22GOnWw0AADtlR8cg/X11afWfqy07GHtg\n9V+qH1UfXHdlAAAAG2xHAem86i+qe1Rvr667zLhbVR+oblG9srp4VxUIAACwUXa0i13Vb1Z3\nrx5ZPaj62+qz1UUNnbnu1XCA+l4NIen43VEoAADA7raagHRx9cDq96rjql8cl0nnVi+v/qC6\nYlcWCAAAsFFWE5Bq+3FIv1fdt6F98f4Nwejr1cfa/MFo/4YmErevrl/t1xD+zq4+V32k2jqt\n4gAAgOlbbUBa8OPq/eNydbFP9ZLqWdU1Vhh3fvX7DbNgV25AXQAAwCaz1oB0dfSW6tHVZ6p3\nVKc1zHxd2nB+p0OrI6rHNwSkw6pnTqVSAABgqmY9IN2rIRz9cfW8lp8Z+pvqRdWrq19p6MT3\nhY0oEAAA2Dx21Ob76u4+DaHohe14t7nLG46zqu0nvIVpOaw6tfrUxPKB6prTLAoAYNbN+gzS\nvg3NIy5a5fjzqm0NDR1gmm615557Hvm0pz2tqgsuuKC3vvWtNbTW/8k0CwMAmGWzHpDOaHiO\nx1Qnr2L8oxtm1b68O4uC1dhzzz17whOeUNV3vvOdhYAEAMBuNOu72L23+nb1xoZzOB2yzLib\nNOxe95fV18bbAQAAc2bWZ5B+Uj2qOrF61bj8oKGL3daGXfAOqa4zjj+9emRDhzsAAGDOzHpA\nqvp0dZvqlxp2tTu87SeKvaQ6q3pfdVL1tuqy6ZTJPDvzzDNraMzwtXHVSufsAgBgN5mHgFTD\nTNJrxgU2nfPOO6+DDjpoy1Of+tRbVH3yk5/s4x//+LTLAgCYO/MSkGroTHdUdfu2zyBdXJ1d\nfa76SMNudzAV+++/fw972MOquvDCCwUkAIApmIeAtE/1kupZrbzb0vnV71d/0I7PmQQAAMyg\neQhIb2lo3/2Z6h3VaQ1NGi5taNJwaHVE9fiGgHRY9cypVAoAAEzVrAekezWEoz+untfyM0N/\nU72oenX1K9Urqy9sRIFcLW1p6Hh44MS6A5cZCwDA1cisB6T7NISiF7bj3eYubzgX0lMbjlVa\nT0Daqzq2YYZqNW67jsdi4+1T3fzXfu3XuslNblLVC17wgulWBADALjHrAWnf6orqolWOP6/a\n1tDQYT1u0jAbtc8qxy+8D3us83HZQD/zMz/TbW8r2wIAzJJZD0hnNDzHY6qTVzH+0dWe1ZfX\n+bjfaDi2abWOrE5NcwgAAJiqPaddwG723urb1Rur46pDlhl3k4bd6/6y4USd792Q6gAAgE1l\n1meQflI9qjqxetW4/KChi93Whl3wDqmuM44/vXpkQ4c7AABgzsx6QKr6dHWb6pcadrU7vO0n\nir2kOqt6X3VS9bbqsumUCQAATNs8BKQaZpJeMy4AAABLmpeAVHVAQ0e7n0ys26N6WMOs0ncb\nZpF+sPGlAQAAm8E8BKTDqhOqn23oEveh6pcbAtF7qp+bGPujhmOQPryxJQIAAJvBPASkt1b3\nqL5Ufa+hpfZfVW9uCEd/Uf1DdUT1rOotDaHqkmkUCwAATM+sB6SfbQhHf1i9YFx31+rj1YHV\nK6pfnxh/RvXK6kHV325cmQAAwGYw6+dBOny8/B8T6z7TEH7u3jCTNOmt4+XtdnNdAADAJjTr\nAemAhuOOzl+0/ozx8sxF6y/a7RUBAACb1qwHpG80dKq7+6L1n204eeyPF61fGPeN3VoVAACw\nKc16QPq7hs50r2s4FmmPcf1bqkd11YB0i+pVDW3AdbEDAIA5NOsB6bzqt6rbV5+oDl1m3GOr\nr1Z3ql5Unbsh1QEAAJvKrHexq/qz6svV06rvLzPmwurU6s+rv96gugAAgE1mHgJS1Snjspz3\njQsAADDHZn0XOwAAgFUTkAAAAEYCEgAAwEhAAgAAGAlIAAAAIwEJAABgJCABAACMBCQAAICR\ngAQAADASkAAAAEYCEgAAwEhAAgAAGAlIAAAAIwEJAABgJCABAACMBCQAAIDR3tMuANixiy++\neOHqydXW8fotqm9P/H1F9azqUxtaHADADBGQ4GrgggsuqOqJT3ziHffff/+2bdvW6173uh7+\n8IcfdOihh1b15je/uYsuuujwBCQAgJ0mIMHVyCMf+ciud73rdfnll/e6172uBz3oQd3xjnes\n6sQTT+yiiy6acoUAAFdvjkECAAAYCUgAAAAjAQkAAGAkIAEAAIwEJAAAgJGABAAAMBKQAAAA\nRgISAADASEACAAAYCUgAAAAjAQkAAGAkIAEAAIwEJAAAgJGABAAAMBKQAAAARgISAADASEAC\nAAAYCUgAAAAjAQkAAGC097QLgE3osOqV1T4T675fPbm6bCoVAQCwIQQk+Gl32HvvvR/6uMc9\nrqof/ehHnXzyyVW/W/2guuYUawMAYDcSkGAJ++yzT894xjOq+tSnPrUQkM6YalEAAOx2AhLs\nwCWXXFLVy172sq51rWt14YUX9vznP3/KVQEAsDsISLBKt7zlLTvwwAM7//zzp10KAAC7iS52\nAAAAIwEJAABgJCABAACMBCQAAICRgAQAADDSxW6+7FVde9G6i6rLplALAABsOvMUkB5QPbS6\nfXX9ar/q4urs6nPVu6pPTK26jfFH1a8vWveu6pFTqAUAADadeQhIN6veXt1jYt3W6tJq3+ru\n1cOr367eWz2p+sEG17hRDrzPfe7TU57ylKre/e53d9JJJx045ZoAAGDTmPVjkLZUJ1dHVC+v\njqwObAhG1x4vD64eWL2+Oro6qRl+XQ488MBuc5vbdJvb3KaDDz542uUAAMCmMuszSA+uDq+e\nXL1hmTHnVR8al89Wr6iOqk7ZgPoAAIBNZGZnSkaHV1dUb17l+NdUV1Z32W0VAQAAm9asB6Qr\nGp7jllWO31Lt0RCSAACAOTPrAenTDYHnuFWOf954Oevd7AAAgCXM+jFIH61OrV5W3at6Z3Va\ndW5DJ7t9q0OqO1VPrI6p3j/eBgAAmDOzHpC2VY+oXls9blxWGntC9ezsYgcAAHNp1gNS1Q+r\nx1S3bpghOrztJ4q9pDqn+nz17urMKdUIAABsAvMQkBacMS4AAABLmqeA9IDqodXt2z6DdHF1\ndvW56l1pzgAAAHNtHgLSzaq3V/eYWLe1urShScPdq4dXv129t3pS9YMNrhEAANgEZr3N95bq\n5OqI6uXVkdWBDcHo2uPlwdUDq9dXR1cnNfuvCwAAsIRZn0F6cENThidXb1hmzHnVh8bls9Ur\nqqOqUzagPgAAYBOZ9YB0eHVF9eZVjn9N9SfVXVpfQLpW9YJqn1WOv9E6HgsAANhFZj0gXdGw\nu9yW6vJVjN9S7dH6z4N0zYaQtd8qxx84Xu6xzscFAADWYdYD0qcbQsdx1R+tYvzzxsv1drP7\nXkPjh9U6sjo1J6gFAICpmvWA9NGG4PGy6l7VO6vTqnMbOtntWx1S3al6YsOJZN8/3gYAAJgz\nsx6QtlWPqF5bPW5cVhp7QvXszOQAAMBcmvWAVPXD6jHVrRtmiA5v+4liL6nOqT5fvbs6c0o1\nMl13bAjGC242rUIAAJiueQhIC84YF1jsAQcccMB/uP/971/VV77ylb7zne9MuSQAAKZhXk6I\nenD189VTqtutMG5Lw252j9qAmthErne96/Wc5zyn5zznOd373veedjkAAEzJPASkh1XfrN7R\nEH6+VL2pOmCJsXs1hKgjNqo4AABg85j1Xez2bzj565bqVdXXqntXT2iYSXpgdf7UqgMAADaV\nWQ9IP1cd2tDC+80T699a/VV14jhm68aXBgAAbDazvovdLRpadv/fRev/T8Ms0v2qV290UbA7\nnH/++VV/1tC58YfV9xt+HAAAYJVmfQbp0mqPap/q4kXbTqqeV/1x9dXqxRtbGuxaV1xxRY96\n1KP2v/Od77x/1QknnNA3v/nNW0+7LgCAq5NZn0H6wnj59GW2v7zhGKUXVc/fkIpgN7rd7W7X\n/e9//+5///t3netcZ9rlAABc7cz6DNKHq09Wf9hwMtDfqb69aMwzx8s/qB60caUBAACbzazP\nIFU9tvrnhvbdhy6xfVv1H6rfqh6wgXXBbnXOOefU8Nn+1MTyH6dZEwDAZjfrM0hV36ruVh1Z\nnb7CuJdWb2to3vCxDagLdqsf//jH3e1ud7vhXe961xtWffjDH+7000//fPW/plwaAMCmNQ8B\nqYZZotWEnq+lWQMz5A53uENPeMITqjrzzDM7/fSVfiMAAGAedrEDAABYFQEJAABgJCABAACM\nBCQAAICRgAQAADASkAAAAEYCEgAAwEhAAgAAGAlIAAAAIwEJAABgJCABAACMBCQAAICRgAQA\nADASkAAAAEYCEgAAwEhAAgAAGO097QJgCq5RHT7x902mVQgAAJuLgMQ8+t3qv0y7CAAANh+7\n2DGP9rvnPe/ZiSee2Iknnth97nOfadcDAMAmYQaJubRly5YOOOCAqvbe238GAAAMzCABAACM\n/HQOc2Lr1q1VN6weNLH6q9U3plEPAMBmJCDBnDj99NOrHjwuCz7YVQMTAMBcs4sdzIkrr7yy\nY445plNOOaVTTjmlpzzlKeVHEgCAqxCQAAAARgISAADASEACAAAYCUgAAAAjB2jDnDr77LOr\n7lR9YGL1P9f/396dh8lV1fkff3en9+4shBAISQghrIEQFlE2CYJsGkSjOICsAvoLiD4/4CfM\ngGOLOqIo/lwwLqAsKirDSFBRGERQRxCIAhEhUZJAIAkJCQlJd6fXmj/OuanqSnfSId11u6ve\nr+epp+ree6rqW7fWT517z+XKVAqSJEkaBAxIKgVfBC7Oma5Nq5DBZOXKlYwdO3aH448//p0A\nixcv5rHHHtsbA5IkSSphBiSVgn0OP/zwHU466SQAvvOd76RczuAxbtw4Lr44ZMcHHniAxx57\nLOWKJEmS0uU+SCoJEyZMYMaMGcyYMYPhw4enXY4kSZIGKQOSJEmSJEUGJEmSJEmKDEiSJEmS\nFBmQJEmSJCkyIEmSJElSZECSJEmSpMiAJEmSJEmRAUmSJEmSIgOSJEmSJEUGJEmSJEmKDEiS\nJEmSFBmQJEmSJCkyIEmSJElSZECSJEmSpMiAJEmSJEmRAUmSJEmSIgOSJEmSJEUVaRdQQO8A\n3gXsD4wFaoAWYDnwDHAv8Hhq1UmSJElKXSkEpEnAXcBhOfPagFagGngLcCpwDfAb4GxgdYFr\nlCRJkjQIFPsmdpXAfcBBwFeBI4GRhGA0Ip6PBo4Dvg+cBPyC4l8vkiRJknpQ7D1IJwJTgXOB\nO3pp8zrwu3h6Cvg6cCzwUAHqkyRJkjSIFHtPyVSgE7izj+2/B2SAgwesIkmSJEmDVrEHpE7C\nY6zsY/tKoIwQkiRJkiSVmGIPSPMIgeeSPra/Mp47mp0kSZJUgop9H6Q/AP8DfBl4G3A38Cyw\nijCSXTWwM3AgcBZwMvBAvI4kSZKkElPsAakLeA9wM3B6PG2p7a3Ax3ATO0mSJKkkFXtAAlgD\nzAL2IvQQTSV7oNiNwApgPvArYGlKNUqSJEkaBEohICX+EU+SJEmS1KNSCkjvAN4F7E+2B6kF\nWA48A9yLgzNIkiRJJa0UAtIk4C7gsJx5bUArYZCGtwCnAtcAvwHOBlYXuEYpdYsWLQLYje77\n4L1G+EPB/fIkSVJJKPaAVAncR9j/6KuEoPQs8EZOmx2AgwjB6ALgF8DRhEEbpJLR3NzMqFGj\nuOaaawBYvHgx3/rWt8YQDgfQmWpxkiRJBVLsAelEwqAM5wJ39NLmdeB38fQU8HXgWOChAtQn\nDSpVVVUceuihmy5LkiSVmmIPSFMJ/3zf2cf23wO+BhzM9gWkXYDvE3qw+mJkPC/bjvuUJEmS\ntJ2KPSB1EjYPqgQ6+tC+khBStnd/i/XAn4HaPrYfT9hHyv08+kcDYb+ZRF1ahUiSJGloKfaA\nNI8QeC4BvtKH9lfG8+0dza4J+Mw2tD+SsA+U+sdvgKPSLkKSJElDT7EHpD8A/wN8GXgbcDdh\nkIZVhJHsqoGdgQOBswgHkn0gXkdD1/Bzzz2Xk046CYBLL7005XIkSZI0VBR7QOoC3gPcDJwe\nT1tqeyvwMdzUbcgbMWIE48aNA6C8vDzlaiRJkjRUFHtAAlgDzCIM9X0yYeCG5ECxG4EVwHzg\nV8DSlGqUJEmSNAiUQkBK/COeJEmSJKlHbnvUXSXwQ0KPkyRJkqQSY0DqbhjwIcKgDZIkSZJK\nTCltYidpG6xbty65eD/ZgUtaCH8irE+jJkmSpIFW7AFp73jqq8qBKkQaal577TUAzjjjjOPL\nyspobm5m7ty5EIbGNyBJkqSiVOwB6Szg02kXIQ1lF110EeXl5axatSoJSJIkSUWr2APSc/H8\nv4An+tC+AvjswJUjSZIkaTAr9oD0U+CDwGHARcDrW2lfgwFJkiRJKlmlMIrdRwhB8Oa0C5Ek\nSZI0uJVCQFoNnAEsZ+sDNmSAVqBjoIuSJEmSNPgU+yZ2id/H09a0Ejaz09ByMrBbzvTotAop\nZplMMtI3ZwAr4+V24E5gYxo1SZIk9bdSCUgqbj8ZPXr0yOrqagBWrFiRcjnFae3atQCMHTv2\ns8OGDQPCus5kMi8Bv02xNEmSpH5jQFIxKL/iiis44ogjADj55JNTLqc4dXV1AfCVr3yF8ePH\nA3DiiSfS0dFRCpvqSpKkEuEPG0mSJEmKDEiSJEmSFBmQJEmSJCkyIEmSJElSZECSJEmSpMiA\nJEmSJEmRAUmSJEmSIgOSJEmSJEUGJEmSJEmKDEiSJEmSFBmQJEmSJCkyIEmSJElSZECSJEmS\npMiAJEmSJElRRdoFSBq6MpkMwF7AmmQWMB9oT6smSZKk7WFAkvSmdXZ2AtyUN/t84LaCFyNJ\nktQPDEiStst1113H9OnTAZg9ezbLli2rTrkkSZKkN82AJGm71NbWMnz4cADKy92tUZIkDW3+\nmpEkSZKkyIAkSZIkSZEBSZIkSZIiA5IkSZIkRQ7SoMFuPPAAUJMzrxk4DliVSkWSJEkqWgYk\nDXa7AlMvu+wyKisraWlpYc6cOQA7Y0AaCnYCdsubt5jsgWUlSZIGFQOShoRTTjmFmpoa1q1b\nlwQkDQ23AKfmzbsdOC+FWiRJkrbKfZAkDaSq97///cydO5e5c+dy8sknA1SlXZQkSVJv7EGS\nNKCqq6s3HUi2srIy5WokSZK2zICkweYoYC7Z3s3eXqMTCIM1gD2hkiRJ6icGJA024+vr63e8\n8sorAXj66ae55557Ni1saWlJLv668KVJkiSp2BmQNOhUVlYyY8YMAFpbW7sta29vB+D6669n\n4sSJAHzoQx8qbIGSJEkqWgYkDUk77bQT48aNA6CsrCzlaiRJklQs3HdDUr9Zs2YNwOeBF+Lp\nmFQLkiRJ2kb2IEnqN21tbZxwwgljpk2bNgbwmFWSJGnIsQdJUr+aNm0aM2fOZObMmVRVecgj\nSZI0tNiDJKlgVq9eDXAY8J2c2X8E7kilIEmSpDwGJEkF88orrzB27Ngp++233xSApUuXsmjR\noukYkCRJ0iBhQJJUUAcccADXXnstAHfeeSeLFi1KuSJJkqQs90GSJEmSpMiAJEmSJEmRm9hJ\nSs3KlSsB9gJ+ljN7IXBtKgVJkqSSZ0CSlJrly5czatSo0UcfffTpyfS8efNWYkCSJEkpMSBJ\nStW4ceO4/PLLAXj44YeZN29eyhVJkqRS5j5IkiRJkhQZkCRJkiQpMiBJkiRJUuQ+SCq0amB8\nzvSpQCOQidNVhS5IkiRJShiQ1N8OIgzbnMgAjwCr4vTngStyrzBhwgQuvPBCAO6//36ef/75\nApQpSZIkbc6ApP724+rq6v2qqkJHUEtLCx0dHZ8DPhWX1x9xxBF87GMfA+BLX/oSbW1tzJgx\nA4Bnn33WgFTCmpubAeqAq3JmrwO+nUpBkiSp5BiQ1N+GXXrppcycOROAq666iieeeGJYboPa\n2lrGjRsHQHV1NW1tbYWvUoPSkiVLqKioaJg+ffr1AE1NTUlgvh1oTrU4SZJUEgxIkgaVhoYG\nbrjhBgAWLFjA7NmzAcpSLUqSJJUMA5IGVHt7O8AuwKFx1k7pVaMh7GCgJV5uA+anWIskSSpi\nBiQNqCVLlgBcEE/SNnn55ZeTi3/IW/TvwKvxcifwU2BDgcqSJElFzICkAZXJZDjrrLO46KKL\nALjgAnOS+q6zsxOA++67j5qaGl555RXOOeccdtppp+sqKsLH14oVK8hkMiuAX/VyM+8FTsmb\ndx8wd4DKliRJQ5gBSdKQ0dXVBcD111/P5MmTAZg5cybNzc1bOuj1+RMmTDhtypQpALzwwgu8\n/PLLYzEgSZKkHhiQtC1GAs8ShmFOdALH4T4hSsnGjRsBfgy058y+EPh5MnH44YdzySWXAHDT\nTTflbrrXm0lA7uiLTWQ36evJH4GpefMuBe7c2h1JkqTBxYCkbTEcGH/11Vez4447AmEY766u\nrg8C+8Y2DWkVp9KUyWT48Ic/3LDffvsBcOONN7J8+fKJ23GTxwG/zZvXAYwG1vdynb3OPvvs\nHaZPnw7AN77xDV566aXdtqMGSZKUEgOScu0CnJc3bw3wvdwZ+++/P+PHjwfCJk+1tbXXJvuD\nrF/f2+9HaeBMmTKFQw8NAyVmMhmAdwK1cfFe23hz9TU1Ndxyyy0ALF26lKuvvroCqKb3gMQe\ne+yxqYb6+vptvEtJkjRYGJCU64Sqqqrrp02bBsCGDRtYsGABwPcJm9L16Lrrrtv0w/DEE08s\nQJlS79asWcPEiRNPHTt27KkAzzzzTLflK1asADgM+FnO7CeAG5KJ8vLyTQczjpvwSZKkElGK\nAakMqAdqCMdVaUq3nEGlbNSoUZsO0jl//nw+8YlPpFyStO1OO+00Zs2aBcAHPvCBbstWrlzJ\nhAkTxr/97W8/HWDhwoXMmzdvH3ICkkrOSCB3oI8mwvG2lK4JwAOE3ttEC2Ez2JWpVCSpJJRK\nQNoFmA28i7Ajde4gA+uBZwgjWn0HeKPg1aWko6MDwn5FyUFcd89dHjdVAjgE6ALGFqg0aUBN\nmjSJiy++GIC77rqLefPm1ZJ9H+yZWmFDz56EcJFrPkMrXJwN3JE370XyPg+VinHAfpdddhmV\nlZW0tLQwZ84cCN9FBiRJA6YUAtKJwH8SgkATsABYBbQS/pXahbC5zVHAFcCphM1tit5zzz0H\nIfw82dPyl156Kbn4eIFKkgouvg/2opf3wZvR0tIC8FbgIzmzHyF8/qRlKnB03ryngT9vx20+\nReiRz3Ub8Kec6QeAJdtxHwNt9IQJE7j22muB0HN+0003jU65JuU45ZRTqKmpYd26dUlAkqQB\nVewBaRTwE2At4V/C+wijUeWrAU4HbiQMDbwPJbDpXVdXF9OnT+erX/0qAKap3AAAE/dJREFU\nEEb/evzxbBaKPUw8+OCDlJeX8/zzz28aKlkqFp2dneyxxx7cfPPNAPzgBz/g7rvv3rS8rW1T\nZ8hXgGSHpEOAv5HtKRmee5uvvvoqw4cPn9XQ0DALYO3atbS0tMwBkjfQ24AP55XyFNCfv/7O\nAmbkTM+oq6vbZ+TI0OGzbt06mpublwG/zGnzLPD1bbiPqhtuuKHbPoj19fXn1dXVnQfw2muv\n0d7e/le6/+l0L70f1Lc/XET40yvXHYSh2HtUVVXF3nvvDcDq1asHrrKB9XFg/7x53wXmpVCL\nJA1pxR6Q3g3sQNi07rEttNtI+AJdQfi38xRCr5OkErdq1SoAjjzyyHMrKytpa2vj0Ucf5ZBD\nDnnL8OEhFz3yyCObXe+MM87gzDPPBOBzn/scDz30UFnO4tNGjx79kWRAlOXLl7Nw4cKFZAPS\nZOCHZPe9GEY4NtMSwuauSZtXyIa0icBqoDlO77377rsPnzRpEgCPP/44xxxzDJ/85CcBuPzy\ny1myZMmu06dP/wjAiy++yJIlS9qAc+P1q4DxwOI4XU7Y7OxFsoO2bPYdcuGFFzJz5kwA3ve+\n9zFy5MiDJ0+efDCEATNef/31D+TcJsCvgU/FywcQRs2sjNOVhP1QlgDJNr8bgFmEETZ7ctme\ne+55YDLS5pNPPklTU9NpQO7Br+4AvtbL9bdmGCHk7Zwzrx24mBCa03LFvvvuu9vOO4eynn76\nadauXbuMwR2Q3gn8B933/3qGzf88kCB8Jv0C2DFnXhtwPrAwjYIGsU8Bp+XNmwPckkItQ1IZ\n2S+dzwCN6ZUyIP6V8Liq+th+GOHNdg1w/Xbc72TCZit9DaAVhH+gq+h+sMv+dnNFRcWFtbVh\n9OOWlhYymQx1dWGXrI0bN9LR0UFDQziUUXt7Oxs3biT5EdjZ2UlzczP19fWUl4fvs/Xr11Nb\nW0vuMN/V1dVUVYVVvmHDBiorK6muDr/zmpqaKC8vZ1traGhooKysjK6uLpqamqirq2PYsGH9\nVkNXV9emoZlbW1tpb2/vtYZMJsOGDRs2q6GmpobKyspN91lVVdWthoqKCmpqagBobm6mrKys\n1xra2tpobW3d6rrflhre7Lr3+d/+57+pqYmurq5WsuGltqKioiapYePGjbS3t3eS3Qcy+UzY\nLrnroS/rvr192z9+tnXdd3V15d9EB9nh0yvp27HU3iAb0oYT1msyPaK6unpYbg05+1Mm2shu\nJVBdXl5el/feyxC2PIDwPTmc7HNTRtg6Id8Gsp/f9fFyEl5rCd+1SQ9kNeH7Jnk9VMZ5G+L0\nsHgbufc5nLCekgczIrZPVujImpqa8tzXYCaTacm5z5p4nkxXEV5nSQ0VsU1SQznhudhaDU1k\n130DYfP1ZD3UxWWtOTWUEQZaSKaT4fgTXcC6nPUwIv+9x5af/3rCek9qqI23mdRQHR9bUkNl\nXBfJ62FYrDt5TeY//8njzl33w+PtJVuopPH8N8TbT2qoi5eTGgbj818VH3uy7isI6ypZ9z3V\n0Jf3ns9/aJ//G/QWQg+7etcIfBqKPyBdCnyT8E9fX3bonAAsjdf71nbcbzlwDH0PSGWEnU5/\ntB332Rfj6L4JRgPhTbQ8TlfFNi/m1DUF+GfOdfbMm55C+Dc4eZPuBrxK9sNoZ8IHR/JG34Hw\n5n8tTtcRPvCWxelKwr/WS7ZSwwtkX7uTCc9b8sEwMd5+8gE4NtaTfOmOJHwYJa+JWmBMvA0I\nz9tEsv9y96WG3Qn/5icfyrsSfmAlH4BjCF8Ur8fpEfF+X43T1YR1lez4VR4f1wt597mlGiYR\nnsvkQ3kc4QM1+dBN9qtI/nn3+Q98/gOf/8Dnf8uP2+ff5z/h8x/szuB//iFsQr0cbUkjMSBB\nWLkZii8cQdgpOUMIHlvrRaonjGTXBew9wHVJkiRJGjwaibmo2PdB+juhJ+gSws7KvyAk6FWE\nlJ2k9gOB9xBS/hdwW1ZJkiSpZBVzDxKE7tGPE7pPM1s4LQTOS6lGSZIkSelppER6kCA80K8D\n3yCMkDSVsE1qDWGHthWEAxs+n1aBkiRJkgaHUghIiQwhCM1PuxBJkiRJg1P51ptIkiRJUmkw\nIEmSJElSZECSJEmSpMiAJEmSJEmRAUmSJEmSIgOSJEmSJEUGJEmSJEmKDEiSJEmSFBmQJEmS\nJCkyIEmSJElSZECSJEmSpMiAJEmSJEmRAUmSJEmSIgOSJEmSJEUGJEmSJEmKDEiSJEmSFFWk\nXYBUou4GZqVdhCRJ2mYzgN+nXYQGjgFJSsdi4FHgsrQLkaJ3A7OBmWkXIkVTgduBtwMtKdci\nAdQCf8DXY9EzIEnpaAPeAOalXYgU7U94Xfqa1GCR7AbwV6ApzUKkqD7tAlQY7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      "text/plain": [
       "Plot with title “'age' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CQAAICJgAQAADARkAAAACYCEgAAwERA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YQGEbrS4YTGKl4hBKWsNYRGEnZmLfDd+KgmNMJwBXAT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\n/5g6LBXmFVoLAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “'income' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oa9SB9ouPDo4Yue1x3bsdey1a7vu/sc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\nDv8AAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “'info_access_use' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AekT1dPqV7WOIL04faNYndNYxS7E6u7NbrFXV09uvrsVhQLAABsrXkPSFWvqD5Y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",
      "text/plain": [
       "Plot with title “'local_knowledge' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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A3shoC05P1jAAAAWNZuCkj3qR5Y3b5DZ5AurT5evaN6TTpnAAB2uYsuuqjq5tWP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      "text/plain": [
       "Plot with title “'social_network' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wAAwKJm/Ryk1fr5hvOPAACAHUxAGhxf3W3aRQAAANM1613sfmacVnJkw4AN/zT+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",
      "text/plain": [
       "Plot with title “'physical_environment' domain histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram visualisation of domain z-scores\n",
    "for( current_domain in unique_domains ) {\n",
    "    domain_scores_filtered <- domain_scores[,current_domain] \n",
