{ "cells": [ { "cell_type": "markdown", "id": "777ee8d3-6725-43e0-a45f-5e7ead0aa26f", "metadata": {}, "source": [ "# Social Vulnerability Logroño - 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": [ "\n", "
  1. 'lubridate'
  2. 'forcats'
  3. 'stringr'
  4. 'dplyr'
  5. 'purrr'
  6. 'readr'
  7. 'tidyr'
  8. 'tibble'
  9. 'ggplot2'
  10. 'tidyverse'
  11. 'sf'
  12. 'pacman'
\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\",\"Spain\",\"Logrono\",\"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 <- c(\"CCA\", \"CPRO\", \"CMUN\", \"CDIS\", \"CSEC\")\n", "GUID_census_indicator_data <- c(\"ccaa\", \"CPRO\", \"CMUN\", \"dist\", \"secc\")\n", "GUID_length = 5" ] }, { "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": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 20
CCACPROCMUNCDISCSECearly_childhood_boyearly_childhood_girlage_middle_to_oldest_old_maleage_middle_to_oldest_old_femaledisabilityone_parent_householdsdependantsunemploymentattending_universityno_higher_educationforeign_nationalsrentedprimary_school_ageone_person_householdsyear_built
<int><int><int><int><int><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
11726111-0.19258164-0.1467916 0.4818082-0.10021618 1.2753218-1.459709836-0.0938240-1.00214549 1.2303104 0.07143877 0.1446865-0.5073936 0.30936208 1.0319635-0.94812767
21726211 0.23745377 0.3146154-0.2611307-0.31519260 0.3880289-0.007254482 0.1987363 0.87022439 0.2231589 0.27339067 0.7793089-0.5005024 0.16550926 0.4903007 0.24638629
31726311-0.96122760-0.1140934 0.3255496 0.83585043-0.4992639-1.459709836-0.0938240-0.37802220 0.7267347 0.20607337 0.3985355-1.0793008 0.26195604 0.7214991 2.13797352
41726411 0.01083739-1.4002199 0.5976241-1.28188374-1.3865567 NA-1.5566254-1.62626879-1.2875683 1.61973671-1.2514828 NA-1.57053273 NA NA
51726511-0.10396496 0.1640514-0.1893316 0.06098884 1.2753218-0.118741371 0.3450164 1.18228604-0.2804168 0.27339067 0.6523844-0.6041755 0.05555571-0.1104731-0.08916627
61726521 2.13595981 1.4136596-0.7006675-0.72507994 0.3880289 1.157623344 1.5152575-0.06596055 0.2231589-0.06319583 0.1446865-0.3682738 1.19901504-0.4718037-0.42805727
\n" ], "text/latex": [ "A data.frame: 6 × 20\n", "\\begin{tabular}{r|llllllllllllllllllll}\n", " & CCA & CPRO & CMUN & CDIS & CSEC & early\\_childhood\\_boy & early\\_childhood\\_girl & age\\_middle\\_to\\_oldest\\_old\\_male & age\\_middle\\_to\\_oldest\\_old\\_female & disability & one\\_parent\\_households & dependants & unemployment & attending\\_university & no\\_higher\\_education & foreign\\_nationals & rented & primary\\_school\\_age & one\\_person\\_households & year\\_built\\\\\n", " & & & & & & & & & & & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 17 & 26 & 1 & 1 & 1 & -0.19258164 & -0.1467916 & 0.4818082 & -0.10021618 & 1.2753218 & -1.459709836 & -0.0938240 & -1.00214549 & 1.2303104 & 0.07143877 & 0.1446865 & -0.5073936 & 0.30936208 & 1.0319635 & -0.94812767\\\\\n", "\t2 & 17 & 26 & 2 & 1 & 1 & 0.23745377 & 0.3146154 & -0.2611307 & -0.31519260 & 0.3880289 & -0.007254482 & 0.1987363 & 0.87022439 & 0.2231589 & 0.27339067 & 0.7793089 & -0.5005024 & 0.16550926 & 0.4903007 & 0.24638629\\\\\n", "\t3 & 17 & 26 & 3 & 1 & 1 & -0.96122760 & -0.1140934 & 0.3255496 & 0.83585043 & -0.4992639 & -1.459709836 & -0.0938240 & -0.37802220 & 0.7267347 & 0.20607337 & 0.3985355 & -1.0793008 & 0.26195604 & 0.7214991 & 2.13797352\\\\\n", "\t4 & 17 & 26 & 4 & 1 & 1 & 0.01083739 & -1.4002199 & 0.5976241 & -1.28188374 & -1.3865567 & NA & -1.5566254 & -1.62626879 & -1.2875683 & 1.61973671 & -1.2514828 & NA & -1.57053273 & NA & NA\\\\\n", "\t5 & 17 & 26 & 5 & 1 & 1 & -0.10396496 & 0.1640514 & -0.1893316 & 0.06098884 & 1.2753218 & -0.118741371 & 0.3450164 & 1.18228604 & -0.2804168 & 0.27339067 & 0.6523844 & -0.6041755 & 0.05555571 & -0.1104731 & -0.08916627\\\\\n", "\t6 & 17 & 26 & 5 & 2 & 1 & 2.13595981 & 1.4136596 & -0.7006675 & -0.72507994 & 0.3880289 & 1.157623344 & 1.5152575 & -0.06596055 & 0.2231589 & -0.06319583 & 0.1446865 & -0.3682738 & 1.19901504 & -0.4718037 & -0.42805727\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 20\n", "\n", "| | CCA <int> | CPRO <int> | CMUN <int> | CDIS <int> | CSEC <int> | early_childhood_boy <dbl> | early_childhood_girl <dbl> | age_middle_to_oldest_old_male <dbl> | age_middle_to_oldest_old_female <dbl> | disability <dbl> | one_parent_households <dbl> | dependants <dbl> | unemployment <dbl> | attending_university <dbl> | no_higher_education <dbl> | foreign_nationals <dbl> | rented <dbl> | primary_school_age <dbl> | one_person_households <dbl> | year_built <dbl> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 17 | 26 | 1 | 1 | 1 | -0.19258164 | -0.1467916 | 0.4818082 | -0.10021618 | 1.2753218 | -1.459709836 | -0.0938240 | -1.00214549 | 1.2303104 | 0.07143877 | 0.1446865 | -0.5073936 | 0.30936208 | 1.0319635 | -0.94812767 |\n", "| 2 | 17 | 26 | 2 | 1 | 1 | 0.23745377 | 0.3146154 | -0.2611307 | -0.31519260 | 0.3880289 | -0.007254482 | 0.1987363 | 0.87022439 | 0.2231589 | 0.27339067 | 0.7793089 | -0.5005024 | 0.16550926 | 0.4903007 | 0.24638629 |\n", "| 3 | 17 | 26 | 3 | 1 | 1 | -0.96122760 | -0.1140934 | 0.3255496 | 0.83585043 | -0.4992639 | -1.459709836 | -0.0938240 | -0.37802220 | 0.7267347 | 0.20607337 | 0.3985355 | -1.0793008 | 0.26195604 | 0.7214991 | 2.13797352 |\n", "| 4 | 17 | 26 | 4 | 1 | 1 | 0.01083739 | -1.4002199 | 0.5976241 | -1.28188374 | -1.3865567 | NA | -1.5566254 | -1.62626879 | -1.2875683 | 1.61973671 | -1.2514828 | NA | -1.57053273 | NA | NA |\n", "| 5 | 17 | 26 | 5 | 1 | 1 | -0.10396496 | 0.1640514 | -0.1893316 | 0.06098884 | 1.2753218 | -0.118741371 | 0.3450164 | 1.18228604 | -0.2804168 | 0.27339067 | 0.6523844 | -0.6041755 | 0.05555571 | -0.1104731 | -0.08916627 |\n", "| 6 | 17 | 26 | 5 | 2 | 1 | 2.13595981 | 1.4136596 | -0.7006675 | -0.72507994 | 0.3880289 | 1.157623344 | 1.5152575 | -0.06596055 | 0.2231589 | -0.06319583 | 0.1446865 | -0.3682738 | 1.19901504 | -0.4718037 | -0.42805727 |\n", "\n" ], "text/plain": [ " CCA CPRO CMUN CDIS CSEC early_childhood_boy early_childhood_girl\n", "1 17 26 1 1 1 -0.19258164 -0.1467916 \n", "2 17 26 2 1 1 0.23745377 0.3146154 \n", "3 17 26 3 1 1 -0.96122760 -0.1140934 \n", "4 17 26 4 1 1 0.01083739 -1.4002199 \n", "5 17 26 5 1 1 -0.10396496 0.1640514 \n", "6 17 26 5 2 1 2.13595981 1.4136596 \n", " age_middle_to_oldest_old_male age_middle_to_oldest_old_female disability\n", "1 0.4818082 -0.10021618 1.2753218\n", "2 -0.2611307 -0.31519260 0.3880289\n", "3 0.3255496 0.83585043 -0.4992639\n", "4 0.5976241 -1.28188374 -1.3865567\n", "5 -0.1893316 0.06098884 1.2753218\n", "6 -0.7006675 -0.72507994 0.3880289\n", " one_parent_households dependants unemployment attending_university\n", "1 -1.459709836 -0.0938240 -1.00214549 1.2303104 \n", "2 -0.007254482 0.1987363 0.87022439 0.2231589 \n", "3 -1.459709836 -0.0938240 -0.37802220 0.7267347 \n", "4 NA -1.5566254 -1.62626879 -1.2875683 \n", "5 -0.118741371 0.3450164 1.18228604 -0.2804168 \n", "6 1.157623344 1.5152575 -0.06596055 0.2231589 \n", " no_higher_education foreign_nationals rented primary_school_age\n", "1 0.07143877 0.1446865 -0.5073936 0.30936208 \n", "2 0.27339067 0.7793089 -0.5005024 0.16550926 \n", "3 0.20607337 0.3985355 -1.0793008 0.26195604 \n", "4 1.61973671 -1.2514828 NA -1.57053273 \n", "5 0.27339067 0.6523844 -0.6041755 0.05555571 \n", "6 -0.06319583 0.1446865 -0.3682738 1.19901504 \n", " one_person_households year_built \n", "1 1.0319635 -0.94812767\n", "2 0.4903007 0.24638629\n", "3 0.7214991 2.13797352\n", "4 NA NA\n", "5 -0.1104731 -0.08916627\n", "6 -0.4718037 -0.42805727" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Reading layer `census_areas_TCD' from data source \n", " `/Cities/2_pipeline/Spain/1b_Copernicus/2021/census_areas_TCD.geojson' \n", " using driver `GeoJSON'\n", "Simple feature collection with 36333 features and 22 fields\n", "Geometry type: MULTIPOLYGON\n", "Dimension: XY\n", "Bounding box: xmin: -1004502 ymin: 3132130 xmax: 1126932 ymax: 4859240\n", "Projected CRS: ETRS89 / UTM zone 30N\n", "Reading layer `census_areas_IMD' from data source \n", " `/Cities/2_pipeline/Spain/1b_Copernicus/2021/census_areas_IMD.geojson' \n", " using driver `GeoJSON'\n", "Simple feature collection with 36333 features and 22 fields\n", "Geometry type: MULTIPOLYGON\n", "Dimension: XY\n", "Bounding box: xmin: -1004502 ymin: 3132130 xmax: 1126932 ymax: 4859240\n", "Projected CRS: ETRS89 / UTM zone 30N\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 17 × 9
domainindicatorsensitivitypreparerespondrecoveradaptive_capacityenhanced_exposureweight
<chr><chr><int><int><int><int><int><int><int>
1age early_childhood_boy 100000 1
2age early_childhood_girl 100000 1
3age age_middle_to_oldest_old_male 100000 1
4age age_middle_to_oldest_old_female100000 1
5health disability 100000 1
6income one_parent_households 011110 1
7income dependants 011110 1
8income unemployment 011110 1
9income attending_university 011110 1
10info_access_use no_higher_education 011110 1
11local_knowledge foreign_nationals 011010 1
12tenure rented 010010 1
13social_network primary_school_age 001110-1
14social_network one_person_households 001110 1
15housing_characteristicsyear_built 000001 1
16physical_environment impervious 000001 1
17physical_environment tree_cover_density 000001 1
\n" ], "text/latex": [ "A data.frame: 17 × 9\n", "\\begin{tabular}{r|lllllllll}\n", " & domain & indicator & sensitivity & prepare & respond & recover & adaptive\\_capacity & enhanced\\_exposure & weight\\\\\n", " & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & age & early\\_childhood\\_boy & 1 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n", "\t2 & age & early\\_childhood\\_girl & 1 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n", "\t3 & age & age\\_middle\\_to\\_oldest\\_old\\_male & 1 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n", "\t4 & age & age\\_middle\\_to\\_oldest\\_old\\_female & 1 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n", "\t5 & health & disability & 1 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n", "\t6 & income & one\\_parent\\_households & 0 & 1 & 1 & 1 & 1 & 0 & 1\\\\\n", "\t7 & income & dependants & 0 & 1 & 1 & 1 & 1 & 0 & 1\\\\\n", "\t8 & income & unemployment & 0 & 1 & 1 & 1 & 1 & 0 & 1\\\\\n", "\t9 & income & attending\\_university & 0 & 1 & 1 & 1 & 1 & 0 & 1\\\\\n", "\t10 & info\\_access\\_use & no\\_higher\\_education & 0 & 1 & 1 & 1 & 1 & 0 & 1\\\\\n", "\t11 & local\\_knowledge & foreign\\_nationals & 0 & 1 & 1 & 0 & 1 & 0 & 1\\\\\n", "\t12 & tenure & rented & 0 & 1 & 0 & 0 & 1 & 0 & 1\\\\\n", "\t13 & social\\_network & primary\\_school\\_age & 0 & 0 & 1 & 1 & 1 & 0 & -1\\\\\n", "\t14 & social\\_network & one\\_person\\_households & 0 & 0 & 1 & 1 & 1 & 0 & 1\\\\\n", "\t15 & housing\\_characteristics & year\\_built & 0 & 0 & 0 & 0 & 0 & 1 & 1\\\\\n", "\t16 & physical\\_environment & impervious & 0 & 0 & 0 & 0 & 0 & 1 & 1\\\\\n", "\t17 & physical\\_environment & tree\\_cover\\_density & 0 & 0 & 0 & 0 & 0 & 1 & 1\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 17 × 9\n", "\n", "| | domain <chr> | indicator <chr> | sensitivity <int> | prepare <int> | respond <int> | recover <int> | adaptive_capacity <int> | enhanced_exposure <int> | weight <int> |\n", "|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | age | early_childhood_boy | 1 | 0 | 0 | 0 | 0 | 0 | 1 |\n", "| 2 | age | early_childhood_girl | 1 | 0 | 0 | 0 | 0 | 0 | 1 |\n", "| 3 | age | age_middle_to_oldest_old_male | 1 | 0 | 0 | 0 | 0 | 0 | 1 |\n", "| 4 | age | age_middle_to_oldest_old_female | 1 | 0 | 0 | 0 | 0 | 0 | 1 |\n", "| 5 | health | disability | 1 | 0 | 0 | 0 | 0 | 0 | 1 |\n", "| 6 | income | one_parent_households | 0 | 1 | 1 | 1 | 1 | 0 | 1 |\n", "| 7 | income | dependants | 0 | 1 | 1 | 1 | 1 | 0 | 1 |\n", "| 8 | income | unemployment | 0 | 1 | 1 | 1 | 1 | 0 | 1 |\n", "| 9 | income | attending_university | 0 | 1 | 1 | 1 | 1 | 0 | 1 |\n", "| 10 | info_access_use | no_higher_education | 0 | 1 | 1 | 1 | 1 | 0 | 1 |\n", "| 11 | local_knowledge | foreign_nationals | 0 | 1 | 1 | 0 | 1 | 0 | 1 |\n", "| 12 | tenure | rented | 0 | 1 | 0 | 0 | 1 | 0 | 1 |\n", "| 13 | social_network | primary_school_age | 0 | 0 | 1 | 1 | 1 | 0 | -1 |\n", "| 14 | social_network | one_person_households | 0 | 0 | 1 | 1 | 1 | 0 | 1 |\n", "| 15 | housing_characteristics | year_built | 0 | 0 | 0 | 0 | 0 | 1 | 1 |\n", "| 16 | physical_environment | impervious | 0 | 0 | 0 | 0 | 0 | 1 | 1 |\n", "| 17 | physical_environment | tree_cover_density | 0 | 0 | 0 | 0 | 0 | 1 | 1 |\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 health disability 1 0 \n", "6 income one_parent_households 0 1 \n", "7 income dependants 0 1 \n", "8 income unemployment 0 1 \n", "9 income attending_university 0 1 \n", "10 info_access_use no_higher_education 0 1 \n", "11 local_knowledge foreign_nationals 0 1 \n", "12 tenure rented 0 1 \n", "13 social_network primary_school_age 0 0 \n", "14 social_network one_person_households 0 0 \n", "15 housing_characteristics year_built 0 0 \n", "16 physical_environment impervious 0 0 \n", "17 physical_environment tree_cover_density 0 0 \n", " respond recover adaptive_capacity enhanced_exposure weight\n", "1 0 0 0 0 1 \n", "2 0 0 0 0 1 \n", "3 0 0 0 0 1 \n", "4 0 0 0 0 1 \n", "5 0 0 0 0 1 \n", "6 1 1 1 0 1 \n", "7 1 1 1 0 1 \n", "8 1 1 1 0 1 \n", "9 1 1 1 0 1 \n", "10 1 1 1 0 1 \n", "11 1 0 1 0 1 \n", "12 0 0 1 0 1 \n", "13 1 1 1 0 -1 \n", "14 1 1 1 0 1 \n", "15 0 0 0 1 1 \n", "16 0 0 0 1 1 \n", "17 0 0 0 1 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load census data\n", "census_indicator_data <- read.csv(\"../../2_pipeline/Spain/Logrono/1a_CensusData/2021/censusDataZ.csv\")\n", "# Update the census indicator data ID to the common GUID\n", "colnames(census_indicator_data)[colnames(census_indicator_data) %in% GUID_census_indicator_data] = GUID\n", "# Change all the GUID column data type to integer, this helps to merge datasets later in the code\n", "census_indicator_data[GUID] <- lapply(census_indicator_data[GUID], as.integer)\n", "# Print head of census indicator data\n", "head(census_indicator_data)\n", "\n", "# Load Coperncius data: tree cover density (TCD) and imperviousness density (IMP)\n", "# Both these datasets already uses the GUID\n", "# Both these datasets contain the census area geometry, so we extract from one of the datasets for use later in the code\n", "tcd_indicator_data <- st_read(\"../../2_pipeline/Spain/1b_Copernicus/2021/census_areas_TCD.geojson\")\n", "imd_indicator_data <- st_read(\"../../2_pipeline/Spain/1b_Copernicus/2021/census_areas_IMD.geojson\")\n", "# Get the geospatial data from the TCD data (the IMD data also has same spatial data)\n", "oa <- subset(tcd_indicator_data, select = c(GUID, 'geometry'))\n", "# Remove the geospatial geometry from the TCS and IMD datasets\n", "tcd_indicator_data <- st_drop_geometry(tcd_indicator_data)\n", "imd_indicator_data <- st_drop_geometry(imd_indicator_data)\n", "# Change all the GUID column data type to integer, this helps to merge datasets later in the code\n", "imd_indicator_data[GUID] <- lapply(imd_indicator_data[GUID], as.integer)\n", "tcd_indicator_data[GUID] <- lapply(tcd_indicator_data[GUID], as.integer)\n", "oa[GUID] <- lapply(st_drop_geometry(oa[GUID]), as.integer)\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": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 22
CCACPROCMUNCDISCSECearly_childhood_boyearly_childhood_girlage_middle_to_oldest_old_maleage_middle_to_oldest_old_femaledisability⋯unemploymentattending_universityno_higher_educationforeign_nationalsrentedprimary_school_ageone_person_householdsyear_builttree_cover_densityimpervious
<int><int><int><int><int><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
11726 111-0.19258164-0.1467916 0.48180824-0.10021618 1.2753218⋯-1.0021455 1.23031040.071438769 0.1446865-0.5073936 0.30936211.0319635-0.9481277-0.4793341-1.232806
21726 1011-0.36188692-0.3284478 1.32081073 0.44848018-1.3865567⋯-1.6262688-1.28756831.619736710-1.2514828 0.0000000-0.60606500.0000000 0.0000000 0.6403542-1.105968
3172610011 0.01083739-1.4002199 0.23798535-0.52201032-1.3865567⋯-1.6262688-1.28756831.619736710-1.2514828 0.0000000-0.90836080.0000000 0.0000000 0.5002384-1.264703
4172610111-1.36078806-1.4002199-0.22155305 0.07955613-1.3865567⋯-1.6262688-1.28756831.619736710-1.2514828 0.0000000-1.57053270.0000000 0.0000000-1.7574746-1.265740
5172610211 0.10257030-0.4581527 0.05129390 0.59299501 0.3880289⋯ 1.0262552 0.22315890.138756071-0.1091625-0.7479582-0.44020110.3954425 1.2637707 0.4972984-1.255886
6172610212 0.64987009 0.6302157-0.04065368 0.64469364 0.3880289⋯ 0.7141936-0.78399260.004121467 0.1446865-0.9688721 0.71340640.8394153 0.7211874 0.4394261-1.178694