    "    domain_scores_filtered[domain_scores_filtered == \"NaN\"] <- 0\n",
    "\n",
    "    title <- paste(\"'\", current_domain, \"' domain histogram\", sep = \"\")\n",
    "    x_label <- paste(\"'\", current_domain, \"' z-score\", sep = \"\")\n",
    "    y_label <- paste(\"Count\", sep = \"\")\n",
    "    hist(domain_scores_filtered, breaks=\"FD\", col=\"grey\", labels=FALSE, main=title, xlab=x_label, ylab=y_label)\n",
    "    box(\"figure\", lwd = 4)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8917dee",
   "metadata": {},
   "source": [
    "### Calculate dimension scores\n",
    "Need to collate the domains into the dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0b6b1a07-abcd-4e93-bf99-c066e3ea0046",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>domain</th><th scope=col>sensitivity</th><th scope=col>prepare</th><th scope=col>respond</th><th scope=col>recover</th><th scope=col>adaptive_capacity</th><th scope=col>enhanced_exposure</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>age   </td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>age   </td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>age   </td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>age   </td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>income</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>income</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & domain & sensitivity & prepare & respond & recover & adaptive\\_capacity & enhanced\\_exposure\\\\\n",
       "  & <chr> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & age    & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\t2 & age    & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\t3 & age    & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\t4 & age    & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\t5 & income & 0 & 1 & 1 & 1 & 1 & 0\\\\\n",
       "\t6 & income & 0 & 1 & 1 & 1 & 1 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 7\n",
       "\n",
       "| <!--/--> | domain &lt;chr&gt; | sensitivity &lt;int&gt; | prepare &lt;int&gt; | respond &lt;int&gt; | recover &lt;int&gt; | adaptive_capacity &lt;int&gt; | enhanced_exposure &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 1 | age    | 1 | 0 | 0 | 0 | 0 | 0 |\n",
       "| 2 | age    | 1 | 0 | 0 | 0 | 0 | 0 |\n",
       "| 3 | age    | 1 | 0 | 0 | 0 | 0 | 0 |\n",
       "| 4 | age    | 1 | 0 | 0 | 0 | 0 | 0 |\n",
       "| 5 | income | 0 | 1 | 1 | 1 | 1 | 0 |\n",
       "| 6 | income | 0 | 1 | 1 | 1 | 1 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "  domain sensitivity prepare respond recover adaptive_capacity\n",
       "1 age    1           0       0       0       0                \n",
       "2 age    1           0       0       0       0                \n",
       "3 age    1           0       0       0       0                \n",
       "4 age    1           0       0       0       0                \n",
       "5 income 0           1       1       1       1                \n",
       "6 income 0           1       1       1       1                \n",
       "  enhanced_exposure\n",
       "1 0                \n",
       "2 0                \n",
       "3 0                \n",
       "4 0                \n",
       "5 0                \n",
       "6 0                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>SEZ2011</th><th scope=col>sensitivity</th><th scope=col>prepare</th><th scope=col>respond</th><th scope=col>recover</th><th scope=col>adaptive_capacity</th><th scope=col>enhanced_exposure</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>151460000001</td><td>-0.7647853635</td><td>-0.696485479</td><td> 0.320951976</td><td> 0.271253971</td><td> 0.320951976</td><td>1.0934219</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>151460000002</td><td>-1.2304165745</td><td> 1.355476641</td><td> 1.575888423</td><td> 1.676990485</td><td> 1.575888423</td><td>1.1258642</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>151460000003</td><td> 0.1046897905</td><td>-2.251790899</td><td>-0.203447695</td><td> 0.312110916</td><td>-0.203447695</td><td>0.9033735</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>151460000004</td><td>-0.4661777348</td><td>-1.478027752</td><td>-2.026764566</td><td>-2.690739562</td><td>-2.026764566</td><td>0.9945343</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>151460000005</td><td> 8.2680954025</td><td>-3.144598085</td><td>-1.038699169</td><td>-0.797332495</td><td>-1.038699169</td><td>1.0451736</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>151460000006</td><td>-0.0003619911</td><td> 0.002318221</td><td> 0.002883523</td><td> 0.002519028</td><td> 0.002883523</td><td>1.1338930</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & SEZ2011 & sensitivity & prepare & respond & recover & adaptive\\_capacity & enhanced\\_exposure\\\\\n",
       "  & <chr> & <dbl{[},1{]}> & <dbl{[},1{]}> & <dbl{[},1{]}> & <dbl{[},1{]}> & <dbl{[},1{]}> & <dbl{[},1{]}>\\\\\n",
       "\\hline\n",
       "\t1 & 151460000001 & -0.7647853635 & -0.696485479 &  0.320951976 &  0.271253971 &  0.320951976 & 1.0934219\\\\\n",
       "\t2 & 151460000002 & -1.2304165745 &  1.355476641 &  1.575888423 &  1.676990485 &  1.575888423 & 1.1258642\\\\\n",
       "\t3 & 151460000003 &  0.1046897905 & -2.251790899 & -0.203447695 &  0.312110916 & -0.203447695 & 0.9033735\\\\\n",
       "\t4 & 151460000004 & -0.4661777348 & -1.478027752 & -2.026764566 & -2.690739562 & -2.026764566 & 0.9945343\\\\\n",
       "\t5 & 151460000005 &  8.2680954025 & -3.144598085 & -1.038699169 & -0.797332495 & -1.038699169 & 1.0451736\\\\\n",
       "\t6 & 151460000006 & -0.0003619911 &  0.002318221 &  0.002883523 &  0.002519028 &  0.002883523 & 1.1338930\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 7\n",
       "\n",
       "| <!--/--> | SEZ2011 &lt;chr&gt; | sensitivity &lt;dbl[,1]&gt; | prepare &lt;dbl[,1]&gt; | respond &lt;dbl[,1]&gt; | recover &lt;dbl[,1]&gt; | adaptive_capacity &lt;dbl[,1]&gt; | enhanced_exposure &lt;dbl[,1]&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 1 | 151460000001 | -0.7647853635 | -0.696485479 |  0.320951976 |  0.271253971 |  0.320951976 | 1.0934219 |\n",
       "| 2 | 151460000002 | -1.2304165745 |  1.355476641 |  1.575888423 |  1.676990485 |  1.575888423 | 1.1258642 |\n",
       "| 3 | 151460000003 |  0.1046897905 | -2.251790899 | -0.203447695 |  0.312110916 | -0.203447695 | 0.9033735 |\n",
       "| 4 | 151460000004 | -0.4661777348 | -1.478027752 | -2.026764566 | -2.690739562 | -2.026764566 | 0.9945343 |\n",
       "| 5 | 151460000005 |  8.2680954025 | -3.144598085 | -1.038699169 | -0.797332495 | -1.038699169 | 1.0451736 |\n",
       "| 6 | 151460000006 | -0.0003619911 |  0.002318221 |  0.002883523 |  0.002519028 |  0.002883523 | 1.1338930 |\n",
       "\n"
      ],
      "text/plain": [
       "  SEZ2011      sensitivity   prepare      respond      recover     \n",
       "1 151460000001 -0.7647853635 -0.696485479  0.320951976  0.271253971\n",
       "2 151460000002 -1.2304165745  1.355476641  1.575888423  1.676990485\n",
       "3 151460000003  0.1046897905 -2.251790899 -0.203447695  0.312110916\n",
       "4 151460000004 -0.4661777348 -1.478027752 -2.026764566 -2.690739562\n",
       "5 151460000005  8.2680954025 -3.144598085 -1.038699169 -0.797332495\n",
       "6 151460000006 -0.0003619911  0.002318221  0.002883523  0.002519028\n",
       "  adaptive_capacity enhanced_exposure\n",
       "1  0.320951976      1.0934219        \n",
       "2  1.575888423      1.1258642        \n",
       "3 -0.203447695      0.9033735        \n",
       "4 -2.026764566      0.9945343        \n",
       "5 -1.038699169      1.0451736        \n",
       "6  0.002883523      1.1338930        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a vector/array of the dimension names\n",
    "dimensions <- c('sensitivity', 'prepare', 'respond', 'recover', 'adaptive_capacity', 'enhanced_exposure')\n",
    "\n",
    "# Get the dimension and their associated indicator ID\n",
    "dimension_indicators <- indicator_mapping %>% select(c('domain', all_of(dimensions)))\n",
    "head(dimension_indicators)\n",
    "\n",
    "# Initialise the dimensions score dataset with the GUID\n",
    "dimension_scores <- indicator_data_weighted %>% select(all_of(GUID))\n",
    "\n",
    "# loop through each of the dimensions and:\n",
    "for (current_dimension in dimensions){\n",
    "    # Identify which indicators are used within this dimension (current_dimension)\n",
    "    # Then select the indicators marked with value 1, which means that the indicator is part of the dimension\n",
    "    current_dimension_info <- dimension_indicators %>% select(c('domain', all_of(current_dimension)))\n",
    "    current_dimension_info <- current_dimension_info %>% filter(dimension_indicators[, current_dimension] == 1)\n",
    "\n",
    "    # Get a array/vector of the unique domains in this dimension\n",
    "    current_dimension_domains <- unique(current_dimension_info$domain)\n",
    "\n",
    "    # Count the number of domains in this dimension\n",
    "    dimension_domain_count <- length(current_dimension_domains)\n",
    "\n",
    "    # Filter the domain scores dataset to only use the domains in the dimension, and add the GUID column name\n",
    "    current_dimension_data <- domain_scores %>% select(c(all_of(GUID), all_of(current_dimension_domains)))  \n",
    "\n",
    "    # Sum each data row to get the total score for the dimension\n",
    "    current_dimension_data[, current_dimension] <- rowSums(current_dimension_data[2:(dimension_domain_count+1)], na.rm = TRUE)\n",
    "\n",
    "    # Add the current dimension score to the overall results\n",
    "    dimension_indicator_score <- current_dimension_data %>% select(all_of(GUID), all_of(current_dimension))\n",
    "    dimension_scores <- merge(dimension_scores, dimension_indicator_score, by=GUID)  \n",
    "}\n",
    "\n",
    "# generate z-scores with the scale function in order to standardise the dimension data\n",
    "dimension_scores <- dimension_scores %>% mutate_if(is.numeric, scale)\n",
    "\n",
    "# Print the first part of the dimension scores, which are now collated into one table\n",
    "head(dimension_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "de22f297-47c5-471d-9fb7-f2528f722353",
   "metadata": {},
   "outputs": [
    {
     "data": {
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BFMjm/swbllo7Pdw6rPzaNYAABgvhY9IFW9oDqrenyjlfeD1hjz6UaIelr14W2r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\nTkSuQmCC",
      "text/plain": [
       "Plot with title “'sensitivity' dimension histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6pzmzrZHdV0ONytq4c2XUj2LeM1AADAgtnpAenq6r7Vc6sHjmlfY19QPS6HugEA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\no2UAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “'prepare' dimension histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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655GsEF1Q9Wn9nQyoCDgoAEbBUrHTAC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",
      "text/plain": [
       "Plot with title “'respond' dimension histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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00aAIA91f0brk10RsNeqUMnntun7dt0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\nJKlg2MVOkiRJkqL/DxADd+AIexs3AAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “'recover' dimension histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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r9smWWt1jWafq1deL4vNW3/32//C3ou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\nkiRJUvTf3l+3sVrcx+4AAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “'adaptive_capacity' dimension histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SxLK+3me6H3OLmezP9/PH9i3OQv/9P+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\n3SbSAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “'enhanced_exposure' dimension histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram visualisation of dimension z-scores\n",
    "for( current_dimension in dimensions ){\n",
    "    dimension_scores_filtered <- dimension_scores[,current_dimension] \n",
    "    dimension_scores_filtered[dimension_scores_filtered == \"NaN\"] <- 0\n",
    "   \n",
    "    title <- paste(\"'\", current_dimension, \"' dimension histogram\", sep = \"\")\n",
    "    x_label <- paste(\"'\", current_dimension, \"' z-score\", sep = \"\")\n",
    "    y_label <- paste(\"Count\", sep = \"\")\n",
    "    hist(dimension_scores_filtered, breaks=\"FD\", col=\"grey\", labels=FALSE, main=title, xlab=x_label, ylab=y_label)\n",
    "    box(\"figure\", lwd = 4)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23966ae2-de19-447d-8cbd-c11cba8bd9f6",
   "metadata": {},
   "source": [
    "## Calculate vulnerability score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a5e64f50-a1d1-47f0-b91c-7da96a9b380e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>SEZ2011</th><th scope=col>social_vulnerability</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>151460000001</td><td> 0.6558794</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>151460000002</td><td> 1.5094801</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>151460000003</td><td> 0.4027326</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>151460000004</td><td>-1.1389091</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>151460000005</td><td> 2.3380431</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>151460000006</td><td> 0.6662274</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & SEZ2011 & social\\_vulnerability\\\\\n",
       "  & <chr> & <dbl{[},1{]}>\\\\\n",
       "\\hline\n",
       "\t1 & 151460000001 &  0.6558794\\\\\n",
       "\t2 & 151460000002 &  1.5094801\\\\\n",
       "\t3 & 151460000003 &  0.4027326\\\\\n",
       "\t4 & 151460000004 & -1.1389091\\\\\n",
       "\t5 & 151460000005 &  2.3380431\\\\\n",
       "\t6 & 151460000006 &  0.6662274\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| <!--/--> | SEZ2011 &lt;chr&gt; | social_vulnerability &lt;dbl[,1]&gt; |\n",
       "|---|---|---|\n",
       "| 1 | 151460000001 |  0.6558794 |\n",
       "| 2 | 151460000002 |  1.5094801 |\n",
       "| 3 | 151460000003 |  0.4027326 |\n",
       "| 4 | 151460000004 | -1.1389091 |\n",
       "| 5 | 151460000005 |  2.3380431 |\n",
       "| 6 | 151460000006 |  0.6662274 |\n",
       "\n"
      ],
      "text/plain": [
       "  SEZ2011      social_vulnerability\n",
       "1 151460000001  0.6558794          \n",
       "2 151460000002  1.5094801          \n",
       "3 151460000003  0.4027326          \n",
       "4 151460000004 -1.1389091          \n",
       "5 151460000005  2.3380431          \n",
       "6 151460000006  0.6662274          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialise the vulnerability score dataset with the GUID\n",
    "vulnerability_scores <- domain_scores %>% select(all_of(GUID))\n",
    "\n",
    "#sum the domains to create a total overall score of vulnerability\n",
    "vulnerability_scores$social_vulnerability <- rowSums(domain_scores[2:(ncol(domain_scores))], na.rm = TRUE)\n",
    "\n",
    "# generate z-scores with the scale function in order to standardise the vulnerability data\n",
    "vulnerability_scores <- vulnerability_scores %>% mutate_if(is.numeric, scale)\n",
    "\n",
    "# Print the first part of the vulnerability scores, which are now collated into one table\n",
    "head(vulnerability_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a6ad33d-3988-4166-b078-9734c25f180d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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C3pkQH2Pqc1/t0yBwmAefKQFs+BtVHz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\nzfdY08v3pnv9yWPYOf59C/ldH5fvz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      "text/plain": [
       "Plot with title “'Vulnerability' histogram”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram visualisation of Vulnerability z-scores\n",
    "title <- paste(\"'Vulnerability' histogram\", sep = \"\")\n",
    "x_label <- paste(\"'Vulnerability' z-score\", sep = \"\")\n",
    "y_label <- paste(\"Count\", sep = \"\")\n",
    "hist(vulnerability_scores$social_vulnerability, breaks=\"FD\", col=\"grey\", labels=FALSE, main=title, xlab=x_label, ylab=y_label)\n",
    "box(\"figure\", lwd = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "26307191-f418-4f79-a734-637fe8e70cc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 26</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>SEZ2011</th><th scope=col>early_childhood_boy</th><th scope=col>early_childhood_girl</th><th scope=col>age_middle_to_oldest_old_male</th><th scope=col>age_middle_to_oldest_old_female</th><th scope=col>dependants</th><th scope=col>unemployment</th><th scope=col>no_higher_education</th><th scope=col>foreign_nationals</th><th scope=col>primary_school_age</th><th scope=col>⋯</th><th scope=col>local_knowledge</th><th scope=col>social_network</th><th scope=col>physical_environment</th><th scope=col>sensitivity</th><th scope=col>prepare</th><th scope=col>respond</th><th scope=col>recover</th><th scope=col>adaptive_capacity</th><th scope=col>enhanced_exposure</th><th scope=col>social_vulnerability</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>151460000001</td><td>-1.230411</td><td>-1.064144</td><td> 2.1196292</td><td>-1.3062152</td><td>-0.7801407</td><td>-0.5060886</td><td>-0.3037669</td><td> 0.142430939</td><td> 1.458348</td><td>⋯</td><td> 0.142430939</td><td> 1.1958366</td><td>1.0118090</td><td>-0.7647853635</td><td>-0.696485479</td><td> 0.320951976</td><td> 0.271253971</td><td> 0.320951976</td><td>1.0934219</td><td> 0.6558794</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>151460000002</td><td>-1.230411</td><td>-1.064144</td><td> 0.2302043</td><td>-0.3189938</td><td> 0.1197864</td><td> 1.3419462</td><td> 0.4424940</td><td> 0.384372102</td><td> 1.458348</td><td>⋯</td><td> 0.384372102</td><td> 0.3778636</td><td>1.0418299</td><td>-1.2304165745</td><td> 1.355476641</td><td> 1.575888423</td><td> 1.676990485</td><td> 1.575888423</td><td>1.1258642</td><td> 1.5094801</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>151460000003</td><td>-1.230411</td><td>-1.064144</td><td> 3.8043177</td><td>-1.3062152</td><td>-2.2200240</td><td>-0.2425543</td><td>-0.8229049</td><td>-0.540697051</td><td> 1.458348</td><td>⋯</td><td>-0.540697051</td><td> 2.3409988</td><td>0.8359458</td><td> 0.1046897905</td><td>-2.251790899</td><td>-0.203447695</td><td> 0.312110916</td><td>-0.203447695</td><td>0.9033735</td><td> 0.4027326</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>151460000004</td><td>-1.230411</td><td>-1.064144</td><td> 2.6982091</td><td>-1.3062152</td><td>-0.5183437</td><td>-1.2391934</td><td>-0.8229049</td><td>-0.002474999</td><td>-1.865648</td><td>⋯</td><td>-0.002474999</td><td>-0.7933508</td><td>0.9203024</td><td>-0.4661777348</td><td>-1.478027752</td><td>-2.026764566</td><td>-2.690739562</td><td>-2.026764566</td><td>0.9945343</td><td>-1.1389091</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>151460000005</td><td>-1.230411</td><td>-1.064144</td><td>19.6216703</td><td>-1.3062152</td><td>-2.2200240</td><td>-2.2981224</td><td>-0.8229049</td><td>-0.540697051</td><td> 1.458348</td><td>⋯</td><td>-0.540697051</td><td> 2.3409988</td><td>0.9671620</td><td> 8.2680954025</td><td>-3.144598085</td><td>-1.038699169</td><td>-0.797332495</td><td>-1.038699169</td><td>1.0451736</td><td> 2.3380431</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>151460000006</td><td> 0.000000</td><td> 0.000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.000000000</td><td> 0.000000</td><td>⋯</td><td> 0.000000000</td><td> 0.0000000</td><td>1.0492594</td><td>-0.0003619911</td><td> 0.002318221</td><td> 0.002883523</td><td> 0.002519028</td><td> 0.002883523</td><td>1.1338930</td><td> 0.6662274</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 26\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & SEZ2011 & early\\_childhood\\_boy & early\\_childhood\\_girl & age\\_middle\\_to\\_oldest\\_old\\_male & age\\_middle\\_to\\_oldest\\_old\\_female & dependants & unemployment & no\\_higher\\_education & foreign\\_nationals & primary\\_school\\_age & ⋯ & local\\_knowledge & social\\_network & physical\\_environment & sensitivity & prepare & respond & recover & adaptive\\_capacity & enhanced\\_exposure & social\\_vulnerability\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl{[},1{]}> & <dbl{[},1{]}> & <dbl{[},1{]}> & <dbl{[},1{]}> & <dbl{[},1{]}> & <dbl{[},1{]}> & <dbl{[},1{]}>\\\\\n",
       "\\hline\n",
       "\t1 & 151460000001 & -1.230411 & -1.064144 &  2.1196292 & -1.3062152 & -0.7801407 & -0.5060886 & -0.3037669 &  0.142430939 &  1.458348 & ⋯ &  0.142430939 &  1.1958366 & 1.0118090 & -0.7647853635 & -0.696485479 &  0.320951976 &  0.271253971 &  0.320951976 & 1.0934219 &  0.6558794\\\\\n",
       "\t2 & 151460000002 & -1.230411 & -1.064144 &  0.2302043 & -0.3189938 &  0.1197864 &  1.3419462 &  0.4424940 &  0.384372102 &  1.458348 & ⋯ &  0.384372102 &  0.3778636 & 1.0418299 & -1.2304165745 &  1.355476641 &  1.575888423 &  1.676990485 &  1.575888423 & 1.1258642 &  1.5094801\\\\\n",
       "\t3 & 151460000003 & -1.230411 & -1.064144 &  3.8043177 & -1.3062152 & -2.2200240 & -0.2425543 & -0.8229049 & -0.540697051 &  1.458348 & ⋯ & -0.540697051 &  2.3409988 & 0.8359458 &  0.1046897905 & -2.251790899 & -0.203447695 &  0.312110916 & -0.203447695 & 0.9033735 &  0.4027326\\\\\n",
       "\t4 & 151460000004 & -1.230411 & -1.064144 &  2.6982091 & -1.3062152 & -0.5183437 & -1.2391934 & -0.8229049 & -0.002474999 & -1.865648 & ⋯ & -0.002474999 & -0.7933508 & 0.9203024 & -0.4661777348 & -1.478027752 & -2.026764566 & -2.690739562 & -2.026764566 & 0.9945343 & -1.1389091\\\\\n",
       "\t5 & 151460000005 & -1.230411 & -1.064144 & 19.6216703 & -1.3062152 & -2.2200240 & -2.2981224 & -0.8229049 & -0.540697051 &  1.458348 & ⋯ & -0.540697051 &  2.3409988 & 0.9671620 &  8.2680954025 & -3.144598085 & -1.038699169 & -0.797332495 & -1.038699169 & 1.0451736 &  2.3380431\\\\\n",
       "\t6 & 151460000006 &  0.000000 &  0.000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.000000000 &  0.000000 & ⋯ &  0.000000000 &  0.0000000 & 1.0492594 & -0.0003619911 &  0.002318221 &  0.002883523 &  0.002519028 &  0.002883523 & 1.1338930 &  0.6662274\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 26\n",
       "\n",
       "| <!--/--> | SEZ2011 &lt;chr&gt; | early_childhood_boy &lt;dbl&gt; | early_childhood_girl &lt;dbl&gt; | age_middle_to_oldest_old_male &lt;dbl&gt; | age_middle_to_oldest_old_female &lt;dbl&gt; | dependants &lt;dbl&gt; | unemployment &lt;dbl&gt; | no_higher_education &lt;dbl&gt; | foreign_nationals &lt;dbl&gt; | primary_school_age &lt;dbl&gt; | ⋯ ⋯ | local_knowledge &lt;dbl&gt; | social_network &lt;dbl&gt; | physical_environment &lt;dbl&gt; | sensitivity &lt;dbl[,1]&gt; | prepare &lt;dbl[,1]&gt; | respond &lt;dbl[,1]&gt; | recover &lt;dbl[,1]&gt; | adaptive_capacity &lt;dbl[,1]&gt; | enhanced_exposure &lt;dbl[,1]&gt; | social_vulnerability &lt;dbl[,1]&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 151460000001 | -1.230411 | -1.064144 |  2.1196292 | -1.3062152 | -0.7801407 | -0.5060886 | -0.3037669 |  0.142430939 |  1.458348 | ⋯ |  0.142430939 |  1.1958366 | 1.0118090 | -0.7647853635 | -0.696485479 |  0.320951976 |  0.271253971 |  0.320951976 | 1.0934219 |  0.6558794 |\n",
       "| 2 | 151460000002 | -1.230411 | -1.064144 |  0.2302043 | -0.3189938 |  0.1197864 |  1.3419462 |  0.4424940 |  0.384372102 |  1.458348 | ⋯ |  0.384372102 |  0.3778636 | 1.0418299 | -1.2304165745 |  1.355476641 |  1.575888423 |  1.676990485 |  1.575888423 | 1.1258642 |  1.5094801 |\n",
       "| 3 | 151460000003 | -1.230411 | -1.064144 |  3.8043177 | -1.3062152 | -2.2200240 | -0.2425543 | -0.8229049 | -0.540697051 |  1.458348 | ⋯ | -0.540697051 |  2.3409988 | 0.8359458 |  0.1046897905 | -2.251790899 | -0.203447695 |  0.312110916 | -0.203447695 | 0.9033735 |  0.4027326 |\n",
       "| 4 | 151460000004 | -1.230411 | -1.064144 |  2.6982091 | -1.3062152 | -0.5183437 | -1.2391934 | -0.8229049 | -0.002474999 | -1.865648 | ⋯ | -0.002474999 | -0.7933508 | 0.9203024 | -0.4661777348 | -1.478027752 | -2.026764566 | -2.690739562 | -2.026764566 | 0.9945343 | -1.1389091 |\n",
       "| 5 | 151460000005 | -1.230411 | -1.064144 | 19.6216703 | -1.3062152 | -2.2200240 | -2.2981224 | -0.8229049 | -0.540697051 |  1.458348 | ⋯ | -0.540697051 |  2.3409988 | 0.9671620 |  8.2680954025 | -3.144598085 | -1.038699169 | -0.797332495 | -1.038699169 | 1.0451736 |  2.3380431 |\n",
       "| 6 | 151460000006 |  0.000000 |  0.000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.000000000 |  0.000000 | ⋯ |  0.000000000 |  0.0000000 | 1.0492594 | -0.0003619911 |  0.002318221 |  0.002883523 |  0.002519028 |  0.002883523 | 1.1338930 |  0.6662274 |\n",
       "\n"
      ],
      "text/plain": [
       "  SEZ2011      early_childhood_boy early_childhood_girl\n",
       "1 151460000001 -1.230411           -1.064144           \n",
       "2 151460000002 -1.230411           -1.064144           \n",
       "3 151460000003 -1.230411           -1.064144           \n",
       "4 151460000004 -1.230411           -1.064144           \n",
       "5 151460000005 -1.230411           -1.064144           \n",
       "6 151460000006  0.000000            0.000000           \n",
       "  age_middle_to_oldest_old_male age_middle_to_oldest_old_female dependants\n",
       "1  2.1196292                    -1.3062152                      -0.7801407\n",
       "2  0.2302043                    -0.3189938                       0.1197864\n",
       "3  3.8043177                    -1.3062152                      -2.2200240\n",
       "4  2.6982091                    -1.3062152                      -0.5183437\n",
       "5 19.6216703                    -1.3062152                      -2.2200240\n",
       "6  0.0000000                     0.0000000                       0.0000000\n",
       "  unemployment no_higher_education foreign_nationals primary_school_age ⋯\n",
       "1 -0.5060886   -0.3037669           0.142430939       1.458348          ⋯\n",
       "2  1.3419462    0.4424940           0.384372102       1.458348          ⋯\n",
       "3 -0.2425543   -0.8229049          -0.540697051       1.458348          ⋯\n",
       "4 -1.2391934   -0.8229049          -0.002474999      -1.865648          ⋯\n",
       "5 -2.2981224   -0.8229049          -0.540697051       1.458348          ⋯\n",
       "6  0.0000000    0.0000000           0.000000000       0.000000          ⋯\n",
       "  local_knowledge social_network physical_environment sensitivity  \n",
       "1  0.142430939     1.1958366     1.0118090            -0.7647853635\n",
       "2  0.384372102     0.3778636     1.0418299            -1.2304165745\n",
       "3 -0.540697051     2.3409988     0.8359458             0.1046897905\n",
       "4 -0.002474999    -0.7933508     0.9203024            -0.4661777348\n",
       "5 -0.540697051     2.3409988     0.9671620             8.2680954025\n",