\n" ], "text/latex": [ "A data.frame: 6 × 22\n", "\\begin{tabular}{r|lllllllllllllllllllll}\n", " & CCA & CPRO & CMUN & CDIS & CSEC & early\\_childhood\\_boy & early\\_childhood\\_girl & age\\_middle\\_to\\_oldest\\_old\\_male & age\\_middle\\_to\\_oldest\\_old\\_female & disability & ⋯ & unemployment & attending\\_university & no\\_higher\\_education & foreign\\_nationals & rented & primary\\_school\\_age & one\\_person\\_households & year\\_built & tree\\_cover\\_density & impervious\\\\\n", " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 17 & 26 & 1 & 1 & 1 & -0.19258164 & -0.1467916 & 0.48180824 & -0.10021618 & 1.2753218 & ⋯ & -1.0021455 & 1.2303104 & 0.071438769 & 0.1446865 & -0.5073936 & 0.3093621 & 1.0319635 & -0.9481277 & -0.4793341 & -1.232806\\\\\n", "\t2 & 17 & 26 & 10 & 1 & 1 & -0.36188692 & -0.3284478 & 1.32081073 & 0.44848018 & -1.3865567 & ⋯ & -1.6262688 & -1.2875683 & 1.619736710 & -1.2514828 & 0.0000000 & -0.6060650 & 0.0000000 & 0.0000000 & 0.6403542 & -1.105968\\\\\n", "\t3 & 17 & 26 & 100 & 1 & 1 & 0.01083739 & -1.4002199 & 0.23798535 & -0.52201032 & -1.3865567 & ⋯ & -1.6262688 & -1.2875683 & 1.619736710 & -1.2514828 & 0.0000000 & -0.9083608 & 0.0000000 & 0.0000000 & 0.5002384 & -1.264703\\\\\n", "\t4 & 17 & 26 & 101 & 1 & 1 & -1.36078806 & -1.4002199 & -0.22155305 & 0.07955613 & -1.3865567 & ⋯ & -1.6262688 & -1.2875683 & 1.619736710 & -1.2514828 & 0.0000000 & -1.5705327 & 0.0000000 & 0.0000000 & -1.7574746 & -1.265740\\\\\n", "\t5 & 17 & 26 & 102 & 1 & 1 & 0.10257030 & -0.4581527 & 0.05129390 & 0.59299501 & 0.3880289 & ⋯ & 1.0262552 & 0.2231589 & 0.138756071 & -0.1091625 & -0.7479582 & -0.4402011 & 0.3954425 & 1.2637707 & 0.4972984 & -1.255886\\\\\n", "\t6 & 17 & 26 & 102 & 1 & 2 & 0.64987009 & 0.6302157 & -0.04065368 & 0.64469364 & 0.3880289 & ⋯ & 0.7141936 & -0.7839926 & 0.004121467 & 0.1446865 & -0.9688721 & 0.7134064 & 0.8394153 & 0.7211874 & 0.4394261 & -1.178694\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 22\n", "\n", "| | CCA <int> | CPRO <int> | CMUN <int> | CDIS <int> | CSEC <int> | early_childhood_boy <dbl> | early_childhood_girl <dbl> | age_middle_to_oldest_old_male <dbl> | age_middle_to_oldest_old_female <dbl> | disability <dbl> | ⋯ ⋯ | unemployment <dbl> | attending_university <dbl> | no_higher_education <dbl> | foreign_nationals <dbl> | rented <dbl> | primary_school_age <dbl> | one_person_households <dbl> | year_built <dbl> | tree_cover_density <dbl> | impervious <dbl> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 17 | 26 | 1 | 1 | 1 | -0.19258164 | -0.1467916 | 0.48180824 | -0.10021618 | 1.2753218 | ⋯ | -1.0021455 | 1.2303104 | 0.071438769 | 0.1446865 | -0.5073936 | 0.3093621 | 1.0319635 | -0.9481277 | -0.4793341 | -1.232806 |\n", "| 2 | 17 | 26 | 10 | 1 | 1 | -0.36188692 | -0.3284478 | 1.32081073 | 0.44848018 | -1.3865567 | ⋯ | -1.6262688 | -1.2875683 | 1.619736710 | -1.2514828 | 0.0000000 | -0.6060650 | 0.0000000 | 0.0000000 | 0.6403542 | -1.105968 |\n", "| 3 | 17 | 26 | 100 | 1 | 1 | 0.01083739 | -1.4002199 | 0.23798535 | -0.52201032 | -1.3865567 | ⋯ | -1.6262688 | -1.2875683 | 1.619736710 | -1.2514828 | 0.0000000 | -0.9083608 | 0.0000000 | 0.0000000 | 0.5002384 | -1.264703 |\n", "| 4 | 17 | 26 | 101 | 1 | 1 | -1.36078806 | -1.4002199 | -0.22155305 | 0.07955613 | -1.3865567 | ⋯ | -1.6262688 | -1.2875683 | 1.619736710 | -1.2514828 | 0.0000000 | -1.5705327 | 0.0000000 | 0.0000000 | -1.7574746 | -1.265740 |\n", "| 5 | 17 | 26 | 102 | 1 | 1 | 0.10257030 | -0.4581527 | 0.05129390 | 0.59299501 | 0.3880289 | ⋯ | 1.0262552 | 0.2231589 | 0.138756071 | -0.1091625 | -0.7479582 | -0.4402011 | 0.3954425 | 1.2637707 | 0.4972984 | -1.255886 |\n", "| 6 | 17 | 26 | 102 | 1 | 2 | 0.64987009 | 0.6302157 | -0.04065368 | 0.64469364 | 0.3880289 | ⋯ | 0.7141936 | -0.7839926 | 0.004121467 | 0.1446865 | -0.9688721 | 0.7134064 | 0.8394153 | 0.7211874 | 0.4394261 | -1.178694 |\n", "\n" ], "text/plain": [ " CCA CPRO CMUN CDIS CSEC early_childhood_boy early_childhood_girl\n", "1 17 26 1 1 1 -0.19258164 -0.1467916 \n", "2 17 26 10 1 1 -0.36188692 -0.3284478 \n", "3 17 26 100 1 1 0.01083739 -1.4002199 \n", "4 17 26 101 1 1 -1.36078806 -1.4002199 \n", "5 17 26 102 1 1 0.10257030 -0.4581527 \n", "6 17 26 102 1 2 0.64987009 0.6302157 \n", " age_middle_to_oldest_old_male age_middle_to_oldest_old_female disability ⋯\n", "1 0.48180824 -0.10021618 1.2753218 ⋯\n", "2 1.32081073 0.44848018 -1.3865567 ⋯\n", "3 0.23798535 -0.52201032 -1.3865567 ⋯\n", "4 -0.22155305 0.07955613 -1.3865567 ⋯\n", "5 0.05129390 0.59299501 0.3880289 ⋯\n", "6 -0.04065368 0.64469364 0.3880289 ⋯\n", " unemployment attending_university no_higher_education foreign_nationals\n", "1 -1.0021455 1.2303104 0.071438769 0.1446865 \n", "2 -1.6262688 -1.2875683 1.619736710 -1.2514828 \n", "3 -1.6262688 -1.2875683 1.619736710 -1.2514828 \n", "4 -1.6262688 -1.2875683 1.619736710 -1.2514828 \n", "5 1.0262552 0.2231589 0.138756071 -0.1091625 \n", "6 0.7141936 -0.7839926 0.004121467 0.1446865 \n", " rented primary_school_age one_person_households year_built\n", "1 -0.5073936 0.3093621 1.0319635 -0.9481277\n", "2 0.0000000 -0.6060650 0.0000000 0.0000000\n", "3 0.0000000 -0.9083608 0.0000000 0.0000000\n", "4 0.0000000 -1.5705327 0.0000000 0.0000000\n", "5 -0.7479582 -0.4402011 0.3954425 1.2637707\n", "6 -0.9688721 0.7134064 0.8394153 0.7211874\n", " tree_cover_density impervious\n", "1 -0.4793341 -1.232806 \n", "2 0.6403542 -1.105968 \n", "3 0.5002384 -1.264703 \n", "4 -1.7574746 -1.265740 \n", "5 0.4972984 -1.255886 \n", "6 0.4394261 -1.178694 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# combine census data with copernicus TCD and IMD data (without geospatial data to avoid duplication)\n", "indicator_data <- merge(census_indicator_data, tcd_indicator_data, by=GUID, all.x=TRUE)\n", "indicator_data <- merge(indicator_data, imd_indicator_data, by=GUID, all.x=TRUE)\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", "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": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 22
CCACPROCMUNCDISCSECearly_childhood_boyearly_childhood_girlage_middle_to_oldest_old_maleage_middle_to_oldest_old_femaledisability⋯unemploymentattending_universityno_higher_educationforeign_nationalsrentedprimary_school_ageone_person_householdsyear_builttree_cover_densityimpervious
<int><int><int><int><int><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
11726 111-0.19258164-0.1467916 0.48180824-0.10021618 1.2753218⋯-1.0021455 1.23031040.071438769 0.1446865-0.5073936 0.30936211.0319635-0.9481277-0.4793341-1.232806
21726 1011-0.36188692-0.3284478 1.32081073 0.44848018-1.3865567⋯-1.6262688-1.28756831.619736710-1.2514828 0.0000000-0.60606500.0000000 0.0000000 0.6403542-1.105968
3172610011 0.01083739-1.4002199 0.23798535-0.52201032-1.3865567⋯-1.6262688-1.28756831.619736710-1.2514828 0.0000000-0.90836080.0000000 0.0000000 0.5002384-1.264703
4172610111-1.36078806-1.4002199-0.22155305 0.07955613-1.3865567⋯-1.6262688-1.28756831.619736710-1.2514828 0.0000000-1.57053270.0000000 0.0000000-1.7574746-1.265740
5172610211 0.10257030-0.4581527 0.05129390 0.59299501 0.3880289⋯ 1.0262552 0.22315890.138756071-0.1091625-0.7479582-0.44020110.3954425 1.2637707 0.4972984-1.255886
6172610212 0.64987009 0.6302157-0.04065368 0.64469364 0.3880289⋯ 0.7141936-0.78399260.004121467 0.1446865-0.9688721 0.71340640.8394153 0.7211874 0.4394261-1.178694
\n" ], "text/latex": [ "A data.frame: 6 × 22\n", "\\begin{tabular}{r|lllllllllllllllllllll}\n", " & CCA & CPRO & CMUN & CDIS & CSEC & early\\_childhood\\_boy & early\\_childhood\\_girl & age\\_middle\\_to\\_oldest\\_old\\_male & age\\_middle\\_to\\_oldest\\_old\\_female & disability & ⋯ & unemployment & attending\\_university & no\\_higher\\_education & foreign\\_nationals & rented & primary\\_school\\_age & one\\_person\\_households & year\\_built & tree\\_cover\\_density & impervious\\\\\n", " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 17 & 26 & 1 & 1 & 1 & -0.19258164 & -0.1467916 & 0.48180824 & -0.10021618 & 1.2753218 & ⋯ & -1.0021455 & 1.2303104 & 0.071438769 & 0.1446865 & -0.5073936 & 0.3093621 & 1.0319635 & -0.9481277 & -0.4793341 & -1.232806\\\\\n", "\t2 & 17 & 26 & 10 & 1 & 1 & -0.36188692 & -0.3284478 & 1.32081073 & 0.44848018 & -1.3865567 & ⋯ & -1.6262688 & -1.2875683 & 1.619736710 & -1.2514828 & 0.0000000 & -0.6060650 & 0.0000000 & 0.0000000 & 0.6403542 & -1.105968\\\\\n", "\t3 & 17 & 26 & 100 & 1 & 1 & 0.01083739 & -1.4002199 & 0.23798535 & -0.52201032 & -1.3865567 & ⋯ & -1.6262688 & -1.2875683 & 1.619736710 & -1.2514828 & 0.0000000 & -0.9083608 & 0.0000000 & 0.0000000 & 0.5002384 & -1.264703\\\\\n", "\t4 & 17 & 26 & 101 & 1 & 1 & -1.36078806 & -1.4002199 & -0.22155305 & 0.07955613 & -1.3865567 & ⋯ & -1.6262688 & -1.2875683 & 1.619736710 & -1.2514828 & 0.0000000 & -1.5705327 & 0.0000000 & 0.0000000 & -1.7574746 & -1.265740\\\\\n", "\t5 & 17 & 26 & 102 & 1 & 1 & 0.10257030 & -0.4581527 & 0.05129390 & 0.59299501 & 0.3880289 & ⋯ & 1.0262552 & 0.2231589 & 0.138756071 & -0.1091625 & -0.7479582 & -0.4402011 & 0.3954425 & 1.2637707 & 0.4972984 & -1.255886\\\\\n", "\t6 & 17 & 26 & 102 & 1 & 2 & 0.64987009 & 0.6302157 & -0.04065368 & 0.64469364 & 0.3880289 & ⋯ & 0.7141936 & -0.7839926 & 0.004121467 & 0.1446865 & -0.9688721 & 0.7134064 & 0.8394153 & 0.7211874 & 0.4394261 & -1.178694\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 22\n", "\n", "| | CCA <int> | CPRO <int> | CMUN <int> | CDIS <int> | CSEC <int> | early_childhood_boy <dbl> | early_childhood_girl <dbl> | age_middle_to_oldest_old_male <dbl> | age_middle_to_oldest_old_female <dbl> | disability <dbl> | ⋯ ⋯ | unemployment <dbl> | attending_university <dbl> | no_higher_education <dbl> | foreign_nationals <dbl> | rented <dbl> | primary_school_age <dbl> | one_person_households <dbl> | year_built <dbl> | tree_cover_density <dbl> | impervious <dbl> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 17 | 26 | 1 | 1 | 1 | -0.19258164 | -0.1467916 | 0.48180824 | -0.10021618 | 1.2753218 | ⋯ | -1.0021455 | 1.2303104 | 0.071438769 | 0.1446865 | -0.5073936 | 0.3093621 | 1.0319635 | -0.9481277 | -0.4793341 | -1.232806 |\n", "| 2 | 17 | 26 | 10 | 1 | 1 | -0.36188692 | -0.3284478 | 1.32081073 | 0.44848018 | -1.3865567 | ⋯ | -1.6262688 | -1.2875683 | 1.619736710 | -1.2514828 | 0.0000000 | -0.6060650 | 0.0000000 | 0.0000000 | 0.6403542 | -1.105968 |\n", "| 3 | 17 | 26 | 100 | 1 | 1 | 0.01083739 | -1.4002199 | 0.23798535 | -0.52201032 | -1.3865567 | ⋯ | -1.6262688 | -1.2875683 | 1.619736710 | -1.2514828 | 0.0000000 | -0.9083608 | 0.0000000 | 0.0000000 | 0.5002384 | -1.264703 |\n", "| 4 | 17 | 26 | 101 | 1 | 1 | -1.36078806 | -1.4002199 | -0.22155305 | 0.07955613 | -1.3865567 | ⋯ | -1.6262688 | -1.2875683 | 1.619736710 | -1.2514828 | 0.0000000 | -1.5705327 | 0.0000000 | 0.0000000 | -1.7574746 | -1.265740 |\n", "| 5 | 17 | 26 | 102 | 1 | 1 | 0.10257030 | -0.4581527 | 0.05129390 | 0.59299501 | 0.3880289 | ⋯ | 1.0262552 | 0.2231589 | 0.138756071 | -0.1091625 | -0.7479582 | -0.4402011 | 0.3954425 | 1.2637707 | 0.4972984 | -1.255886 |\n", "| 6 | 17 | 26 | 102 | 1 | 2 | 0.64987009 | 0.6302157 | -0.04065368 | 0.64469364 | 0.3880289 | ⋯ | 0.7141936 | -0.7839926 | 0.004121467 | 0.1446865 | -0.9688721 | 0.7134064 | 0.8394153 | 0.7211874 | 0.4394261 | -1.178694 |\n", "\n" ], "text/plain": [ " CCA CPRO CMUN CDIS CSEC early_childhood_boy early_childhood_girl\n", "1 17 26 1 1 1 -0.19258164 -0.1467916 \n", "2 17 26 10 1 1 -0.36188692 -0.3284478 \n", "3 17 26 100 1 1 0.01083739 -1.4002199 \n", "4 17 26 101 1 1 -1.36078806 -1.4002199 \n", "5 17 26 102 1 1 0.10257030 -0.4581527 \n", "6 17 26 102 1 2 0.64987009 0.6302157 \n", " age_middle_to_oldest_old_male age_middle_to_oldest_old_female disability ⋯\n", "1 0.48180824 -0.10021618 1.2753218 ⋯\n", "2 1.32081073 0.44848018 -1.3865567 ⋯\n", "3 0.23798535 -0.52201032 -1.3865567 ⋯\n", "4 -0.22155305 0.07955613 -1.3865567 ⋯\n", "5 0.05129390 0.59299501 0.3880289 ⋯\n", "6 -0.04065368 0.64469364 0.3880289 ⋯\n", " unemployment attending_university no_higher_education foreign_nationals\n", "1 -1.0021455 1.2303104 0.071438769 0.1446865 \n", "2 -1.6262688 -1.2875683 1.619736710 -1.2514828 \n", "3 -1.6262688 -1.2875683 1.619736710 -1.2514828 \n", "4 -1.6262688 -1.2875683 1.619736710 -1.2514828 \n", "5 1.0262552 0.2231589 0.138756071 -0.1091625 \n", "6 0.7141936 -0.7839926 0.004121467 0.1446865 \n", " rented primary_school_age one_person_households year_built\n", "1 -0.5073936 0.3093621 1.0319635 -0.9481277\n", "2 0.0000000 -0.6060650 0.0000000 0.0000000\n", "3 0.0000000 -0.9083608 0.0000000 0.0000000\n", "4 0.0000000 -1.5705327 0.0000000 0.0000000\n", "5 -0.7479582 -0.4402011 0.3954425 1.2637707\n", "6 -0.9688721 0.7134064 0.8394153 0.7211874\n", " tree_cover_density impervious\n", "1 -0.4793341 -1.232806 \n", "2 0.6403542 -1.105968 \n", "3 0.5002384 -1.264703 \n", "4 -1.7574746 -1.265740 \n", "5 0.4972984 -1.255886 \n", "6 0.4394261 -1.178694 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 22
CCACPROCMUNCDISCSECearly_childhood_boyearly_childhood_girlage_middle_to_oldest_old_maleage_middle_to_oldest_old_femaledisability⋯unemploymentattending_universityno_higher_educationforeign_nationalsrentedprimary_school_ageone_person_householdsyear_builttree_cover_densityimpervious
<int><int><int><int><int><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
11726 111-0.19258164-0.1467916 0.48180824-0.10021618 1.2753218⋯-1.0021455 1.23031040.071438769 0.1446865-0.5073936-0.30936211.0319635-0.9481277-0.4793341-1.232806
21726 1011-0.36188692-0.3284478 1.32081073 0.44848018-1.3865567⋯-1.6262688-1.28756831.619736710-1.2514828 0.0000000 0.60606500.0000000 0.0000000 0.6403542-1.105968
3172610011 0.01083739-1.4002199 0.23798535-0.52201032-1.3865567⋯-1.6262688-1.28756831.619736710-1.2514828 0.0000000 0.90836080.0000000 0.0000000 0.5002384-1.264703
4172610111-1.36078806-1.4002199-0.22155305 0.07955613-1.3865567⋯-1.6262688-1.28756831.619736710-1.2514828 0.0000000 1.57053270.0000000 0.0000000-1.7574746-1.265740
5172610211 0.10257030-0.4581527 0.05129390 0.59299501 0.3880289⋯ 1.0262552 0.22315890.138756071-0.1091625-0.7479582 0.44020110.3954425 1.2637707 0.4972984-1.255886
6172610212 0.64987009 0.6302157-0.04065368 0.64469364 0.3880289⋯ 0.7141936-0.78399260.004121467 0.1446865-0.9688721-0.71340640.8394153 0.7211874 0.4394261-1.178694
\n" ], "text/latex": [ "A data.frame: 6 × 22\n", "\\begin{tabular}{r|lllllllllllllllllllll}\n", " & CCA & CPRO & CMUN & CDIS & CSEC & early\\_childhood\\_boy & early\\_childhood\\_girl & age\\_middle\\_to\\_oldest\\_old\\_male & age\\_middle\\_to\\_oldest\\_old\\_female & disability & ⋯ & unemployment & attending\\_university & no\\_higher\\_education & foreign\\_nationals & rented & primary\\_school\\_age & one\\_person\\_households & year\\_built & tree\\_cover\\_density & impervious\\\\\n", " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 17 & 26 & 1 & 1 & 1 & -0.19258164 & -0.1467916 & 0.48180824 & -0.10021618 & 1.2753218 & ⋯ & -1.0021455 & 1.2303104 & 0.071438769 & 0.1446865 & -0.5073936 & -0.3093621 & 1.0319635 & -0.9481277 & -0.4793341 & -1.232806\\\\\n", "\t2 & 17 & 26 & 10 & 1 & 1 & -0.36188692 & -0.3284478 & 1.32081073 & 0.44848018 & -1.3865567 & ⋯ & -1.6262688 & -1.2875683 & 1.619736710 & -1.2514828 & 0.0000000 & 0.6060650 & 0.0000000 & 0.0000000 & 0.6403542 & -1.105968\\\\\n", "\t3 & 17 & 26 & 100 & 1 & 1 & 0.01083739 & -1.4002199 & 0.23798535 & -0.52201032 & -1.3865567 & ⋯ & -1.6262688 & -1.2875683 & 1.619736710 & -1.2514828 & 0.0000000 & 0.9083608 & 0.0000000 & 0.0000000 & 0.5002384 & -1.264703\\\\\n", "\t4 & 17 & 26 & 101 & 1 & 1 & -1.36078806 & -1.4002199 & -0.22155305 & 0.07955613 & -1.3865567 & ⋯ & -1.6262688 & -1.2875683 & 1.619736710 & -1.2514828 & 0.0000000 & 1.5705327 & 0.0000000 & 0.0000000 & -1.7574746 & -1.265740\\\\\n", "\t5 & 17 & 26 & 102 & 1 & 1 & 0.10257030 & -0.4581527 & 0.05129390 & 0.59299501 & 0.3880289 & ⋯ & 1.0262552 & 0.2231589 & 0.138756071 & -0.1091625 & -0.7479582 & 0.4402011 & 0.3954425 & 1.2637707 & 0.4972984 & -1.255886\\\\\n", "\t6 & 17 & 26 & 102 & 1 & 2 & 0.64987009 & 0.6302157 & -0.04065368 & 0.64469364 & 0.3880289 & ⋯ & 0.7141936 & -0.7839926 & 0.004121467 & 0.1446865 & -0.9688721 & -0.7134064 & 0.8394153 & 0.7211874 & 0.4394261 & -1.178694\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 22\n", "\n", "| | CCA <int> | CPRO <int> | CMUN <int> | CDIS <int> | CSEC <int> | early_childhood_boy <dbl> | early_childhood_girl <dbl> | age_middle_to_oldest_old_male <dbl> | age_middle_to_oldest_old_female <dbl> | disability <dbl> | ⋯ ⋯ | unemployment <dbl> | attending_university <dbl> | no_higher_education <dbl> | foreign_nationals <dbl> | rented <dbl> | primary_school_age <dbl> | one_person_households <dbl> | year_built <dbl> | tree_cover_density <dbl> | impervious <dbl> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 17 | 26 | 1 | 1 | 1 | -0.19258164 | -0.1467916 | 0.48180824 | -0.10021618 | 1.2753218 | ⋯ | -1.0021455 | 1.2303104 | 0.071438769 | 0.1446865 | -0.5073936 | -0.3093621 | 1.0319635 | -0.9481277 | -0.4793341 | -1.232806 |\n", "| 2 | 17 | 26 | 10 | 1 | 1 | -0.36188692 | -0.3284478 | 1.32081073 | 0.44848018 | -1.3865567 | ⋯ | -1.6262688 | -1.2875683 | 1.619736710 | -1.2514828 | 0.0000000 | 0.6060650 | 0.0000000 | 0.0000000 | 0.6403542 | -1.105968 |\n", "| 3 | 17 | 26 | 100 | 1 | 1 | 0.01083739 | -1.4002199 | 0.23798535 | -0.52201032 | -1.3865567 | ⋯ | -1.6262688 | -1.2875683 | 1.619736710 | -1.2514828 | 0.0000000 | 0.9083608 | 0.0000000 | 0.0000000 | 0.5002384 | -1.264703 |\n", "| 4 | 17 | 26 | 101 | 1 | 1 | -1.36078806 | -1.4002199 | -0.22155305 | 0.07955613 | -1.3865567 | ⋯ | -1.6262688 | -1.2875683 | 1.619736710 | -1.2514828 | 0.0000000 | 1.5705327 | 0.0000000 | 0.0000000 | -1.7574746 | -1.265740 |\n", "| 5 | 17 | 26 | 102 | 1 | 1 | 0.10257030 | -0.4581527 | 0.05129390 | 0.59299501 | 0.3880289 | ⋯ | 1.0262552 | 0.2231589 | 0.138756071 | -0.1091625 | -0.7479582 | 0.4402011 | 0.3954425 | 1.2637707 | 0.4972984 | -1.255886 |\n", "| 6 | 17 | 26 | 102 | 1 | 2 | 0.64987009 | 0.6302157 | -0.04065368 | 0.64469364 | 0.3880289 | ⋯ | 0.7141936 | -0.7839926 | 0.004121467 | 0.1446865 | -0.9688721 | -0.7134064 | 0.8394153 | 0.7211874 | 0.4394261 | -1.178694 |\n", "\n" ], "text/plain": [ " CCA CPRO CMUN CDIS CSEC early_childhood_boy early_childhood_girl\n", "1 17 26 1 1 1 -0.19258164 -0.1467916 \n", "2 17 26 10 1 1 -0.36188692 -0.3284478 \n", "3 17 26 100 1 1 0.01083739 -1.4002199 \n", "4 17 26 101 1 1 -1.36078806 -1.4002199 \n", "5 17 26 102 1 1 0.10257030 -0.4581527 \n", "6 17 26 102 1 2 0.64987009 0.6302157 \n", " age_middle_to_oldest_old_male age_middle_to_oldest_old_female disability ⋯\n", "1 0.48180824 -0.10021618 1.2753218 ⋯\n", "2 1.32081073 0.44848018 -1.3865567 ⋯\n", "3 0.23798535 -0.52201032 -1.3865567 ⋯\n", "4 -0.22155305 0.07955613 -1.3865567 ⋯\n", "5 0.05129390 0.59299501 0.3880289 ⋯\n", "6 -0.04065368 0.64469364 0.3880289 ⋯\n", " unemployment attending_university no_higher_education foreign_nationals\n", "1 -1.0021455 1.2303104 0.071438769 0.1446865 \n", "2 -1.6262688 -1.2875683 1.619736710 -1.2514828 \n", "3 -1.6262688 -1.2875683 1.619736710 -1.2514828 \n", "4 -1.6262688 -1.2875683 1.619736710 -1.2514828 \n", "5 1.0262552 0.2231589 0.138756071 -0.1091625 \n", "6 0.7141936 -0.7839926 