       "6  0.000000000     0.0000000     1.0492594            -0.0003619911\n",
       "  prepare      respond      recover      adaptive_capacity enhanced_exposure\n",
       "1 -0.696485479  0.320951976  0.271253971  0.320951976      1.0934219        \n",
       "2  1.355476641  1.575888423  1.676990485  1.575888423      1.1258642        \n",
       "3 -2.251790899 -0.203447695  0.312110916 -0.203447695      0.9033735        \n",
       "4 -1.478027752 -2.026764566 -2.690739562 -2.026764566      0.9945343        \n",
       "5 -3.144598085 -1.038699169 -0.797332495 -1.038699169      1.0451736        \n",
       "6  0.002318221  0.002883523  0.002519028  0.002883523      1.1338930        \n",
       "  social_vulnerability\n",
       "1  0.6558794          \n",
       "2  1.5094801          \n",
       "3  0.4027326          \n",
       "4 -1.1389091          \n",
       "5  2.3380431          \n",
       "6  0.6662274          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Merge all the indicators, domains, dimensions, and total vulnerability into one dataset\n",
    "output_dataset <- merge(indicator_data_weighted, domain_scores, by=GUID)\n",
    "output_dataset <- merge(output_dataset, dimension_scores, by=GUID)\n",
    "output_dataset <- merge(output_dataset, vulnerability_scores, by=GUID)\n",
    "\n",
    "head(output_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "228968f5-50a5-442a-9344-f4857f11086e",
   "metadata": {},
   "source": [
    "## Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "752d49bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 25 × 25 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>early_childhood_boy</th><th scope=col>early_childhood_girl</th><th scope=col>age_middle_to_oldest_old_male</th><th scope=col>age_middle_to_oldest_old_female</th><th scope=col>dependants</th><th scope=col>unemployment</th><th scope=col>no_higher_education</th><th scope=col>foreign_nationals</th><th scope=col>primary_school_age</th><th scope=col>one_person_households</th><th scope=col>⋯</th><th scope=col>local_knowledge</th><th scope=col>social_network</th><th scope=col>physical_environment</th><th scope=col>sensitivity</th><th scope=col>prepare</th><th scope=col>respond</th><th scope=col>recover</th><th scope=col>adaptive_capacity</th><th scope=col>enhanced_exposure</th><th scope=col>social_vulnerability</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>early_childhood_boy</th><td> 1.00000000</td><td> 0.229097528</td><td>-0.14133786</td><td>-0.152371506</td><td> 0.479381856</td><td>-0.06813950</td><td>-0.05906216</td><td> 0.06968821</td><td>-0.211541089</td><td>-0.053140400</td><td>⋯</td><td> 0.06968821</td><td>-0.17126794</td><td> 0.041952673</td><td> 0.468965905</td><td> 0.177996543</td><td> 0.06209890</td><td> 0.046053168</td><td> 0.06209890</td><td> 0.041952673</td><td> 0.21671079</td></tr>\n",
       "\t<tr><th scope=row>early_childhood_girl</th><td> 0.22909753</td><td> 1.000000000</td><td>-0.12233710</td><td>-0.115415283</td><td> 0.447880059</td><td>-0.09983243</td><td>-0.07541606</td><td> 0.02251899</td><td>-0.131044555</td><td>-0.087782855</td><td>⋯</td><td> 0.02251899</td><td>-0.14149308</td><td> 0.012169251</td><td> 0.497075108</td><td> 0.124612566</td><td> 0.03031025</td><td> 0.028487985</td><td> 0.03031025</td><td> 0.012169251</td><td> 0.18312793</td></tr>\n",
       "\t<tr><th scope=row>age_middle_to_oldest_old_male</th><td>-0.14133786</td><td>-0.122337104</td><td> 1.00000000</td><td> 0.291926773</td><td>-0.219142202</td><td>-0.20364703</td><td> 0.01344422</td><td>-0.22114688</td><td> 0.156731495</td><td>-0.058133618</td><td>⋯</td><td>-0.22114688</td><td> 0.06398469</td><td>-0.130727845</td><td> 0.515454373</td><td>-0.265588094</td><td>-0.20945491</td><td>-0.162603657</td><td>-0.20945491</td><td>-0.130727845</td><td>-0.08165958</td></tr>\n",
       "\t<tr><th scope=row>age_middle_to_oldest_old_female</th><td>-0.15237151</td><td>-0.115415283</td><td> 0.29192677</td><td> 1.000000000</td><td>-0.235062655</td><td>-0.23129506</td><td> 0.23646828</td><td>-0.25730892</td><td> 0.166348557</td><td>-0.001316863</td><td>⋯</td><td>-0.25730892</td><td> 0.10686905</td><td>-0.133976721</td><td> 0.513279034</td><td>-0.205041719</td><td>-0.12666491</td><td>-0.033731192</td><td>-0.12666491</td><td>-0.133976721</td><td>-0.01975176</td></tr>\n",
       "\t<tr><th scope=row>dependants</th><td> 0.47938186</td><td> 0.447880059</td><td>-0.21914220</td><td>-0.235062655</td><td> 1.000000000</td><td>-0.08266693</td><td> 0.11096832</td><td> 0.08006488</td><td>-0.735608824</td><td>-0.291803934</td><td>⋯</td><td> 0.08006488</td><td>-0.66460705</td><td>-0.024206633</td><td> 0.237217276</td><td> 0.467697674</td><td> 0.03233285</td><td> 0.001091004</td><td> 0.03233285</td><td>-0.024206633</td><td> 0.08373068</td></tr>\n",
       "\t<tr><th scope=row>unemployment</th><td>-0.06813950</td><td>-0.099832433</td><td>-0.20364703</td><td>-0.231295057</td><td>-0.082666926</td><td> 1.00000000</td><td> 0.10782418</td><td> 0.33788259</td><td> 0.083462147</td><td> 0.010301782</td><td>⋯</td><td> 0.33788259</td><td> 0.06069221</td><td>-0.046306921</td><td>-0.302233026</td><td> 0.573835450</td><td> 0.57384691</td><td> 0.585589861</td><td> 0.57384691</td><td>-0.046306921</td><td> 0.32713626</td></tr>\n",
       "\t<tr><th scope=row>no_higher_education</th><td>-0.05906216</td><td>-0.075416063</td><td> 0.01344422</td><td> 0.236468283</td><td> 0.110968315</td><td> 0.10782418</td><td> 1.00000000</td><td> 0.26173870</td><td>-0.045445576</td><td>-0.029993040</td><td>⋯</td><td> 0.26173870</td><td>-0.04877900</td><td>-0.128676167</td><td> 0.057830642</td><td> 0.624357416</td><td> 0.55436679</td><td> 0.599521006</td><td> 0.55436679</td><td>-0.128676167</td><td> 0.37411283</td></tr>\n",
       "\t<tr><th scope=row>foreign_nationals</th><td> 0.06968821</td><td> 0.022518991</td><td>-0.22114688</td><td>-0.257308923</td><td> 0.080064879</td><td> 0.33788259</td><td> 0.26173870</td><td> 1.00000000</td><td>-0.074132287</td><td> 0.291800349</td><td>⋯</td><td> 1.00000000</td><td> 0.14025860</td><td> 0.058567612</td><td>-0.193601159</td><td> 0.707212143</td><td> 0.74713737</td><td> 0.469628941</td><td> 0.74713737</td><td> 0.058567612</td><td> 0.55682335</td></tr>\n",
       "\t<tr><th scope=row>primary_school_age</th><td>-0.21154109</td><td>-0.131044555</td><td> 0.15673150</td><td> 0.166348557</td><td>-0.735608824</td><td> 0.08346215</td><td>-0.04544558</td><td>-0.07413229</td><td> 1.000000000</td><td> 0.196787249</td><td>⋯</td><td>-0.07413229</td><td> 0.77451044</td><td> 0.003812838</td><td>-0.009809013</td><td>-0.325644138</td><td> 0.16757171</td><td> 0.261335750</td><td> 0.16757171</td><td> 0.003812838</td><td> 0.12973201</td></tr>\n",
       "\t<tr><th scope=row>one_person_households</th><td>-0.05314040</td><td>-0.087782855</td><td>-0.05813362</td><td>-0.001316863</td><td>-0.291803934</td><td> 0.01030178</td><td>-0.02999304</td><td> 0.29180035</td><td> 0.196787249</td><td> 1.000000000</td><td>⋯</td><td> 0.29180035</td><td> 0.77260594</td><td> 0.284224811</td><td>-0.100459534</td><td>-0.008700115</td><td> 0.46292243</td><td> 0.462342309</td><td> 0.46292243</td><td> 0.284224811</td><td> 0.49619186</td></tr>\n",
       "\t<tr><th scope=row>tree_cover_density</th><td> 0.04948625</td><td> 0.013555773</td><td>-0.11879223</td><td>-0.119645043</td><td>-0.001507900</td><td>-0.03736943</td><td>-0.08206047</td><td> 0.05235133</td><td>-0.012114734</td><td> 0.218214737</td><td>⋯</td><td> 0.05235133</td><td> 0.13294221</td><td> 0.925360125</td><td>-0.087913902</td><td>-0.028978401</td><td> 0.05394525</td><td> 0.044286154</td><td> 0.05394525</td><td> 0.925360125</td><td> 0.55702025</td></tr>\n",
       "\t<tr><th scope=row>impervious</th><td> 0.02815641</td><td> 0.008966105</td><td>-0.12314844</td><td>-0.128308388</td><td>-0.043291805</td><td>-0.04833173</td><td>-0.15608312</td><td> 0.05604093</td><td> 0.019171231</td><td> 0.307805877</td><td>⋯</td><td> 0.05604093</td><td> 0.21100349</td><td> 0.925360125</td><td>-0.107434095</td><td>-0.080913361</td><td> 0.05295268</td><td> 0.041038921</td><td> 0.05295268</td><td> 0.925360125</td><td> 0.55026267</td></tr>\n",
       "\t<tr><th scope=row>age</th><td> 0.46896590</td><td> 0.497075108</td><td> 0.51545437</td><td> 0.513279034</td><td> 0.237217276</td><td>-0.30223303</td><td> 0.05783064</td><td>-0.19360116</td><td>-0.009809013</td><td>-0.100459534</td><td>⋯</td><td>-0.19360116</td><td>-0.07116596</td><td>-0.105552418</td><td> 1.000000000</td><td>-0.084199177</td><td>-0.12216143</td><td>-0.061054149</td><td>-0.12216143</td><td>-0.105552418</td><td> 0.14962341</td></tr>\n",
       "\t<tr><th scope=row>income</th><td> 0.30405399</td><td> 0.257398729</td><td>-0.31214939</td><td>-0.344305665</td><td> 0.678122645</td><td> 0.67637489</td><td> 0.16153269</td><td> 0.30835420</td><td>-0.482128040</td><td>-0.208071286</td><td>⋯</td><td> 0.30835420</td><td>-0.44644375</td><td>-0.052040916</td><td>-0.047564415</td><td> 0.768857522</td><td> 0.44709337</td><td> 0.432663034</td><td> 0.44709337</td><td>-0.052040916</td><td> 0.30313843</td></tr>\n",
       "\t<tr><th scope=row>info_access_use</th><td>-0.05906216</td><td>-0.075416063</td><td> 0.01344422</td><td> 0.236468283</td><td> 0.110968315</td><td> 0.10782418</td><td> 1.00000000</td><td> 0.26173870</td><td>-0.045445576</td><td>-0.029993040</td><td>⋯</td><td> 0.26173870</td><td>-0.04877900</td><td>-0.128676167</td><td> 0.057830642</td><td> 0.624357416</td><td> 0.55436679</td><td> 0.599521006</td><td> 0.55436679</td><td>-0.128676167</td><td> 0.37411283</td></tr>\n",
       "\t<tr><th scope=row>local_knowledge</th><td> 0.06968821</td><td> 0.022518991</td><td>-0.22114688</td><td>-0.257308923</td><td> 0.080064879</td><td> 0.33788259</td><td> 0.26173870</td><td> 1.00000000</td><td>-0.074132287</td><td> 0.291800349</td><td>⋯</td><td> 1.00000000</td><td> 0.14025860</td><td> 0.058567612</td><td>-0.193601159</td><td> 0.707212143</td><td> 0.74713737</td><td> 0.469628941</td><td> 0.74713737</td><td> 0.058567612</td><td> 0.55682335</td></tr>\n",
       "\t<tr><th scope=row>social_network</th><td>-0.17126794</td><td>-0.141493083</td><td> 0.06398469</td><td> 0.106869046</td><td>-0.664607054</td><td> 0.06069221</td><td>-0.04877900</td><td> 0.14025860</td><td> 0.774510442</td><td> 0.772605938</td><td>⋯</td><td> 0.14025860</td><td> 1.00000000</td><td> 0.185844241</td><td>-0.071165963</td><td>-0.216483284</td><td> 0.40717750</td><td> 0.467519929</td><td> 0.40717750</td><td> 0.185844241</td><td> 0.40413915</td></tr>\n",
       "\t<tr><th scope=row>physical_environment</th><td> 0.04195267</td><td> 0.012169251</td><td>-0.13072785</td><td>-0.133976721</td><td>-0.024206633</td><td>-0.04630692</td><td>-0.12867617</td><td> 0.05856761</td><td> 0.003812838</td><td> 0.284224811</td><td>⋯</td><td> 0.05856761</td><td> 0.18584424</td><td> 1.000000000</td><td>-0.105552418</td><td>-0.059377835</td><td> 0.05776018</td><td> 0.046103713</td><td> 0.05776018</td><td> 1.000000000</td><td> 0.59829838</td></tr>\n",
       "\t<tr><th scope=row>sensitivity</th><td> 0.46896590</td><td> 0.497075108</td><td> 0.51545437</td><td> 0.513279034</td><td> 0.237217276</td><td>-0.30223303</td><td> 0.05783064</td><td>-0.19360116</td><td>-0.009809013</td><td>-0.100459534</td><td>⋯</td><td>-0.19360116</td><td>-0.07116596</td><td>-0.105552418</td><td> 1.000000000</td><td>-0.084199177</td><td>-0.12216143</td><td>-0.061054149</td><td>-0.12216143</td><td>-0.105552418</td><td> 0.14962341</td></tr>\n",
       "\t<tr><th scope=row>prepare</th><td> 0.17799654</td><td> 0.124612566</td><td>-0.26558809</td><td>-0.205041719</td><td> 0.467697674</td><td> 0.57383545</td><td> 0.62435742</td><td> 0.70721214</td><td>-0.325644138</td><td>-0.008700115</td><td>⋯</td><td> 0.70721214</td><td>-0.21648328</td><td>-0.059377835</td><td>-0.084199177</td><td> 1.000000000</td><td> 0.80354306</td><td> 0.697613287</td><td> 0.80354306</td><td>-0.059377835</td><td> 0.56521567</td></tr>\n",
       "\t<tr><th scope=row>respond</th><td> 0.06209890</td><td> 0.030310255</td><td>-0.20945491</td><td>-0.126664909</td><td> 0.032332845</td><td> 0.57384691</td><td> 0.55436679</td><td> 0.74713737</td><td> 0.167571709</td><td> 0.462922431</td><td>⋯</td><td> 0.74713737</td><td> 0.40717750</td><td> 0.057760176</td><td>-0.122161434</td><td> 0.803543060</td><td> 1.00000000</td><td> 0.937690173</td><td> 1.00000000</td><td> 0.057760176</td><td> 0.77518413</td></tr>\n",
       "\t<tr><th scope=row>recover</th><td> 0.04605317</td><td> 0.028487985</td><td>-0.16260366</td><td>-0.033731192</td><td> 0.001091004</td><td> 0.58558986</td><td> 0.59952101</td><td> 0.46962894</td><td> 0.261335750</td><td> 0.462342309</td><td>⋯</td><td> 0.46962894</td><td> 0.46751993</td><td> 0.046103713</td><td>-0.061054149</td><td> 0.697613287</td><td> 0.93769017</td><td> 1.000000000</td><td> 0.93769017</td><td> 0.046103713</td><td> 0.73856429</td></tr>\n",
       "\t<tr><th scope=row>adaptive_capacity</th><td> 0.06209890</td><td> 0.030310255</td><td>-0.20945491</td><td>-0.126664909</td><td> 0.032332845</td><td> 0.57384691</td><td> 0.55436679</td><td> 0.74713737</td><td> 0.167571709</td><td> 0.462922431</td><td>⋯</td><td> 0.74713737</td><td> 0.40717750</td><td> 0.057760176</td><td>-0.122161434</td><td> 0.803543060</td><td> 1.00000000</td><td> 0.937690173</td><td> 1.00000000</td><td> 0.057760176</td><td> 0.77518413</td></tr>\n",