0.004121467 0.1446865 \n", " rented primary_school_age one_person_households year_built\n", "1 -0.5073936 -0.3093621 1.0319635 -0.9481277\n", "2 0.0000000 0.6060650 0.0000000 0.0000000\n", "3 0.0000000 0.9083608 0.0000000 0.0000000\n", "4 0.0000000 1.5705327 0.0000000 0.0000000\n", "5 -0.7479582 0.4402011 0.3954425 1.2637707\n", "6 -0.9688721 -0.7134064 0.8394153 0.7211874\n", " tree_cover_density impervious\n", "1 -0.4793341 -1.232806 \n", "2 0.6403542 -1.105968 \n", "3 0.5002384 -1.264703 \n", "4 -1.7574746 -1.265740 \n", "5 0.4972984 -1.255886 \n", "6 0.4394261 -1.178694 " ] }, "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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s+9NLyxY3tInMe8m1nejZi9WOoPNB1d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\nAAAAAElFTkSuQmCC", "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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HW0qWmghk+NOmv/CQPn5SNNB/8+oOkY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\nw9DvDpzfAAAAAElFTkSuQmCC", "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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Wrj7bTFHzZ9+/qdTRcjn9a0k/Ppph2Q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", "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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oNPauoY39m0WX0eJ067uulcA7X6qDev\naRqutG55gOnfzjz3bzfxms9pGkL0a6t/0vQL6h80nXTv9jPPremg6Rvn8Nz1atqNeV3175o6\n+oc17aLxvqaVrhM+3HQyvs9vWlG6e9OC50NNbfFLTSMnzctW328n23izbXFt9UVNu6N8UXVZ\n04rL0aaVk1c2zeOVC+f12mIrlnF+bsVfdnJQo7et8vhm22gn+pmt9Mlnr1PHbvUD22nPnfqO\nLUMbrFfj0U6dNx9dZZrVbOd113ruTvU921kObHWdY715VDvzP7eV7x07ZDbZwiJ9Y34dAQB2\nnnUOVjqSLUgswMOa9sO9U9NZoD+r6ReXmn4VnB0y8/e38T4XtLVRl97X6icq5PSWbV4vWz17\n3X6Zn/vlcwAb97B2Z52DfUhAYjdd1bSJ/MTJ4/5308GWx6tHd3JYzI9X/2Ub73On6ju38Lwr\nqp/exvseRMs2r5etnr1uv8zP/fI5gI3brXUO9im72LGbLm/av/x0B1t+qGmITACA7bDOwWYc\nyS52LMhvNY2Q89im8wR8UtOoRO9r+nXnBU1DcQIAbId1DrbMFiQAAOAgO9LIRbdacCEAAABL\nQ0ACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAA\ngEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQA\nAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQk\nAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgE\nJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgOL7qABThUnVedXV1ffXSx5QAAAMvi\noGxBuqR6RvXa6rrq2urqcf2a6tXVt1afsKgCAQCAxTsIW5AeXv1ydUHT1qI3N4Wjo9VZTeHp\nAdWDq2+pvrQpSAEAAAfQ8XE5suA6dsKF1d9X764e1ekD4dnVv2oKTu9p2gUPAAA4GI40ctF+\n38XukdVtq8dWv1bddJrpPl49v3pC9cnV5btSHQAAsFT2e0C6c3Vj9ccbnP4V1bHqrjtWEQAA\nsLT2e0C6pjqzuniD01/aNE+u2bGKAACApbXfA9Lvjb8/Vt16nWnPq57dtO/h7+5kUQAAwHLa\n76PYvaH6ieqp1UOrX6+ubBqM4YamUezuUN27aRCH21c/UF21iGIBAIDF28+j2NV0YthvrP6m\nk591tctV1VcvqEYAAGBxjjRywX7fglTTB31m9azqXtU9mo5JOrtp9Lr3V6+v3jTH97xV9ZA2\nvoXu0KjpF+ZYA7DzDjf9r++33ZWPVa/s9CN/AsC+dRAC0gnHm4LQ63fhvT6lelEbn7+Hm05k\n+6KmUfeAveGfHTp06KXnn3/+ouuYq+uuu67jx48/ovqdRdcCALvtoASkR1X/vGmL0Ys7OXjD\n/1P9u+ou1fuq51Xf1/Z/NX1HGx85r+pB1R82bUkC9o7DZ511VldcccWi65iryy+/vKNHjx6U\n5QMAnOIgLAC/q/qemdv/tvqmpqG8n1N9rHpvdcfqu5u2/vzrXa4RAABYAvttv/mVLqm+o+kE\nsF9QfV7TsUjPqJ5W/ffqNtVl1e2qX6q+prr7AmoFAAAWbL9vQXpYdUb12OpD474/rh5cfWpT\nSDqxO9211VPGtA/NUN8AAHDg7PeAdOfqPZ0MRyf8RdPxPh9fcf81Tbvbbeb4IQAAYJ/Y77vY\nfbi67Sr3X1xdtMr9h5p2tfvoThYFAAAsp/0ekP6yKSB93cx9n1c9Ytx/+Yrpn1idO54HAAAc\nMPt9F7vXNA3p/dPVN1dHq3uP+/6y+l9NQ3u/s7pn9fimE8b+wQJqBQAAFmy/B6SqJ1Q/07S1\n6MamUPSU6vrqPtXXz0z79uorms4iDwAAHDAHISC9v+kksWdWN3dq+Pmi6v7VXasPVK9uClEA\nAMABdBAC0gmnCz5/Oi4AAMABt98HaQAAANgwAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABg\nEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAA\nYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkA\nAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJ\nAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYB\nCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAG\nAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAA\nBgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAA\nAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAA\nAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQ\nAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQ\nkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABg\nEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAACGw4suYBedVz2sumd1cXV2\ndX31vuqvqldWNyyqOAAAYPEOQkC6dfX91b+pzlljug9XP1j9UHV8F+oCAACWzEEISC+o/kX1\nuuqXqyurq6uj1VnVJdV9qsc3BaS7VE9ZSKUAAMBC7feA9LlN4ehHq6d3+i1DL66+t/qp6snV\nj1d/vRsFAgAAy2O/D9LweU2h6Bmtv9vcTdW3jesP28GaAACAJbXfA9JZ1c3VdRuc/h+qY00D\nOgAAAAfMfg9Ib2najfCLNzj9v2iaJ2/asYoAAICltd8D0kur91Q/Xz21usNpprtT0+51/6N6\n23geAABwwOz3QRo+Vj26uqJ69rh8qGkUuxuadsG7Q3XhmP6q6suaRrgDAAAOmP0ekKr+rLp7\n9S+bdrW7RydPFPvx6r3Vb1e/Xr2ounExZQIAAIt2EAJSTVuSfnpcdsNdqte08fl7YrpDO1MO\nAACwEQclIFWd0TSi3QnnV4+sLmvape4vqt9vGsVuu95VPbaNz997Vv+19YciBwAAdtBBCEh3\nqZ7XtPXo+eO+h1e/WN1uxbR/1TSS3du3+Z7HmsLWRn1sm+8HAADMwX4fxe7M6uXV53Zy69En\nVb9anVv9WNOxSV9bvaD6zKZjkQ5CcAQAAFbY70HgkU1bkB7XNABD1eObTgT7z6pXzEz73OpP\nqx9p2sL0m7tXJgAAsAz2+xakuzVtOfqVmfv+SdMxQq9YZfqfajoO6J47XhkAALB09ntA+njT\n4AwXzNz3d53+PEc3NwWkm3a4LgAAYAnt94D0e+Pv987c9+KmrUj3WGX6b2maJ6/d2bIAAIBl\ntN+PQfrr6jnVN1T3Htf/pPr26orq+6qrqjtWT6weVb2sevUiigUAABZrvwekqqdW72kKRb+w\n4rGfW3H7BdXX70JNAADAEjoIAelY05aiZ1WXVw+oPqXpRLE3VR+sXl+9pHrzgmoEAACWwEEI\nSCd8pGkL0QsWXQgAALCc9vsgDQAAABsmIAEAAAwHaRc7YLEe2TRS5H5y50UXAADMl4AE7Jav\nvOMd7/g197nPfRZdx9xcddVVvfvd7150GQDAHAlIwK65173u1dOe9rRFlzE3z3ve8wQkANhn\nHIMEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAA\nAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgA\nAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhI\nAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAI\nSAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAw\nCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAw+FFFwDAcjl+/HjVfaobFlzKvF1ZvW/RRQCw\n3AQkAE5xww03dM4553z/4cP7ZxFx/fXXd9NNN/1s9XWLrgWA5bZ/ln4AzM13fdd39cAHPnDR\nZczND/3QD/XSl770jEXXAcDycwwSAADAICABAAAMAhIAAMAgIAEAAAwCEgAAwCAgAQAADAIS\nAADAICABAAAMAhIAAMAgIAEAAAwCEgAAwCAgAQAADIcXXQAA7LS3vvWtVU+ovmzBpczbu6r7\nLroIgP1EQAJg37v++uu7//3vf+vHPe5xt150LfPyxje+sec+97n75vMALAsBCYAD4fa3v333\nu9/9Fl3G3Nx4442LLgFgX3IMEgAAwCAgAQAADAISAADAICABAAAMAhIAAMAgIAEAAAwCEgAA\nwCAgAQAADAISAADAICABAAAMAhIAAMAgIAEAAAwCEgAAwCAgAQAADAISAADAICABAAAMAhIA\nAMAgIAEAAAwCEgAAwCAgAQAADAISAADAICABAAAMAhIAAMAgIAEAAAwCEgAAwCAgAQAADAIS\nAADAICABAAAMAhIAAMCwmYD0f1c/uYHXe3f1yC1XBAAAsCCbCUiXVQ9cZ5pzq4urT9tyRQAA\nAAtyeAPT/PH4e8fqtjO3VzpU3aU6q/r77ZcGAACwuzYSkH6zekB1t+qc6j5rTHtN9fzqF7df\nGgAAwO7aSED6nvH3SPXo1g5IAAAAe9ZGAtIJP1W9aKcKAQAAWLTNBKT3jssl1b2rC5qOO1rN\nG8YFAABgz9hMQKr6oepbWn/0u2c07ZIHAACwZ2wmIH1O9a3V66tfrz5UHTvNtKcb6Q4AAGBp\nbTYg/U3TiHZHd6YcAACAxdnMiWLPrq5MOAIAAPapzQSkP6s+vdMPzAAAALCnbSYg/X5TSPrh\n6qwdqQYAAGCBNnMM0kOqd1ZfVz2x+ovqg6eZ9lfHBQAAYM/YTED6v5qG+K66TfWINaZ9awIS\nAACwx2wmID2r+h/VzRuY9pqtlbPjPqHpOKqLmwaduL56X/Wm6mMLrAsAAFgCmwlIHxqXveiR\n1b+vHlydscrjN1Yvq76/+qNdrAsAAFgimwlIdx6X9ZxRvad625Yqmr9vr36gaXjylzcNVX71\nuH1WdUl1n6ZdBr+4+vrquQupFAAAWKjNBKQnVd+9wWmfUR3ZdDXzd5fq+6pXVF9V/d06076o\nenb1W0273gEAAAfIZgLSK5t2QVvNJ1af08lA8vJt1jUvD2/aovWvWzscVb2j+lfVG6vLsxUJ\nAAAOnM0EpFeMy1q+qfqK6se2XNF8XdR0fNG7Nzj9m6tj1R12rCIAAGBpbeZEsRvx35q2Jn3R\nnF93q95XnVndc4PTf3bTPHnvjlUEAAAsrXkHpKp3Vffegdfdit9qGsr756t7rDPt51a/WF1b\n/cYO1wUAACyhzexitxEXVvetXjzn192qD1RPrX6mafS6N3VyFLsbmkaxu0NToLusaWS7J1Qf\nXESxAADAYm0mIH3xuKzmUNPxPl9Y3a569Tbrmqefq/6q+pamoby/YpVp3t8Uon64umrXKgMA\nAJbKZgLSA5sGYVjLNdW/a9pKs0xeV/3Lcf0O1cXV2dXHm8LR1XN+v9s2jea30flrUAgAAFgC\nmwlIP1W95DSPHa+uq97eNGrcMvvAuMz68uqTqh/f/XIAAIBlsZmA9N727+huX5ahv5IAACAA\nSURBVFLdp/kFpH+o/s0mpn9Q9WVzem8AAGCLtjJIwyXVE5tODHvxuO991R82jRb34fmUNheP\nGpf1fH7TsVM/M27/2rgAAAAHyGYD0iOrX6ouWOWxx1ff2bQl5DXbrGtePrv62k1Mf2La9yQg\nAQDAgbOZ8yDdpmkL0Uerb6g+s2lwgTtUn9U0StwZ1S83DYCwDH6tenPTYAw/Vl3aNIDCysvz\nq7+Yuf2DiygWAABYrM1sQXpE03mO7l/92YrH/q5pKO1XVq+tHt5ybIF5XVN4+4/Vt1f/vHpy\n9Xsrpruhurnl2j0QAADYZZvZgnRZ07FGK8PRrD+t3l19+naKmrOj1X9qOoHtB6tXVD/btKUI\nAADgH20mIN1cnbvB1zy2tXJ21JVNgzH82+ox1Rurxy20IgAAYKlsJiBd2XQc0pevMc0jqju2\nfCeKPeFY01De96j+pHpBdUX1iYssCgAAWA6bOQbpZdXbmgZq+KmmXdXeWx1qOsnqF1ZfV11V\n/e58y5y79zQN//2Y6plNQ5evtesgAABwAGwmIN3YFCr+V/VN47LSG6tHj2n3gv/ZFPy+sfrI\ngmsBAAAWbLPnQXpDdc/qS6oHNQ2bfbxp8IZXVb9d3TTPAnfBh6vvWXQRAADA4m0mIB1qCkM3\nNh23c8XMY7duCkbLODgDAADAhmx0kIbPaTq/0ekGM/jm6g+qT51HUQAAAIuwkYD0WU0DMtyv\naZjs1VxYPXhMd/F8SgMAANhdGwlIP1udUz2+evFppvkP1b+q7lQ9ez6lAQAA7K71AtJnNm05\nenb1wnWm/YXq56p/0RSUAAAA9pT1AtJ9x9+f3+DrPbc6o2mEOwAAgD1lvYB06fj79g2+3tvG\n3ztvrRwAAIDFWS8gnTjh61kbfL3zxt+Pba0cAACAxVkvIL1j/H3gBl/vYePvu7ZUDQAAwAKt\nF5B+vzpafVt15jrT3qb6juoj1cu3XRkAAMAuWy8g/UP1nOoB1f+sbnea6e5avay6rPrx6vp5\nFQgAALBbDm9gmm+v7l99WfWF1Uuqv6iuqy6qPrd6RNPodS+rjuxEoQAAADttIwHp+uoLqu+p\nnlo9blxmXV39WPVD1c3zLBAAAGC3bCQg1cnjkL6nenB1t6YR665uGgL81QlGAADAHrfRgHTC\nR6vfGRcAAIB9Zb1BGgAAAA4MAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkA\nAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJ\nAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYB\nCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgOLzoAoBV\nXVRduOgi5uyCRRcAALAeAQmW0xurixddBADAQSMgwXI67+lPf3r3ve99F13H3DztaU9bdAkA\nAOsSkGBJXXTRRV166aWLLmNuDh/W3QAAy88gDQAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACD\ngAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAA\ng4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAA\nAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMBxedAEAwOYdO3as6ozq\nCxdcyrwdq15Z3bToQoCDSUACgD3obW97W4cOHTr7/PPPf9mia5mn6667ruPHjz+i+p1F1wIc\nTAISAOxBx44d66yzzuqKK65YdClzdfnll3f06FHrJ8DCOAYJAABgEJAAAAAGAQkAAGAQkAAA\nAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAA\nAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQ\nAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAAhsOLLmABDlXnVWdX11cfXWw5\nAADAsjgoW5AuqZ5Rvba6rrq2unpcv6Z6dfWt1ScsqkAAAGDxDsIWpIdXv1xd0LS16M1N4eho\ndVZTeHpA9eDqW6ovbQpSAADAAbPfA9KF1QuqD1dPrH6zummV6c6uHlP9aPXi6tOy6x0AABw4\n+30Xu0dWt60eW/1aq4ejqo9Xz6+eUH1ydfmuVAcAACyV/R6Q7lzdWP3xBqd/RXWsuuuOVQQA\nACyt/R6QrqnOrC7e4PSXNs2Ta3asIgAAYGnt94D0e+Pvj1W3Xmfa86pnV8er393JogAAgOW0\n3wdpeEP1E9VTq4dWv15d2TSK3Q1No9jdobp39ajq9tUPVFctolgAAGCx9ntAqvqGpqG9v7V6\nyhrTvaV6evW83SgKAABYPgchIB2vnlk9q7pXdY+mY5LObhq97v3V66s3zfE9b1U9pI3P33vO\n8b0BAIAtOggB6YTjTUHo9bvwXp9SvaiNz98T0x3amXIAAICNOCgB6YuajjE6v3pN9XNNW49W\nOqtpd7z/Oi5b9Y42PnJe1YOqP2wKcQAAwIIchID0ndX3ztz+mqbjkR7dLbcmHWra+nPhrlQG\nAAAslf0+zPcdmwLS26vHVJ/dNKLdbas/qD5zcaUBAADLZr9vQfqnTbvNPbH63+O+P69+e1x+\nq/rc6m8XUh0AALBU9vsWpDs1Hdfz2hX3v716ZHVOdUV17i7XBQAALKH9HpA+2HRc0aWrPHZV\n9ZVNJ4n9pfb/1jQAAGAd+z0gvaZpC9J/rs5Y5fHfazp57JdWv1J9wu6VBgAALJv9HpCurH6h\n6RikN7f6CVmfW3119SXtzjmSAACAJbXfA1LVk6pnVXdo9a1IVc+vvqD6yG4VBQAALJ+DcNzN\njdU3Np376Nga072qukf1wOo9u1AXAACwZA5CQDrh6Aamual69U4XAgAALKeDsIsdAADAhghI\nAAAAw0HaxQ4AWHI33HBD1QubjiHeT55ZHVl0EcD6BCQAYGkcP368Jz3pSed/xmd8xqJLmZsX\nvvCF/emf/umnLLoOYGMEJABgqdz1rnftfve736LLmJuXv/zliy4B2ATHIAEAAAwCEgAAwCAg\nAQAADAISAADAICABAAAMAhIAAMAgIAEAAAwCEgAAwCAgAQAADAISAADAICABAAAMhxddAGzT\nJ1Xf1f4L+2ctugAAgINIQGKvu+8ZZ5zxlMsvv3zRdczVS17ykkWXAABwIAlI7HlnnnlmT3va\n0xZdxlwJSAAAi7HfdksCAADYMgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJ\nAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYB\nCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAG\nAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAA\nBgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAA\nAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAA\nAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAAhsOLLgAAYD87duxY1QXVZQsuZd4+XP39oouAeROQ\nAAB20Jve9KaqrxiX/eTvqjssugiYNwEJAGAH3XzzzT3kIQ/pyU9+8qJLmZs///M/70d+5EfO\nW3QdsBMEJACAHXbuued26aWXLrqMuXnXu9616BJgxxikAQAAYBCQAAAABgEJAABgEJAAAAAG\nAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGA4vOgC2FVfXz150UXM2W0W\nXQAAAPuHgHSwPOjud7/7/R760Icuuo65ed3rXteVV1656DIAANgnBKQD5rLLLuurvuqrFl3G\n3Nxwww0CEgAAc+MYJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAA\nGAQkAACAQUACAAAYBCQAAIDh8KILAABgbzl69GjVmdW3LbiUebup+unqmkUXwuIISAAAbMo7\n3/nODh06dOu73e1uP7joWubpLW95S8ePH39D9VuLroXFEZAAANi0s846q5/8yZ9cdBlzdfnl\nl3f06NFDi66DxXIMEgAAwCAgAQAADAISAADAICABAAAMAhIAAMAgIAEAAAwCEgAAwCAgAQAA\nDAISAADAICABAAAMAhIAAMBweNEFAADAErmkumzRRczZ31ZHF13EXiEgAQBAdcMNN1T97KLr\n2AHPrr5h0UXsFQISAABUx48f7+lPf3r3ve99F13K3DznOc/pla985XmLrmMvOYgB6VB1XnV2\ndX310cWWAwDAsrjooou69NJLF13G3Jx77rmLLmHPOSiDNFxSPaN6bXVddW119bh+TfXq6lur\nT1hUgQAAwOIdhC1ID69+ubqgaWvRm5vC0dHqrKbw9IDqwdW3VF/aFKQAAIADZr8HpAurF1Qf\nrp5Y/WZ10yrTnV09pvrR6sXVp2XXOwAAOHD2+y52j6xuWz22+rVWD0dVH6+eXz2h+uTq8l2p\nDgAAWCqHquPj+jOqI4srZUd8R9PnuvUGpz+juqH6j9UPbuN971K9po1voTvctAvgrasbt/G+\n6/mZw4cPf+0555yzg2+xu44ePdqNN97Y+eefv+hS5uraa6/tnHPO6fDh/bOR96Mf/Wi3utWt\n8v1bfr5/e4Pv397h+7d37Mfv3/XXX99NN930s9XXLbqWJXek+u7a/7vYXVOdWV1c/d0Gpr+0\naavaNdt833c1bbXa6Pw91FTjToajqu+66aabXnDttdfu8NvsqsPVna+99tq3L7qQObvs+uuv\nf1d186ILmaOLjh071rXXXvv3iy5kjnz/9g7fv73D929v8P3bW65cdAF7zfFxObLgOnbCPZo+\n2y+0/lak86orqmPV3Xe4LgAAYHkcaeSi/b4F6Q3VT1RPrR5a/XpTgr66aVe6s6o7VPeuHlXd\nvvqB6qpFFAsAACzeft6CVNPua99Y/U0nP+tql6uqr15QjQAAwOIc6YBsQarpgz6zelZ1r6bd\n7i5uGtr749X7q9dXb1pUgQAAwHI4CAHphONNQej1iy4EAABYTvv9PEgAAAAbJiABAAAMAhIA\nAMAgIAEAAAwCEgAAwCAgAQAADAISAADAICABAAAMAhIAAMAgIAEAAAwCEgAAwCAgAQAADAIS\nAADAICABAAAMAhIAAMAgIAEAAAyHF10AbNNXVz+36CIAgH3ja6rnLboIFkdAYq/7UHV99U8X\nXQjr+u7x9xkLrYKNeFX1HdWrF10Ia/r86gfS/+0F+r+941VN6xYcYAISe93x6lj1Z4suhHWd\nWOBoq+V3rHpr2mrZXZL+b6/Q/+0dx5rWLTjAHIMEAAAwCEgAAACDgAQAADAISAAAAIOABAAA\nMAhIAAAAg4AEAAAw/J/27j5IkrI+4Pj3joPjOOA47uAOBO5ACOSIp0ggItR5YhCkQKIQNGIA\nwRAtwRgsEiysuFHwJYpEMVUCIi8lEBPUBAR5MQKCFgQ4lBQgIt7xKm/H8X5wy+3mj98zNbO9\nPbvTs7PdMzvfT9XUzHb39jz9dPczz6/7eZ42QJIkSZKkxABJkiRJkpIZVSdAmqB16aXu537q\nHZ5XvcH91DvcT73D80oADKfXQMXpkNoxHVhcdSLUkrnppe63GFsY9ALLv95h+dc7FmP5168G\nSHGRd5DU64aAVVUnQi1ZU3UC1LJVVSdALbH86x2Wf71jVdUJUPWMkCVJkiQpMUCSJEmSpMQA\nSZIkSZISAyRJkiRJSgyQJEmSJCkxQJIkSZKkxABJkiRJkhIDJEmSJElKDJAkSZIkKTFAkiRJ\nkqTEAEmSJEmSEgMkSZIkSUoMkCRJkiQpMUCSJEmSpGRG1QmQOmgO8EZgLbASeLXa5GgMGwBv\nB14B7qw4LRppPrAT8BpwH7Cu2uRoDFsAbwEeAR6sOC1qbibx2zQT+D3wfLXJ0RjmATsCLwKr\niHJQfWo4vQYqTofUrh2Ay6kfy8NEofZ1YJMK06V8OwK3EPvpjorTorr5wA+A9dTPozXASVUm\nSk3tTwRGw8DXKk6L8m0M/Atx0a7x9+m/gcXVJUs53grcwMj99ArwVWI/qj8MUN//BkjqaZsT\nV7lfB84E3g28D7iROK4vrixlynMM8AJwFzCIAVK3mAbcTARHXwOWAYemacPAR6pLmjJmEvto\nCLgdA6Rudgmxf64A3gscBJyTpj0AbFRd0tRgZ+KO0fPAZ4iLD0cANxH76oLqkqaSDWCApCni\nY+Qfv7OAR4lK+OyS06R884h99U2ikvcqBkjd4lBi35yZmT6bOI8eI5pFqnqHAy8DxwNvwwCp\nW+1G7JsbiAsQjX6Y5h1YdqKU62xifxyRmT4LeJxokTKz7ESpEgOkuMhBGtTrfgOcBpyXmb4W\nWEH0s9u67EQp1zriKuonsV13t3lfej83M/1l4FJgW2CfUlOkZlYCewLnV50QjWk98A/AZ0lX\noxvckt63LTVFauYi4Cjgysz0tcC9xJ2+WWUnStVykAb1uhvTK88iooL3h7ISozG9yOgfIHWH\ntwAvAffnzLujYZlbcuarXCuqToBa8gDRfyXP4oZlVL07yG/NMI8o934HPFdqilQ5AyRNVR8G\nlgL/iqPZSePZjuYXEh5P79uXlBZpKtuN6NO3AvhFxWnRaH8CvAHYBTiRaB55fKUpUiUMkDQV\n7U80FVpBNL+TNLZNgCeazFub3u3LJ03MDsQIdq8TTbqyTe9UvdOBw9LnO4Cjgf+tLjmqin2Q\n1AuuJfoaNb6adRg/DrgGuJsY0e6VMhIoABYwej9dUmmK1KrXaX7BrDbd5yFJ7dsLuI14dtW7\niPJR3edLwAeAU4h6xi/xQmtf8g6SesEzjD+CzHTieROfJp6JdDT1K98qxxCj70I8W0VCVNhq\nYG6TeVumd/el1J4PEkNFPwgcQjyAVN3ptvQCOIu44PoF4DpiWH31EYf51lRwHnEcn8HoIVXV\nnRzmu3tcTQS4c3LmnUycW4eXmiK1wmG+u9/RxLl1DbBZxWlRvk1oPqLg8cQ5dnJ5yVGFBnCY\nb00hXwQ+StwSPw3bdUtF/ZS4sPCenHmHEk3wflZqiqTedzDwXaLf0SHESJ7qPncSw+fnBbAL\n0rtNjPuQd5DUy/Ylrs75pOve4x2k7jEfeIFoArRdw/TjiN8Hn7nTnbyD1L3mAE8B9+AzdLrd\nPxPn0SWMHIxmKfA0cYFolwrSpfINkOIi+yCp151CXPneG7i1yTJfAK4qLUVq5q+BTzT8vREx\n5G3jfjsCeLTMRAmIfn7HEQ+FfYBoa78lsDvwa6Jvn7rDWdQf2rtpev8QsF/6/BTxQGZV6yPA\nVsRDsW9ossxVxO+TqnU68HbiPDqYGEBjU2AJUb84BZ9Z1XcMkNTrVgE3jbPM+hLSofGtZ+Qz\nqX6es4zNI6tzOXAfcAKwK3Hl9Byif5/PEuseg9T3x6uMLv9eKzc5auI5xv9tGiwjIRrXIDHq\n7SFEM+NFxKA01xF3lXw4c5+yiZ0kSZKkfjaAgzRIkiRJ0kgGSJIkSZKUGCBJkiRJUmKAJEmS\nJEmJAZIkSZIkJQZIkiRJkpQYIEmSJElSYoAkSZIkSYkBkiRJkiQlBkiSJEmSlBggSZIkSVJi\ngCRJkiRJiQGSJEmSJCUGSJIkSZKUGCBJkiRJUmKAJEmSJEmJAZIkSZIkJQZIkiRJkpQYIEmS\nJElSYoAkSZIkSYkBkiRJkiQlBkiSJEmSlBggSZIkSVJigCRJkiRJiQGSJEmSJCUGSJIkSZKU\nGCBJkiRJUmKAJEmSJEmJAZIkSZIkJQZIkiRJkpQYIEmSJElSYoAkSZIkSYkBktSflgF7Vp2I\niv07MAwsbGHZ14FbW1xvdtna92xXKHVTS5E8KJLXvaLM7f9m+q5h4NsTWE9ZJnJ+WI5JmhQG\nSFJ/uho4u+pEVOxS4DPAi1UnpEOmAdcAe1edkB4wVfNqG+Ak4LfAPsCXqk3OpLMckzQpDJCk\n/vQSUycwaNcVwJeBl6tOSIfsAhwIbFl1QnrAVM2rHdL71cRdqIcqTEsZLMckTYoZVSdAUiWa\nVSw2BBYB84EngEeA9U3WsQWwK/A88Js0bW9gCLgjZ/mdiOZsteWbrbcVS4CtgJuIuwFLgNnA\n/Wn9NbsCm6Xp2e1dAmw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"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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ReJ/bbq9k3t9D9dvbP646amEgBzISAB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\ncS5YfxL9MHAtcBxx24W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"text/plain": [ "Plot with title “'disability' domain histogram”" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" }, { "data": { "image/png": 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Wz4h5o2Pr+n6ZoVd2j6gvtk0+ER230B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\ns2tAul99z5VcnuTmXSy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"text/plain": [ "Plot with title “'one_parent_households' domain histogram”" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" }, { "data": { "image/png": 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PqB6hfX2fbp1YUbrgYAAGCONhKQ3lw9a5V5X1h9ZXWH6peqv9pkXQAAAMfdRgLS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\ng4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAA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ybURyWn483mNMxXadqleqqPd8h+XF9zK/Uv50h3Z63T8kqTHsQZKk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", "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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RdIfNL4e0ZTE5wXt3ynCId7/69q2gB/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", "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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gEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkA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\nkpT+H/wLPn8bmgReAAAAAElFTkSuQmCC", "text/plain": [ "Plot with title “'attending_university' domain histogram”" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" }, { "data": { "image/png": 