       "\t<tr><th scope=row>enhanced_exposure</th><td> 0.04195267</td><td> 0.012169251</td><td>-0.13072785</td><td>-0.133976721</td><td>-0.024206633</td><td>-0.04630692</td><td>-0.12867617</td><td> 0.05856761</td><td> 0.003812838</td><td> 0.284224811</td><td>⋯</td><td> 0.05856761</td><td> 0.18584424</td><td> 1.000000000</td><td>-0.105552418</td><td>-0.059377835</td><td> 0.05776018</td><td> 0.046103713</td><td> 0.05776018</td><td> 1.000000000</td><td> 0.59829838</td></tr>\n",
       "\t<tr><th scope=row>social_vulnerability</th><td> 0.21671079</td><td> 0.183127935</td><td>-0.08165958</td><td>-0.019751765</td><td> 0.083730678</td><td> 0.32713626</td><td> 0.37411283</td><td> 0.55682335</td><td> 0.129732006</td><td> 0.496191862</td><td>⋯</td><td> 0.55682335</td><td> 0.40413915</td><td> 0.598298378</td><td> 0.149623413</td><td> 0.565215672</td><td> 0.77518413</td><td> 0.738564294</td><td> 0.77518413</td><td> 0.598298378</td><td> 1.00000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 25 × 25 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & early\\_childhood\\_boy & early\\_childhood\\_girl & age\\_middle\\_to\\_oldest\\_old\\_male & age\\_middle\\_to\\_oldest\\_old\\_female & dependants & unemployment & no\\_higher\\_education & foreign\\_nationals & primary\\_school\\_age & one\\_person\\_households & ⋯ & local\\_knowledge & social\\_network & physical\\_environment & sensitivity & prepare & respond & recover & adaptive\\_capacity & enhanced\\_exposure & social\\_vulnerability\\\\\n",
       "\\hline\n",
       "\tearly\\_childhood\\_boy &  1.00000000 &  0.229097528 & -0.14133786 & -0.152371506 &  0.479381856 & -0.06813950 & -0.05906216 &  0.06968821 & -0.211541089 & -0.053140400 & ⋯ &  0.06968821 & -0.17126794 &  0.041952673 &  0.468965905 &  0.177996543 &  0.06209890 &  0.046053168 &  0.06209890 &  0.041952673 &  0.21671079\\\\\n",
       "\tearly\\_childhood\\_girl &  0.22909753 &  1.000000000 & -0.12233710 & -0.115415283 &  0.447880059 & -0.09983243 & -0.07541606 &  0.02251899 & -0.131044555 & -0.087782855 & ⋯ &  0.02251899 & -0.14149308 &  0.012169251 &  0.497075108 &  0.124612566 &  0.03031025 &  0.028487985 &  0.03031025 &  0.012169251 &  0.18312793\\\\\n",
       "\tage\\_middle\\_to\\_oldest\\_old\\_male & -0.14133786 & -0.122337104 &  1.00000000 &  0.291926773 & -0.219142202 & -0.20364703 &  0.01344422 & -0.22114688 &  0.156731495 & -0.058133618 & ⋯ & -0.22114688 &  0.06398469 & -0.130727845 &  0.515454373 & -0.265588094 & -0.20945491 & -0.162603657 & -0.20945491 & -0.130727845 & -0.08165958\\\\\n",
       "\tage\\_middle\\_to\\_oldest\\_old\\_female & -0.15237151 & -0.115415283 &  0.29192677 &  1.000000000 & -0.235062655 & -0.23129506 &  0.23646828 & -0.25730892 &  0.166348557 & -0.001316863 & ⋯ & -0.25730892 &  0.10686905 & -0.133976721 &  0.513279034 & -0.205041719 & -0.12666491 & -0.033731192 & -0.12666491 & -0.133976721 & -0.01975176\\\\\n",
       "\tdependants &  0.47938186 &  0.447880059 & -0.21914220 & -0.235062655 &  1.000000000 & -0.08266693 &  0.11096832 &  0.08006488 & -0.735608824 & -0.291803934 & ⋯ &  0.08006488 & -0.66460705 & -0.024206633 &  0.237217276 &  0.467697674 &  0.03233285 &  0.001091004 &  0.03233285 & -0.024206633 &  0.08373068\\\\\n",
       "\tunemployment & -0.06813950 & -0.099832433 & -0.20364703 & -0.231295057 & -0.082666926 &  1.00000000 &  0.10782418 &  0.33788259 &  0.083462147 &  0.010301782 & ⋯ &  0.33788259 &  0.06069221 & -0.046306921 & -0.302233026 &  0.573835450 &  0.57384691 &  0.585589861 &  0.57384691 & -0.046306921 &  0.32713626\\\\\n",
       "\tno\\_higher\\_education & -0.05906216 & -0.075416063 &  0.01344422 &  0.236468283 &  0.110968315 &  0.10782418 &  1.00000000 &  0.26173870 & -0.045445576 & -0.029993040 & ⋯ &  0.26173870 & -0.04877900 & -0.128676167 &  0.057830642 &  0.624357416 &  0.55436679 &  0.599521006 &  0.55436679 & -0.128676167 &  0.37411283\\\\\n",
       "\tforeign\\_nationals &  0.06968821 &  0.022518991 & -0.22114688 & -0.257308923 &  0.080064879 &  0.33788259 &  0.26173870 &  1.00000000 & -0.074132287 &  0.291800349 & ⋯ &  1.00000000 &  0.14025860 &  0.058567612 & -0.193601159 &  0.707212143 &  0.74713737 &  0.469628941 &  0.74713737 &  0.058567612 &  0.55682335\\\\\n",
       "\tprimary\\_school\\_age & -0.21154109 & -0.131044555 &  0.15673150 &  0.166348557 & -0.735608824 &  0.08346215 & -0.04544558 & -0.07413229 &  1.000000000 &  0.196787249 & ⋯ & -0.07413229 &  0.77451044 &  0.003812838 & -0.009809013 & -0.325644138 &  0.16757171 &  0.261335750 &  0.16757171 &  0.003812838 &  0.12973201\\\\\n",
       "\tone\\_person\\_households & -0.05314040 & -0.087782855 & -0.05813362 & -0.001316863 & -0.291803934 &  0.01030178 & -0.02999304 &  0.29180035 &  0.196787249 &  1.000000000 & ⋯ &  0.29180035 &  0.77260594 &  0.284224811 & -0.100459534 & -0.008700115 &  0.46292243 &  0.462342309 &  0.46292243 &  0.284224811 &  0.49619186\\\\\n",
       "\ttree\\_cover\\_density &  0.04948625 &  0.013555773 & -0.11879223 & -0.119645043 & -0.001507900 & -0.03736943 & -0.08206047 &  0.05235133 & -0.012114734 &  0.218214737 & ⋯ &  0.05235133 &  0.13294221 &  0.925360125 & -0.087913902 & -0.028978401 &  0.05394525 &  0.044286154 &  0.05394525 &  0.925360125 &  0.55702025\\\\\n",
       "\timpervious &  0.02815641 &  0.008966105 & -0.12314844 & -0.128308388 & -0.043291805 & -0.04833173 & -0.15608312 &  0.05604093 &  0.019171231 &  0.307805877 & ⋯ &  0.05604093 &  0.21100349 &  0.925360125 & -0.107434095 & -0.080913361 &  0.05295268 &  0.041038921 &  0.05295268 &  0.925360125 &  0.55026267\\\\\n",
       "\tage &  0.46896590 &  0.497075108 &  0.51545437 &  0.513279034 &  0.237217276 & -0.30223303 &  0.05783064 & -0.19360116 & -0.009809013 & -0.100459534 & ⋯ & -0.19360116 & -0.07116596 & -0.105552418 &  1.000000000 & -0.084199177 & -0.12216143 & -0.061054149 & -0.12216143 & -0.105552418 &  0.14962341\\\\\n",
       "\tincome &  0.30405399 &  0.257398729 & -0.31214939 & -0.344305665 &  0.678122645 &  0.67637489 &  0.16153269 &  0.30835420 & -0.482128040 & -0.208071286 & ⋯ &  0.30835420 & -0.44644375 & -0.052040916 & -0.047564415 &  0.768857522 &  0.44709337 &  0.432663034 &  0.44709337 & -0.052040916 &  0.30313843\\\\\n",
       "\tinfo\\_access\\_use & -0.05906216 & -0.075416063 &  0.01344422 &  0.236468283 &  0.110968315 &  0.10782418 &  1.00000000 &  0.26173870 & -0.045445576 & -0.029993040 & ⋯ &  0.26173870 & -0.04877900 & -0.128676167 &  0.057830642 &  0.624357416 &  0.55436679 &  0.599521006 &  0.55436679 & -0.128676167 &  0.37411283\\\\\n",
       "\tlocal\\_knowledge &  0.06968821 &  0.022518991 & -0.22114688 & -0.257308923 &  0.080064879 &  0.33788259 &  0.26173870 &  1.00000000 & -0.074132287 &  0.291800349 & ⋯ &  1.00000000 &  0.14025860 &  0.058567612 & -0.193601159 &  0.707212143 &  0.74713737 &  0.469628941 &  0.74713737 &  0.058567612 &  0.55682335\\\\\n",
       "\tsocial\\_network & -0.17126794 & -0.141493083 &  0.06398469 &  0.106869046 & -0.664607054 &  0.06069221 & -0.04877900 &  0.14025860 &  0.774510442 &  0.772605938 & ⋯ &  0.14025860 &  1.00000000 &  0.185844241 & -0.071165963 & -0.216483284 &  0.40717750 &  0.467519929 &  0.40717750 &  0.185844241 &  0.40413915\\\\\n",
       "\tphysical\\_environment &  0.04195267 &  0.012169251 & -0.13072785 & -0.133976721 & -0.024206633 & -0.04630692 & -0.12867617 &  0.05856761 &  0.003812838 &  0.284224811 & ⋯ &  0.05856761 &  0.18584424 &  1.000000000 & -0.105552418 & -0.059377835 &  0.05776018 &  0.046103713 &  0.05776018 &  1.000000000 &  0.59829838\\\\\n",
       "\tsensitivity &  0.46896590 &  0.497075108 &  0.51545437 &  0.513279034 &  0.237217276 & -0.30223303 &  0.05783064 & -0.19360116 & -0.009809013 & -0.100459534 & ⋯ & -0.19360116 & -0.07116596 & -0.105552418 &  1.000000000 & -0.084199177 & -0.12216143 & -0.061054149 & -0.12216143 & -0.105552418 &  0.14962341\\\\\n",
       "\tprepare &  0.17799654 &  0.124612566 & -0.26558809 & -0.205041719 &  0.467697674 &  0.57383545 &  0.62435742 &  0.70721214 & -0.325644138 & -0.008700115 & ⋯ &  0.70721214 & -0.21648328 & -0.059377835 & -0.084199177 &  1.000000000 &  0.80354306 &  0.697613287 &  0.80354306 & -0.059377835 &  0.56521567\\\\\n",
       "\trespond &  0.06209890 &  0.030310255 & -0.20945491 & -0.126664909 &  0.032332845 &  0.57384691 &  0.55436679 &  0.74713737 &  0.167571709 &  0.462922431 & ⋯ &  0.74713737 &  0.40717750 &  0.057760176 & -0.122161434 &  0.803543060 &  1.00000000 &  0.937690173 &  1.00000000 &  0.057760176 &  0.77518413\\\\\n",
       "\trecover &  0.04605317 &  0.028487985 & -0.16260366 & -0.033731192 &  0.001091004 &  0.58558986 &  0.59952101 &  0.46962894 &  0.261335750 &  0.462342309 & ⋯ &  0.46962894 &  0.46751993 &  0.046103713 & -0.061054149 &  0.697613287 &  0.93769017 &  1.000000000 &  0.93769017 &  0.046103713 &  0.73856429\\\\\n",
       "\tadaptive\\_capacity &  0.06209890 &  0.030310255 & -0.20945491 & -0.126664909 &  0.032332845 &  0.57384691 &  0.55436679 &  0.74713737 &  0.167571709 &  0.462922431 & ⋯ &  0.74713737 &  0.40717750 &  0.057760176 & -0.122161434 &  0.803543060 &  1.00000000 &  0.937690173 &  1.00000000 &  0.057760176 &  0.77518413\\\\\n",
       "\tenhanced\\_exposure &  0.04195267 &  0.012169251 & -0.13072785 & -0.133976721 & -0.024206633 & -0.04630692 & -0.12867617 &  0.05856761 &  0.003812838 &  0.284224811 & ⋯ &  0.05856761 &  0.18584424 &  1.000000000 & -0.105552418 & -0.059377835 &  0.05776018 &  0.046103713 &  0.05776018 &  1.000000000 &  0.59829838\\\\\n",
       "\tsocial\\_vulnerability &  0.21671079 &  0.183127935 & -0.08165958 & -0.019751765 &  0.083730678 &  0.32713626 &  0.37411283 &  0.55682335 &  0.129732006 &  0.496191862 & ⋯ &  0.55682335 &  0.40413915 &  0.598298378 &  0.149623413 &  0.565215672 &  0.77518413 &  0.738564294 &  0.77518413 &  0.598298378 &  1.00000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 25 × 25 of type dbl\n",
       "\n",
       "| <!--/--> | early_childhood_boy | early_childhood_girl | age_middle_to_oldest_old_male | age_middle_to_oldest_old_female | dependants | unemployment | no_higher_education | foreign_nationals | primary_school_age | one_person_households | ⋯ | local_knowledge | social_network | physical_environment | sensitivity | prepare | respond | recover | adaptive_capacity | enhanced_exposure | social_vulnerability |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| early_childhood_boy |  1.00000000 |  0.229097528 | -0.14133786 | -0.152371506 |  0.479381856 | -0.06813950 | -0.05906216 |  0.06968821 | -0.211541089 | -0.053140400 | ⋯ |  0.06968821 | -0.17126794 |  0.041952673 |  0.468965905 |  0.177996543 |  0.06209890 |  0.046053168 |  0.06209890 |  0.041952673 |  0.21671079 |\n",
       "| early_childhood_girl |  0.22909753 |  1.000000000 | -0.12233710 | -0.115415283 |  0.447880059 | -0.09983243 | -0.07541606 |  0.02251899 | -0.131044555 | -0.087782855 | ⋯ |  0.02251899 | -0.14149308 |  0.012169251 |  0.497075108 |  0.124612566 |  0.03031025 |  0.028487985 |  0.03031025 |  0.012169251 |  0.18312793 |\n",
       "| age_middle_to_oldest_old_male | -0.14133786 | -0.122337104 |  1.00000000 |  0.291926773 | -0.219142202 | -0.20364703 |  0.01344422 | -0.22114688 |  0.156731495 | -0.058133618 | ⋯ | -0.22114688 |  0.06398469 | -0.130727845 |  0.515454373 | -0.265588094 | -0.20945491 | -0.162603657 | -0.20945491 | -0.130727845 | -0.08165958 |\n",
       "| age_middle_to_oldest_old_female | -0.15237151 | -0.115415283 |  0.29192677 |  1.000000000 | -0.235062655 | -0.23129506 |  0.23646828 | -0.25730892 |  0.166348557 | -0.001316863 | ⋯ | -0.25730892 |  0.10686905 | -0.133976721 |  0.513279034 | -0.205041719 | -0.12666491 | -0.033731192 | -0.12666491 | -0.133976721 | -0.01975176 |\n",
       "| dependants |  0.47938186 |  0.447880059 | -0.21914220 | -0.235062655 |  1.000000000 | -0.08266693 |  0.11096832 |  0.08006488 | -0.735608824 | -0.291803934 | ⋯ |  0.08006488 | -0.66460705 | -0.024206633 |  0.237217276 |  0.467697674 |  0.03233285 |  0.001091004 |  0.03233285 | -0.024206633 |  0.08373068 |\n",
       "| unemployment | -0.06813950 | -0.099832433 | -0.20364703 | -0.231295057 | -0.082666926 |  1.00000000 |  0.10782418 |  0.33788259 |  0.083462147 |  0.010301782 | ⋯ |  0.33788259 |  0.06069221 | -0.046306921 | -0.302233026 |  0.573835450 |  0.57384691 |  0.585589861 |  0.57384691 | -0.046306921 |  0.32713626 |\n",
       "| no_higher_education | -0.05906216 | -0.075416063 |  0.01344422 |  0.236468283 |  0.110968315 |  0.10782418 |  1.00000000 |  0.26173870 | -0.045445576 | -0.029993040 | ⋯ |  0.26173870 | -0.04877900 | -0.128676167 |  0.057830642 |  0.624357416 |  0.55436679 |  0.599521006 |  0.55436679 | -0.128676167 |  0.37411283 |\n",
       "| foreign_nationals |  0.06968821 |  0.022518991 | -0.22114688 | -0.257308923 |  0.080064879 |  0.33788259 |  0.26173870 |  1.00000000 | -0.074132287 |  0.291800349 | ⋯ |  1.00000000 |  0.14025860 |  0.058567612 | -0.193601159 |  0.707212143 |  0.74713737 |  0.469628941 |  0.74713737 |  0.058567612 |  0.55682335 |\n",
       "| primary_school_age | -0.21154109 | -0.131044555 |  0.15673150 |  0.166348557 | -0.735608824 |  0.08346215 | -0.04544558 | -0.07413229 |  1.000000000 |  0.196787249 | ⋯ | -0.07413229 |  0.77451044 |  0.003812838 | -0.009809013 | -0.325644138 |  0.16757171 |  0.261335750 |  0.16757171 |  0.003812838 |  0.12973201 |\n",
       "| one_person_households | -0.05314040 | -0.087782855 | -0.05813362 | -0.001316863 | -0.291803934 |  0.01030178 | -0.02999304 |  0.29180035 |  0.196787249 |  1.000000000 | ⋯ |  0.29180035 |  0.77260594 |  0.284224811 | -0.100459534 | -0.008700115 |  0.46292243 |  0.462342309 |  0.46292243 |  0.284224811 |  0.49619186 |\n",
       "| tree_cover_density |  0.04948625 |  0.013555773 | -0.11879223 | -0.119645043 | -0.001507900 | -0.03736943 | -0.08206047 |  0.05235133 | -0.012114734 |  0.218214737 | ⋯ |  0.05235133 |  0.13294221 |  0.925360125 | -0.087913902 | -0.028978401 |  0.05394525 |  0.044286154 |  0.05394525 |  0.925360125 |  0.55702025 |\n",