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ckj0i6sedu0w5DmrkPzO2kbQidUDZfU\n8yfLt8VqPxvDLlOrMUw7jms+/z27rq/T35nAd3oe+52e+1fTznstUce42mJc67Kr0j4PJ9Xf\no9JC0/VpwejctOW3PxgstZwnw63nx7VuTUbf3snqljvoTP+vegBr5ZnxqyHAqFm3wsptjj1I\nMHX2y+p6bLskC1/EchROSDs+/ci0c2PukvZLZNJ+Le/tSvafx1TDKEziewvMrhMyHetWmEgC\nEkyPI5P84Soe9/4kbx5xLXMuSDt0ZO6iip9KOwl5Z1rvanPdxV6X5JVjqmEUJvG9BWbXtKxb\nYWI5xA4YpwelnXex2EnIl6V1HQvA4KxbYbQ2xyF2wBr5cNqJ/o9Ou37Gj6b11nVJ2q+e707r\nohaAwVm3whjZgwQAAMyyzalcdLOOCwEAAJgYAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAU\nAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJAACgCEgAAABF\nQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAoAhIAAEAR\nkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFAE\nJAAAgCIgAQAAFAEJAACgCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQB\nCQAAoAhIAAAARUACAAAoAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAAAGV9\n1wUAAGN3eJKjuy6iY+cluaTrIoDJJyABwPR70fr1639977337rqOTmzdujXbt2//yyRP7boW\nYPIJSAAw/fY48cQTc+qpp3ZdRyde/vKX56yzztqj6zqA3YNzkAAAAIqABAAAUAQkAACAIiAB\nAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJAACgCEgA\nAABFQAIAACgCEgAAQBGQAAAAyvquCwCANbAuyQFdF9GhDV0XALC7EJAAmAWvTPI7XRcBwOQT\nkACYBQfe8573zK/92q91XUcnnv/853ddAsBuQ0ACYCbsv//+2bRpU9dldGLDBkfYAQxq1gLS\nzZPcIckhSfZKsjXJJUnOT3Jth3UBAAATYFYC0oOTnJrkXkn2WGD8tiRnJ3lJkk+uYV0AAMAE\nmYWA9LwkL01yfZKPJjkvyZb6f2OSw5Ick+SBSU5K8rQkb+mkUgAAoFPTHpBuk+TFSc5J8tgk\n31tm2vcmeX2SD6cdegcAAMyQab9Q7APSDql7cpYOR0lyUZInpp2b9KAx1wUAAEygaQ9IB6Wd\nX/TNAaf/SpIdSQ4dW0UAAMDEmvaAdEmSPZMcPeD0P5P2nlw8tooAAICJNe0B6cNpXXm/M8kd\nl5n22CTvSnJVkg+OuS4AAGACTXsnDZcmOTnJX6T1Xnd+dvVid0NaL3aHJrlzkqPSerZ7XJLv\nd1EsAADQrWkPSEnytiRfSPLstK68H7HANN9NC1GvSHLBmlUGAABMlFkISEny2SSPr9uHJjkk\nrbe669LC0ZYRv97Nkzw3C1+UdiEbk2xKu6AtAADQkVkJSL0urWHOYUmOS+vQ4Rsjeo2NaddV\nGvT9vUWS+yXZkHboHwAA0IFZCEj7JnlRkl9M22N0RpKX1Li3pV1Ads7H6/9hLxK7Je1cpkEd\nnxaQAACADs1CQHprkkcl2Z52TaQ/SnJE2qF1j0lydpJvp/Vyd9+0AHVcJ5UCAACdmvZuvo9O\n8sgkf5y2J2nftE4aHp/kKUmenOQBdfu4tI4cjk3bowMAAMyYaQ9Id09yTZLfTzu3Z2eS96Ud\nSrczydv7pv/TtOsm3W0NawQAACbEtB9id2hahwzb+u7/VlrHCP12pJ0/tN+Y6wIAACbQtO9B\nujjJ4Wldevf6ySS3zk274d67pr987JUBAAATZ9oD0ifTutx+bZJbpl2f6HfTDr37YZI/7Jl2\njySnJ9kzyb+ubZkAAMAkmPZD7C5M8qYkv5HkqT33vzrJV5L8WVp33N9IcockRyb5YJIvrG2Z\nAADAJJj2gJQkz0zytSQPTXJ9Wjfeb0g73+igJM9NsimtE4d3Jjm5mzIBAICuzUJA2p7Wzfcf\nLzDuJUlemuSQJFekXUgWAACYUbMQkJazI+2isQAAwIyb9k4aAAAABiYgAQAAFAEJAACgCEgA\nAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAoAhIA\nAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACKgAQA\nAFAEJAAAgCIgAQAAFAEJAACgCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEA\nABQBCQAAoAhIAAAARUACAAAoAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAA\nAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJAACgCEgAAABFQAIAACgCEgAA\nQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAoAhIAAEARkAAAAIqABAAA\nUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAA\nFAEJAACgCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAA\nRUACAAAoAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABA\nEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJAACgCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQ\nBCQAAIAiIAEAABQBCQAAoAhIAAAAZX3XBQAAwBjtneTwrovo2CVJtnZdxO5CQAIAYJq9Mslv\ndF1Ex96Q5OSui9hdCEgAAEyzve93v/vllFNO6bqOTrz61a/OOeecs3fXdexOBCQAAKbahg0b\nst9++3VdRic2bNjQdQm7nVkLSDdPcockhyTZK+1YzEuSnJ/k2g7rAgAAJsCsBKQHJzk1yb2S\n7LHA+G1Jzk7ykiSfXMO6AACACTILAel5SV6a5PokH01yXpIt9f/GJIclOSbJA5OclORpSd7S\nSaUAAECnpj0g3SbJi5Ock+SxSb63zLTvTfL6JB9OO/QOAACYIdN+odgHpB1S9+QsHY6S5KIk\nT0w7N+lBY64LAACYQNMekA5KO7/omwNO/5UkO5IcOraKAACAiTXtAemSJHsmOXrA6X8m7T25\neGwVAQAAE2vaA9KH07ryfmeSOy4z7bFJ3pXkqiQfHHNdAADABJr2ThouTXJykr9I673u/Ozq\nxe6GtF7sDk1y5yRHpfVs97gk3++iWAAAoFvTHpCS5G1JvpDk2WldeT9igWm+mxaiXpHkgjWr\nDAAAmCizEJCS5LNJHl+3D01ySFpvddelhaMtI369g5P8aZINA05/i/q7bsR1AAAAKzArAanX\npTUkLSzdLsmRSf4r7XylUdiW5PK0Q/gGMRekdo7o9QEAgFWYhYC0V5LfT/KJJB+p+34syZvT\nrpM05/q679QMH5R+mOSZK5j++CQPHfI1AQCAIc1CQDojyUlJfjctIO2d5Jwkt0079O4zaSHq\nPkl+K8mtkjy8k0oBAIBOTXtAundaODo9ySvrvsekhaPfS/Kynmk3pHXo8Ngkd0/y6TWrEgAA\nmAjTfh2ku6ad1/Oi7Dq/585p3Xif3jftDWk93SXtkDcAAGDGTHtA2jPJjrTwM2drWs91C3WI\ncGmSG9MOuQMAAGbMtAekzyXZI8mv9tz3sbRD7A5aYPqH1vTnj780AABg0kx7QPpYkk8meUOS\nF6Z1wHB2kr9N8q4kP17T3TLJ7yT5qyQXJjlrzSsFAAA6N+2dNOxI2yv07iTPr+E7aYfY3SXJ\n19MOv5u7DtFFSR6S1uU3AAAwY6Y9ICWtQ4YTk9w/yaPSeqi7ddq5RtfX+C8m+Ye0PUjXdVIl\nAADQuVkISHM+WgMAAMCCpv0cJAAAgIEJSAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAA\ngCIgAQAAFAEJAACgCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAA\noAhIAAAARUACAAAoAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAA\nKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJAACgCEgAAABFQAIAACgCEgAAQBGQAAAA\nioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAoAhIAAEARkAAAAIqABAAAUAQkAACA\nIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJAACg\nCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAo\nAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACK\ngAQAAFAEJAAAgCIgAQAAFAEJAACgrCQg/WqSPx/g+b6Z5MGrrggAAKAjKwlIRyU5bplp9kly\nSJKfWHVFAAAAHVk/wDT/Vn+PSHJgz//91iW5TZKNSS4fvjQAAIC1NUhA+lCSuye5fZK9kxyz\nxLRXJnlHkncNXxoAAMDaGiQgvbD+bk7ysCwdkAAAAHZbgwSkOW9K8t5xFQIAANC1lQSki2s4\nLMmdk+yXdt7RQr5cAwAAwG5jJQEpSV6e5NlZvve709IOyQMAANhtrCQg3SPJ7yb5YpJ/SHJZ\nkh2LTLtYT3cAAAATa6UB6VtpPdpdP55yAAAAurOSC8XuleS8CEcAAMCUWklA+kySO2TxjhkA\nAAB2aysJSP+cFpJekWTjWKoBAADo0ErOQbpPkq8neWqSJyT5fJLvLzLt+2oAAADYbawkIP1c\nWhffSbJ/kgcuMe1/R0ACAAB2MysJSK9N8tYkNw4w7ZWrKwcAAKA7KwlIl9UAAAAwlVYSkH6s\nhuXskeTbSS5cVUUAAAAdWUlAekqSFww47WlJNq+4GgAAgA6tJCCdm+Qli4y7ZZJ7JLlNkhcn\n+eiQdQEAAKy5lQSkc2pYyilJHpHkT1ZdEQAAQEdWcqHYQbw6bW/Sz4/4eQEAAMZu1AEpSb6R\n5M5jeF4AAICxGnVAOiDJTyf54YifFwAAYOxWcg7SSTUsZF2Sg5KcmOQWST4xZF0AAABrbiUB\n6bi0ThiWcmWS30ly3qorAgAA6MhKAtKbkpy5yLidSa5O8rUk24YtCgAAoAsrCUgX1wAAADCV\nVhKQ5hyW5AlpF4Y9pO67JMm/JnlnkitGUxoAAMDaWmlAenCSv0my3wLjHpPkD5M8NMm/D1kX\nAADAmltJN9/7p+0huibJbyX5qSSH1nCXJM9OskeSv0uy12jLBAAAGL+V7EF6YNp1ju6W5DN9\n476X5AtJzk3y6SQPSPKBURQIAACwVlYSkI5KO9eoPxz1+n9JvpnkDpm8gLRvkhOSHJ127tRe\nSbamzdNcuLuhq+IAAIDurSQg3ZhknwGmu1mSHasrZyw2JHlJkt9MsvcS012R5GVJXp7WbTkA\nADBjVhKQzks7D+nhSd63yDQPTHJEJutCse9O8stJPpt2ftR5SbYkuT7JxrRe+Y5J62TiZUlu\nk+QZnVQKAAB0aiUB6ewkF6Z11PCmJOekXRdpXZIfTXJikqcmuSDJP422zFU7Ni0cvSrJc7L4\nnqEzkrwobb6enuR1Sb60FgUCAACTYyUBaVuShyT5v0lOqaHffyV5WE07Ce6ZFopOy/KHzW1P\n8twkT047V0lAAgCAGbPS6yB9Oa2Tg19IcnySw9OCxyVJ/iXJP6YFjUmxMe3cqasHnP4HaedP\n7Tu2igAAgIm1koC0Li0MbUvy/hrmbEgLRpPUOUOSfDVtHk9K8qEBpv/ltE4mzjlH7CwAACAA\nSURBVB9nUQAAwGQa9EKx90i7vtEtFxn/20k+nuS2oyhqhM5K8u2086ZOTruo7UKOTDu87q1p\n51mdtSbVAQAAE2WQgHSXtA4Z7prk3otMc0CSe9V0h4ymtJG4Nu2cqGuTvD7Jd5N8P+1cqf9M\n21P0g7RrN70syXeS/GJaD3cAAMCMGSQg/WXa9YMek9bb20J+P8kT0/bEvH40pY3MZ5JsSvK/\n0ron35IW4m6Xtkfs4iTvSfKEJHeKw+sAAGBmLXcO0k+l7Tl6bVqIWMpfJ7l/kl9NC0rfGrq6\n0bk2yZtrWAuHpx2ut8eA0+9ff9eNpxwAAGAQywWkn66/7xzw+d6S1k328Vk+UE2S30vby/Tk\nET3fD9OuGzVoJxg/nuTuWb4rcgAAYIyW24A/vP5+bcDnu7D+/tjqyunMbdP2lo3KtUleuYLp\nj0/yGyN8fQAAYBWWC0hzF3zdOODzzV0/6NrVlTNyz6xhObdMm8f/rv9fUwMAADBDluuk4aL6\ne9yAz3dC/f3GqqoZvQPT9g7dKu1isYsNc9dwmvv/hi6KBQAAurXcHqR/Tuvy+rlJPpBde5QW\nsn/auTw/TPLRURQ3Aq9NcuskT0pyeZLfTOviu99fJDkmyd3WqjAAAGDyLLcH6QdJ3pjWgcDf\nJrnFItPdLq1TgqOSvC7J1lEVOKTL0zpeODHtvKjPJ9mcZEOHNQEAABNqkF7Wnpe2Z+WhaUHj\nzLSgcXWSg5Icm+SBaV1an50WQCbNR9M6Ydic5A+SPDrtukif6LAmgLV0epJTuy4CACbdIAFp\na5L7JXlhkpOT/EoNvbYk+ZMkL09y4ygLHKGtaYcK/k3a9ZDOTds79rwuiwJYI4fc7W53y6/8\nSv/qezacfvrpXZcAwG5i0Ov0zJ2H9MIk90py+7Qe67akdQH+iUxuMOr3+bROJ05Jm58HJ/lu\npxUBrIGDDz44d73rXbsuoxMbNw7aGSsAs27QgDTnmiQfqWF3dmOSVyV5X5I/TztE8DOdVgQA\nAHRupQFp2nw9yUlJ7pPWzTcAADDDZj0gzTm36wIAAIDuLdfNNwAAwMwQkAAAAIqABAAAUAQk\nAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJ\nAACgCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUAC\nAAAoAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAA\nAACKgAQAAFAEJAAAgCIgAQAAFAEJAACgCEgAAABFQAIAACgCEgAAQFnfdQEAa+TpSZ7WdREd\nunXXBQDA7kBAAmbFcZs2bbrrfe97367r6MR73vOerksAgN2CgATMjKOOOiqPfexjuy6jEx/6\n0Ie6LgEAdgvOQQIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUAC\nAAAoAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAA\nAACKgAQAAFAEJAAAgCIgAQAAFAEJAACgCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQA\nAIAiIAEAABQBCQAAoAhIAAAARUACAAAoAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkA\nAKAISAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJAACgCEgAAABFQAIA\nACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAoAhIAAEARkAAA\nAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFDWd13A\nGto3yQlJjk5ySJK9kmxNckmSLyQ5N8kNXRUHAAB0bxYC0oYkL0nym0n2XmK6K5K8LMnLk+xc\ng7oAAIAJMwsB6d1JfjnJZ5P8XZLzkmxJcn2SjUkOS3JMksekBaTbJHlGJ5UCAACdmvaAdGxa\nOHpVkudk8T1DZyR5UZI3JXl6ktcl+dJaFAgAAEyOae+k4Z5poei0LH/Y3PYkz63bJ4yxJgAA\nYEJNe0DamOTGJFcPOP0PkuxI69ABAACYMdMekL6adhjhSQNO/8tp78n5Y6sIAACYWNMekM5K\n8u0k70xycpJDF5nuyLTD696a5MJ6HAAAMGOmvZOGa5M8LMn7k7y+hsvSerG7Ie0QvEOTHFDT\nX5DkoWk93AEAADNm2gNSknwmyaYkj0871O6O2XWh2OuSXJzkH5P8Q5L3JtnWTZkAAEDXZiEg\nJW1P0ptrWAtHpoWujQNOv1f9XTeecgAAgEHMSkBKklsk+ZEk30rrqW4heyR5YpLP17BalyY5\nPYMHpNsmOTXLd0UOAACM0SwEpNsneVuS4+v/i9Oui/SmBabdM62jhtMyXEC6IcnbVzD98WkB\nCQAA6NC092K3Lu28ouOTfCnJB9L20rwx7XA7h7QBAAD/Y9r3IP1ckmOSvDytG++k7SX64yTP\nTHJNkt/upjQAAGDSTHtAukP9fVnPfduSnJLkiiR/lHYx2devcV0AAMAEmvaAtFfaIXXXLjDu\nBWnnJ706Lg4LAFNrx44dSbJfkqM6LqVL3047RxpYxrQHpP9OO8/o55OcucD4p6T1IPe3SX4h\nyafXrjQAYC2cf/75SfKIGmbV65L8766LgN3BtAeks5N8J60Xu+ekBaFresZfl+TBaXuPzk7y\nojWuDwAYsxtvvDH3uc998vSnP73rUjrxxje+Meeee+6PdF0H7C6mPSBtTfKkJH+f1n33V5J8\nqm+a7ye5X1pvdy9ey+IAgLWxzz775PDDD++6jE7ss88+XZcAu5VpD0hJ8k9pPdk9PsnXF5nm\nyiQPSrtI7K8uMR0AADDFZiEgJclFWX7v0M4kf1UDAAAwg6b9QrEAAAADE5AAAACKgAQAAFAE\nJAAAgCIgAQAAFAEJAACgCEgAAABFQAIAACizcqFYIHlwkod0XUSHju+6AABg8glIMDseecQR\nRzzpmGOO6bqOTnzsYx/rugQAYDcgIMEMudOd7pRnPetZXZfRic997nNdlwAA7AacgwQAAFAE\nJAAAgCIgAQAAFAEJAACgCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQB\nCQAAoAhIAAAARUACAAAoAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAAAEVA\nAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJAACgCEgAAABFQAIAACgCEgAAQBGQ\nAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAoAhIAAEARkAAAAIqABAAAUAQk\nAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJ\nAACgCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUAC\nAAAoAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAWd91\nAbCG7pTk+K6L6NCmrgsAAJh0AhKz5Fk3v/nNn3zYYYd1XUcnLrrooq5LAACYeAISs2Td8ccf\nn1NPPbXrOjrxxCc+sesSAAAmnnOQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUAC\nAAAoAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAA\nAACKgAQAAFAEJAAAgCIgAQAAFAEJAACgCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQA\nAIAiIAEAABQBCQAAoAhIAAAARUACAAAo67sugDX1K0me2nURHbpj1wUAADDZBKTZctJRRx11\n4rHHHtt1HZ0488wzuy4BAIAJN4sBaV2SfZPslWRrkmu6LWdtbdq0KU972tO6LqMT5557btcl\nAAAw4WblHKTDkpyW5NNJrk5yVZItdfvKJJ9I8rtJbt5VgQAAQPdmYQ/SA5L8XZL90vYWfSUt\nHF2fZGNaeLp7knsleXaSX0oLUgAAwIyZ9oB0QJJ3J7kiyROSfCjJ9gWm2yvJo5K8KskZSX4i\nM3boHQAAMP2H2D04yYFJHp3kA1k4HCXJdUnekeRxSW6V5EFrUh0AADBR1iXZWbdPS7K5u1LG\n4vfS5mvDgNPvkeSGJH+Q5GVDvO5tkvx7Bt9Dtz7tEMANSbYN8brL+Yv169f/+t577z3Gl5hc\n11xzTW52s5vF/Jv/WWT+zb/5n+3537Fjx/VJru26lo7ss379+o2z2v5bt27N9u3b/zKzfamX\nQWxO8oJk+g+xuzLJnkkOSfK9AaY/PG2v2pVDvu430vZaDfr+rkurcZzhKEmev3379ndfddVV\nY36ZiXXQjh07ctVVV13edSEdMf/m3/ybf/M/mw6qvzM7/9u3b5/l9k+S87ouYHezs4bNHdcx\nDndMm7e/zvJ7kfZN8v4kO5JsGnNdAADA5NicykXTvgfpy0n+LMnJSe6b5B/SEvSWtEPpNiY5\nNMmdkzwkycFJXprkgi6KBQAAujfNe5CSdvjaM5N8K7vmdaHhgiS/1lGNAABAdzZnRvYgJW1G\nX5PktUnulHbY3SFpXXtfl+S7Sb6Y5PyuCgQAACbDLASkOTvTgtAXuy4EAACYTNN+HSQAAICB\nCUgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAo\nAhIAAEARkAAAAIqABAAAUAQkAACAIiABAACU9V0XwJr6VJLjui4CAIA19W9J7tl1EbsLAWm2\nfC3JliSndV0InXhB/dX+s0n7zzbtP9u0/2x7QZKrui5idyIgzZYbklyW5DNdF0InLqu/2n82\naf/Zpv1nm/afbZctPwm9nIMEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAoAhIAAEAR\nkAAAAIqABAAAUNZ3XQBr6oauC6BT2n+2af/Zpv1nm/afbdp/FXbWsLnjOhi/A2tgNmn/2ab9\nZ5v2n23af7Zp/8FsTuUie5Bmyw+6LoBOaf/Zpv1nm/afbdp/tmn/FXIOEgAAQBGQAAAAioAE\nAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAoAhIAAEARkAAAAIqABAAAUAQkAACAIiAB\nAAAUAQkAAKCs77oAOrEuya1quCTJd5Lc2GlFrLVDk/x4ku8l+Va0/6zZI8nxSa7N/2/v3qP0\nqMsDjn8DAYEEQgTlImBSq6QUkUKDIAih4kGRpohAARUKyqGSQ7XSSmipLge1WKrlKBWhXkCw\nelqPXAoSEAooVZCrXEoJEFAgh6uGcElCLts/nt/wzs7Om5333d2Z7Pt+P+e8Z/adyzvPO/Pb\n3Xnmdxm4o+FYNP42AXYkzvtDwAvNhqMGzEive4HnG41EdXsd8JY0XYS//5UNptdAw3GoHkcQ\n/yAHc68ngY83GZRqsw9xQZw//08DJzYZlGo1E7iZOPe3NxyLxtd6wBeBl2n9vr8KXABs1GBc\nqs8k4GRgGXH+D242HNVoI+CfaJ377HU5kSxruAFax8kEqY8cSZzrh4CPAXOIxOjRNP/YxiJT\nHXYlLpSeAz4N/AlwPK3zf3xzoakmxwJLgbuAlZgg9bovEL/bVwDvA/YHvpXmXdRgXKrHNsAC\n4nf9bkyQ+s33aP3+zyX+BpxP6zpww+ZCW2cNYILUl+4FVhBN6/LeQZSBm2qPSHX6AXGe5xTm\n75Lm/6LugFSrLYjz/FWiqcVyTJB62ZbEOb6N4f2NLwPWADvVHZRq9TXiBtiewHxMkPrJLOJ8\n30DUIub9KC07sO6gJoABUl7kIA395TPAh4gmdXn3EHeYptUekep0JXAacGNh/j3Ai8C2dQek\nWr1K3EX8K+JGiXrbQUQi/E0iGcq7gLhoOrTuoFSrBUTLgVuaDkS1W01c851Oqg3JuTlN/Z+/\nFg7S0F+ubjN/X2AD4k6jetclbeZvAUwFflljLKrfi8B/NR2EarNrmpYNwnF7YR31pquaDkCN\neQg4u82yGbl11IYJUv/aB3g9sDvRgfMe4LONRqSmnEXcTf5a04FIGjPbpenikmXPAquA7esL\nR9I6YBZwHHAn8D8Nx7JOM0HqX1fSalL3feBviCG/1V9OJQbq+AYxso2k3rBJmi4vWTaY5k+p\nLxxJDduB+D+/Cvgww5veKccEqbdsxfCBFu4gfhGKPgxMJ56NcTxwP3AU0WZZE1Mn538ycC4x\nvPf5wLzxDU016OT8q/etStN2/+cnE/3SJPW+2cRodusB7wH+r9lw1n0mSL1lDfBUYd5v26yb\nb5t8LjEE6MVEk4uyO45a91U9/9OBHxKj2c0HvjS+Yakmnfz+q/dlDwOdTjwQOm8T4hkplg+p\n9x0JfAd4hBjF8LFGo5lAHOa7f2xODP1a5mKiHOxWXzhqwDSiVuFl4JCGY1GzHOa7t32G+Js+\nt2TZbmmZ/Q77h8N896djiJtnC4BNG45lIhjAYb77ztbE3cJ2o1htlaY2uehdk4nzPwt4P/Es\nFEm96bo0Pahk2Z+m6TU1xSKpfgcB3yb6HR1MjGSqDliD1D9uIs71Jxn64LCjiTsMjzH8gYLq\nHacR5//YpgPROsEapN73c+KZV3Ny83YFlgIPYjP7fmINUn+ZRjStvR/YuOFYJpIBUl40idYo\nFmdgktTrdiCGddyOeFjsY8CbiDHxlxJNMYqdvNU7lhB/NG9dyzofxNEMe9VHGToYxx7AK8B9\nuXmHAU/UGZTG1duAnwJvJJrWriTO+4tER+07mwtNNbiJeFgwxENBtycS4yVp3gK87utVnwL+\nhfh7/mSbda4CzqwtoolhAPgcePeo3/wGeCtRg/BOIjm6G/gW0YGv3S+ResPdFdZx2M/etZqh\nA7D8tGQdz39vWQjsDJxEjGI1CfhH4DzKn4+k3rKC1u/0ovTKW1lvOKrREka+4e35H4FN7CRJ\nkiT1swEcpEGSJEmShjJBkiRJkqTEBEmSJEmSEhMkSZIkSUpMkCRJkiQpMUGSJEmSpMQESZIk\nSZISEyRJkiRJSkyQJEmSJCkxQZIkSZKkxARJkiRJkhITJEmSJElKTJAkSZIkKTFBkiRJkqTE\nBEmSJEmSEhMkSZIkSUpMkCRJkiQpMUGSJEmSpMQESZIkSZISEyRJkiRJSkyQJEmSJCkxQZIk\nSZKkxARJkiRJkhITJEmSJElKTJAkSZIkKTFBkiRJkqTEBEmSJEmSEhMkSZIkSUpMkCRJkiQp\nMUGSJEmSpMQESZIkSZISEyRJkiRJSkyQJPW6OcAWTQeRsx0wCFxSYd1Zad1vjPN+JrLTie/5\nvqYDGcFozmUndmDdK/OSNKGYIEnqdTcAuzcdRJeeAU4DLm06EHXkaODrhXl1ncsjmNhlXpIa\nN7npACSpBi82HUCXfguc1XQQ6tjBwO8V5tV1Ll9K04la5iWpcSZIkvpBdrG4CbAH8Cjw6/T+\nD4DlwMPAijbbbwa8DVg/bfvMGMQ0mKYbpxhWlMSQxbsYWFjYflaKayGwBHgjsBNwH/Bch/vJ\nm5I+ezLl37V4DGcBbwB+tpbvOpKR9pmZRHyHTYGHiKSjaFOi9uQJ4nvmzQTeDNwFvFBYNh14\nK1FWHgFebRPDlsD2xHFdBCwt2fdewDKiqdvvgF+x9nM5UvnqpNxWTZC2Sp/Rzr3A8yN8RmYD\nomnfG4hzsghY1Wbdqse502PSrhxWLVuSNMRgeg00HIckjYdBYEb6eWZ6/0VgHnGRtizNex44\npLDtVOAiYCWtv5WDwH/nPrNTWd+g7wAfIS6gs899rhBDWb+VtwMP5LZZBvwD8In0/gNd7Adg\nI+BfiYvt/He9rvBds5jOBP4t/XxfZ4eg430C/CFDv/dK4Bziu+f7IP1xen9Oyf4+n5btk5s3\nBfgucUGfffYzwHGFbXdIca3JrbeGKB/TCvsufhcoP5dVy1cn5fYQhpb5dj5SEmv+dfAI22fm\nAU8Vtl3M8ONX9ThXPSYjlcNOypYkQeRC2d8KEyRJPW1nWrXl2Z3/+4HriQtPiJqXp4kLz6m5\nba9O659F3M3eEfhb4iLvYaJWplNZ4nIbUQtyeIrxWOBlokZiSlq3eFG9IXEXfDUwPy0/APjf\n9Hn5RKGT/QD8B3FRejpRs/AW4MS03sPEHXtoXaxfT9TEzAX27OI4dLLPDdJ3WA2cktbbm+hr\n80jhe3eaIF2e5v0zUfNzAPALIvnJJx73ELUdJxPJ2juALzF0IIz1gc2Jmp3b0s/tziVUL1+d\nlNupDC3z7UwFfr/wmkM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"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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X9P0/C3P7ys3Qvb/0DqQ/UDHdrwt9/X\n1K8fYCO2+jMOmAFd7IB5Or76s9Y+8Hlv9Yqm45wAjjQ+4+DIcG662AHbxJXVg5tOoPh91dc3\nnZX+i9X7qlc2HXANcCTyGQdHGAEJ2C7+z5gAFpHPODhCHO55IwAAABaGgAQAADAISAAAAIOA\nBAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACD\ngAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAA\ng4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAA\nAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAcM+8CtthJ1W2qU6vjqy9XF1Xvq740x7oA\nAIBtYKcEpPtXT6zuXh29yvxrqjdUT63+bgvrAgAAtpGdEJB+rnpadVX1xur86jPj9nHVjao7\nVPep7lv9aPW8uVQKAADM3d4xnTvnOjbDLaqvNAWjU9fR9p1N3e5uvMl1AQAA28e5jVy06IM0\nnNPUpe6Hq389SNuPVD/YdGzSd21yXQAAwDa06AHplKbjiz6+zvYXVnuq0zatIgAAYNta9IB0\nUbW7uu06239L03PyqU2rCAAA2LYWPSD9ZdMxRX9UnXmQtnepXlRdXr1mk+sCAAC2oUUfxe7T\n1WOq5zaNXve+9o1id3XTKHanVWdVt2wa2e4R1WfnUSwAADB/izyK3ZJvqf64KfjsXWW6qHpO\ndet5FQgAAMzNuY1ssOh7kJa8u/qBcf20piG/j6+urC5u2qM0S9evnlwdu872xzbtwfr2GdcB\nAABswKIHpOtXJ1efaBqdrqZud5/e5PUePdZ7/Drbf031bU1B6erNKgoAADi4Re5id27TY/v7\n6nbzLeWA7tZU53r3OAEAALNzbjvkRLFLTm3qZvf0pj07AAAAX2WnBKQ7NoWj/1R9sPqp6jpz\nrQgAANh2dkpAurL62er21T9Wz6j+pSk03anaNb/SAACA7WKnBKQlF1TnVPeq/k/1uOqd1aeq\nV1e/Xj2xOnteBQIAAPOz6KPYreXNY7pd9ajqu6sHjKnqSU3BCQAA2EF2akBacl71hDHduCkw\n3WzcD9vd0U3Dwx8970K2wNXVW5pGlwEA2DQ7PSAtd9GY4Ehxj+pNJ5544rzr2FR79+7ti1/8\nYtU3Vu+bczkAwIJb9ID0leqq/OrMYjrmqKOO6pWvfOW869hUX/jCF3rwgx9ci/95BQBsA4s+\nSMNTquObQhIAAMABLXpAAgAAWDcBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYB\nCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAIZj5l0AW+oh1cPnXcQW+Yfq6fMuAgCA\nI4uAtLM88KY3venD7nCHO8y7jk314Q9/uAsuuOCMBCQAADZIQNphvumbvqmf/umfnncZm+pl\nL3tZF1xwwbzLAADgCOQYJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFA\nAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBB\nQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYjpl3AQAHcu211y5dfU71xTmWshX2VI+vzpt3\nIQCwUwlIwLZ2xRVXVHW/+93vrieffPKcq9lcr3zlK/vSl750VgISAMyNgAQcER760Id2i1vc\nYt5lbKo3vOENfelLX5p3GQCwozkGCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYB\nCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAG\nAQkAAGAQkAAAAAYBCQAAYBCQAAAAhmPmXcAWO6m6TXVqdXz15eqi6n3Vl+ZYFwAAsA3slIB0\n/+qJ1d2ro1eZf031huqp1d9tYV0AAMA2shMC0s9VT6uuqt5YnV99Ztw+rrpRdYfqPtV9qx+t\nnjeXSgEAgLla9IB0i+op1Zuq76/+9SBtX1L9XvWXTV3vAACAHWTRB2k4p6lL3Q934HBU9ZHq\nB5uOTfquTa4LAADYhhY9IJ3SdHzRx9fZ/sJqT3XaplUEAABsW4sekC6qdle3XWf7b2l6Tj61\naRUBAADb1qIHpL9sGsr7j6ozD9L2LtWLqsur12xyXQAAwDa06IM0fLp6TPXcptHr3te+Ueyu\nbhrF7rTqrOqWTSPbPaL67DyKBQAA5mvRA1LVC6r3Vj/TNJT3Q1dpc3FTiPrN6v1bVhkAALCt\n7ISAVPXu6gfG9dOqU5tGq7uyKRx9ZsbrO6F6fHWddba/yYzXDwAAHIKdEpCW+/SYagpLZ1Q3\nq/656XilWTix6Zim3etsf/K43DWj9QMAAIdgJwSk46tfqN5avX7cd/PqOU3nSVpy1bjviR1+\nULq4ut8G2t+telu19zDXCwAAHIadEJD+rLpv9YSmgHSd6k3V6U1d797VFKK+rXpsU3e3h8yl\nUgAAYK4WPSDdoykc/Xr12+O+hzeFo5+vfm1Z22ObBnT4/urs6p1bViUAALAtLPp5kO7Y1G3t\nye3rvnZW0zDev76i7dVNI93V1OUNAADYYRY9IO2u9jSFnyVfbjpGaLXjfT5dXdvU5Q4AANhh\nFj0g/WN1dPWoZfe9uamL3SmrtP/u0f59m18aAACw3Sx6QHpz9XfV/6h+pWkAhjdUL61eVH3d\naHfD6nHVH1Yfql635ZUCAABzt+iDNOxp2iv0J9V/GdO/NHWxu3310abud8eO9h+pHtQ05DcA\nALDDLHpAqmlAhntX/656WNMIdV/fdKzRVWP+edWrm/YgXTmXKgEAgLnbCQFpyRvHBAAAsKpF\nPwYJAABg3QQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQA\nAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQk\nAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgE\nJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAY\nBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAA\nGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgOGYeRcAwH5OqG4w7yI22d7q0nkXAQCrEZAA\ntolLL7206lljWnRPrH5z3kUAwEoCEsA2sWfPnn7kR36kO9/5zvMuZVM985nP7Pzzz/+aedcB\nAKsRkAC2kRvf+Mbd+ta3nncZm+qEE06YdwkAsCaDNAAAAAwCEgAAwCAgAQAADDvxGKRdTcPo\nHl99ubpivuUAAADbxU7Zg3Sj6knVO6svVpdXnxnXL6veWj2hOmleBQIAAPO3E/YgnVO9rDqx\naW/RhU3h6KrquKbwdHZ19+pnqgc2BSkAAGCHWfSAdP3qT5rO2P7I6rXVV1Zpd3z1sOrp1Z9V\n35CudwAAsOMsehe7+1c3qL63elWrh6OqK6sXVo+oblJ915ZUBwAAbCuLHpBuXl1T/cM627+p\n2lOdsWkVAQAA29aiB6TLqt3Vqetsf+Om5+SyTasIAADYthY9IL15XD6jOvYgbU+ofq/aW/3V\nZhYFAABsT4s+SMMF1X+vHlN9e/Xq6vymUeyubhrF7rTqrOpB1b+pnla9fx7FAgAA87XoAanq\nsU1Dez+h+okDtPtA9fjqD7aiKAAAYPvZCQFpb/W71TOrb6rObDom6fim0esurs6r3jfDdR5X\n/UDrf35Pn+G6AQCAQ7QTAtKSvU1B6LwV9x/VNHLdLN2w+vHq6HW2v96M1w8AAByCRQ9INx/T\n25oC0pLd1c9WP1J9fXVV9fam44/+1wzW+8nqLhtof7dRIwAAMEeLPordj1RvaeryttxLqyc3\nhacPVl9oGsThL6tHb2WBAADA9rHoAWk196m+uyk43by6dXWj6q7Vx6rfqU6ZW3UAAMDc7MSA\ndE5Td7tHVJ9adv/bqx9rOh/SOXOoCwAAmLOdGJBOqj7RdJzQSm9pCk9fv5UFAQAA28NODEgf\nra6/xrzjql3V5VtWDQAAsG3sxID00uq61b1WmffD43KW50QCAACOEIs+zPeSy5tGqrt0TFc2\nDem9NBT3UdXTq8dWF1ZvnkONAADAnC16QHp39adNXeqWpps2Pe7rLmu3p2lI8E9VD272J44F\nAACOAIsekF41ptWsfOwPrt7adNJYAABgB1r0gHQgX1lx+41zqQIAANg2duIgDQAAAKsSkAAA\nAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAA\nAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQ\nAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQ\nkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABg\nEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAA\nYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAACGY+Zd\nwBzsqk6ojq++XF0x33IAAIDtYqfsQbpR9aTqndUXq8urz4zrl1VvrZ5QnTSvAgEAgPnbCXuQ\nzqleVp3YtLfowqZwdFV1XFN4Oru6e/Uz1QObghQAALDDLHpAun71J9Wl1SOr11ZfWaXd8dXD\nqqdXf1Z9Q7reAQDAjrPoXezuX92g+t7qVa0ejqqurF5YPaK6SfVdW1IdAACwrSx6QLp5dU31\nD+ts/6ZqT3XGplUEAABsW4sekC6rdlenrrP9jZuek8s2rSIAAGDbWvSA9OZx+Yzq2IO0PaH6\nvWpv9VebWRQAALA9LfogDRdU/716TPXt1aur85tGsbu6aRS706qzqgdV/6Z6WvX+eRQLAADM\n16IHpKrHNg3t/YTqJw7Q7gPV46s/2IqiAACA7WcnBKS91e9Wz6y+qTqz6Zik45tGr7u4Oq96\n3wzXeXTTCHrHrbP9N8xw3TveVVddVdMQ7w+bcymb7XbzLgAAYNHshIC0ZG9TEDpvlXm3rf5t\n9fczWtfNqme1/oC09DrsmtH6d7QLL7ywo4466hYnnHDCS+Zdy2a6+uqru+aaa+ZdBgDAQtlJ\nAelAHlfdobrTjJb30aYR8dbrbtXbmkIch2nv3r2dfvrpPetZz5p3KZvqJS95Sc9+9rPnXQYA\nwEJZ9IB01pgO5vTqlOqR4/Z7xwQAAOwgix6QHlL98gbav3BcPikBCQAAdpxFD0jvra5q6rr2\n+9XfrNHuP1a3aBrFrmY7YAMAAHCEWPSA9Irq9tWzq59qOs/R46rPrmj3gOoG1Z9vaXUAAMC2\nctS8C9gCF1b3rH6sKQj9c/WD8ywIAADYnnZCQKqpi91zms6B9DfVH1avb+pWBwAAUO2cgLTk\noup7qn/fFJb+qanLnfMPAQAAOy4gLXllU0B6QfXb6XIHAAC0cwNS1WVNo9fdo3prdcF8ywEA\nAOZt0UexW4+/q+417yIAAID528l7kAAAAPYjIAEAAAwCEgAAwCAgAQAADAISAADAICABAAAM\nAhIAAMAgIAEAAAwbCUiPqn5/Hcv7eHX/Q64IAABgTjYSkG5Z3fUgba5bnVp9wyFXBAAAMCfH\nrKPNP4zLm1Y3WHZ7pV3VLarjqksOvzQAAICttZ6A9Nrq7OpW1XWqOxyg7WXVC6sXHX5pAAAA\nW2s9AelXxuW51b/vwAEJAADgiLWegLTk2dVLNqsQAACAedtIQPrUmG5UnVWd2OjTh5oAACAA\nSURBVHTc0WouGBMAAMARYyMBqeo3qp/p4KPfPampSx4AAMARYyMB6c7VE6rzqldXn6v2rNF2\nrZHuAAAAtq2NBqRPNI1od9XmlAMAADA/GzlR7PHV+QlHAADAgtpIQHpXdZvWHpgBAADgiLaR\ngPTXTSHpN6vjNqUaAACAOdrIMUjfVn20enT1yOo91WfXaPuKMQEAABwxNhKQvqNpiO+qk6v7\nHKDtBxOQAACAI8xGAtIzq+dX166j7WWHVg4AAMD8bCQgfW5MAAAAC2kjAenmYzqYo6tPVh86\npIoAAADmZCMB6UeqX15n2ydV5264GgAAgDnaSED62+qpa8y7YXXn6hbVU6o3HmZdAAAAW24j\nAelNYzqQn6weWj3jkCsCAACYk42cKHY9/mvT3qTvnPFyAQAANt2sA1LVx6qzNmG5AAAAm2rW\nAen61TdXX5jxcgEAADbdRo5Buu+YVrOrOqW6d/U11VsPsy4AAIAtt5GAdNemQRgO5LLqcdX5\nh1wRAADAnGwkID27+os15u2tvlh9uLrmcIsCAACYh40EpE+NCQAAYCFtJCAtuVH1yKYTw546\n7ruoelv1R9WlsykNAABga200IN2/enF14irzHl79UvXd1dsPsy4AAIAtt5Fhvk9u2kN0RfXY\n6nbVaWO6ffUz1dHVy6rjZ1smAADA5tvIHqT7NJ3n6E7Vu1bM+9fqvdXfVu+szqleNYsCAQAA\ntspG9iDdsulYo5XhaLn/XX28us3hFAUAADAPGwlI11bXXecy9xxaOQAAAPOzkYB0ftNxSA85\nQJv7VDfNiWIBAIAj0EaOQXpD9aGmgRqeXb2p6bxIu6qvre5dPbp6f/VXsy0TAABg820kIF1T\nPaj68+onx7TSP1f/frQFAAA4omz0PEgXVLet7lfdrbpxtbdp8Ia3VP+r+sosCwQAANgqGwlI\nu5rC0DXVK8e05NimYGRwBgAA4Ii13kEa7tx0fqMbrjH/p6q/qU6fRVEAAADzsJ6AdPumARnu\nWN1jjTbXr+4+2p06m9IAAAC21noC0v+srlM9vPqzNdr8QvWD1c2q35tNaQAAAFvrYAHpdk17\njn6v+tODtP3j6gXVg5uCEgAAwBHlYAHpm8flH61zec+rjm4a4Q4AAOCIcrCAdONx+eF1Lu9D\n4/Lmh1YOAADA/BwsIC2d8PW4dS7vhHH5pUMrBwAAYH4Odh6kj4zLu1YvX8fy7jkuP3aoBQGw\n2D75yU9W/Xj1sDmXshVeXP3SvIsAYP0OFpD+urqq+tnqVe3bo7Sak6ufr75QvXEWxQGweK64\n4orOPvvs63/rt37r9eddy2Z661vf2jve8Y47zrsOADbmYAHp89Wzqv9cvbT6D9XnVml3RvWi\n6pbVU6svz7BGABbMGWec0QMe8IB5l7GpLrroot7xjnfMuwwANuhgAanq56o7Vd9d3bv6i+o9\n1RerU6q7VPdpGr3uDdW5m1EoAADAZltPQPpyda/qV6rHVN83puU+Uz2j+o3q2lkWCAAAsFXW\nE5Bq33FIv1LdvbpV04h1n2kaAvytCUYAAMARbr0BackV1evHBAAAsFA2GpCOZN9R3a+6bXVq\ndXxT98GLqvc2jdLnaFoAANjBdkJA+rqmEfjOXnbf1U3dBo9rGoDigdUvVq+rHtnqI/UBAAAL\n7qh5F7DJdlevre7QNIjE3ZrO13RcddK4PKVpEIrnNY3G9+oW/3kBAABWseh7kM6pzqweVb1w\njTafr948pvdUv1vds3rTFtQHAABsI4u+p+TMptH1XrzO9s+p9lbfvGkVAQAA29aiB6Rrmx7j\n7nW2313tagpJAADADrPoAeldTYHnMets//hxaTQ7AADYgRb9GKS3VG+rfqu6S/Xy6vymE9xe\n3TRIw2nVWdUjqvs2nePpbfMoFgAAmK9FD0h7qgdVz60eNqYDtX1B9dh0sQMAgB1p0QNS1SXV\nQ6pbNe0hOrN9J4q9srq4Oq96TfWJGa1zV/Vvq+uus/1tZ7ReAADgMOyEgLTkA2PaCreo/qad\n9fwCAMARb9EHaVhy/eoB1XeN60tOq367+sum7nX3ntH6Pty+EfHWM919RusFAAAOw07Yw3Gf\n6iXVSeP255qOS7qgaZS7myxr+0PVTzadLBYAANhhFn0P0nHV/2w63uh/VE9qCkh/UP3H6sTq\n+5u6xD24+pfqN6obzqNYAABgvhZ9D9I5TXuIfqB60bjvd6oPNZ0b6dzqT8b9H62uqf6i+s5l\n7QEAgB1i0fcgnT4u/3zZfZdWr6q+tvpfK9r/9bi8+eaWBQAAbEeLHpD2LpuW++S4vHjF/Ut7\n1K7YzKIAAIDtadG72L2/aZS4+1cvW3b/a5qORVoZhO47Lj+8+aUBAADbzaIHpDc0hZ3nNQ3E\n8N+qL1f/MKYlJ1UPrZ7eNFDDX21tmQAAwHaw6F3svlL9f+PyN6oT1mj3oKYQtbv60eqqrSgO\nAADYXhZ9D1LVW6pbVQ+pLlmjzQXVU6o/qi7coroAAIBtZicEpJqON3rOAea/e0wAAMAOtuhd\n7AAAANZNQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAA\nGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIA\nABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUAC\nAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFA\nAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBB\nQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACA\nQUACAAAYBCQAAIDhmHkXsIW+o7pfddvq1Or46svVRdV7q1dV75hbdQAAwNzthID0ddVLq7OX\n3Xd1dVV1XHWn6oHVL1avqx5ZfW6LawQAALaBRe9it7t6bXWH6hnV3aqTm4LRSePylOpe1fOq\n+1SvbvGfFwAAYBWLvgfpnOrM6lHVC9do8/nqzWN6T/W71T2rN21BfQAAwDay6HtKzqyurV68\nzvbPqfZW37xpFQEAANvWogeka5se4+51tt9d7WoKSQAAwA6z6AHpXU2B5zHrbP/4cWk0OwAA\n2IEW/Rikt1Rvq36rukv18ur86jNNI9kdV51WnVU9orpv9frxNwAAwA6z6AFpT/Wg6rnVw8Z0\noLYvqB6bLnYAALAjLXpAqrqkekh1q6Y9RGe270SxV1YXV+dVr6k+McP13qz1H/v0tTNcLwAA\ncIh2QkBa8oExbYXTqw8ewt/tmnUhAADA+u2UgHRK9R3V9aq3V+9bo93upqG+/3xMh+pD1U2a\n9lKtx7dUL03XPgAAmKudEJAe0HQepOstu+/F1Y9Xl69oe3T1Q9VHO7yAVPWpDbS90WGuCwAA\nmIFFD0gnNO0R2l39XtOenbtW31/dprpXdencqgMAALaVRQ9I39m0d+YRTXuNlvxp9YfVK0eb\nq7e+NAAAYLtZ9BPF3rLpuJ6V3eVe0bQX6R7Vs7e6KAAAYHta9IB0VdPIcMeuMu/V1eObjjn6\npa0sCgAA2J4WPSD907h89Brzn9F0jNKTqydsSUUAAMC2tejHIP1N9c7qN6vbNe0p+uSKNj8x\nLn+juvfWlQYAAGw3i74Hqep7qv/T1JVuteG091Q/Vv1C07mSAACAHWonBKSPV3esvrV6/wHa\nPa36xuq/VH+9+WUBAADbzaJ3sVuyp3rrOtp9qHrKJtcCAABsUzthDxIAAMC6CEgAAACDgAQA\nADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AE\nAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOA\nBAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACD\ngAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAA\ng4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAA\nAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwHDPv\nArbQd1T3q25bnVodX325uqh6b/Wq6h1zqw4AAJi7nRCQvq56aXX2svuurq6qjqvuVD2w+sXq\nddUjq89tcY0AAMA2sOhd7HZXr63uUD2jult1clMwOmlcnlLdq3pedZ/q1S3+8wIAAKxi0fcg\nnVOdWT2qeuEabT5fvXlM76l+t7pn9aYtqA8AANhGFn1PyZnVtdWL19n+OdXe6ps3rSIAAGDb\nWvSAdG3TY9y9zva7q11NIQkAANhhFj0gvasp8Dxmne0fPy6NZgcAADvQoh+D9JbqbdVvVXep\nXl6dX32maSS746rTqrOqR1T3rV4//gYAANhhFj0g7akeVD23etiYDtT2BdVj08UOAAB2pEUP\nSFWXVA+pbtW0h+jM9p0o9srq4uq86jXVJ2a43hOqY9fZ9sQZrhcAADhEOyEgLfnAmLbC6dX7\n2/gxXrs2oRYAAGCddlJAWo/d1fOrV4zpUH2o6eS0692DdFbTiWp17QMAgDkSkPZ3dPUD1Qc7\nvIBUU7e99TruMNcFAADMwKIP8w0AALBui74H6dZjWq/1nlAWAABYQIsekB5R/fK8iwAAAI4M\nix6Q/nlcvqJ65zraH1M9efPKAQAAtrNFD0h/Wn1vdXb16OrzB2l/fAISAADsWDthkIYfawqC\nz513IQAAwPa2EwLS56qHVxd18AEb9lZXVV/Z7KIAAIDtZ9G72C352zEdzFVN3ewAAIAdaCfs\nQQIAAFgXAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAA\nYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkA\nAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJ\nAABgOGbeBQDAIrr00kurbl392pxL2QrnVy+cdxGb7LjqCdX15l3IFriyenp12bwLgXkQkABg\nE3zsYx/rpJNOuuWtbnWrn513LZvpkksu6SMf+cinWvyAdEb15Nvf/vYdc8xif31697vf3d69\ne/+2etO8a4F5WOx3OADM0W1uc5t+7dcWewfSG9/4xp761KfOu4ytsKvq3HPP7eSTT553LZvq\n3ve+d3v37t017zpgXhyDBAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAYxQ4AOGR79+6t2l3d\ncc6lbLbT513AFrt1dem8i9hke6t/qq6edyFsLwISAHDILrzwwqobVv97zqUwI3v27Kn67/Ou\nY4v8x3bOY2WdBCQA4JBde+21nXLKKT3/+c+fdymb6u1vf3u/+qu/Ou8ytsyTn/zkzjrrrHmX\nsake97jH9eEPf/j4edfB9iMgAQCHZdeuXZ144onzLmNTXec615l3CVvqOte5zsK/pkcfffS8\nS2CbMkgDAADAICABAAAMAhIAAMAgIAEAAAwCEgAAwCAgAQAADAISAADAICABAAAMAhIAwP9t\n786D5KzrPI6/x9zJJCYagSBH4kEQhcUDV2QV3MKDFVFXxHN1PShLEC2lXMGjGFe8VxcB0XVx\nlXVVdC3jyaKiSGGpnIJBjhhgBTZGwplOZiaZSWb/+H67ptPpmemZebqf6c77VdX1TD/P0898\np59O5/k8v+f3eyQpGZAkSZIkKRmQJEmSJCkZkCRJkiQpGZAkSZIkKRmQJEmSJCkZkCRJkiQp\nGZAkSZIkKRmQJEmSJCkZkCRJkiQpGZAkSZIkKRmQJEmSJCkZkCRJkiQpzS67AEmSJKndhoaG\nAA4Cji25lHb4PXBv2UV0CgOSJEmS9jgbN24EeFs+ut2FwMllF9EpDEiSJEna44yMjHDKKadw\n4oknll1KS33qU5/i0ksv9Zh/EuyDJEmSJElpT0yTPcAiYD4wAGwttxxJkiRJM8We0oK0D/Bh\n4BpgC1ABNuXPm4FfAe8FlpRVoCRJkqTy7QktSC8AvgMsJlqLbiPC0TZgHhGejgCOAk4HXkIE\nKUmSJEl7mG4PSEuBi4GHgNcDlwDDDdabD7wS+CywBliNl95JkiRJe5xuv8TuxcAy4CTgBzQO\nRwCDwNeA1wKPBY5rS3WSJEmSZpQeYCR//jDQV14pLXEm8XfNbXL9WcB24APAJ6bxe1cBV9F8\nC91s4hLAucDQNH7vRC6cPXv2WxYsWNDCX1G+gYEBdu7cyaJFi8oupaWGhoYYHBxk8eLFZZfS\nUjt37mTr1q0sXLiQWbNmlV1OS1UqFebPn8+cOXPKLqWltmzZwpw5c5g3b17ZpbRUf38/PT09\ndPt37uDgIMPDw/T29pZdSktVv3N7e3vp6ekpu5yWqlQqe8x37rx585g7t9nDxM40MDDA8PDw\nl4G3ll3LDNcHnAXdf4ndZmAOsBfN3T14BdGqtnmav/dPRKtVs+9vD1FjK8MRwIeGh4cvrlQq\nLf41pVsIPKpSqdxTdiEt9ghgVaVSub3sQtrgCf39/bczekKnWx04ODj458HBwe1lF9Jie2/f\nvn1g+/bt0/2unemWAAsqlcpfyi6kxeYCKyqVyp/KLqTFeoDHb9myZX3ZhbTB4/v7++8EdpZd\nSIvtt23btge2bdvWX3YhbfCHsgvoNCP56Cu5jlY4hPjbvs7ErUiLgO8TXwYHtbguSZIkSTNH\nH5mLur0F6WbgAuAU4Gjgh0SC3kRcSjcP2Bs4DDgBWA58HFhXRrGSJEmSytfNLUgQTeLvBO5m\n9G9t9FgHvLGkGiVJkiSVp489pAUJ4g89FzgPeApx2d1exNDeg8BGYC1wa1kFSpIkSZoZ9oSA\nVDVCBKG1ZRciSZIkaWbq9vsgSZIkSVLTDEiSJEmSlAxIkiRJkpQMSJIkSZKUDEiSJEmSlAxI\nkiRJkpQMSJIkSZKUDEiSJEmSlAxIkiRJkpQMSJIkSZKUDEiSJEmSlAxIkiRJkpQMSJIkSZKU\nDEiSJEmSlAxIkiRJkpQMSJIkSZKUZpddgNQC5wHvKLsISZKkGeK3wJFlF9EpDEjqRncBtwGv\nK7sQFWIpcBnwKuD2kmtRMc4F/kiczFDnexHwLuC4sgtRYa4mTjReXXYhKsRZQKXsIjqJAUnd\naBjoB64ruxAVYnlObwZuKrMQFeZhYCP+G+0Wq4Eh3J/dZARYh/u0W9xfdgGdxj5IkiRJkpQM\nSJIkSZKUDEiSJEmSlAxIkiRJkpQMSJIkSZKUDEiSJEmSlAxIkiRJkpQMSJIkSZKUDEiSJEmS\nlGaXXYDUAtvzoe4wRNzV3X3aPfw32l3cn93Hfdpd3JdTMJKPvpLrkIoyH9i37CJUqMeVXYAK\ntRfQW3YRKsxs4ICyi1ChVgE9ZRehwizLh8bXR+YiW5DUjQaBDWUXoULdUXYBKtS9ZRegQg0D\nd5VdhAp1Z9kFqFAPll1Ap7EPkiRJkiQlA5IkSZIkJQOSJEmSJCUDkiRJkiQlA5IkSZIkJQOS\nJEmSJCUDkiRJkiQlA5IkSZIkJQOSJEmSJCUDkiRJkiQlA5IkSZIkJQOSJEmSJCUDkiRJkiQl\nA5IkSZIkpdllFyC1weHAUuBKYEfJtWjyVgIrgPuBdeWWooKszMdaYr+qc80CDgAeA9wFbCy3\nHBXg0cAqoAL8L7Ct1GpUpGcAvcBvcL9OaCQffSXXIRVtEfAlRj/jveWWo0k6HLie0f03AqwH\nnldmUZqWHuA0YIDYn8eXW46m6S3ABnb9N3ojcEyJNWnqngZczq77sx/4NDC/xLpUjGcRJ4lH\ngP1KrmWm6mP0s29AUld6JtHasIk4A2ZA6iwriH33IPAO4CjgDcDdxH/YTy6vNE3RCuBSYAi4\nAQNSpzuZ2IdrgX8A/hZ4P7AFGASeVF5pmoInEC1GDwNnEvvzROAKYj9/pbzSVIA5xL/V6jG/\nAamxPgxI6nJrgZ8D+xIHZQakzvIZYp+9pG7+4Tn/W22vSNN1HnAncRbzDAxInayHaDm6n7gc\nq9Y7iX378XYXpWk5j9hvJ9bNX0Ds623AvHYXpcJ8iDg5dQkGpPH0kbnIQRrUrT4KPJ/4Ylfn\neTnwZ+BHdfNvAK4hDqzntrsoTculRMD9bdmFaNoWAp8kwlB9H7Jf5XTftlak6boIeB3ww7r5\nA8DNxPftgnYXpUKsBj4AnIP9eJtmQFK3uhjYWXYRmpIlRAfhav+jetcSB2gHtbMoTduPict3\n1Pm2Ap8Dvt5g2cqc/rFt1agI1wLfYPeO+48mTmysBx5qd1Gath6iL/YG4KySa+kojmInaaap\nNv2P1fpXnb8/cFPry5HUpIXEQdhW7LPSyZ4CPBZ4ItEHtIcYkEOd52TgucALif67apIBSdJM\nszCng2MsH8jpojbUIqk584kWpUOJS7X+r9xyNA1nAy/Nn68lBsi5urxyNEUriEthvwb8tORa\nOo6X2KlT/QS4te4xq9SKVJThnI51Aqc6f3sbapE0sb2I4aGPB94MfLPccjRNHwdeBbyX+H/1\n10QfFnWW84j/T99TdiGdyBYkdar7cESdblXt9L1sjOWPyukDbahF0vgOIzr2LwH+DvhZueWo\nAFflA+BfiQFWPkK0QlxTVlGalJcCryCG4L+v5Fo6kgFJnep1ZReglrmH6MOweozl1fur3NKe\nciSN4VDgl0Tn/SOJlnx1poXAUnbv+7mDGPToWOA5GJA6wWzg88Q9IAFeX7Ps4Jy+nLjP4Ddw\nQKuGDEiSZpoR4BfE2ej9iMBUtRg4BriO3YcXltQ++xOtRfcBRxPD8qtzXQc8DlhO3DC21t45\n9bLmzjCfGGQDov9RI+fm9DuM3d93j2YfJEkz0fnEte8XEF/25PPPESHpcyXVJSlcCDySuJmz\n4ajzfZu419EX2XUAnMOAdxMtST8poS5N3hbi/8lGjwtyndX53HA0jpF89JVch1SUJxM3o6w+\nHiI+41fXzDuhtOrUrM8S++1eogP4Pfn8q8Sws+osVzD67+8uYl/eWjOvr7TKNFmHE/vvYXb9\nrq19rCmtOk3FHKJFcIS49Oo3wFoiGO0ETi+vNBXoHGIf7zfRinuoPjIXeYmdutEIu54VuaHB\nOjvaVIum7j1Ep+BXE8OVXg58Fw+8OtU2Rm/8e0c+ag21txxN0xUTLK+/4ahmtiHgBcRIhMcB\nBxID4fyUGL79+vJKU4HWE/92/ffZBFuQJEmSJO3J+shcZB8kSZIkSUoGJEmSJElKBiRJkiRJ\nSgYkSZIkSUoGJEmSJElKBiRJkiRJSgYkSZIkSUoGJEmSJElKBiRJkiRJSgYkSZIkSUoGJEmS\nJElKBiRJkiRJSgYkSZIkSUoGJEmSJElKBiRJkiRJSgYkSZIkSUoGJEmSJElKBiRJkiRJSgYk\nSZIkSUoGJEmSJElKBiRJkiRJSgYkSZIkSUoGJEmSJElKBiRJkiRJSgYkSZIkSUoGJEmSJElK\nBiRJkiRJSgYkSZIkSUoGJEmSJElKBiRJkiRJSgYkSZIkSUoGJEmSJElKBiRJmtgxwKNrnh8C\n/BkYycc+JdQEcHD+/i+W9Ptngpn4HlRrunCSrzuA3T9rkqQ2MyBJ0sQuB55e8/ydRCj6APBU\n4P4yigLuBc4E1pT0+9vttcAFdfO66T04id0/a5KkNptddgGS1CEqNT8fmNPPAw+XUEvVA8An\nSvz97XY88Li6ed30HmzJaWXctSRJLWVAkqTmVIAlwNOAvXLeXwPbgV/ntGoJcBAwC7iTaOWo\ntRB4Zi77E3FJ1mOAK+vWW5TLZk+wnQ3AurplB2cd64CHsuZDgJuA+xrUsBB4EjAIrAe2jflO\njG+q210O7E9cmnYHsLlm2WKiVeVIYIC4DO1B4EbGfw8mux+KqHU8c4jL6B5DBLs7gOGa5c0G\npL2zzrGspflWzYlqqrUMeGLWdzu7fuZrtevzL0ktU72Gvq/kOiRpphoBVgLPYvQ7s/ZR7YPU\nC1wEDNUt/0W+vqraR+UjwL/nzzfVLJ9PtE5tq9vOZWNsp7b/zaHALTWvGQA+BLw9n78411uV\nzz8GnEoc9A7kvPuBlzXzxjQw2e0ekH/XzpqadxLv4yNznWew+3t+2TjvQbP7oRW11tZU2wfp\nVGBjXT0bgDfVrPMyRj9r43k9u78ftY/jJ3j9ZGqCCCr/SQSn6nr3Nliv3Z9/SSpSH6PfNwYk\nSZrAU4iz2LOApcAvie/NvfN5T673Pzn/E8QZ9NXAe4kDy/XAglyvemD+c+B3wAlE+Kr6NnGQ\n+UGipeDxwNuIlor1xBl42D0czCXOtO8AzsjlxwI3A9fkui/KdastIH/IOlbl/EOAvxBhoXcS\n71HVZLf7e6Il4jTgycBfAZ/MbfxXrlN93wfz71hKHLQ3eg+g+f3Qilpra6oGpKPz+U+BZxOX\nCT43n48AR+V6vYx+1sbTCzyh7nEMEe7uA1ZM8PrJ1ATw/Zz3L0Qr3rHAb4hwWBsi2/35l6Qi\n9WFAkqQpu4z43pxfM+/ZOe87Ddb/aC77x3y+Xz7fwWh/pqqnM3owWu+0XFY9c18fDk7I5+fX\nvW4VcWBfG5CqNWwhLm+qdU4ue26DGiYyme32EgNdnNJgO7cA/ew6mNAg8Nu69erfg6nsh6Jr\nrQ9IH8znx9S9bilwNnGp5nQ8ghjcYQR4aZOvabamI3K9r9Sttw8RfH6WETKBjwAABcVJREFU\nz8v4/EtSkfrIXGQfJEkqxrE5/W6DZT8A3k+ctf9qzfzriT4YtY7L6TDw6rplc3P6HHY/YIXR\ng9pL6+bfSYS649jdtcCmunn35HQ6w003s90txMFzD9G3ZQWjf+NWosWhl+b7+MDU9kOra707\np6cS/aYezOcPEUFlus4ggs4XiNaeZjRb0wtz+qO6128kWvGq/bRmwudfkgphQJKkYqzM6R0N\nllUPAvevm39P/YqMjtL2vnF+11j3Xdo3p3c3WHYjjQPShgbzqp30Z41Tw0Sa3e4rgc8y2qpQ\n7QNUbZ2b7O0oVuZ0Mvuh1bV+A/h74ESihecqouVlDTGgwnQcAXyYuIzy9LplH8maa72JuDyu\n2Zqqn8dGn9XaQSxW5rTMz78kFcL7IElSMebktNHIXkM5nVc3f+s423kh0SrR6DHWZVTVM+xD\nDZb1j/GanWPMn65mtnsEcDFR7/OI+hcRLTGXjfO68UxlP7S61iFinz2PuOzusUSo+T3wPUb7\n5kxWLxF0dgCvIQJbrc1ES0/to/q+NFtT9f0ca2S7qpnw+ZekQtiCJEnFeCCnjS5Le1ROmxl6\n+b6cLif63ExGdZjoxQ2WzcSz7q8hTtSdTgx8UWv5FLdZ1H6oV0Stv6x57WpGW3jOAM6aQk3n\nEwM0vIsINvU+nY/p1DTe+1lrJnz+JakQtiBJUjGuy+kzGyw7Iqe/a2I71+a00eVw+xB9PcY6\nuVW9vOmQuvk9wPOb+N3ttiyn9ZdlrQKeOsVtFrUf6k2n1iVEkKl1GzFc9zC7jhjXrFcBbwQu\nAc6dwuubren6nD6L3X2J0QFBZsLnX5IKYUCSpGJ8j+jofiq7DrPcS/SnGGLXoaDH8n3iLPor\nGT2whLj06Hyin8jTxnht9VKvtzM6DDbAe4hLqGaaatioPfheRtxz55aa51WD7D7aXL2i9sN0\na621hhh9b1Xd/MOIg/1GfaDGcyDwb8RQ5FMd0a3Zmr5HDNxwKruOOPcK4GRGw8pM+PxLUiE8\nCyNJxdhMDGP838RNL39M9G15AXHm+1Tg9ia2s4U4i78GuJK4t8xW4G+IA9SzgavHeO11xDDL\nJxId7X+Vr9mXOLj8p0n/Va31ZeI+OecSw2nvJG5kew7xPnwG+DoRBi4iWiCOIf6uDcBJDbZZ\n1H6YTq1X1b32TOLA/mZin24C9srtbCJGx5uMs4kb095FDBpR77s0Hk1uKjU9DLwZ+BZxv6gr\n83cfCazL7cDM+PxLUiEMSJI0eTcS35/1nft/ABwKvJW44WcPcdPLrwI31Ky3DbiC0ZaHej8h\nbpB5MnEz0mXEAec3iXBQ1Z/bWVcz77U57/n5usuIcHRaLq92oh+vhntyWf3Q182YzHY3EJen\nvZu4Gegm4uD4EqIFbCUxpPaOXP8txH0qlgF/zHmN3oMi9sN0aq3WdFu+9uqs5w05XU4MmPA+\n4D+Y3DDmEKPCXZE/79dg+ZImtjGZmtYQn8OTic/l3UQQ/wK7DgzR7s+/JLWMN4qVpO4yp8G8\ni4jv+tVtrkWSpE7QR+Yi+yBJUvdYQrQCXAcsrJl/MNGn43Z2bWmRJEl1vMROkrrHZuAC4n42\n64g+HIuJe90AvI04OzYZRzP24AP1KsDPJ7l9SZJmFAOSJHWXfyaC0UnAAUQgOo+4Gej6KWzv\nk8Azmlz3VqLviSRJHcuAJEnd5/J8FKHR/W8kSepa9kGSJEmSpGRAkiRJkqRkQJIkSZKkZECS\nJEmSpGRAkiRJkqRkQJIkSZKkZECSJEmSpGRAkiRJkqRkQJIkSZKkZECSJEmSpGRAkiRJkqRk\nQJIkSZKkZECSJEmSpGRAkiRJkqRkQJIkSZKkZECSJEmSpGRAkiRJkqRkQJIkSZKkZECSJEmS\npGRAkiRJkqRkQJIkSZKkZECSJEmSpGRAkiRJkqQ0u+bno4D3lVWIJEmSJJXkqOoPPcBIiYVI\nkiRJ0ozhJXaSJEmSlP4fK+S+KgYWSgUAAAAASUVORK5CYII=", "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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2M5pzq/OqPpvKPVvG8sAAAAx431BKSqn6ye0dG73z23aUoeAADAcWM9AelLq2dV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\nRU5ErkJggg==", "text/plain": [ "Plot with title “'rented' domain histogram”" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" }, { "data": { "image/png": 