       "| impervious |  0.02815641 |  0.008966105 | -0.12314844 | -0.128308388 | -0.043291805 | -0.04833173 | -0.15608312 |  0.05604093 |  0.019171231 |  0.307805877 | ⋯ |  0.05604093 |  0.21100349 |  0.925360125 | -0.107434095 | -0.080913361 |  0.05295268 |  0.041038921 |  0.05295268 |  0.925360125 |  0.55026267 |\n",
       "| age |  0.46896590 |  0.497075108 |  0.51545437 |  0.513279034 |  0.237217276 | -0.30223303 |  0.05783064 | -0.19360116 | -0.009809013 | -0.100459534 | ⋯ | -0.19360116 | -0.07116596 | -0.105552418 |  1.000000000 | -0.084199177 | -0.12216143 | -0.061054149 | -0.12216143 | -0.105552418 |  0.14962341 |\n",
       "| income |  0.30405399 |  0.257398729 | -0.31214939 | -0.344305665 |  0.678122645 |  0.67637489 |  0.16153269 |  0.30835420 | -0.482128040 | -0.208071286 | ⋯ |  0.30835420 | -0.44644375 | -0.052040916 | -0.047564415 |  0.768857522 |  0.44709337 |  0.432663034 |  0.44709337 | -0.052040916 |  0.30313843 |\n",
       "| info_access_use | -0.05906216 | -0.075416063 |  0.01344422 |  0.236468283 |  0.110968315 |  0.10782418 |  1.00000000 |  0.26173870 | -0.045445576 | -0.029993040 | ⋯ |  0.26173870 | -0.04877900 | -0.128676167 |  0.057830642 |  0.624357416 |  0.55436679 |  0.599521006 |  0.55436679 | -0.128676167 |  0.37411283 |\n",
       "| local_knowledge |  0.06968821 |  0.022518991 | -0.22114688 | -0.257308923 |  0.080064879 |  0.33788259 |  0.26173870 |  1.00000000 | -0.074132287 |  0.291800349 | ⋯ |  1.00000000 |  0.14025860 |  0.058567612 | -0.193601159 |  0.707212143 |  0.74713737 |  0.469628941 |  0.74713737 |  0.058567612 |  0.55682335 |\n",
       "| social_network | -0.17126794 | -0.141493083 |  0.06398469 |  0.106869046 | -0.664607054 |  0.06069221 | -0.04877900 |  0.14025860 |  0.774510442 |  0.772605938 | ⋯ |  0.14025860 |  1.00000000 |  0.185844241 | -0.071165963 | -0.216483284 |  0.40717750 |  0.467519929 |  0.40717750 |  0.185844241 |  0.40413915 |\n",
       "| physical_environment |  0.04195267 |  0.012169251 | -0.13072785 | -0.133976721 | -0.024206633 | -0.04630692 | -0.12867617 |  0.05856761 |  0.003812838 |  0.284224811 | ⋯ |  0.05856761 |  0.18584424 |  1.000000000 | -0.105552418 | -0.059377835 |  0.05776018 |  0.046103713 |  0.05776018 |  1.000000000 |  0.59829838 |\n",
       "| sensitivity |  0.46896590 |  0.497075108 |  0.51545437 |  0.513279034 |  0.237217276 | -0.30223303 |  0.05783064 | -0.19360116 | -0.009809013 | -0.100459534 | ⋯ | -0.19360116 | -0.07116596 | -0.105552418 |  1.000000000 | -0.084199177 | -0.12216143 | -0.061054149 | -0.12216143 | -0.105552418 |  0.14962341 |\n",
       "| prepare |  0.17799654 |  0.124612566 | -0.26558809 | -0.205041719 |  0.467697674 |  0.57383545 |  0.62435742 |  0.70721214 | -0.325644138 | -0.008700115 | ⋯ |  0.70721214 | -0.21648328 | -0.059377835 | -0.084199177 |  1.000000000 |  0.80354306 |  0.697613287 |  0.80354306 | -0.059377835 |  0.56521567 |\n",
       "| respond |  0.06209890 |  0.030310255 | -0.20945491 | -0.126664909 |  0.032332845 |  0.57384691 |  0.55436679 |  0.74713737 |  0.167571709 |  0.462922431 | ⋯ |  0.74713737 |  0.40717750 |  0.057760176 | -0.122161434 |  0.803543060 |  1.00000000 |  0.937690173 |  1.00000000 |  0.057760176 |  0.77518413 |\n",
       "| recover |  0.04605317 |  0.028487985 | -0.16260366 | -0.033731192 |  0.001091004 |  0.58558986 |  0.59952101 |  0.46962894 |  0.261335750 |  0.462342309 | ⋯ |  0.46962894 |  0.46751993 |  0.046103713 | -0.061054149 |  0.697613287 |  0.93769017 |  1.000000000 |  0.93769017 |  0.046103713 |  0.73856429 |\n",
       "| adaptive_capacity |  0.06209890 |  0.030310255 | -0.20945491 | -0.126664909 |  0.032332845 |  0.57384691 |  0.55436679 |  0.74713737 |  0.167571709 |  0.462922431 | ⋯ |  0.74713737 |  0.40717750 |  0.057760176 | -0.122161434 |  0.803543060 |  1.00000000 |  0.937690173 |  1.00000000 |  0.057760176 |  0.77518413 |\n",
       "| enhanced_exposure |  0.04195267 |  0.012169251 | -0.13072785 | -0.133976721 | -0.024206633 | -0.04630692 | -0.12867617 |  0.05856761 |  0.003812838 |  0.284224811 | ⋯ |  0.05856761 |  0.18584424 |  1.000000000 | -0.105552418 | -0.059377835 |  0.05776018 |  0.046103713 |  0.05776018 |  1.000000000 |  0.59829838 |\n",
       "| social_vulnerability |  0.21671079 |  0.183127935 | -0.08165958 | -0.019751765 |  0.083730678 |  0.32713626 |  0.37411283 |  0.55682335 |  0.129732006 |  0.496191862 | ⋯ |  0.55682335 |  0.40413915 |  0.598298378 |  0.149623413 |  0.565215672 |  0.77518413 |  0.738564294 |  0.77518413 |  0.598298378 |  1.00000000 |\n",
       "\n"
      ],
      "text/plain": [
       "                                early_childhood_boy early_childhood_girl\n",
       "early_childhood_boy              1.00000000          0.229097528        \n",
       "early_childhood_girl             0.22909753          1.000000000        \n",
       "age_middle_to_oldest_old_male   -0.14133786         -0.122337104        \n",
       "age_middle_to_oldest_old_female -0.15237151         -0.115415283        \n",
       "dependants                       0.47938186          0.447880059        \n",
       "unemployment                    -0.06813950         -0.099832433        \n",
       "no_higher_education             -0.05906216         -0.075416063        \n",
       "foreign_nationals                0.06968821          0.022518991        \n",
       "primary_school_age              -0.21154109         -0.131044555        \n",
       "one_person_households           -0.05314040         -0.087782855        \n",
       "tree_cover_density               0.04948625          0.013555773        \n",
       "impervious                       0.02815641          0.008966105        \n",
       "age                              0.46896590          0.497075108        \n",
       "income                           0.30405399          0.257398729        \n",
       "info_access_use                 -0.05906216         -0.075416063        \n",
       "local_knowledge                  0.06968821          0.022518991        \n",
       "social_network                  -0.17126794         -0.141493083        \n",
       "physical_environment             0.04195267          0.012169251        \n",
       "sensitivity                      0.46896590          0.497075108        \n",
       "prepare                          0.17799654          0.124612566        \n",
       "respond                          0.06209890          0.030310255        \n",
       "recover                          0.04605317          0.028487985        \n",
       "adaptive_capacity                0.06209890          0.030310255        \n",
       "enhanced_exposure                0.04195267          0.012169251        \n",
       "social_vulnerability             0.21671079          0.183127935        \n",
       "                                age_middle_to_oldest_old_male\n",
       "early_childhood_boy             -0.14133786                  \n",
       "early_childhood_girl            -0.12233710                  \n",
       "age_middle_to_oldest_old_male    1.00000000                  \n",
       "age_middle_to_oldest_old_female  0.29192677                  \n",
       "dependants                      -0.21914220                  \n",
       "unemployment                    -0.20364703                  \n",
       "no_higher_education              0.01344422                  \n",
       "foreign_nationals               -0.22114688                  \n",
       "primary_school_age               0.15673150                  \n",
       "one_person_households           -0.05813362                  \n",
       "tree_cover_density              -0.11879223                  \n",
       "impervious                      -0.12314844                  \n",
       "age                              0.51545437                  \n",
       "income                          -0.31214939                  \n",
       "info_access_use                  0.01344422                  \n",
       "local_knowledge                 -0.22114688                  \n",
       "social_network                   0.06398469                  \n",
       "physical_environment            -0.13072785                  \n",
       "sensitivity                      0.51545437                  \n",
       "prepare                         -0.26558809                  \n",
       "respond                         -0.20945491                  \n",
       "recover                         -0.16260366                  \n",
       "adaptive_capacity               -0.20945491                  \n",
       "enhanced_exposure               -0.13072785                  \n",
       "social_vulnerability            -0.08165958                  \n",
       "                                age_middle_to_oldest_old_female dependants  \n",
       "early_childhood_boy             -0.152371506                     0.479381856\n",
       "early_childhood_girl            -0.115415283                     0.447880059\n",
       "age_middle_to_oldest_old_male    0.291926773                    -0.219142202\n",
       "age_middle_to_oldest_old_female  1.000000000                    -0.235062655\n",
       "dependants                      -0.235062655                     1.000000000\n",
       "unemployment                    -0.231295057                    -0.082666926\n",
       "no_higher_education              0.236468283                     0.110968315\n",
       "foreign_nationals               -0.257308923                     0.080064879\n",
       "primary_school_age               0.166348557                    -0.735608824\n",
       "one_person_households           -0.001316863                    -0.291803934\n",
       "tree_cover_density              -0.119645043                    -0.001507900\n",
       "impervious                      -0.128308388                    -0.043291805\n",
       "age                              0.513279034                     0.237217276\n",
       "income                          -0.344305665                     0.678122645\n",
       "info_access_use                  0.236468283                     0.110968315\n",
       "local_knowledge                 -0.257308923                     0.080064879\n",
       "social_network                   0.106869046                    -0.664607054\n",
       "physical_environment            -0.133976721                    -0.024206633\n",
       "sensitivity                      0.513279034                     0.237217276\n",
       "prepare                         -0.205041719                     0.467697674\n",
       "respond                         -0.126664909                     0.032332845\n",
       "recover                         -0.033731192                     0.001091004\n",
       "adaptive_capacity               -0.126664909                     0.032332845\n",
       "enhanced_exposure               -0.133976721                    -0.024206633\n",
       "social_vulnerability            -0.019751765                     0.083730678\n",
       "                                unemployment no_higher_education\n",
       "early_childhood_boy             -0.06813950  -0.05906216        \n",
       "early_childhood_girl            -0.09983243  -0.07541606        \n",
       "age_middle_to_oldest_old_male   -0.20364703   0.01344422        \n",
       "age_middle_to_oldest_old_female -0.23129506   0.23646828        \n",
       "dependants                      -0.08266693   0.11096832        \n",
       "unemployment                     1.00000000   0.10782418        \n",
       "no_higher_education              0.10782418   1.00000000        \n",
       "foreign_nationals                0.33788259   0.26173870        \n",
       "primary_school_age               0.08346215  -0.04544558        \n",
       "one_person_households            0.01030178  -0.02999304        \n",
       "tree_cover_density              -0.03736943  -0.08206047        \n",
       "impervious                      -0.04833173  -0.15608312        \n",
       "age                             -0.30223303   0.05783064        \n",
       "income                           0.67637489   0.16153269        \n",
       "info_access_use                  0.10782418   1.00000000        \n",
       "local_knowledge                  0.33788259   0.26173870        \n",
       "social_network                   0.06069221  -0.04877900        \n",
       "physical_environment            -0.04630692  -0.12867617        \n",
       "sensitivity                     -0.30223303   0.05783064        \n",
       "prepare                          0.57383545   0.62435742        \n",
       "respond                          0.57384691   0.55436679        \n",
       "recover                          0.58558986   0.59952101        \n",
       "adaptive_capacity                0.57384691   0.55436679        \n",
       "enhanced_exposure               -0.04630692  -0.12867617        \n",
       "social_vulnerability             0.32713626   0.37411283        \n",
       "                                foreign_nationals primary_school_age\n",
       "early_childhood_boy              0.06968821       -0.211541089      \n",
       "early_childhood_girl             0.02251899       -0.131044555      \n",
       "age_middle_to_oldest_old_male   -0.22114688        0.156731495      \n",
       "age_middle_to_oldest_old_female -0.25730892        0.166348557      \n",
       "dependants                       0.08006488       -0.735608824      \n",
       "unemployment                     0.33788259        0.083462147      \n",
       "no_higher_education              0.26173870       -0.045445576      \n",
       "foreign_nationals                1.00000000       -0.074132287      \n",
       "primary_school_age              -0.07413229        1.000000000      \n",
       "one_person_households            0.29180035        0.196787249      \n",
       "tree_cover_density               0.05235133       -0.012114734      \n",
       "impervious                       0.05604093        0.019171231      \n",
       "age                             -0.19360116       -0.009809013      \n",
       "income                           0.30835420       -0.482128040      \n",
       "info_access_use                  0.26173870       -0.045445576      \n",
       "local_knowledge                  1.00000000       -0.074132287      \n",
       "social_network                   0.14025860        0.774510442      \n",
       "physical_environment             0.05856761        0.003812838      \n",
       "sensitivity                     -0.19360116       -0.009809013      \n",
       "prepare                          0.70721214       -0.325644138      \n",
       "respond                          0.74713737        0.167571709      \n",
       "recover                          0.46962894        0.261335750      \n",
       "adaptive_capacity                0.74713737        0.167571709      \n",
       "enhanced_exposure                0.05856761        0.003812838      \n",
       "social_vulnerability             0.55682335        0.129732006      \n",
       "                                one_person_households ⋯ local_knowledge\n",
       "early_childhood_boy             -0.053140400          ⋯  0.06968821    \n",
       "early_childhood_girl            -0.087782855          ⋯  0.02251899    \n",
       "age_middle_to_oldest_old_male   -0.058133618          ⋯ -0.22114688    \n",
       "age_middle_to_oldest_old_female -0.001316863          ⋯ -0.25730892    \n",
       "dependants                      -0.291803934          ⋯  0.08006488    \n",
       "unemployment                     0.010301782          ⋯  0.33788259    \n",
       "no_higher_education             -0.029993040          ⋯  0.26173870    \n",
       "foreign_nationals                0.291800349          ⋯  1.00000000    \n",
       "primary_school_age               0.196787249          ⋯ -0.07413229    \n",
       "one_person_households            1.000000000          ⋯  0.29180035    \n",
       "tree_cover_density               0.218214737          ⋯  0.05235133    \n",
       "impervious                       0.307805877          ⋯  0.05604093    \n",
       "age                             -0.100459534          ⋯ -0.19360116    \n",
       "income                          -0.208071286          ⋯  0.30835420    \n",
       "info_access_use                 -0.029993040          ⋯  0.26173870    \n",
       "local_knowledge                  0.291800349          ⋯  1.00000000    \n",
       "social_network                   0.772605938          ⋯  0.14025860    \n",
       "physical_environment             0.284224811          ⋯  0.05856761    \n",
       "sensitivity                     -0.100459534          ⋯ -0.19360116    \n",
       "prepare                         -0.008700115          ⋯  0.70721214    \n",
       "respond                          0.462922431          ⋯  0.74713737    \n",
       "recover                          0.462342309          ⋯  0.46962894    \n",
       "adaptive_capacity                0.462922431          ⋯  0.74713737    \n",
       "enhanced_exposure                0.284224811          ⋯  0.05856761    \n",
       "social_vulnerability             0.496191862          ⋯  0.55682335    \n",
       "                                social_network physical_environment\n",
       "early_childhood_boy             -0.17126794     0.041952673        \n",
       "early_childhood_girl            -0.14149308     0.012169251        \n",
       "age_middle_to_oldest_old_male    0.06398469    -0.130727845        \n",
       "age_middle_to_oldest_old_female  0.10686905    -0.133976721        \n",
       "dependants                      -0.66460705    -0.024206633        \n",
       "unemployment                     0.06069221    -0.046306921        \n",
       "no_higher_education             -0.04877900    -0.128676167        \n",
       "foreign_nationals                0.14025860     0.058567612        \n",
       "primary_school_age               0.77451044     0.003812838        \n",
       "one_person_households            0.77260594     0.284224811        \n",
       "tree_cover_density               0.13294221     0.925360125        \n",
       "impervious                       0.21100349     0.925360125        \n",
       "age                             -0.07116596    -0.105552418        \n",
       "income                          -0.44644375    -0.052040916        \n",
       "info_access_use                 -0.04877900    -0.128676167        \n",
       "local_knowledge                  0.14025860     0.058567612        \n",
       "social_network                   1.00000000     0.185844241        \n",
       "physical_environment             0.18584424     1.000000000        \n",
       "sensitivity                     -0.07116596    -0.105552418        \n",
       "prepare                         -0.21648328    -0.059377835        \n",
       "respond                          0.40717750     0.057760176        \n",
       "recover                          0.46751993     0.046103713        \n",
       "adaptive_capacity                0.40717750     0.057760176        \n",
       "enhanced_exposure                0.18584424     1.000000000        \n",
       "social_vulnerability             0.40413915     0.598298378        \n",
       "                                sensitivity  prepare      respond    \n",
       "early_childhood_boy              0.468965905  0.177996543  0.06209890\n",
       "early_childhood_girl             0.497075108  0.124612566  0.03031025\n",
       "age_middle_to_oldest_old_male    0.515454373 -0.265588094 -0.20945491\n",
       "age_middle_to_oldest_old_female  0.513279034 -0.205041719 -0.12666491\n",
       "dependants                       0.237217276  0.467697674  0.03233285\n",
       "unemployment                    -0.302233026  0.573835450  0.57384691\n",
       "no_higher_education              0.057830642  0.624357416  0.55436679\n",
       "foreign_nationals               -0.193601159  0.707212143  0.74713737\n",
       "primary_school_age              -0.009809013 -0.325644138  0.16757171\n",
       "one_person_households           -0.100459534 -0.008700115  0.46292243\n",
       "tree_cover_density              -0.087913902 -0.028978401  0.05394525\n",
       "impervious                      -0.107434095 -0.080913361  0.05295268\n",
       "age                              1.000000000 -0.084199177 -0.12216143\n",
       "income                          -0.047564415  0.768857522  0.44709337\n",
       "info_access_use                  0.057830642  0.624357416  0.55436679\n",
       "local_knowledge                 -0.193601159  0.707212143  0.74713737\n",
       "social_network                  -0.071165963 -0.216483284  0.40717750\n",
       "physical_environment            -0.105552418 -0.059377835  0.05776018\n",
       "sensitivity                      1.000000000 -0.084199177 -0.12216143\n",
       "prepare                         -0.084199177  1.000000000  0.80354306\n",
       "respond                         -0.122161434  0.803543060  1.00000000\n",
       "recover                         -0.061054149  0.697613287  0.93769017\n",
       "adaptive_capacity               -0.122161434  0.803543060  1.00000000\n",
       "enhanced_exposure               -0.105552418 -0.059377835  0.05776018\n",
       "social_vulnerability             0.149623413  0.565215672  0.77518413\n",
       "                                recover      adaptive_capacity\n",
       "early_childhood_boy              0.046053168  0.06209890      \n",
       "early_childhood_girl             0.028487985  0.03031025      \n",
       "age_middle_to_oldest_old_male   -0.162603657 -0.20945491      \n",
       "age_middle_to_oldest_old_female -0.033731192 -0.12666491      \n",
       "dependants                       0.001091004  0.03233285      \n",
       "unemployment                     0.585589861  0.57384691      \n",
       "no_higher_education              0.599521006  0.55436679      \n",
       "foreign_nationals                0.469628941  0.74713737      \n",
       "primary_school_age               0.261335750  0.16757171      \n",
       "one_person_households            0.462342309  0.46292243      \n",
       "tree_cover_density               0.044286154  0.05394525      \n",
       "impervious                       0.041038921  0.05295268      \n",
       "age                             -0.061054149 -0.12216143      \n",
       "income                           0.432663034  0.44709337      \n",
       "info_access_use                  0.599521006  0.55436679      \n",
       "local_knowledge                  0.469628941  0.74713737      \n",
       "social_network                   0.467519929  0.40717750      \n",
       "physical_environment             0.046103713  0.05776018      \n",
       "sensitivity                     -0.061054149 -0.12216143      \n",
       "prepare                          0.697613287  0.80354306      \n",
       "respond                          0.937690173  1.00000000      \n",
       "recover                          1.000000000  0.93769017      \n",
       "adaptive_capacity                0.937690173  1.00000000      \n",
       "enhanced_exposure                0.046103713  0.05776018      \n",
       "social_vulnerability             0.738564294  0.77518413      \n",
       "                                enhanced_exposure social_vulnerability\n",
       "early_childhood_boy              0.041952673       0.21671079         \n",
       "early_childhood_girl             0.012169251       0.18312793         \n",
       "age_middle_to_oldest_old_male   -0.130727845      -0.08165958         \n",
       "age_middle_to_oldest_old_female -0.133976721      -0.01975176         \n",
       "dependants                      -0.024206633       0.08373068         \n",
       "unemployment                    -0.046306921       0.32713626         \n",
       "no_higher_education             -0.128676167       0.37411283         \n",
       "foreign_nationals                0.058567612       0.55682335         \n",
       "primary_school_age               0.003812838       0.12973201         \n",
       "one_person_households            0.284224811       0.49619186         \n",
       "tree_cover_density               0.925360125       0.55702025         \n",
       "impervious                       0.925360125       0.55026267         \n",
       "age                             -0.105552418       0.14962341         \n",
       "income                          -0.052040916       0.30313843         \n",
       "info_access_use                 -0.128676167       0.37411283         \n",
       "local_knowledge                  0.058567612       0.55682335         \n",
       "social_network                   0.185844241       0.40413915         \n",
       "physical_environment             1.000000000       0.59829838         \n",
       "sensitivity                     -0.105552418       0.14962341         \n",
       "prepare                         -0.059377835       0.56521567         \n",
       "respond                          0.057760176       0.77518413         \n",
       "recover                          0.046103713       0.73856429         \n",
       "adaptive_capacity                0.057760176       0.77518413         \n",
       "enhanced_exposure                1.000000000       0.59829838         \n",
       "social_vulnerability             0.598298378       1.00000000         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the correlations\n",
    "correlation <- cor(output_dataset %>% select(-c(all_of(GUID))), use=\"pairwise.complete.obs\")\n",
    "correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34ed4484-ecc8-4aa2-8f92-cec176dc6ed5",
   "metadata": {},
   "source": [