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5dY7l3V02b+X6pj/sWdvF0vvlzeyTu6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\nGcMudpIkSZKU/h+kOqtox+U9EAAAAABJRU5ErkJggg==", "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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VUfWDZvf1N3hheO5d6h6cT6Tzd9y/ii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\np+/4xwAAAABJRU5ErkJggg==", "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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F7RjadZ38gxdaz7+FPztX+wMYnLKY2p\ntV/SGA74rxfarp56/Gi/o+3Y38dy3AFLpgcJ2Cm/36F/c35zuaUAe9BJ6ZEBjs356UECdthD\nGlN819rX3uw2X9WxXYT9ltzXBLbTrzR6lW/fmHp8cUjbozo0jf6BxqQOAJsiIAHb6dzGdTJn\nNSYcWPGSxtCz3ex7O7Zpfb+rcb0CsD0+16Hhb09rXKf09sYEEIvXNb60wyeLANgwQ+yA7XLf\nbnxB8+IMVQCbdXrjesb1Jnc4WP1ea99oGWA952eIHbADPto4kbl54+LjNzQmG7j+SE8COIKr\nq0c0pt3+rupLG1Pnf7Z6T3VxY1IJgGMiIAHb6dLqXyy7CGBP+t/t/qG6wAnIjWIBAAAmAQkA\nAGASkAAAACYBCQAAYBKQAAAAJgEJAABgEpAAAAAmAQkAAGASkAAAACYBCQAAYBKQAAAAJgEJ\nAABgEpAAAAAmAQkAAGASkAAAACYBCQAAYBKQAAAAJgEJAABgEpAAAAAmAQkAAGASkAAAACYB\nCQAAYBKQAAAAJgEJAABgEpAAAAAmAQkAAGASkAAAACYBCQAAYBKQAAAAJgEJAABgEpAAAAAm\nAQkAAGASkAAAACYBCQAAYBKQAAAAJgEJAABgEpAAAAAmAQkAAGASkAAAACYBCQAAYBKQAAAA\nJgEJAABgEpAAAAAmAQkAAGASkAAAACYBCQAAYBKQAAAAJgEJAABgEpAAAAAmAQkAAGASkAAA\nACYBCQAAYBKQAAAAJgEJAABgEpAAAAAmAQkAAGA6edkFsKNuW91t2UWc4C6pPrrsIgAA2B4C\n0v7yjJNPPvlfn3HGGcuu44R01VVXdf311/969YRl1wIAwPYQkPaXm97//vfvaU972rLrOCFd\ncMEFveY1r7npsusAAGD77MeAdFJ1s+r06qrqyuWWAwAA7Bb7ZZKGs6unV2+pPltdUX18Pr68\nemP11OrmyyoQAABYvv3Qg/TA6qLqzEZv0aWNcHRNdVojPN2z+ubqx6qHNoIUAACwz+z1gHTL\n6sLqM9Vjq1dX16/R7vTq0dWzq1dWd8nQOwAA2Hf2+hC7h1S3qv5l9QetHY6qrq5eWn1P9cXV\nt+9IdQAAwK6y1wPSHarrqr/aYPvXVQeqO29bRQAAwK611wPS5dUp1W022P62jd/J5dtWEQAA\nsGvt9YD05/Prc6pTj9L2ZtULqoPVn25nUQAAwO601ydpeHf1X6onVfepXlVd0pjF7trGLHZn\nVXevvrO6dfXz1XuXUSwAALBcez0gVf1wY2rvp1ZPPEK7y6qnVC/eiaIAAIDdZz8EpIPVL1XP\nq766umvjmqTTG7PXfax6Z/WeZRUIAADsDvshIK042AhC71x2IQAAwO601ydpWPGARg/SixrD\n7E5fp91p1QeqH92ZsgAAgN1kP/Qg/XT1jIXv/1XjeqSHd+PepJOqO1a33JHKAACAXWWv9yB9\nSSMgvb96dPX1jRntblX9RfU1yysNAADYbfZ6D9K3NobNPbZ601z319Vr5/JH1b2qDy+lOgAA\nYFfZ6wHp9o3JGd6yav37q4c0QtPF1bdVn9vCn3u76qKOfnPaFac07sd0u+rAFtYBAABswl4P\nSJ9oXFd02+qDq7a9t3pUoyfpd6pHbuHP/VT18kbv1Ubcsfq/G/vj2i2sAwAA2IS9HpDe3OhB\nelZjcoYbVm3/88asdr9WvaL6N1v0c6+ufnET7c9rBCQAAGCJ9vokDZdUv9W4BunS6m5rtPmN\n6vHVd+QeSQAAsK/t9YBU9QONeyCdVd10nTYvre5X/dNOFQUAAOw+e32IXdV11Y807n10pAkQ\n3lDdtfqm6kM7UBcAALDL7IeAtOKaDbS5vnrjdhcCAADsTvthiN1mnNboPfr3yy4EAADYeQLS\n4U6qvri6+bILAQAAdp6ABAAAMO31a5AeOJeNWm+WOwAAYB/Y6wHpvOrHll0EAABwYtjrAemP\nqp+ufr160Qban1r9xbZWBAAA7Fp7PSC9uXpm4x5Iz6vedZT2p297RQAAwK61HyZpeEb1jurC\n6owl1wIAAOxie70HqcbNXx9VfVN16+qDR2h7Q/Xa6m92oC4AAGCX2Q8BqcbNXy/aQLvrqgdv\ncy0AAMAutR+G2AEAAGyIgAQAADAJSAAAAJOABAAAMAlIAAAAk4AEAAAwCUgAAACTgAQAADAJ\nSAAAAJOABAAAMAlIAAAAk4AEAAAwCUgAAACTgAQAADAJSAAAAJOABAAAMAlIAAAAk4AEAAAw\nCUgAAACTgAQAADAJSAAAAJOABAAAMAlIAAAAk4AEAAAwCUgAAACTgAQAADAJSAAAAJOABAAA\nMAlIAAAAk4AEAAAwCUgAAACTgAQAADAJSAAAAJOABAAAMAlIAAAAk4AEAAAwCUgAAACTgAQA\nADAJSAAAAJOABAAAMAlIAAAAk4AEAAAwCUgAAACTgAQAADAJSAAAAJOABAAAMAlIAAAAk4AE\nAAAwCUgAAACTgAQAADAJSAAAAJOABAAAMAlIAAAAk4AEAAAwCUgAAACTgAQAADAJSAAAAJOA\nBAAAMAlIAAAAk4AEAAAwnbzsAnbQzar7VnerblOdXl1VfbR6R/X66tplFQcAACzffghIp1bP\nrH6oOuMI7T5T/UJ1QXVwB+riBHP99ddXfWF1jyWXciK7rLp82UUAAKxnPwSkC6tHVG+rLqou\nqT5eXVOdVp1dnVs9phGQ7lQ9cSmVsqtdeumlVQ+dC8fmV/L3BQDsYns9IN2rEY6eXT2l9XuG\nXlk9o3ph9YPV86t37USBnDgOHDjQ/e53v5785Ccvu5QT0nOf+9xe97rXnbbsOgAAjmSvB6R7\nN0LR0zv6sLnrqx+vvr9xrZKAxI2ceuqpnXnmmcsu44R06qmnLrsEAICj2uuz2J1W3VB9doPt\nP10daEzoAAAA7DN7PSBd1ugle/AG2z+i8Tt5z7ZVBAAA7Fp7PSC9pvpQ9bLqSdVZ67S7fWN4\n3Yuq983nAQAA+8xevwbpc9XDq4urF8zlk41Z7K5tDME7q7rlbP/e6mGNGe4AAIB9Zq8HpKq3\nVudU39sYanfXDt0o9urqI9Vrq1dVL6+uW06ZAADAsu2HgFSjJ+lX5wIAALCm/RKQasxMd9/q\nbh3qQbqq+mj1jur1jWF3AADAPrUfAtKp1TOrH6rOOEK7z1S/UF3Q0e+ZBAAA7EH7ISBd2Ji+\n+23VRdUljUkarmlM0nB2dW71mEZAulP1xKVUCgAALNVeD0j3aoSjZ1dPaf2eoVdWz6heWP1g\n9fzqXTtRIAAAsHvs9YB070YoenpHHzZ3feNeSN/fuFbpeALSTauHNHqoNuIux/GzAACALbLX\nA9Jp1Q3VZzfY/tPVgcaEDsfj9tWvtPGAtLIfTjrOnwsAAByHvR6QLmu8xwdXr95A+0dUN6ne\nc5w/9wPVbTfR/rzqLzM5BAAALNVNll3ANntN9aHqZdWTqrPWaXf7xvC6F1Xvm88DAAD2mb3e\ng/S56uHVxdUL5vLJxix21zaGwJ1V3XK2f2/1sMYMdwAAwD6z1wNS1Vurc6rvbQy1u2uHbhR7\ndfWR6rXVq6qXV9ctp0wAAGDZ9kNAqtGT9KtzAQAAWNNevwZps25aPaH6+mUXAgAA7DwB6XCn\nNHqZvnPZhQAAADtPQAIAAJj2+jVIp85lozZ6Y1cAAGAP2usB6Sern1l2EQAAwIlhrwekz8yv\nb2vc++hoblI9YPvKAQAAdrO9HpD+a/Wvqyurh1Q3HKX96dVV210UAACwO+31SRquadwg9hur\nn15yLQAAwC631wNS1Tuqp1SPqM5Zci0AAMAutteH2K14/lyO5prq3tWHtrccAABgN9ovAWmj\nDlZ/tewiAACA5dgPQ+wAAAA2REACAACYBCQAAIBJQAIAAJgEJAAAgElAAgAAmAQkAACASUAC\nAACYBCQAAIBJQAIAAJgEJAAAgElAAgAAmAQkAACASUACAACYBCQAAIBJQAIAAJgEJAAAgElA\nAgAAmAQkAACASUACAACYBCQAAIBJQAIAAJgEJAAAgElAAgAAmAQkAACASUACAACYBCQAAIBJ\nQAIAAJgEJAAAgElAAgAAmAQkAACASUACAACYBCQAAIBJQAIAAJgEJAAAgElAAgAAmAQkAACA\nSUACAACYBCQAAIBJQAIAAJgEJAAAgElAAgAAmAQkAACASUACAACYBCQAAIBJQAIAAJgEJAAA\ngElAAgAAmAQkAACASUACAACYBCQAAIBJQAIAAJgEJAAAgElAAgAAmAQkAACASUACAACYBCQA\nAIBJQAIAAJgEJAAAgElAAgAAmAQkAACASUACAACYBCQAAIBJQAIAAJgEJAAAgOnkZRewg25W\n3be6W3Wb6vTqquqj1Tuq11fXLqs4AABg+fZDQDq1emb1Q9UZR2j3meoXqguqgztQFwAAsMvs\nh4B0YfWI6m3VRdUl1cera6rTqrOrc6vHNALSnaonLqVSAABgqfZ6QLpXIxw9u3pK6/cMvbJ6\nRvXC6ger51fv2okCAQCA3WOvT9Jw70YoenpHHzZ3ffXj8/F9t7EmAABgl9rrAem06obqsxts\n/+nqQGNCBwAAYJ/Z6wHpssYwwgdvsP0jGr+T92xbRQAAwK611wPSa6oPVS+rnlSdtU672zeG\n172oet98HgAAsM/s9UkaPlc9vLq4esFcPtmYxe7axhC8s6pbzvbvrR7WmOEOAADYZ/Z6QKp6\na3VO9b2NoXZ37dCNYq+uPlK9tnpV9fLquuWUCQAALNt+CEg1epJ+dS474faN0HXaBtufPr+e\ntD3lAAAAG7FfAlLV7Ro9Rp9aWHd6Y0jdnaqPNnqRPnXjp27aP1QXVKdusP2XV0/r6FORAwAA\n22g/BKRzqgurr5vfv776nkYYeWMjHK34dPWdc/3xuLb6zU20P68RkAAAgCXaDwHpd6qvbQSj\nqxph5MLq/dXNq59qzHR318ZMdy9vhCYTNQAAwD6z1wPSfaqvrx5VvWKu+9LqHXP9tzUmcVjx\nx9WfVQ+o/nDHqgQAAHaFvX4fpK+qPtGhcFT1gcYECh/p8HBU9brqM9VX7kRxAADA7rLXA9KZ\n1RVrrL+y+uw6z/lcG59cAQAA2EP2ekD6QGPK7dsurDu5cR3SOR26QeyKL55tP7wTxQEAALvL\nXg9If9roLfqDxnVID20Mt7t19dfVSxrTf1d9RfVb1YH5PAAAYJ/Z65M0fLoxffYvV/9trjtY\n/UD1941JGT7cmJZ7ZVjds9KDBAAA+9JeD0hVL2z0Fn1nY+rui6t3zm3/vHpqdefGzV1fUv36\nEmoEAAB2gf0QkKreMpfV/mIuAAAAe/4aJAAAgA0TkAAAACYBCQAAYBKQAAAAJgEJAABgEpAA\nAAAmAQkAAGASkAAAACYBCQAAYBKQAAAAJgEJAABgEpAAAAAmAQkAAGASkAAAACYBCQAAYBKQ\nAAAAJgEJAABgEpAAAAAmAQkAAGASkAAAACYBCQAAYBKQAAAAJgEJAABgEpAAAAAmAQkAAGAS\nkAAAACYBCQAAYBKQAAAAJgEJAABgEpAAAAAmAQkAAGASkAAAACYBCQAAYBKQAAAAJgEJAABg\nEpAAAAAmAQkAAGASkAAAACYBCQAAYBKQAAAAJgEJAABgEpAAAAAmAQkAAGASkAAAACYBCQAA\nYBKQAAAAps0EpO+rfnkDr/f31UOOuSIAAIAl2UxA+rLqm47S5vOq21R3OeaKAAAAluTkDbT5\nq/n1S6pbLXy/2knVnarTqk8df2kAAAA7ayMB6dXVPauvqM6ozj1C28url1a/ffylAQAA7KyN\nBKSfnV/Prx7ekQMSAADACWsjAWnFC6uXb1chAAAAy7aZgPSRuZxd3b06s3Hd0VrePRcAAIAT\nxmYCUtUF1Y919Nnvnt4YkgcAAHDC2ExA+sbqqdU7q1dVn6wOrNN2vZnuAAAAdq3NBqQPNma0\nu2Z7ygEAAFiezdwo9vTqkoQjAABgj9pMQHpr9ZWtPzEDAADACW0zAem/N0LSf6pO25ZqAAAA\nlmgz1yB9W/WB6gnVY6u3V59Yp+3vzQUAAOCEsZmA9M8aU3xX3aJ60BHa/k0CEgAAcILZTEB6\nXvWi6oYNtL382MoBAABYns0EpE/OBQAAYE/aTEC6w1yO5qbVh6r3HVNFAAAAS2npAcgAACAA\nSURBVLKZgPQD1c9ssO3Tq/M3XQ0AAMASbSYgvb565jrbvqj6xupO1c9Vf3acdQEAAOy4zQSk\n183lSJ5cPbJ6zjFXBAAAsCSbuVHsRjy30Zv0gC1+XQAAgG231QGp6u+qu2/D6wIAAGyrrQ5I\nt6y+rvqnLX5dAACAbbeZa5AePJe1nFR9QXX/6gurNx5nXdvppOpm1enVVdWVyy0HAADYLTYT\nkL6pMQnDkVxe/bvqkmOuaHucXf3f1XdUd60+b2HbFdU7qourX2m8BwAAYB/aTEB6YfWH62w7\nWH22en913fEWtcUeWF1UndnoLbq0+nh1TXVaIzzds/rm6seqh1ZvWUqlAADAUm0mIH1kLieS\nW1YXVp+pHlu9urp+jXanV4+unl29srpLht4BAMC+s5mAtOLsRtj4xuo2c91Hq7+sXtYII7vF\nQ6pbNYbW/dUR2l1dvbT6WPXH1bc3ep0AAIB9ZLMB6SHV7zSGq632mOqnq4dVbz7OurbKHRpD\n/o4Ujha9rjpQ3XnbKgIAAHatzUzzfYtGD9GV1Q9XX1OdNZevbVy/c9NGz8vpW1vmMbu8OqVD\nPV1Hc9vG78REDQAAsA9tpgfpQY1rer6heuuqbf/YmAnu9Y0JDh5Y/cFWFHic/nx+fU71/dW1\nR2h7s+oFjQkn/nSb6wIAAHahzQSkL2tca7Q6HC36X9XfV1/Z7ghI767+S/Wk6j7VqxpTkH+8\nEZZOa/SA3b36zurW1c9X711GsQAAwHJtJiDd0OH3D1rPTRrX8ewWP9yY2vup1ROP0O6y6inV\ni3eiKAAAYPfZTEC6pHEd0r+ofm+dNg+qvqTddaPYg9UvVc+rvrpxo9jbNK6Turoxc907q/ds\n4c88pfruNn4t1pdv4c8GAACO0WYC0p9U72tM1PDCxoxvH6lOqm5X3b96QmN42m68hudgIwi9\ncwd+1m2rn2wEpY1YCVInbU85AADARmwmIF3XuE7n96snz2W1/1M9fLbdTe7XuO5oJRyd0hhO\n932Na6uuqd5ePbd6xRb8vJXrsDbqvMZ9pA5uwc8GAACO0Wbvg/Tu6m6NG6+e1+gpOdiYvOEN\n1Wur67eywC3ws9V/bASidzZ6aX6/8R6uqz7Q6MH5lupbZ9ufW0ahAADAcm0mIJ3UCEPXVRfP\nZcWpjWC0myZnqDqn+qlGcLtwrvuOufxe9W+rT871X1X9dvUzjWGEH9jJQgEAgOXb6I1iv7Fx\nf6MvWmf7j1Z/0e6bbOCfNd7jD1Qfnuu+rXGz28d3KBzVGB74+EZofOAO1ggAAOwSGwlIX9uY\nkOEejWFoa7ll9c2z3W22prQtcatGz9ZHFtadXL2/+uwa7d/ZmM78C7e/NAAAYLfZSED69eqM\n6jHVK9dp85PV46rbVy/YmtK2xPsagejbFtb9dWMq8rWGF969ummHepsAAIB95GgB6WsaPUcv\nqH73KG1/q/rN6hGNoLQb/H+NsPPbjZnsqi6qPtiYvGFxWu2vr36nuqJ69Q7WCAAA7BJHC0hf\nN7++bIOv9xuNHpjzjrmirfW5xo1tT63+rHGPpl9rXE/11EYP02sbN7Z9a3XnxvVKn1hGsQAA\nwHIdbRa7286v79/g671vfr3DsZWzLf5n455E/656dPW9C9vuNJfLG71Hz6retdMFAgAAu8PR\nAtLKDV9P2+Dr3Wx+/dyxlbNtPtW4v9F/rM6s7lh9fmMCh09Uf5ebtAIAwL53tID0t/PrN1Wv\n2MDr3Xd+/btjLWgHXJFeIgAAYA1Huwbpv1fXVD9enXKUtreofqL6p8b1PgAAACeUowWkT1e/\nUt2z+m+tf3+gO1d/Un1Z9fzqqq0qEAAAYKccbYhd1X+ovqF6WHX/6g+rtzdutPoF1b2qBzVm\nr/uT6vztKBQAAGC7bSQgXdW4h9DPVk+qvmsuiz5ePae6oLphKwsEAADYKRsJSHXoOqSfrb65\n+orGjHUfb0wB/sYEIwAA4AS30YC04srqj+cCAACwpxxtkgYAAIB9Q0ACAACYBCQAAIBJQAIA\nAJgEJAAAgElAAgAAmAQkAACASUACAACYBCQAAIBJQAIAAJgEJAAAgElAAgAAmAQkAACASUAC\nAACYBCQAAIBJQAIAAJgEJAAAgElAAgAAmAQkAACASUACAACYBCQAAIBJQAIAAJgEJAAAgElA\nAgAAmAQkAACASUACAACYBCQAAIBJQAIAAJgEJAAAgElAAgAAmAQkAACASUACAACYBCQAAIBJ\nQAIAAJgEJAAAgElAAgAAmAQkAACASUACAACYBCQAAIBJQAIAAJgEJAAAgElAAgAAmAQkAACA\nSUACAACYBCQAAIBJQAIAAJgEJAAAgElAAgAAmAQkAACASUACAACYBCQAAIBJQAIAAJgEJAAA\ngElAAgAAmAQkAACASUACAACYBCQAAIBJQAIAAJgEJAAAgElAAgAAmAQkAACASUACAACYBCQA\nAIBJQAIAAJhOXnYBO+hm1X2ru1W3qU6vrqo+Wr2jen117bKKAwAAlm8/BKRTq2dWP1SdcYR2\nn6l+obqgOrgDdQEAALvMfghIF1aPqN5WXVRdUn28uqY6rTq7Ord6TCMg3al64lIqBQAAlmqv\nB6R7NcLRs6untH7P0CurZ1QvrH6wen71rp0oEAAA2D32+iQN926Eoqd39GFz11c/Ph/fdxtr\nAgAAdqm9HpBOq26oPrvB9p+uDjQmdAAAAPaZvR6QLmsMI3zwBts/ovE7ec+2VQQAAOxaez0g\nvab6UPWy6knVWeu0u31jeN2LqvfN5wEAAPvMXp+k4XPVw6uLqxfM5ZONWeyubQzBO6u65Wz/\n3uphjRnuAACAfWavB6Sqt1bnVN/bGGp31w7dKPbq6iPVa6tXVS+vrltOmQAAwLLth4BUoyfp\nV+eyE+5Y/Y+OfGPaRSv74aTtKQcAANiI/RKQjuYnGr1M379Fr/fh6oeqUzbY/i6N+zAdbSpy\nAABgGwlIw5dXX7OFr3d99fubaH9eIyABAABLtNcD0o/M5Wi+qDFhw9/M739pLgAAwD6y1wPS\nrRq9Q1dXlx6l3SkduqHstdtcFwAAsAvt9YD0vOpLq39VfapxXdD/WaPdr1XnVt+wU4UBAAC7\nz16/UeynGhMv3L+6Q/X26vzq1CXWBAAA7FJ7PSCt+LPGJAy/WP1UIyh9y1IrAgAAdp39EpCq\nrqp+vLpndWX1+uq/VrdYZlEAAMDusZ8C0oq3V99UPaV6XPXO6u5LrQgAANgV9mNAqrqhenb1\n1dW7G71KAADAPrfXZ7E7mg9UD66+rTqw3FIAAIBl2+8BacXrl10AAACwfPt1iB0AAMCNCEgA\nAACTgAQAADAJSAAAAJOABAAAMAlIAAAAk4AEAAAwCUgAAACTgAQAADAJSAAAAJOABAAAMAlI\nAAAAk4AEAAAwCUgAAACTgAQAADAJSAAAAJOABAAAMAlIAAAAk4AEAAAwCUgAAACTgAQAADAJ\nSAAAAJOABAAAMAlIAAAA08nLLgCADfmJ6qnLLuIE95+qn192EQDsbgISwInhnHPPPfdWD3vY\nw5Zdxwnp4osv7u1vf/s5y64DgN1PQAI4QZx99tnd5z73WXYZJ6Q3v/nNyy4BgBOEa5AAAAAm\nPUjAjrjmmmuqblfdf8mlnKhut+wCAGA/EJCAHXHZZZd18sknP/CMM8544LJrORFdeeWVyy4B\nAPYFAQnYEQcPHuz+979/T3va05ZdygnpcY973LJLAIB9wTVIAAAAk4AEAAAwCUgAAACTgAQA\nADAJSAAAAJOABAAAMAlIAAAAk4AEAAAwCUgAAACTgAQAADAJSAAAAJOABAAAMAlIAAAAk4AE\nAAAwCUgAAACTgAQAADAJSAAAAJOABAAAMAlIAAAAk4AEAAAwCUgAAACTgAQAADAJSAAAAJOA\nBAAAMAlIAAAAk4AEAAAwCUgAAACTgAQAADAJSAAAAJOABAAAMAlIAAAAk4AEAAAwCUgAAACT\ngAQAADAJSAAAAJOABAAAMAlIAAAAk4AEAAAwnbzsApbgpOpm1enVVdWVyy0HAADYLfZLD9LZ\n1dOrt1Sfra6oPj4fX169sXpqdfNlFQgAACzffuhBemB1UXVmo7fo0kY4uqY6rRGe7ll9c/Vj\n1UMbQQoAANhn9npAumV1YfWZ6rHVq6vr12h3evXo6tnVK6u7ZOgdAADsO3t9iN1DqltV/7L6\ng9YOR1VXVy+tvqf64urbd6Q6AABgV9nrPUh3qK6r/mqD7V9XHajuvG0VAbAspzY+NOPYfKY6\nuOwiALbbXg9Il1enVLep/nED7W/b6FW7fDuLAmBnvec976kxSuB7llzKiew51b9fdhEA222v\nB6Q/n1+fU31/de0R2t6sekHj07E/3ea6ANhB1113Xfe+9717/OMfv+xSTkgvfvGLe9Ob3qT3\nDdgX9npAenf1X6onVfepXlVd0pjF7trGLHZnVXevvrO6dfXz1XuXUSwA2+cWt7hF55xzzrLL\nOCHd4ha3WHYJADtmrwekqh9uTO391OqJR2h3WfWU6sU7URQAALD77IeAdLD6pep51VdXd21c\nk3R6Y/a6j1XvrN6zhT/ztMa04jfdYPsv38KfDQBb6oorrqg6p/q3Sy7lRPamxvkGsMvth4C0\n4mDjH6ad+Mfp1tW/aeO/38/fxloA4Lh84AMf6OY3v/l5Z5999nnLruVE9LGPfazLL7/8RdUP\nLLsW4Oj2S0B6QOMao8+v3lz9ZqP3aLXTGsPxfnEux+rD1Tdtov151V8ex88DgG113nnn9bSn\nPW3ZZZyQLrjggl7zmtectOw6gI3ZDwHpp6tnLHz/rxrXIz28G/cmnVTdsbrljlQGAADsKjdZ\ndgHb7EsaAen91aOrr2/MaHer6i+qr1leaQAAwG6z13uQvrVDEya8aa776+q1c/mj6l6NIXEA\nAMA+t9d7kG7fmJzhLavWv796SHVGdXH1eTtcFwAAsAvt9YD0icZ1RbddY9t7q0c1bhL7O+39\n3jQAAOAo9npAenOjB+lZrX1Poj9v3Dz2odUrqpvvXGkAAMBus9cD0iXVbzWuQbq0utsabX6j\nenz1HbmBGwAA7Gt7PSDVuCnb86qzWrsXqeql1f2qf9qpogAAgN1nP1x3c131I417Hx04Qrs3\nVHdt3OD1QztQFwAAsMvsh4C04poNtLm+euN2FwIAAOxO+2GIHQAAwIYISAAAAJOABAAAMAlI\nAAAAk4AEAAAwCUgAAACTgAQAADAJSAAAAJOABAAAMAlIAAAAk4AEAAAwCUgAAACTgAQAADAJ\nSAAAAJOABAAAMAlIAAAAk4AEAAAwCUgAAACTgAQAADAJSAAAAJOABAAAMAlIAAAAk4AEAAAw\nCUgAAACTgAQAADAJSAAAAJOABAAAMAlIAAAAk4AEAAAwCUgAAACTgAQAADAJSAAAAJOABAAA\nMJ287AIAAPay66+/vuoLq3ssuZQT2WXV5csugv1BQAIA2EaXXnpp1UPnwrH5leqJyy6C/UFA\nAgDYRgcOHOh+97tfT37yk5ddygnpuc99bq973etOW3Yd7B8CEgDANjv11FM788wzl13GCenU\nU09ddgnsMyZpAAAAmAQkAACASUACAACYBCQAAIBJQAIAAJgEJAAAgElAAgAAmAQkAACASUAC\nAACYBCQAAIBJQAIAAJgEJAAAgElAAgAAmAQkAACASUACAACYBCQAAIBJQAIAAJgEJAAAgElA\nAgAAmAQkAACASUACAACYBCQAAID/v707D5erru84/r7k5mZjTQIJEFCQTQREKQqFh01R9vZh\nVSylpa1VK0vLY6u1yIVHli7qA1hakE3LVlFRghTUkqKo2BBReFhCgLCXEhIgIWS73Ns/vt/p\nnEzmbtzcOXdm3q/nOc9Mzjl35jtnfpk7n/v7nd9JBiRJkiRJSgYkSZIkSUoGJEmSJElKBiRJ\nkiRJSgYkSZIkSUoGJEmSJElKBiRJkiRJSgYkSZIkSUoGJEmSJElKBiRJkiRJSgYkSZIkSUqd\nZRcgSZIk9aenpwdgGrBXyaU0syeA18suolkYkCRJkjRmzZ8/H+DoXPT2XAF8quwimoUBSZIk\nSWNWb28vhxxyCGeeeWbZpTSlSy65hLvvvntC2XU0EwOSJEmSxrSuri422mijsstoSl1dXWWX\n0HScpEGSJEmSUjv2IHUAU4CJwApgebnlSJIkSRor2qUHaSZwHjAXeANYBizK+0uBe4HPARuX\nVaAkSZKk8rVDD9JHgO8AGxG9RfOJcLQKmECEp72B/YCziRlS5pZSqSRJkqRStXpA2hS4GXgN\n+APgDqCnzn4TgROArwK3Ajvj0DtJkiSp7bT6ELsjgc2AE4HbqB+OAFYC/wacDGwNHN6Q6iRJ\nkiSNKR1AX94/D+gur5RR8QXidQ11fsNxwGrgi8DFI3je7YBfMfQeuk5iCGAXsGYEzzuYqzo7\nO/9k0qRJo/gUrWv58uVssMEGePzeHo/fyHj8RsbjNzIev5Hx+I2Mx29kVqxYQU9Pz9XAn5Zd\nyxjXDZwLrT/EbikwHtgCeHkI+29J9KotHeHzPkP0Wg31+HYQNY5mOAI4p6en5+Zly5aN8tO0\nrKm9vb0sW7ZsSdmFNCmP38h4/EbG4zcyHr+R8fiNjMdv5B4uu4Bm05dLd8l1jIZdidd2A4P3\nIk0BfgD0AjuNcl2SJEmSxo5uMhe1eg/SI8DlwGeAA4HZRIJeRAylmwDMAPYAjgGmAxcBj5dR\nrCRJkqTytXIPEsTwtTOA56i+1nrL48CpJdUoSZIkqTzdtEkPEsQLvRS4DNiNGHa3BTG190rg\nJeAh4LGyCpQkSZI0NrRDQKroI4LQQ2UXIkmSJGlsavXrIEmSJEnSkBmQJEmSJCkZkCRJkiQp\nGZAkSZIkKRmQJEmSJCkZkCRJkiQpGZAkSZIkKRmQJEmSJCkZkCRJkiQpGZAkSZIkKRmQJEmS\nJCkZkCRJkiQpGZAkSZIkKRmQJEmSJCkZkCRJkiQpGZAkSZIkKXWWXYAa6pfAPmUXIUmSpIa6\nD9i37CKahQGpvTwFLALOK7sQtaVz89b2pzLY/lQm25/KdC6wrOwimokBqb2sBhYD88ouRG1p\ncd7a/lQG25/KZPtTmRYPvouKPAdJkiRJkpIBSZIkSZKSAUmSJEmSkgFJkiRJkpIBSZIkSZKS\nAUmSJEmSkgFJkiRJkpIBSZIkSZKSAUmSJEmSUmfZBaihVpddgNqa7U9lsv2pTLY/lcn29zb0\n5dJdch0afZvlIpXB9qcy2f5UJtufymT7G5puMhfZg9ReXi27ALU125/KZPtTmWx/KpPtb5g8\nB0mSJEmSkgFJkiRJkpIBSZIkSZKSAUmSJEmSkgFJkiRJkpIBSZIkSZKSAUmSJEmSkgFJkiRJ\nkpIBSZIkSZKSAUmSJEmSkgFJkiRJkpIBSZIkSZKSAUmSJEmSkgFJkiRJklJn2QWoNOOA3wXe\nBOaVXIta32RgZ6LdLQBeL7cctaF35vIQsLjUStRuJgDvytun8PNPjTWFaH8dwEJgabnlNI++\nXLpLrkONsx1wL/G+319yLWptGwAXAsupftasBq4EJpZYl9pHB3A6sIJof0eVW47ayETgH6i2\nvcryAyKsS6NpC+AaYA3VttcL3AK8o8S6xrJuqsfKgNRmTiX+evAA8Z/GgKTRdAHx+XIbcBhw\nMHB1rvtmiXWpPWwJ3El81v0GA5Ia6waqn3/HEJ+BV+S6BUBXeaWpxXUBDxJt7XLgcOAI4o+T\nfcB8YHxp1Y1d3RiQ2tI04r2+lOjqX4kBSaNnOtHG5rLu+Y7fJ/6StWuji1JbuYwYUrIP8HkM\nSGqcXYj2NofoxSz6Xm77aKOLUts4jmhjX6+z7dbcdnBDK2oO3WQucpKG9rKa+CvWGcCqkmtR\n6zuCCOJXEWGo6EriS8OxjS5KbeVOYE/gvrILUdt5C/hr4O/Iv0YX3Ju3WzW0IrWTXwMnAv9Y\nZ1vlvPNNGldO83GShvayDJhddhFqG3vmbb1JQO6v2UcaDT8suwC1rQXU/3IK1fOPFjSmFLWh\nhbnU6iB6jnqIUy3UDwOSpNEyK29frLNtEfEBvU3jypGk0u0C/DHxF/6fl1yL2sMsYhbZrYFT\ngAOBs4FnyixqrDMgSRotk/N2ZZ1tfbl+SuPKkaRSbUvMYNcDfIJ1h95Jo+F44Gt5/0XgD4Eb\nyyunORiQWssM4J6adfOID2Kp0Xrytr/PmU7ivDhJanV7E7PZbQB8CHis3HLURmYDzwIziZkU\nrwdOAk7A38H9cpKG1tILvFSzLCm1IrWzysU4N6uzbTJxjRDbp6RW9zHgp8Rn4geJ4XVSozxJ\nzJx4OTFRVzfVCbvUDwNSa1kEHFSznF5aNWp38/N25zrbdsnbRxtUiySVoTKc6R5gX+DpUqtR\nuxhPnHM0rs62G/L2gMaV03wMSJJGy0/y9og6247O27saVIskNdoRwDXEeUdHETPJSo1wKfA8\n8OE622bkrcPrBuGFYtuXF4rVaPsFcc2tgwrr9gSWEj1MngepRvFCsWqkTYCXgYeBSSXXovZz\nMPF59xBrzxa7OXEdrj7gtBLqGuu6qeYiA1IbOYW4YGJl6QXeqFk3q9+floZvJ+JcuF5gLhGY\neoBXgfeXWJfawz1UP9ueJX7XPVZY111aZWp1ZxHt7TnW/h1bXM4prTq1g/OJNriKOO/tv4Hl\nue671B9+1+66yVzkX2/by1usPeXyT+vs47SjWp8eB3YDPkPM4tQBXAT8C/WvjyStT6uofqY9\nlUvRmsaWozbyGuvOKlvL9qfR9CUiCJ0CbE9cVuNG4HZi2KcGYQ+SJEmSpHbWTeYiJ2mQJEmS\npGRAkiRJkqRkQJIkSZKkZECSJEmSpGRAkiRJkqRkQJIkSZKkZECSJEmSpGRAkiRJkqRkQJIk\nSZKkZECSJEmSpGRAkiRJkqRkQJIkSZKkZECSJEmSpGRAkiRJkqRkQJIkSZKkZECSJEmSpGRA\nkiRJkqRkQJIkSZKkZECSJEmSpGRAkiRJkqRkQJIkSZKkZECSJEmSpGRAkiRJkqRkQJIkSZKk\nZECSJEmSpGRAkiRJkqRkQJIkSZKkZECSJEmSpGRAkiRJkqRkQJIkSZKkZECSJEmSpGRAkiRJ\nkqRkQJKkse8AYK+yixgFNwN9wKxRetyZw3yeVj3OkqRhMCBJ0th3B3BZ2UU0kRuBLwDL+tne\nAdwJfKBmvcdZkmRAkqQm8Ab9f9nXum4DLgaW97N9R+CjwNSa9R5nSRKdZRcgSRpU8Yv7vsRn\n98/62Xc/YjjZL2rWb08MOXsdeAx4q5+fHw+8A5gOvAQ8V2ffXYHNgXuAjYE9gIXAC0N6Nf3b\nMZ/3aeB/arbtDkwD7gV6Cus7gAOBV4HfFurbgjgGq+s8zrF5fw9gJfAAcVyGGpA+CEzqZ9sy\nYN4QHqNiGrAt8QfLZ4BX+tmvA9iFON4LgZcHeMwdifenv/d6qO/fUNuMJLWcvly6S65DklTf\nb4Dr8v6/E5/Z9c6V2SG33VpYdxDwCNXP+j5gMXBGnZ8/E/jfmn2fBo6p2a9yTs97iWDSBxw/\nnBdU8zj7EKGi+LzfBiYW9r09129a8xiduf4ndR633jlIlccpLvvnfsXjPJAn6jxGZbl/CD8P\nEUJ/CPQWfrY3121es+8RxPtQfJ7ZwJY1+x0FPFWz3xLifS0a7P07iKG3GUlqFd1UP/MMSJI0\nxm0PbJX3DyU+s+udK/PF3FYJNO8DVhFf2g8FtiF6oP4j9/vzws8em+v+i+iR2Yn4Yv44sIbo\nvaj4JtVQ0k1MbjCT4at8UX8IuCjrPQS4O9f/U2Hf9RWQplD9JXhcPt643K94nAeyLRFGi8sN\n+ZgXDOHnAeYQvVd/RhzbXYDPAm8CPy7s9wHi+C/IevcCziZ60eZRHSq/X66bTwwfnEUEnblZ\n1ycLjznQ+zecNiNJraQbA5IkNaUOYjjUK0BXzbbfEj1AleHTs4nzcGbU7DcJeJ4Y0lXxEeBC\n4st+0UnE74jPF9ZdleuufluvoKoSXK6pWT8FeI3o/aiEl/UVkCBeSx9w2Ajrr/gQ0fvzK4Y+\ndH0NEUZrnUgEoMrrrvQybVez3z/n+gPy3z8iXtPuNfttRgwdXFhYN9D7N5w2I0mtpJvMRZ6D\nJEnNpRIozgeOBr6b63ciziX5GtGTMB74MHFeycF1Huc5YmjbtsCzxBfsHwGbAHsDGxG9E5Uv\nylvUeYzvjfjVhFtq/r2cONfoSCKwzV9PzzMapgHfIkLIyax9ftRAngd+h+jtuauw/tuF+51E\nj9rDrB1wAM4CTidC0ngiKC0geuOKXiXOVzuMGNZXDDi1799w24wktSQDkiQ1n2uBc4E/ohqQ\nTipsgzg/ZSLwLuCmAR5rJvFldyrwr8RQu3HE5AZrqA7hqjfr6fNvq/p1PTHAY89kbAekq4lh\neacCTxbWzyAmQSiaB3wi738S+A4x3fgLwH8Sw9huI4bZkY87kfrHeU3h/pbABOL8o3oqoWgb\n1g5ItY873DYjSS3Jab4lqfk8T/Q6HEa1Z+ck4NdUexDG5+3PiOFR/S1zc7/rgBOIHqiZxBfu\nDYkejP70N432cK2qs64SAMbyH/I+DfweESa+VbOtl5gFsLgsKWz/MXHO01nAo8TQupuIXpqj\nc5/KezhYr1Rlv9oZ+yoqx3JCzfra92+4bUaSWtJY/sUjSerfVcQkCscTJ/y/hxhyVbE4b2cS\nkwEMZFNiBrQHgc/VbJs+4koHt8kA614f5Gc3XM+1DNW7ga8Qs8t9us72RcQkCQNZDFySy0Ri\nEobLgOuJYWyVQDVtkMcZbL/K9Z4W97Odmu1DaTOS1LLsQZKk5jSbmJDhBODjRO/BjYXtrxFD\n13YgrotT61Bg67y/CTH5Q70hWsetp3oH8v4663YjemEez39Xepmm1Oz37tEqagATiN6eLmLI\n3GAhrlYH8Z4UX8tKYia8y4hrE+1OnD/0NHFuWe11lw4lhugdkPstzP1q5WsqsQAAA39JREFU\ne4kgzilbSfRUDWQ4bUaSWpYBSZKaUw8xXfMBwGnEuStLava5ivgyfgHVWdEgpm2+Dbgy//0C\nEUDex9oz430810HMhjZa/oq1ez8Oy+edAyzNdQvydt/CfuOAvyWnZB2GSu9I7fWGhurviWsI\nnc+6F+Qdiv2J4Hd+zfoOqmGxcqHca4kgdW5hv8nAl4nhfU8V9tuQtWcbhDg3akci0NUbylhr\nqG1Gklqa03xLUnPakepn+JF1to+nev2aR4kv0XcR4epp4mT8iq/kfg8CVxCzyD0DvJMIXm8C\n3yCG3FWmia6dEny4Khe9vZAIBDcTPWMrifNjihfD3TXXLwYuBr4E3EdcK+kV4tpJFYNN831g\n/ntRPt/vD6PmHYierbeI0HF9nWUobsoaHidmrruF6gVoLynsN5E4J6hyvajbifOZeonrJlVM\nIAJlX+5/BXGeU2/+XHGo5EDv33DajCS1km7yd+o4qsHoHupfk0GSNDYtAU4hgsNfsG5PSi/x\nRfwRondhK2I41rXAp6j2UkB8mX6BGG43Ffg5cRHTF4nQND0ffzYxXXQXEXCWjaD+92aNpxFB\n4T3E9OJ353M/WNh3ETGkbDJxUdWNiYklvkpMl72Q+GIPEaY2IIYcrij8+yYi6D1DBK3JeTzm\nMPQZ+aZm3c9mrRvXWa4bwuPcCjxAHMepWd/9RG/aNwr79RCh65l87ElEr9Vnge8X9nsr93uS\nmLhjFtE+vk60jaWFfXem//dvOG1GklrJQRTOHbUHSZKa0z7E5/c5ZRciSVKT6yZzkecgSVJz\n2ow4H6TSSyBJktYDp/mWpOayP/CXeTsd+BgxBKpsM1h7AoXBzCWG9EmSNKYYkCSpubxFnDsz\nhzgRf0655fy/vYhzhIbqZGJyAkmSxhQDkiQ1l18Ch5ddRB134O8USVIL8BwkSZIkSUoGJEmS\nJElKBiRJkiRJSgYkSZIkSUoGJEmSJElKBiRJkiRJSgYkSZIkSUoGJEmSJElKBiRJkiRJSgYk\nSZIkSUoGJEmSJElKBiRJkiRJSgYkSZIkSUoGJEmSJElKBiRJkiRJSgYkSZIkSUoGJEmSJElK\nBiRJkiRJSgYkSZIkSUoGJEmSJElKBiRJkiRJSgYkSZIkSUoGJEmSJElKnYX7+wF/U1YhkiRJ\nklSS/Sp3OoC+EguRJEmSpDHDIXaSJEmSlP4PKq3ItCqF8AAAAAAASUVORK5CYII=", "text/plain": [ "Plot with title “'year_built' domain histogram”" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" }, { "data": { "image/png": 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Xpk9cVdrhEAANgDFr2b72OrN1SnVU+v7lxdqSkYXXH8vEp19+r51b2q17b4nwsA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", "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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qruWH3RCVcEAACwIKfNsc7vj593rW438/typ1RfUO2vPrPx0gAAALbXPAHpLdUD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\nUBcLIkmSJEmThkPsJEmSJCn5f6lwLFnK4HetAAAAAElFTkSuQmCC", "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", "index_start = GUID_length+1\n", "index_end = ncol(indicator_data_weighted)\n", "indicator_columns <- colnames(indicator_data_weighted)[index_start:index_end]\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": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 14
CCACPROCMUNCDISCSECagehealthincomeinfo_access_uselocal_knowledgetenuresocial_networkhousing_characteristicsphysical_environment
<int><int><int><int><int><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
11726 111 0.01055472 1.2753218-0.33134220.071438769 0.1446865-0.50739360.36130070-0.9481277-0.8560700
21726 1011 0.26973904-1.3865567-1.11761561.619736710-1.2514828 0.00000000.30303248 0.0000000-0.2328068
3172610011-0.41835186-1.3865567-1.11761561.619736710-1.2514828 0.00000000.45418041 0.0000000-0.3822322
4172610111-0.72575121-1.3865567-1.11761561.619736710-1.2514828 0.00000000.78526637 0.0000000-1.5116073
5172610211 0.07217662 0.3880289 0.14393330.138756071-0.1091625-0.74795820.41782180 1.2637707-0.3792937
6172610212 0.47103144 0.3880289 0.22971420.004121467 0.1446865-0.96887210.06300444 0.7211874-0.3696338
\n" ], "text/latex": [ "A data.frame: 6 × 14\n", "\\begin{tabular}{r|llllllllllllll}\n", " & CCA & CPRO & CMUN & CDIS & CSEC & age & health & income & info\\_access\\_use & local\\_knowledge & tenure & social\\_network & housing\\_characteristics & physical\\_environment\\\\\n", " & & & & & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 17 & 26 & 1 & 1 & 1 & 0.01055472 & 1.2753218 & -0.3313422 & 0.071438769 & 0.1446865 & -0.5073936 & 0.36130070 & -0.9481277 & -0.8560700\\\\\n", "\t2 & 17 & 26 & 10 & 1 & 1 & 0.26973904 & -1.3865567 & -1.1176156 & 1.619736710 & -1.2514828 & 0.0000000 & 0.30303248 & 0.0000000 & -0.2328068\\\\\n", "\t3 & 17 & 26 & 100 & 1 & 1 & -0.41835186 & -1.3865567 & -1.1176156 & 1.619736710 & -1.2514828 & 0.0000000 & 0.45418041 & 0.0000000 & -0.3822322\\\\\n", "\t4 & 17 & 26 & 101 & 1 & 1 & -0.72575121 & -1.3865567 & -1.1176156 & 1.619736710 & -1.2514828 & 0.0000000 & 0.78526637 & 0.0000000 & -1.5116073\\\\\n", "\t5 & 17 & 26 & 102 & 1 & 1 & 0.07217662 & 0.3880289 & 0.1439333 & 0.138756071 & -0.1091625 & -0.7479582 & 0.41782180 & 1.2637707 & -0.3792937\\\\\n", "\t6 & 17 & 26 & 102 & 1 & 2 & 0.47103144 & 0.3880289 & 0.2297142 & 0.004121467 & 0.1446865 & -0.9688721 & 0.06300444 & 0.7211874 & -0.3696338\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 14\n", "\n", "| | CCA <int> | CPRO <int> | CMUN <int> | CDIS <int> | CSEC <int> | age <dbl> | health <dbl> | income <dbl> | info_access_use <dbl> | local_knowledge <dbl> | tenure <dbl> | social_network <dbl> | housing_characteristics <dbl> | physical_environment <dbl> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 17 | 26 | 1 | 1 | 1 | 0.01055472 | 1.2753218 | -0.3313422 | 0.071438769 | 0.1446865 | -0.5073936 | 0.36130070 | -0.9481277 | -0.8560700 |\n", "| 2 | 17 | 26 | 10 | 1 | 1 | 0.26973904 | -1.3865567 | -1.1176156 | 1.619736710 | -1.2514828 | 0.0000000 | 0.30303248 | 0.0000000 | -0.2328068 |\n", "| 3 | 17 | 26 | 100 | 1 | 1 | -0.41835186 | -1.3865567 | -1.1176156 | 1.619736710 | -1.2514828 | 0.0000000 | 0.45418041 | 0.0000000 | -0.3822322 |\n", "| 4 | 17 | 26 | 101 | 1 | 1 | -0.72575121 | -1.3865567 | -1.1176156 | 1.619736710 | -1.2514828 | 0.0000000 | 0.78526637 | 0.0000000 | -1.5116073 |\n", "| 5 | 17 | 26 | 102 | 1 | 1 | 0.07217662 | 0.3880289 | 0.1439333 | 0.138756071 | -0.1091625 | -0.7479582 | 0.41782180 | 1.2637707 | -0.3792937 |\n", "| 6 | 17 | 26 | 102 | 1 | 2 | 0.47103144 | 0.3880289 | 0.2297142 | 0.004121467 | 0.1446865 | -0.9688721 | 0.06300444 | 0.7211874 | -0.3696338 |\n", "\n" ], "text/plain": [ " CCA CPRO CMUN CDIS CSEC age health income info_access_use\n", "1 17 26 1 1 1 0.01055472 1.2753218 -0.3313422 0.071438769 \n", "2 17 26 10 1 1 0.26973904 -1.3865567 -1.1176156 1.619736710 \n", "3 17 26 100 1 1 -0.41835186 -1.3865567 -1.1176156 1.619736710 \n", "4 17 26 101 1 1 -0.72575121 -1.3865567 -1.1176156 1.619736710 \n", "5 17 26 102 1 1 0.07217662 0.3880289 0.1439333 0.138756071 \n", "6 17 26 102 1 2 0.47103144 0.3880289 0.2297142 0.004121467 \n", " local_knowledge tenure social_network housing_characteristics\n", "1 0.1446865 -0.5073936 0.36130070 -0.9481277 \n", "2 -1.2514828 0.0000000 0.30303248 0.0000000 \n", "3 -1.2514828 0.0000000 0.45418041 0.0000000 \n", "4 -1.2514828 0.0000000 0.78526637 0.0000000 \n", "5 -0.1091625 -0.7479582 0.41782180 1.2637707 \n", "6 0.1446865 -0.9688721 0.06300444 0.7211874 \n", " physical_environment\n", "1 -0.8560700 \n", "2 -0.2328068 \n", "3 -0.3822322 \n", "4 -1.5116073 \n", "5 -0.3792937 \n", "6 -0.3696338 " ] }, "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.double, function(x) {x*internal_domain_weight})\n", "\n", " # Sum each data row to get the total score for the domain\n", " index_start = GUID_length+1\n", " index_end = domain_indicator_count+5\n", " current_domain_data_weighted[, current_domain] <- rowSums(current_domain_data_weighted[index_start:index_end], 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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CAgAQAADAISAADAICABAAAMAhIAAMAg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\nAeRIXNQIOs9t9Pbsxjv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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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POYY47Z8AVfffXVXX755Z+urr/hCwcA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\nll1IK3CInSRJkqTJrIv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"text/plain": [ "Plot with title “'health' domain histogram”" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" }, { "data": { "image/png": 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+vY399WDz/qijbXpU2B6Jtm1v15ddtWnqDiHtUJ3dDbBAAA7CIbCUjnVfc9QpvT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\nkSRJkqRVhlPsJEmSJCn9P2/jCFJXJO51AAAAAElFTkSuQmCC", "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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9Oa5AEAAOw2RglI907yu0m+lOQfklyWZNsC8y7U0x0AAMCaNWpA+k5aj3Y3TKYc\nAACA/oxyo9i9k5wb4QgAAJhSowSkzya5cxbumAEAAGC3NkpA+ue0kPSqJBsnUg0AAECPRrkG\n6f5Jvpnk6UmelOQLSX64wLzvrwEAAGC3MUpA+m9pXXwnyQFJHrLIvF+PgAQAAOxmRglIr0vy\n9iQ3DzHvVeOVAwAA0J9RAtJlNQAAAEylUQLSbWtYyp5JvpvkgrEqAgAA6MkoAelpSV405Lyn\nJtk8cjUAAAA9GiUgnZPkZQtMu1WSeye5fZKXJvnYMusCAABYdaMEpLNrWMzJSR6V5I/HrggA\nAKAno9wodhivTTub9HMrvF4AAICJW+mAlCTfSnK3CawXAABgolY6IB2Y5KeTXLnC6wUAAJi4\nUa5BOrGG+axLcnCSE5LcMsknl1kXAADAqhslIB2X1gnDYq5K8jtJzh27IgAAgJ6MEpDekuSM\nBaZtT3JNkm8kuWm5RQEAAPRhlIB0UQ0AAABTaZSANOfwJE9KuzHsoTXu4iT/muRdSa5YmdIA\nAABW16gB6WFJ/ibJ/vNMe3ySP0jyiCT/vsy6AAAAVt0o3XwfkHaG6Nokv5Xkp5IcVsPdkzw3\nyZ5J/i7J3itbJgAAwOSNcgbpIWn3Obpnks8OTPtBki8mOSfJZ5I8OMkHV6JAAACA1TJKQDoq\n7VqjwXDU9X+TfDvJnbP2AtJ+SR6Y5Oi0a6f2TrIl7TXNhbsb+yoOAADo3ygB6eYk+w4x3x5J\nto1XzkRsSPKyJL+ZZJ9F5rsiySuSvDKt23IAAGDGjBKQzk27DumXk7x/gXkekuTIrK0bxb4n\nyS8l+Vza9VHnJrk0yQ1JNqb1yndMWicTr0hy+yTP6qVSAACgV6MEpLOSXJDWUcNbkpyddl+k\ndUl+PMkJSZ6e5Pwk/7SyZY7t2LRw9Jokz8vCZ4ZOT/KStNf1zCSvT/Ll1SgQAABYO0YJSDcl\neXiS/53k5BoG/WeSR9a8a8F90kLRqVm62dzWJM9P8tS0a5UEJAAAmDGj3gfpK2mdHPx8kuOT\nHJEWPC5O8i9J/jEtaKwVG9OunbpmyPl/lHb91H4TqwgAAFizRglI69LC0E1JPlDDnA1pwWgt\ndc6QJF9Le40nJvnwEPP/UlonE+dNsigAAGBtGvZGsfdOu7/RrRaY/ttJPpHkDitR1Ao6M8l3\n066bOintprbzuU1a87q3p11ndeaqVAcAAKwpwwSku6d1yHCPJPdbYJ4Dk9y35oFJW7IAAB30\nSURBVDt0ZUpbEdelXRN1XZI3JPl+kh+mXSv1H2lnin6Udu+mVyT5XpJfSOvhDgAAmDHDBKS/\nSLt/0OPTenubz+8neXLamZg3rExpK+azSTYl+e9p3ZNfmhbi7ph2RuyiJO9N8qQkd43mdQAA\nMLOWugbpp9LOHL0uLUQs5q+T/GySX00LSt9ZdnUr57okb61hNRyR1lxvzyHnP6B+rptMOQAA\nwDCWCkg/XT/fNeT63pbWTfbxWTpQrSW/l3aW6akrtL4r0+4bNWwnGD+R5F5ZuityAABggpY6\ngD+ifn5jyPVdUD9vO145vblD2tmylXJdklePMP/xSX5jBZ8fAAAYw1IBae6GrxuHXN/c/YOu\nG6+cFffsGpZyq7TX+PX6/U9rAAAAZshSnTRcWD+PG3J9D6yf3xqrmpV3UNrZoVun3Sx2oWHu\nHk5zv9/YR7EAAEC/ljqD9M9pXV4/P8kHs+OM0nwOSLuW58okH1uJ4lbA65LcLslTklye5DfT\nuvge9OdJjklyz9UqDAAAWHuWOoP0oyRvTutA4G+T3HKB+e6Y1inBUUlen2TLShW4TJendbxw\nQtp1UV9IsjnJhh5rAgAA1qhhell7QdqZlUekBY0z0oLGNUkOTnJskoekdWl9VloAWWs+ltYJ\nw+Yk/zPJY9Pui/TJHmsCWE2nJTml7yIAYK0bJiBtSfKgJC9OclKSx9XQdWmSP07yyiQ3r2SB\nK2hLWlPBv0m7H9I5aWfHXtBnUQCr5NB73vOeedzjBr++Z8Npp53WdwkA7CaGvU/P3HVIL05y\n3yR3Suux7tK0LsA/mbUbjAZ9Ia3TiZPTXs/Dkny/14oAVsEhhxySe9zjHn2X0YuNG4ftjBWA\nWTdsQJpzbZKP1rA7uznJa5K8P8mb0poIfrbXigAAgN6NGpCmzTeTnJjk/mndfAMAADNs1gPS\nnHP6LgAAAOjfUt18AwAAzAwBCQAAoAhIAAAARUACAAAoAhIAAEARkAAAAIqABAAAUAQkAACA\nIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJAACg\nCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAo\nAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACK\ngAQAAFAEJAAAgCIgAQAAlPV9FwCwSp6Z5Bl9F9Gj2/VdAADsDgQkYFYct2nTpns84AEP6LuO\nXrz3ve/tuwQA2C0ISMDMOOqoo/KEJzyh7zJ68eEPf7jvEgBgt+AaJAAAgCIgAQAAFAEJAACg\nCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAo\nAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACK\ngAQAAFAEJAAAgCIgAQAAFAEJAACgCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAi\nIAEAABQBCQAAoAhIAAAARUACAAAoAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAI\nSAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJAACgCEgAAABFQAIAACgC\nEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAoAhIAAEARkAAAAIqA\nBAAAUAQkAACAIiABAAAUAQkAAKAISAAAAGV93wWsov2SPDDJ0UkOTbJ3ki1JLk7yxSTnJLmx\nr+IAAID+zUJA2pDkZUl+M8k+i8x3RZJXJHllku2rUBcAALDGzEJAek+SX0ryuSR/l+TcJJcm\nuSHJxiSHJzkmyePTAtLtkzyrl0oBAIBeTXtAOjYtHL0myfOy8Jmh05O8JMlbkjwzyeuTfHk1\nCgQAANaOae+k4T5poejULN1sbmuS59fjB06wJgAAYI2a9oC0McnNSa4Zcv4fJdmW1qEDAAAw\nY6Y9IH0trRnhiUPO/0tp78l5E6sIAABYs6Y9IJ2Z5LtJ3pXkpCSHLTDfbdKa1709yQW1HAAA\nMGOmvZOG65I8MskHkryhhsvSerG7Ma0J3mFJDqz5z0/yiLQe7gAAgBkz7QEpST6bZFOSJ6Y1\ntbtLdtwo9vokFyX5xyT/kOR9SW7qp0wAAKBvsxCQknYm6a01rIbbpIWujUPOv3f9XDeZcgAA\ngGHMSkBKklsm+bEk30nrqW4+eyZ5cpIv1DCuS5KcluED0h2SnJKluyIHAAAmaBYC0p2SvCPJ\n8fX7RWn3RXrLPPPuldZRw6lZXkC6MclfjjD/8WkBCQAA6NG092K3Lu26ouOTfDnJB9PO0rw5\nrbmdJm0AAMD/N+1nkP5bkmOSvDKtG++knSX6oyTPTnJtkt/upzQAAGCtmfaAdOf6+YrOuJuS\nnJzkiiR/mHYz2Tescl0AAMAaNO0Bae+0JnXXzTPtRWnXJ702bg4LAFNr27ZtSbJ/kqN6LqVP\n3027RhpYwrQHpK+nXWf0c0nOmGf609J6kPvbJD+f5DOrVxoAsBrOO++8JHlUDbPq9Un+R99F\nwO5g2gPSWUm+l9aL3fPSgtC1nenXJ3lY2tmjs5K8ZJXrAwAm7Oabb87973//PPOZz+y7lF68\n+c1vzjnnnPNjfdcBu4tpD0hbkjwlyd+ndd/91SSfHpjnh0kelNbb3UtXszgAYHXsu+++OeKI\nI/ouoxf77rtv3yXAbmXaA1KS/FNaT3ZPTPLNBea5KslD024S+6uLzAcAAEyxWQhISXJhlj47\ntD3JX9UAAADMoGm/USwAAMDQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAoAhIAAEARkAAA\nAMqs3CgWSB6W5OF9F9Gj4/suAABY+wQkmB2PPvLII59yzDHH9F1HLz7+8Y/3XQIAsBsQkGCG\n3PWud81znvOcvsvoxec///m+SwAAdgOuQQIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEA\nABQBCQAAoAhIAAAARUACAAAoAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAA\nAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJAACgCEgAAABFQAIAACgCEgAA\nQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAoAhIAAEARkAAAAIqABAAA\nUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAA\nFAEJAACgCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAA\nRUACAAAoAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABA\nEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJAACgrO+7AFhFd01yfN9F9GhT3wUAAKx1AhKz5Dm3\nuMUtnnr44Yf3XUcvLrzwwr5LAABY8wQkZsm6448/PqecckrfdfTiyU9+ct8lAACsea5BAgAA\nKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJAACgCEgAAABFQAIAACgCEgAAQBGQAAAA\nioAEAABQBCQAAIAiIAEAABQBCQAAoAhIAAAARUACAAAoAhIAAEARkAAAAIqABAAAUAQkAACA\nIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAAFAEJAACg\nrO+7AFbV45I8ve8ienSXvgsAAGBtE5Bmy4lHHXXUCccee2zfdfTijDPO6LsEAADWuFkMSOuS\n7Jdk7yRbklzbbzmra9OmTXnGM57Rdxm9OOecc/ouAQCANW5WrkE6PMmpST6T5JokVye5tB5f\nleSTSX43yS36KhAAAOjfLJxBenCSv0uyf9rZoq+mhaMbkmxMC0/3SnLfJM9N8otpQQoAAJgx\n0x6QDkzyniRXJHlSkg8n2TrPfHsneUyS1yQ5PclPZsaa3gEAANPfxO5hSQ5K8tgkH8z84ShJ\nrk/yziS/kuTWSR66KtUBAABryrok2+vxqUk291fKRPxe2uvaMOT8eya5Mcn/TPKKZTzv7ZP8\ne4Y/Q7c+rQnghiQ3LeN5l/Ln69ev//V99tlngk+xdl177bXZY4894vV7/bPI6/f6vf7Zfv3b\ntm27Icl1fdfSk33Xr1+/cVa3/5YtW7J169a/yGzf6mUYm5O8KJn+JnZXJdkryaFJfjDE/Eek\nnVW7apnP+620s1bDvr/r0mqcZDhKkhdu3br1PVdfffWEn2bNOnjbtm25+uqrL++7kJ54/V6/\n1+/1e/2z6eD6ObOvf+vWrbO8/ZPk3L4L2N1sr2Fzz3VMwl3SXttfZ+mzSPsl+UCSbUk2Tbgu\nAABg7dicykXTfgbpK0n+LMlJSR6Q5B/SEvSlaU3pNiY5LMndkjw8ySFJXp7k/D6KBQAA+jfN\nZ5CS1nzt2Um+kx2vdb7h/CS/1lONAABAfzZnRs4gJe2F/mmS1yW5a1qzu0PTuva+Psn3k3wp\nyXl9FQgAAKwNsxCQ5mxPC0Jf6rsQAABgbZr2+yABAAAMTUACAAAoAhIAAEARkAAAAIqABAAA\nUAQkAACAIiABAAAUAQkAAKAISAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAA\nFAEJAACgrO+7AFbVp5Mc13cRAACsqn9Lcp++i9hdCEiz5RtJLk1yat+F0IsX1U/bfzbZ/rPN\n9p9ttv9se1GSq/suYnciIM2WG5NcluSzfRdCLy6rn7b/bLL9Z5vtP9ts/9l22dKz0OUaJAAA\ngCIgAQAAFAEJAACgCEgAAABFQAIAACgCEgAAQBGQAAAAioAEAABQBCQAAICyvu8CWFU39l0A\nvbL9Z5vtP9ts/9lm+882238M22vY3HMdTN5BNTCbbP/ZZvvPNtt/ttn+s832H87mVC5yBmm2\n/KjvAuiV7T/bbP/ZZvvPNtt/ttn+I3INEgAAQBGQAAAAioAEAABQBCQAAIAiIAEAABQBCQAA\noAhIAAAARUACAAAoAhIAAEARkAAAAIqABAAAUAQkAACAIiABAAAUAQkAAKCs77sAerEuya1r\nuDjJ95Lc3GtFrLbDkvxEkh8k+U5s/1mzZ5Ljk1yX5LM918Lk7ZvkJ9O2+9eSXNlvOfTgdjV8\n6f+1d/dBktTlAce/B3dwcMjxJgcIKC+akwqIEFCLq8tFgxiCqGAMSiVXIS8mMRHLpPRMpXRj\nglGTaCJYEcIFjOiZxEqQFzkDCR5vYuCU4AsCwl0IRzTCeXKAcC+7+eN5pqa3b2a3e3a3Z3fm\n+6mamp3u33Q//TbbT/fv92vgib5GoqbtCRyT7w/j8V/ZWL5G+hyHmvEW4h/kWOG1CfiNfgal\nxiwjToiL2/8HwNv7GZQadRRwG7Ht7+5zLJpZuwEfAp6mfbxvAy4DFvYxLjVnHvD7wE+I7X9W\nf8NRgxYCH6W97VuvLxLJsnY1Qns9mSANkfOIbf0g8OvACiIx2pDDV/YtMjXhROJE6XHg3cCr\ngQtob/8L+heaGrISeBL4BrAdE6RBdxFxbF8DvA74OWB1Dvt0H+NSMw4F1hLH+j2YIA2bz9I+\n/s8mfgMupX0euEf/Qpu1RjBBGkrfBJ4jqtYVvYzYB9Y1HpGa9HliO68oDT8hh3+16YDUqAOJ\n7fwJoqrFs5ggDbKDiG18F7u2N74aGAWOazooNepi4gLYK4FVmCANk6XE9r6ZuItY9C857oym\ng5oDRsi8yE4ahst7gHOJKnVF9xJXmBY3HpGadB3wPuArpeH3AluBw5oOSI3aRlxFfCdxoUSD\n7UwiEb6cSIaKLiNOms5pOig1ai1Rc+DOfgeixu0kzvn+mLwbUnBbvvs/fwJ20jBcbugyfDmw\ngLjSqMF1VZfhBwL7AP/ZYCxq3lbg2n4HocacmO+dOuG4u1RGg+n6fgegvnkQ+Isu415UKKMu\nTJCG1zLgAOBkogHnvcD7+xqR+uXDxNXki/sdiKRpc3i+P9Zh3A+BHcARzYUjaRZYCvwa8HXg\n9j7HMquZIA2v62hXqVsD/CHR5beGy3uJjjo+RfRsI2kw7J3vz3YYN5bDFzUXjqQ+O5L4P78D\nOJ9dq96pwARpsCxh144W1hMHQtn5wP7EszEuAL4NvJWos6y5qc72nw9cQnTvfSnwjpkNTQ2o\ns/01+Hbke7f/8/OJdmmSBt8pRG92uwGvAb7b33BmPxOkwTIKfL80bHOXssW6yZcQXYB+hqhy\n0emKo2a/qtt/f+ALRG92q4CPzGxYakid41+Dr/Uw0P2JB0IX7U08I8X9Qxp85wFXAA8RvRhu\n7Gs0c4jdfA+P/YiuXzv5DLEfnNRcOOqDxcRdhaeBN/Y5FvWX3XwPtvcQv+lndxh3Uo6z3eHw\nsJvv4fSrxMWztcDz+hzLXDCC3XwPnUOIq4XderFaku9WuRhc84ntvxT4BeJZKJIG0035fmaH\nca/P9y83FIuk5p0J/D3R7ugsoidT1eAdpOGxjtjWFzL+wWFvI64wbGTXBwpqcLyP2P4r+x2I\nZgXvIA2+O4hnXq0oDDsReBK4H6vZDxPvIA2XxUTV2m8De/U5lrlkhMyL5tHuxeJPMEkadEcS\n3ToeTjwsdiPwAqJP/CeJqhjlRt4aHFuIH82vTVDmTdib4aD6FcZ3xnEq8AzwrcKwNwOPNhmU\nZtRLgFuAg4mqtduJ7b6VaKj99f6FpgasIx4WDPFQ0COIxHhLDluL532D6l3Ax4nf801dylwP\n/GljEc0NI8AHwKtHw+YR4MXEHYRXEMnRPcBqogFft4NIg+GeCmXs9nNw7WR8Byy3dCjj9h8s\nDwA/Dfwu0YvVPODPgb+l8/ORNFieo31MP5yvou3NhqMGbWHyC95u/0lYxU6SJEnSMBvBThok\nSZIkaTwTJEmSJElKJkiSJEmSlEyQJEmSJCmZIEmSJElSMkGSJEmSpGSCJEmSJEnJBEmSJEmS\nkgmSJEmSJCUTJEmSJElKJkiSJEmSlEyQJEmSJCmZIEmSJElSMkGSJEmSpGSCJEmSJEnJBEmS\nJEmSkgmSJEmSJCUTJEmSJElKJkiSJEmSlEyQJEmSJCmZIEmSJElSMkGSJEmSpGSCJEmSJEnJ\nBEmSJEmSkgmSJEmSJCUTJEmSJElKJkiSJEmSlEyQJEmSJCmZIEmSJElSMkGSJEmSpGSCJEmS\nJEnJBEmSJEmSkgmSJFWzHDi58PnzwBhwSI/T+0R+fwz41NRC0wAo71+SpD5q/YMe6XMckjSb\nPQXcUfh8NrAKWNTDtA4lfnfvB14JvHDK0WmuK+9fkqRmjZB50fw+ByJJc8VTwNbC52vy1Ysj\n8/1LwJ1TCUoDo7x/SZL6xARJkqopn8AeBxxMXPXflp+fD6zL8Ufl+P8Gvl/43gnAqfn3EmAF\n8BjwQGl+L87p/Rj4LrBzGpZhAXG36qCM6X8mmO48YCmwL7AB+L8plgM4mqiSONkyHUgkkbsR\n6+/xKZar4vic3m3AjsLwecDPAj8C/msK859s2asmSK8A9uoybiuwvsI0WqrGX2cbT7bfFo+T\nfYnjYQOwqVSu6r4iSTPCKnaSNLl7gCsLn8ttkD6dn48GbgW2EyfaY8DVwD5Zbi3t391ObZDO\nAh4ujd8MXDjF+C8EflCa7kaiqmDZmTmuWPZaompgL+VWAN8plXsCeGep3AuB64HRQrnRHPb8\nHsrVcV1OZ7/S8Pk5/KYe57+Caste3r+6+R677j+t190Vvl83/qrbuOp+2zpuXkYknWPAmwvj\nV1BtfUnSdBuh/btjgiRJFRwNHFb4XE6QVufn9cBKYCGwB/BXOfwDWW4R8Loc9nHihLx1R+A0\nIqm6HzgDOJw4Ybwry/9Wj7Gfk9//CnE35CXEie8DRCK3tFD21Bz2IHAu0XHAH2Rc62l37lO1\n3MuB54iT99OBI4BXATdkTG8vzPtm4FngNzOmpcDvAc8AN/ZQro46CVLV+ddZ9vL+1c2RwLGl\n12dzehdV+H6d+Ktu4zr7betCwk3Eecdy2sdQnfUlSdNtBBMkSZqScoJ0eX7+aKnc/jn8PwrD\nluWwD5fK/lsOP77DNJ4iqiL14rXAh4iT6aJfzvmtKgxr3Vk4qlT2kzl8ec1y1wJPE9UJi/YC\nHiWqdrVsJ5K4srcQJ+a71yxXR50Eqer86yx7r15DrO+vUb3afNX4q27jOvtt6zhZ3WH+Tawv\nSepmBBMkSZqSbgnSig5ln2J8+5VOCdIC4qp+uS1SS+sq+lR6vFsMnAK8Gvh5otrSGPCxHD8f\n+AnwzQ7fXUD7jkHVcguy3PeA8zq8vprzb3VasYFYV2dMshxVy9VRJ0GqMv+6y96LA4m2O08C\nx9T4XpX462zjOvtt6zj5xQ7TnOn1JUkTGcFe7CRpRjzWYdgOJr+rcSiwJ9GOo5PW1fMjqH8l\n/QCindM5Gcc24i5C6yS39X4YUTXw0Q7T2F74u2q5Q7PcMcCaCeI7BHiEqIr1BaKd1ibg34kT\n7GuI6l8tVcvNlCrzr7vsvVhNbIuVwEOF4UtodxbSsh44v0b8dbZxL/ttebpNrC9JqsQHxUrS\n9Brt8XsL8n1bl/Gtk9I9e5j2lcAvEW2eDslp7EPcSeoUww4mVrfcrUQ1qW6vu7LcjURbnHcB\n9xFVvtYQve29vjDdquVmSpX51132un4HeEPO9x9K40aJXgqLr809xl91G9fdb5/uMp2ZWl+S\nVItV7CSpvm5V7MrtfAC2AN8qfO5Uxa7VVun2LvNrNcQ/sWac+xEnzOUuqiF6HhsD/roUw2QP\nLK1abr8s16361WQWEnc9NhPdPS+eYrmJdKti11qGm3b5xsTzn+qyT+SlxJ2eDfS2rGWd4q+7\nL1Tdb7sdJzO5viSpihEyL/IOkiTNDj8iTnhPoPNdolOIth731ZzuYuI5Np2qQJ3bIYaNGUP5\nWTunE9Wyltcot4VoU3Is8XycstOBF+Tf87LMosL4Z4kT7IuJZ+YcX6NcXc/l+6LS8JeWPled\nf51lr2NP4m7PHkRS8+Oa368af519YTr225laX5JUmwmSJM0eVxBV31aVhq8kThrX0D6Rr2pT\nfuflxEl1y1tzGMRdgGIMi2h3Sw6wN/BnRJWuh2uWu5w4Kb+I8e2wXkW0ebksPy8j7h58sBT/\nPOCk/Pt/a5Sr68FCXC27A39E9mhUM06ovux1fIR4htAHmfzuTid14q+zL0zHfjsT60uSemIV\nO0mqb7qr2EFcgb85x90KXEq0FxklehM7qMdYW89iujeneRvRYP5FRLWqZ4C/y+kvzHmP5Tyv\nI9qwjBLPymmpWm4B7Z7M7iNOpr9MtG3ZyPje19bQrmb1T8A/034w6t/0UK6O44g7HU8Q2+X9\nwJ3AXwKPM76b9qrzr7PsVRxLrN+dGcNVHV5VVI2/6jaus99OdJxM9/qSpDpGyLxod9qJ0To6\nPxdBkrSr44i78J8juif+KeIOzT8CW0tllxF3KL6Ynxfn928hehdr2Umc5D4EHEw8cHMzcAnw\nDqI7517cSNxJWkz0aHc78ZDQx4ik6SDin8K1RPfPVxEJ1L5E9ao7iBPiqwvT3FGx3ChxQv4d\n4i7DYUS1rCuA32b83Z5/Bb5BrMcDiPV7N/BuIoGrW66OHxLVxvYmHpy6L9G5xceAnyGqkd1Q\nc/51lr2KA4i7R48Az8sYy68rK0ynavxVt3Gd/Xai42S615ck1bGCwqM6vIMkSZIkaZiNYCcN\nkiRJkjSeD4qVpLllCeM7EpjMXUT1umHiOpIk9cwESZLmlpOJtjJVvY1oiD9MXEeSpJ6ZIEnS\n3PIl/O2ejOtIktQz2yBJkiRJUjJBkiRJkqRkgiRJkiRJyQRJkiRJkpIJkiRJkiQlEyRJkiRJ\nSiZIkiRJkpRMkCRJkiQpmSBJkiRJUjJBkiRJkqRkgiRJkiRJyQRJkiRJkpIJkiRJkiQlEyRJ\nkiRJSiZIkiRJkpRMkCRJkiQpmSBJkiRJUjJBkiRJkqRkgiRJkiRJyQRJkiRJkpIJkiRJkiQl\nEyRJkiRJSiZIkiRJkpTmF/4+DXhvvwKRJEmSpD45rfXHPGCsj4FIkiRJ0qxhFTtJkiRJSv8P\nAmC25R1aqSkAAAAASUVORK5CYII=", "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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TPzLmoKJqup4QUz81+1wvyNfMbPXdb2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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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ACDgAQAADAISAAAAIOABAAAMAhIAAAA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\nwKLhEDsAAIDyf9xuwIBRYP9OAAAAAElFTkSuQmCC", "text/plain": [ "Plot with title “'tenure' domain histogram”" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" }, { "data": { "image/png": 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r8Uy+AFnXE35O66N04ev+npUmzzC9l+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\nLCECiC1Znp2JUYIhYrS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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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+qpc6kUAACYq70ekO7dFI5eUD2z5Y8MvaF6bvXy6ierl1Qf2okCAQCA3WOvd9Jw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\n21QfqM6sDsyxJgAAYJfa6wFpwV80dcLwG9UvNgWl755rRQAAwK6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SoByQr3d2mLaG+IF+6PDCkaTaHQb8IXGF/zs1x6JmOIDoRfYhwKuB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", "text/plain": [ "Plot with title “'housing_characteristics' domain histogram”" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" }, { "data": { "image/png": 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ALgVri46ZpUP9wUZlf+P58Yz72Vrmzq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\nv91PE9NjnkN0Y7C7VO9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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": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 7