    "## Add geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d2dc8bd1-e2b3-4c92-94d4-37a61766265a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 27</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>SEZ2011</th><th scope=col>early_childhood_boy</th><th scope=col>early_childhood_girl</th><th scope=col>age_middle_to_oldest_old_male</th><th scope=col>age_middle_to_oldest_old_female</th><th scope=col>dependants</th><th scope=col>unemployment</th><th scope=col>no_higher_education</th><th scope=col>foreign_nationals</th><th scope=col>primary_school_age</th><th scope=col>⋯</th><th scope=col>social_network</th><th scope=col>physical_environment</th><th scope=col>sensitivity</th><th scope=col>prepare</th><th scope=col>respond</th><th scope=col>recover</th><th scope=col>adaptive_capacity</th><th scope=col>enhanced_exposure</th><th scope=col>social_vulnerability</th><th scope=col>geometry</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;POLYGON [m]&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>151460000001</td><td>-1.230411</td><td>-1.064144</td><td> 2.1196292</td><td>-1.3062152</td><td>-0.7801407</td><td>-0.5060886</td><td>-0.3037669</td><td> 0.142430939</td><td> 1.458348</td><td>⋯</td><td> 1.1958366</td><td>1.0118090</td><td>-0.7647853635</td><td>-0.696485479</td><td> 0.320951976</td><td> 0.271253971</td><td> 0.320951976</td><td>1.0934219</td><td> 0.6558794</td><td>POLYGON ((1515164 5034507, ...</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>151460000002</td><td>-1.230411</td><td>-1.064144</td><td> 0.2302043</td><td>-0.3189938</td><td> 0.1197864</td><td> 1.3419462</td><td> 0.4424940</td><td> 0.384372102</td><td> 1.458348</td><td>⋯</td><td> 0.3778636</td><td>1.0418299</td><td>-1.2304165745</td><td> 1.355476641</td><td> 1.575888423</td><td> 1.676990485</td><td> 1.575888423</td><td>1.1258642</td><td> 1.5094801</td><td>POLYGON ((1515138 5034525, ...</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>151460000003</td><td>-1.230411</td><td>-1.064144</td><td> 3.8043177</td><td>-1.3062152</td><td>-2.2200240</td><td>-0.2425543</td><td>-0.8229049</td><td>-0.540697051</td><td> 1.458348</td><td>⋯</td><td> 2.3409988</td><td>0.8359458</td><td> 0.1046897905</td><td>-2.251790899</td><td>-0.203447695</td><td> 0.312110916</td><td>-0.203447695</td><td>0.9033735</td><td> 0.4027326</td><td>POLYGON ((1515050 5034427, ...</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>151460000004</td><td>-1.230411</td><td>-1.064144</td><td> 2.6982091</td><td>-1.3062152</td><td>-0.5183437</td><td>-1.2391934</td><td>-0.8229049</td><td>-0.002474999</td><td>-1.865648</td><td>⋯</td><td>-0.7933508</td><td>0.9203024</td><td>-0.4661777348</td><td>-1.478027752</td><td>-2.026764566</td><td>-2.690739562</td><td>-2.026764566</td><td>0.9945343</td><td>-1.1389091</td><td>POLYGON ((1515095 5034409, ...</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>151460000005</td><td>-1.230411</td><td>-1.064144</td><td>19.6216703</td><td>-1.3062152</td><td>-2.2200240</td><td>-2.2981224</td><td>-0.8229049</td><td>-0.540697051</td><td> 1.458348</td><td>⋯</td><td> 2.3409988</td><td>0.9671620</td><td> 8.2680954025</td><td>-3.144598085</td><td>-1.038699169</td><td>-0.797332495</td><td>-1.038699169</td><td>1.0451736</td><td> 2.3380431</td><td>POLYGON ((1514881 5034455, ...</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>151460000006</td><td> 0.000000</td><td> 0.000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.000000000</td><td> 0.000000</td><td>⋯</td><td> 0.0000000</td><td>1.0492594</td><td>-0.0003619911</td><td> 0.002318221</td><td> 0.002883523</td><td> 0.002519028</td><td> 0.002883523</td><td>1.1338930</td><td> 0.6662274</td><td>POLYGON ((1514764 5034456, ...</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 27\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & SEZ2011 & early\\_childhood\\_boy & early\\_childhood\\_girl & age\\_middle\\_to\\_oldest\\_old\\_male & age\\_middle\\_to\\_oldest\\_old\\_female & dependants & unemployment & no\\_higher\\_education & foreign\\_nationals & primary\\_school\\_age & ⋯ & social\\_network & physical\\_environment & sensitivity & prepare & respond & recover & adaptive\\_capacity & enhanced\\_exposure & social\\_vulnerability & geometry\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl{[},1{]}> & <dbl{[},1{]}> & <dbl{[},1{]}> & <dbl{[},1{]}> & <dbl{[},1{]}> & <dbl{[},1{]}> & <dbl{[},1{]}> & <POLYGON {[}m{]}>\\\\\n",
       "\\hline\n",
       "\t1 & 151460000001 & -1.230411 & -1.064144 &  2.1196292 & -1.3062152 & -0.7801407 & -0.5060886 & -0.3037669 &  0.142430939 &  1.458348 & ⋯ &  1.1958366 & 1.0118090 & -0.7647853635 & -0.696485479 &  0.320951976 &  0.271253971 &  0.320951976 & 1.0934219 &  0.6558794 & POLYGON ((1515164 5034507, ...\\\\\n",
       "\t2 & 151460000002 & -1.230411 & -1.064144 &  0.2302043 & -0.3189938 &  0.1197864 &  1.3419462 &  0.4424940 &  0.384372102 &  1.458348 & ⋯ &  0.3778636 & 1.0418299 & -1.2304165745 &  1.355476641 &  1.575888423 &  1.676990485 &  1.575888423 & 1.1258642 &  1.5094801 & POLYGON ((1515138 5034525, ...\\\\\n",
       "\t3 & 151460000003 & -1.230411 & -1.064144 &  3.8043177 & -1.3062152 & -2.2200240 & -0.2425543 & -0.8229049 & -0.540697051 &  1.458348 & ⋯ &  2.3409988 & 0.8359458 &  0.1046897905 & -2.251790899 & -0.203447695 &  0.312110916 & -0.203447695 & 0.9033735 &  0.4027326 & POLYGON ((1515050 5034427, ...\\\\\n",
       "\t4 & 151460000004 & -1.230411 & -1.064144 &  2.6982091 & -1.3062152 & -0.5183437 & -1.2391934 & -0.8229049 & -0.002474999 & -1.865648 & ⋯ & -0.7933508 & 0.9203024 & -0.4661777348 & -1.478027752 & -2.026764566 & -2.690739562 & -2.026764566 & 0.9945343 & -1.1389091 & POLYGON ((1515095 5034409, ...\\\\\n",
       "\t5 & 151460000005 & -1.230411 & -1.064144 & 19.6216703 & -1.3062152 & -2.2200240 & -2.2981224 & -0.8229049 & -0.540697051 &  1.458348 & ⋯ &  2.3409988 & 0.9671620 &  8.2680954025 & -3.144598085 & -1.038699169 & -0.797332495 & -1.038699169 & 1.0451736 &  2.3380431 & POLYGON ((1514881 5034455, ...\\\\\n",
       "\t6 & 151460000006 &  0.000000 &  0.000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.000000000 &  0.000000 & ⋯ &  0.0000000 & 1.0492594 & -0.0003619911 &  0.002318221 &  0.002883523 &  0.002519028 &  0.002883523 & 1.1338930 &  0.6662274 & POLYGON ((1514764 5034456, ...\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 27\n",
       "\n",
       "| <!--/--> | SEZ2011 &lt;chr&gt; | early_childhood_boy &lt;dbl&gt; | early_childhood_girl &lt;dbl&gt; | age_middle_to_oldest_old_male &lt;dbl&gt; | age_middle_to_oldest_old_female &lt;dbl&gt; | dependants &lt;dbl&gt; | unemployment &lt;dbl&gt; | no_higher_education &lt;dbl&gt; | foreign_nationals &lt;dbl&gt; | primary_school_age &lt;dbl&gt; | ⋯ ⋯ | social_network &lt;dbl&gt; | physical_environment &lt;dbl&gt; | sensitivity &lt;dbl[,1]&gt; | prepare &lt;dbl[,1]&gt; | respond &lt;dbl[,1]&gt; | recover &lt;dbl[,1]&gt; | adaptive_capacity &lt;dbl[,1]&gt; | enhanced_exposure &lt;dbl[,1]&gt; | social_vulnerability &lt;dbl[,1]&gt; | geometry &lt;POLYGON [m]&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 151460000001 | -1.230411 | -1.064144 |  2.1196292 | -1.3062152 | -0.7801407 | -0.5060886 | -0.3037669 |  0.142430939 |  1.458348 | ⋯ |  1.1958366 | 1.0118090 | -0.7647853635 | -0.696485479 |  0.320951976 |  0.271253971 |  0.320951976 | 1.0934219 |  0.6558794 | POLYGON ((1515164 5034507, ... |\n",
       "| 2 | 151460000002 | -1.230411 | -1.064144 |  0.2302043 | -0.3189938 |  0.1197864 |  1.3419462 |  0.4424940 |  0.384372102 |  1.458348 | ⋯ |  0.3778636 | 1.0418299 | -1.2304165745 |  1.355476641 |  1.575888423 |  1.676990485 |  1.575888423 | 1.1258642 |  1.5094801 | POLYGON ((1515138 5034525, ... |\n",
       "| 3 | 151460000003 | -1.230411 | -1.064144 |  3.8043177 | -1.3062152 | -2.2200240 | -0.2425543 | -0.8229049 | -0.540697051 |  1.458348 | ⋯ |  2.3409988 | 0.8359458 |  0.1046897905 | -2.251790899 | -0.203447695 |  0.312110916 | -0.203447695 | 0.9033735 |  0.4027326 | POLYGON ((1515050 5034427, ... |\n",
       "| 4 | 151460000004 | -1.230411 | -1.064144 |  2.6982091 | -1.3062152 | -0.5183437 | -1.2391934 | -0.8229049 | -0.002474999 | -1.865648 | ⋯ | -0.7933508 | 0.9203024 | -0.4661777348 | -1.478027752 | -2.026764566 | -2.690739562 | -2.026764566 | 0.9945343 | -1.1389091 | POLYGON ((1515095 5034409, ... |\n",
       "| 5 | 151460000005 | -1.230411 | -1.064144 | 19.6216703 | -1.3062152 | -2.2200240 | -2.2981224 | -0.8229049 | -0.540697051 |  1.458348 | ⋯ |  2.3409988 | 0.9671620 |  8.2680954025 | -3.144598085 | -1.038699169 | -0.797332495 | -1.038699169 | 1.0451736 |  2.3380431 | POLYGON ((1514881 5034455, ... |\n",
       "| 6 | 151460000006 |  0.000000 |  0.000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.000000000 |  0.000000 | ⋯ |  0.0000000 | 1.0492594 | -0.0003619911 |  0.002318221 |  0.002883523 |  0.002519028 |  0.002883523 | 1.1338930 |  0.6662274 | POLYGON ((1514764 5034456, ... |\n",
       "\n"
      ],
      "text/plain": [
       "  SEZ2011      early_childhood_boy early_childhood_girl\n",
       "1 151460000001 -1.230411           -1.064144           \n",
       "2 151460000002 -1.230411           -1.064144           \n",
       "3 151460000003 -1.230411           -1.064144           \n",
       "4 151460000004 -1.230411           -1.064144           \n",
       "5 151460000005 -1.230411           -1.064144           \n",
       "6 151460000006  0.000000            0.000000           \n",
       "  age_middle_to_oldest_old_male age_middle_to_oldest_old_female dependants\n",
       "1  2.1196292                    -1.3062152                      -0.7801407\n",
       "2  0.2302043                    -0.3189938                       0.1197864\n",
       "3  3.8043177                    -1.3062152                      -2.2200240\n",
       "4  2.6982091                    -1.3062152                      -0.5183437\n",
       "5 19.6216703                    -1.3062152                      -2.2200240\n",
       "6  0.0000000                     0.0000000                       0.0000000\n",
       "  unemployment no_higher_education foreign_nationals primary_school_age ⋯\n",
       "1 -0.5060886   -0.3037669           0.142430939       1.458348          ⋯\n",
       "2  1.3419462    0.4424940           0.384372102       1.458348          ⋯\n",
       "3 -0.2425543   -0.8229049          -0.540697051       1.458348          ⋯\n",
       "4 -1.2391934   -0.8229049          -0.002474999      -1.865648          ⋯\n",
       "5 -2.2981224   -0.8229049          -0.540697051       1.458348          ⋯\n",
       "6  0.0000000    0.0000000           0.000000000       0.000000          ⋯\n",
       "  social_network physical_environment sensitivity   prepare      respond     \n",
       "1  1.1958366     1.0118090            -0.7647853635 -0.696485479  0.320951976\n",
       "2  0.3778636     1.0418299            -1.2304165745  1.355476641  1.575888423\n",
       "3  2.3409988     0.8359458             0.1046897905 -2.251790899 -0.203447695\n",
       "4 -0.7933508     0.9203024            -0.4661777348 -1.478027752 -2.026764566\n",
       "5  2.3409988     0.9671620             8.2680954025 -3.144598085 -1.038699169\n",
       "6  0.0000000     1.0492594            -0.0003619911  0.002318221  0.002883523\n",
       "  recover      adaptive_capacity enhanced_exposure social_vulnerability\n",
       "1  0.271253971  0.320951976      1.0934219          0.6558794          \n",
       "2  1.676990485  1.575888423      1.1258642          1.5094801          \n",
       "3  0.312110916 -0.203447695      0.9033735          0.4027326          \n",
       "4 -2.690739562 -2.026764566      0.9945343         -1.1389091          \n",
       "5 -0.797332495 -1.038699169      1.0451736          2.3380431          \n",
       "6  0.002519028  0.002883523      1.1338930          0.6662274          \n",
       "  geometry                      \n",
       "1 POLYGON ((1515164 5034507, ...\n",
       "2 POLYGON ((1515138 5034525, ...\n",
       "3 POLYGON ((1515050 5034427, ...\n",
       "4 POLYGON ((1515095 5034409, ...\n",
       "5 POLYGON ((1514881 5034455, ...\n",
       "6 POLYGON ((1514764 5034456, ..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add st_drop_geometry\n",
    "output_dataset_geom <- merge(output_dataset, oa, by.x=GUID, by.y=GUID, all.x = TRUE)\n",
    "head(output_dataset_geom)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64e09111",
   "metadata": {},
   "source": [
    "# Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bfa864c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleting source `../../3_outputs/Italy/Milan/2021/social_vulnerability_index_milan_2021.geojson' using driver `GeoJSON'\n",
      "Writing layer `social_vulnerability_index_milan_2021' to data source \n",
      "  `../../3_outputs/Italy/Milan/2021/social_vulnerability_index_milan_2021.geojson' using driver `GeoJSON'\n",
      "Writing 6079 features with 26 fields and geometry type Polygon.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in abbreviate_shapefile_names(obj):\n",
      "“Field names abbreviated for ESRI Shapefile driver”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleting layer `social_vulnerability_index_milan_2021' using driver `ESRI Shapefile'\n",
      "Writing layer `social_vulnerability_index_milan_2021' to data source \n",
      "  `../../3_outputs/Italy/Milan/2021/social_vulnerability_index_milan_2021.shp' using driver `ESRI Shapefile'\n",
      "Writing 6079 features with 26 fields and geometry type Polygon.\n"
     ]
    }
   ],
   "source": [
    "# CSV\n",
    "write.csv(output_dataset, file.path(output_dir, \"social_vulnerability_index_milan_2021.csv\"), row.names = FALSE)\n",
    "\n",
    "# GeoJSON\n",
    "st_write(output_dataset_geom, file.path(output_dir, \"social_vulnerability_index_milan_2021.geojson\"), delete_dsn=TRUE)\n",
    "\n",
    "# Shapefile\n",
    "# Need to manually rename these fields, otherwise we get a shapefile creation error\n",
    "names(output_dataset_geom)[names(output_dataset_geom) == 'early_childhood_boy'] <- 'erly_cld_b'\n",
    "names(output_dataset_geom)[names(output_dataset_geom) == 'early_childhood_girl'] <- 'erly_cld_g'\n",
    "names(output_dataset_geom)[names(output_dataset_geom) == 'age_middle_to_oldest_old_male'] <- 'age_old_m'\n",
    "names(output_dataset_geom)[names(output_dataset_geom) == 'age_middle_to_oldest_old_female'] <- 'age_old_f'\n",
    "st_write(output_dataset_geom, file.path(output_dir, \"social_vulnerability_index_milan_2021.shp\"), append = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ad01c2-b342-4f7f-b9d9-c03ccb0ed11b",
   "metadata": {},
   "source": [
    "**END**"
   ]
  }
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