domainsensitivitypreparerespondrecoveradaptive_capacityenhanced_exposure
<chr><int><int><int><int><int><int>
1age 100000
2age 100000
3age 100000
4age 100000
5health100000
6income011110
\n" ], "text/latex": [ "A data.frame: 6 × 7\n", "\\begin{tabular}{r|lllllll}\n", " & domain & sensitivity & prepare & respond & recover & adaptive\\_capacity & enhanced\\_exposure\\\\\n", " & & & & & & & \\\\\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 & health & 1 & 0 & 0 & 0 & 0 & 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 <chr> | sensitivity <int> | prepare <int> | respond <int> | recover <int> | adaptive_capacity <int> | enhanced_exposure <int> |\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 | health | 1 | 0 | 0 | 0 | 0 | 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 health 1 0 0 0 0 \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": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 11
CCACPROCMUNCDISCSECsensitivitypreparerespondrecoveradaptive_capacityenhanced_exposure
<int><int><int><int><int><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]>
11726 111 1.1377171-0.3378133 0.184577960.1073270-0.15329655-1.00827077
21726 1011-0.9881374-0.4065854-0.334774370.8522391-0.26183757 0.04893220
3172610011-1.5969460-0.4065854-0.221404121.0122262-0.17316713-0.05159845
4172610111-1.8689267-0.4065854 0.026930721.3626742 0.02106336-0.81142134
5172610211 0.4071804-0.3116725 0.443547900.7414772-0.09187444 0.80062025
6172610212 0.7600790-0.3203095 0.331172140.3141994-0.30936550 0.44207919
\n" ], "text/latex": [ "A data.frame: 6 × 11\n", "\\begin{tabular}{r|lllllllllll}\n", " & CCA & CPRO & CMUN & CDIS & CSEC & sensitivity & prepare & respond & recover & adaptive\\_capacity & enhanced\\_exposure\\\\\n", " & & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 17 & 26 & 1 & 1 & 1 & 1.1377171 & -0.3378133 & 0.18457796 & 0.1073270 & -0.15329655 & -1.00827077\\\\\n", "\t2 & 17 & 26 & 10 & 1 & 1 & -0.9881374 & -0.4065854 & -0.33477437 & 0.8522391 & -0.26183757 & 0.04893220\\\\\n", "\t3 & 17 & 26 & 100 & 1 & 1 & -1.5969460 & -0.4065854 & -0.22140412 & 1.0122262 & -0.17316713 & -0.05159845\\\\\n", "\t4 & 17 & 26 & 101 & 1 & 1 & -1.8689267 & -0.4065854 & 0.02693072 & 1.3626742 & 0.02106336 & -0.81142134\\\\\n", "\t5 & 17 & 26 & 102 & 1 & 1 & 0.4071804 & -0.3116725 & 0.44354790 & 0.7414772 & -0.09187444 & 0.80062025\\\\\n", "\t6 & 17 & 26 & 102 & 1 & 2 & 0.7600790 & -0.3203095 & 0.33117214 & 0.3141994 & -0.30936550 & 0.44207919\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 11\n", "\n", "| | CCA <int> | CPRO <int> | CMUN <int> | CDIS <int> | CSEC <int> | sensitivity <dbl[,1]> | prepare <dbl[,1]> | respond <dbl[,1]> | recover <dbl[,1]> | adaptive_capacity <dbl[,1]> | enhanced_exposure <dbl[,1]> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 17 | 26 | 1 | 1 | 1 | 1.1377171 | -0.3378133 | 0.18457796 | 0.1073270 | -0.15329655 | -1.00827077 |\n", "| 2 | 17 | 26 | 10 | 1 | 1 | -0.9881374 | -0.4065854 | -0.33477437 | 0.8522391 | -0.26183757 | 0.04893220 |\n", "| 3 | 17 | 26 | 100 | 1 | 1 | -1.5969460 | -0.4065854 | -0.22140412 | 1.0122262 | -0.17316713 | -0.05159845 |\n", "| 4 | 17 | 26 | 101 | 1 | 1 | -1.8689267 | -0.4065854 | 0.02693072 | 1.3626742 | 0.02106336 | -0.81142134 |\n", "| 5 | 17 | 26 | 102 | 1 | 1 | 0.4071804 | -0.3116725 | 0.44354790 | 0.7414772 | -0.09187444 | 0.80062025 |\n", "| 6 | 17 | 26 | 102 | 1 | 2 | 0.7600790 | -0.3203095 | 0.33117214 | 0.3141994 | -0.30936550 | 0.44207919 |\n", "\n" ], "text/plain": [ " CCA CPRO CMUN CDIS CSEC sensitivity prepare respond recover \n", "1 17 26 1 1 1 1.1377171 -0.3378133 0.18457796 0.1073270\n", "2 17 26 10 1 1 -0.9881374 -0.4065854 -0.33477437 0.8522391\n", "3 17 26 100 1 1 -1.5969460 -0.4065854 -0.22140412 1.0122262\n", "4 17 26 101 1 1 -1.8689267 -0.4065854 0.02693072 1.3626742\n", "5 17 26 102 1 1 0.4071804 -0.3116725 0.44354790 0.7414772\n", "6 17 26 102 1 2 0.7600790 -0.3203095 0.33117214 0.3141994\n", " adaptive_capacity enhanced_exposure\n", "1 -0.15329655 -1.00827077 \n", "2 -0.26183757 0.04893220 \n", "3 -0.17316713 -0.05159845 \n", "4 0.02106336 -0.81142134 \n", "5 -0.09187444 0.80062025 \n", "6 -0.30936550 0.44207919 " ] }, "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", " index_start = GUID_length+1\n", " index_end = dimension_domain_count+5\n", " current_dimension_data[, current_dimension] <- rowSums(current_dimension_data[index_start:index_end], 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.double, 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": { "image/png": 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4LWnZu6Fr62qYXrpqZWqh9p6jb48aZR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\nAABJRU5ErkJggg==", "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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AAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgE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\nYCRjIZIkSZLUNjzFTpIkSZLC/wF3muUVZyITmwAAAABJRU5ErkJggg==", "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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6i+rnrXllYG7AgCErBbrLTDCGD3e11T\nr3xf3XRvors19cp4ZdMRpZdVn5pbdcC2p5MGAABgL7sonTQAAAAcS0ACAAAYBCQAAIBBQAIA\nABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUAC\nAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFA\nAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBB\nQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACA\nQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAA\ngEFAAgAAGAQkAACAQUACAAAY9s+7AAD2vE+rHladNO9C1umm6o3VLfMuBIC1E5AAmLcHVZed\nccYZ865jXQ4ePFj1kOryOZcCwDoISADM2/6q3/qt32rfvn3zrmVNbrnllh75yEeW/6sAO55r\nkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABg\nEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAA\nYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkA\nAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJ\nAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYB\nCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAG\nAQkAAGAQkAAAAAYBCQAAYNg/7wK22O2qe1dnVadWh6orq3dX182xLgAAYBvYKwHpcdX3Vg+t\nTlri+RurN1TPrt68hXUBAADbyF4ISN9fPac6XP1+9Y7qqvH4QHWX6gHVo6vHVN9aXTyXSgEA\ngLna7QHp3OrHq8uqJ1cfPUHbl1e/WL2u6dQ7AABgD9ntnTRc2HRK3Td2/HBU9XfV1zVdm/Sl\nm1wXAACwDe32gHTHpuuLPrjC9n9T3VKdvWkVAQAA29ZuD0hXVidX911h+89vWidXbFpFAADA\ntrXbA9LrmrryvqS6zwnaPrD69epg9ZpNrgsAANiGdnsnDR+pnlb9SlPvde/uaC92NzT1Ynd2\ndf/qvKae7Z5SfWwexQIAAPO12wNS1Yuqt1XPaOrK+yuXaPPhphD1k9V7tqwyAABgW9kLAanq\nr6qnjvGzq7Oaequ7vikcXbXBy7tN9czqtBW2P7k6p/qaDa4DAABYhb0SkGZ9ZAwL7lI9qKlD\nhw9s0DLOaLqm6eQVtr99dX719U2n+QEAAHOwFwLSbaofq76s6YjRpdWzx3MvarqB7II/Go/X\ne5PYD1ePXUX7h1Rvqo6sc7kAAMA67IWA9KvVE6ubmu6J9J+bTmf7cPWk6g3VPzT1cvewpgD1\noLlUCgAAzNVu7+b7vtVXVT/VdCTpNk2dNDy1+qbqG6sLx/iDmjpyeGDTER0AAGCP2e0B6fzq\n2uoHmrr1PlK9qulUuiPVixe1/7mm+yZ94RbWCAAAbBO7/RS7s5s6ZLhx0fQPVXdaov0tTT3a\nnbHJdQEAANvQbj+CdEV116YuvWf9i+ozqpMWTT9ttP/EplcGAABsO7s9IL25OlA9r7pzdbvq\ne5pOvftk9UMzbU+q/mtT19xv2toyAQCA7WC3n2L3vuoF1b+vvmVm+nOrv6l+qXpK0/2P7l3d\nvXpN9batLRMAANgOdntAqvqO6v3VE5puwnpp9d+brje6Y/V91Wc1deJwSfW0+ZQJAADM214I\nSDc1dfP9U0s89+zqOdVZ1dVNN5IFAAD2qL0QkE7klqabxgIAAHvcbu+kAQAAYMUEJAAAgEFA\nAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBB\nQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACA\nQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAA\ngEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQA\nAIBBQAIAABj2z7sAANblnOqx8y5ine417wIAYIGABLCzPfXUU0/9iXvc4x7zrmPNrr766j76\n0Y/OuwwAqAQkgJ3u084777x+4Rd+Yd51rNmll17a8573vHmXAQCVa5AAAAD+iYAEAAAwCEgA\nAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhI\nAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAI\nSAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAw\nCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAA\nMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQA\nADAISAAAAIOABAAAMAhIAAAAg4AEAAAw7J93AVvoNtXDq/tWZ1WnVoeqK6u3VW+sbphXcQAA\nwPzthYB0SvXs6tur047T7urqJ6r/Vh3ZgroAAIBtZi8EpJdV/6b6q+oV1Tuqq6rD1YHqLtUD\nqic1BaRzq2+bS6UAAMBc7faA9MCmcPQz1TNb/sjQpdWPVS+o/l31C9Vfb0WBAADA9rHbO2l4\ncFMoelYnPm3upur7xvjDN7EmAABgm9rtAelAdXN1zQrb/2N1S1OHDgAAwB6z2wPSe5tOI3zM\nCtv/m6Z18u5NqwgAANi2dntA+l/VP1SXVE+rzl6m3d2bTq/71ep943UAAMAes9s7abiu+vLq\nt6tfHMPHm3qxu6HpFLyzqzNH+/dUT2jq4Q4AANhjdntAqnpL9VnVU5tOtbtPR28Ue311RfV7\n1e9WL69unE+ZAADAvO2FgFTTkaQXjmEr3KN6fXXyCtufOn7u25xyAACAldgrAanqpKYe7Rbc\ntnpcdV7TKXVvrf6wqRe79bqy+i8dDT4n8pnV93birsgBAIBNtBcC0rnVi5uOHr10TLuw+vXq\nTovavq2pJ7v3r3OZN1YvWUX7hzQFJAAAYI52ey92J1e/Xz2wo0eP7la9qjq9+tmma5O+uXpZ\n9TlN1yLtheAIAAAsstuDwOOajiB9TVMHDFVParoR7COqy2baXlz9ZfVTTUeYXrt1ZQIAANvB\nbj+CdK+mI0evnJn2GdUHOjYcLXhB03VA9930ygAAgG1ntwek65s6ZzhjZtpHW/4+Rzc3BaSb\nNrkuAABgG9rtAekPxs8fm5l2adNRpPss0f4ZTevkLza3LAAAYDva7dcg/XX1/Orp1f3H+J9X\n31/9dvXj1Xuqc6qvrR5fvaH6k3kUCwAAzNduD0hVT6v+oSkU/dqi51606PHLqm/dgpoAAIBt\naC8EpFuajhQ9r/rS6vzqnk03ir2p+lj19urV1d/MqUYAAGAb2AsBacEnm44QvWzehQAAANvT\nbu+kAQAAYMUEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQk\nAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgE\nJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAY\nBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAA\nGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIA\nABgEJAAAgGE1Aenrq19ewfw+WD1uzRUBAADMyWoC0nnVg07Q5vTqrOqz11wRAADAnOxfQZs/\nHT/Pqe4w83ixfdW51YHqE+svDQAAYGutJCC9tjq/uld1WvWA47T9VPXS6tfXXxoAAMDWWklA\n+tHx86Lqyzt+QAIAANixVhKQFrygevlmFQIAADBvqwlIV4zhLtX9qzOarjtayjvHAAAAsGOs\nJiBV/bfqGZ2497tnNZ2SBwAAsGOsJiB9UfU91dur360+Xt2yTNvleroDAADYtlYbkD7U1KPd\n4c0pBwAAYH5Wc6PYU6t3JBwBAAC71GoC0luqe7d8xwwAAAA72moC0h82haSfrA5sSjUAAABz\ntJprkL6k+vvqW6qvrd5afWyZtq8aAwAAwI6xmoD0L5u6+K66ffXo47T92wQkAABgh1lNQHpe\n9avVzSto+6m1lQMAADA/qwlIHx8DAADArrSagHSPMZzISdU/VO9bU0UAAABzspqA9E3Vj6yw\n7bOqi1ZdDQAAwBytJiC9sXr2Ms/dufqi6tzqx6vfX2ddAAAAW241AemyMRzPd1ZfWf3smisC\nAACYk9XcKHYlntt0NOlRGzxfAACATbfRAanqA9X9N2G+AAAAm2qjA9KZ1edVn9zg+QIAAGy6\n1VyD9JgxLGVfdcfqkdWdqj9ZZ10AAABbbjUB6UFNnTAcz6eq/1i9Y80VAQAAzMlqAtILqlcv\n89yR6prq/dWN6y0KAABgHlYTkK4YAwAAwK60moC04C7V1zbdGPasMe3K6k3VJdXVG1MaAADA\n1lptQHpc9RvVGUs896Tqh6onVH+2zroAAAC23Gq6+b590xGia6unV59TnT2Gz62eUZ1UvaI6\ndWPLBAAA2HyrOYL06Kb7HH1h9ZZFz320elv1xuovqgur39mIAgEAALbKao4gndd0rdHicDTr\nL6sPVvdeT1EAAADzsJqAdHN1+grnecvaygEAAJif1QSkdzRdh/QVx2nz6Oqc3CgWAADYgVZz\nDdIbqvc1ddTwguqypvsi7avuVj2y+pbqPdX/3tgyAQAANt9qAtKN1eOr36q+cwyLvav68tEW\nAABgR1ntfZDeWd23emz1kOqu1ZGmzhv+uPq96qaNLBAAAGCrrCYg7WsKQzdWvz2GBac0BSOd\nMwAAADvWSjtp+KKm+xvdeZnnv6v6o+ozN6IoAACAeVhJQPrcpg4ZvqC6YJk2Z1YPHe3O2pjS\nAAAAttZKAtL/qE6rnlRdukybH6i+rrp79YsbUxoAAMDWOlFA+pymI0e/WP3mCdr+WvWi6t80\nBSUAAIAd5UQB6fPGz0tWOL+Lq5OaergDAADYUU7Ui91dx8/3r3B+7xs/77G2cgC21M9U/3be\nRazTqfMuAAB2kxMFpIUbvh5Y4fxuM35et7ZyALbUvR784Aff4cILL5x3HWt2ySUrPcAPAKzE\niQLS342fD6peuYL5PXz8/MBaCwLYSne/+9172MMeNu8y1uy1r31t11577bzLAIBd40TXIP1h\ndbj6vurkE7S9ffWfqk9Wv7/uygAAALbYiQLSP1bPr86v/md1p2Xa/fPqDdV51S9UhzaqQAAA\ngK1yolPsqr6/+sLqCdUjq1dXb62uqe5YPbB6dFPvdW+oLtqMQgEAADbbSgLSoepfVT9aPa36\nmjHMuqr62eq/VTdvZIEAAABbZSUBqY5eh/Sj1UOrezX1WHdVUxfgf5JgBAAA7HArDUgLrq1e\nPwYAAIBdZbUBaSf7l9Vjq/tWZzXdXPFQdWX1tup3qj+fW3UAAMDc7YWAdM+mHvjOn5l2Q9Np\ngweaOqD419UPVv+r+trq41tcIwAAsA2cqJvvne7k6rXVA5o6kXhI0/2aDlS3G3cZqV0AAB9Y\nSURBVD/v2NQJxcVNvfH9brt/vQAAAEvY7UeQLqzuU3199dJl2vxj9QdjeGv189XDq8u2oD4A\nAGAb2e1HSu7T1Lveb6yw/QurI9XnbVpFAADAtrXbA9LNTe/x5BW2P7na1xSSAACAPWa3B6S3\nNAWep62w/TPHT73ZAQDAHrTbr0H64+pN1U9VD6xeWb2j6Qa3NzR10nB2df/qKdVjmu7x9KZ5\nFAsAAMzXbg9It1SPr36leuIYjtf2RdXTc4odAADsSbs9IFV9ovqK6l5NR4ju09EbxV5ffbh6\ne/Wa6kMbtMx91YOr01fY/r4btFwA5uuzq+vmXcQ6vaud/x4A1mwvBKQF7x3DUva3sddjnVv9\nUXtr/QLsWUeO/NOJBy+cZx0b5JnVT8+7CIB5sQM/+eWmm8l+4QbN7/2tvOe8mm5g67ongB3u\n537u57r//e8/7zLW7OlPf3rvfOc7T5l3HQDztNsD0u3GcCKnNwWac8bjT40BAADYQ3Z7QPru\n6kdW0X7hGqRnVRdteDUAAMC2ttsD0ifHz+ur36yuXqbdI6s7V78xHv/pJtcFAABsQ7s9IP1s\nUy92P93U3ff3VP9jiXa/0nQN0ndtXWkAAMB2s5E9t21XL67+RfW6piD0B01dfgMAABxjLwSk\nqquqp1aPbeqC+23VD7S6nuYAAIBdbq8EpAWva7op6y9XP1a9pTp/rhUBAADbxl4LSFXXVv+x\nelB1S/Xm6tFzrQgAANgW9mJAWvAXTTeG/eHq0+dcCwAAsA3s5YBUdVP1E9WZ1RfPuRYAAGDO\ndns33yt1eN4FAAAA87fXjyABAAD8EwEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAA\nBgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAA\nAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAA\nAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQ\nAAAABgEJAABgEJAAAAAGAQkAAGAQkAAAAAYBCQAAYBCQAAAABgEJAABgEJAAAAAGAQkAAGAQ\nkAAAAAYBCQAAYBCQAAAAhv3zLgAA2B6uuOKKqh+uvmfOpazXxdUz510EsDMJSABAVYcPH+4R\nj3jEaRdccMFp865lrV7/+td3+eWXf/a86wB2LgEJAPgn5557bg972MPmXcaavetd7+ryyy+f\ndxnADuYaJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACA\nQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAA\ngEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAY9s+7\nAGDHen513ryLWKcHzLsAAGB7EZCAtXriox71qDt8xmd8xrzrWLNLLrlk3iUAANuMgASs2Rd/\n8Rd3wQUXzLuMNXvFK14x7xIAgG3GNUgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAw\nCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAA\nMAhIAAAAg4AEAAAw7J93AVvsdtW9q7OqU6tD1ZXVu6vr5lgXAACwDeyVgPS46nurh1YnLfH8\njdUbqmdXb97CugAAgG1kLwSk76+eUx2ufr96R3XVeHygukv1gOrR1WOqb60unkulAADAXO32\ngHRu9ePVZdWTq4+eoO3Lq1+sXtd06h0AALCH7PZOGi5sOqXuGzt+OKr6u+rrmq5N+tJNrgsA\nANiGdntAumPT9UUfXGH7v6luqc7etIoAAIBta7cHpCurk6v7rrD95zetkys2rSIAAGDb2u0B\n6XVNXXlfUt3nBG0fWP16dbB6zSbXBQAAbEO7vZOGj1RPq36lqfe6d3e0F7sbmnqxO7u6f3Ve\nU892T6k+No9iAQCA+drtAanqRdXbqmc0deX9lUu0+XBTiPrJ6j1bVhkAALCt7IWAVPVX1VPH\n+NnVWU291V3fFI6u2uDlnVn9WHXKCtvrFAIAALaBvRKQZn1kDEvZV92zunoMa3VSU0g6sML2\nZ6xjWQAAwAbZCwHptOqHqsdXt63+rOnmsX+9RNsDTfdDelZ10TqW+fGmeyqt1EOqf7WO5QEA\nABtgt/diV/WS6geq+zXdF+lrqre0ugADAADsAbs9IH1u9VXV7zVdd3T7pu6+31q9uHry/EoD\nAAC2m90ekL5g/HxaRztieFf1JU33SHpRdcHWlwUAAGxHuz0g3bk6Un1g0fTDTafavat6VdM9\nkAAAgD1utwekDzT1THf/JZ67pqnjhluajibdZQvrAgAAtqHdHpD+sLquemF17hLPf7D6sqYj\nTW+qvmjLKgMAALad3R6QPlz9cNO1SO+vHrxEm7+sHtZ049g/2rrSAACA7Wa3B6Sqn2nqye73\nm+5PtJS3N/V496vVzVtUFwAAsM3shRvFVr1yDMfzseqbxgAAAOxBe+EIEgAAwIoISAAAAIOA\nBAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACD\ngAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAA\ng4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAA\nAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgA\nAACDgAQAADAISAAAAIOABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAMP+eRcAq3Ra\n9ZBq37wLWafzqvfPu4h18vkBAOw6dnDYaZ5UXTzvIgAA2J0EJHaa/eecc04veclL5l3Hml1+\n+eX94A/+YK961as688wz513Omj3iEY+YdwkAABvONUgAAACDgAQAADAISAAAAIOABAAAMAhI\nAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAAMOyfdwEAABzjftXF7fwvsq+vnlB9fN6F\nwGoISAAA28u9Dhw4cP7Xf/3Xz7uONTt06FCXXHJJ1dkJSOwwAhIAwDZzyimn9OQnP3neZazZ\n1VdfvRCQYMfZ6YduAQAANoyABAAAMAhIAAAAg4AEAAAwCEgAAACDgAQAADAISAAAAIOABAAA\nMAhIAAAAw/55FwAAsFEOHTpUddfqiXMuZT2+aN4FwF4mIAEAu8Z73/ve9u/f/wWnnXbay+dd\ny1odPnx43iXAniYgAQC7xpEjRzr//PN79rOfPe9S1uyFL3xhr371q+ddBuxZrkECAAAYBCQA\nAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgGH/vAsAAGB3\nueGGGxZGf6+64ThNt7sj1f9XXTbvQtg6AhIAABvq0KFDVX3DN3zDOXe6053mXM3aXXzxxV19\n9dWfmYC0pwhIAABsioc//OHd8573nHcZa/byl7+8q6++et5lsMVcgwQAADAISAAAAIOABAAA\nMLgGaW/58uop8y5inc6bdwEAAOxeAtLe8oRzzjnniQ94wAPmXceavfWtb513CQAA7GIC0h5z\nv/vdr+/+7u+edxlr9pznPKd3vetd8y4DAIBdyjVIAAAAg4AEAAAwCEgAAACDgAQAADAISAAA\nAIOABAAAMAhIAAAAg4AEAAAwCEgAAADD/nkXAAAA29HNN99cdc/qC+Zcynq9pzo47yJ2CgEJ\nAACW8LGPfazqB8ewkz2/+rZ5F7FT7MWAtK+6TXVqdai6dr7lAACwHR05cqRv//Zv78ILL5x3\nKWv23Oc+t8suu+zAvOvYSfZKQLpL9e+rx1b3qU6fee5g9bbqt5vS9ae2vDoAALalU089tTPO\nOGPeZazZKaecMu8Sdpy9EJAurF5RndF0tOhvqquqw9WBpvB0fvXQ6hnVv67+Yi6VAgAAc7Xb\nA9KZ1cuqq6uvrV5b3bREu1OrJ1Y/U11afXZOvQMAgD1nt3fz/bjqDtVXV7/T0uGo6vrqpdVT\nqn9WfemWVAcAAGwr+6ojY/xZ1UXzK2VT/Kem97XSky9Pqm5o6qnkJ9ax3HOrP2vlR+j2N50C\neEp14zqWeyK/sn///m8+7bTTNnERm+vQoUPdfPPN3fa2t513KWt20003dejQoW5729u2b9++\neZezZgcPHuy0005r//6deyD6mmuu6eSTT+7AgZ177eqhQ4c6cuRIp59++okbb1M33nhj119/\n/Y4+x7+mv4nTTz+9k046ad6lrNk111zTKaecsqOvWbjuuuvat29fO/l/3eHDh7vxxht39P+6\nW265pWuvvXbH/00cPHiwAwcO7Oi/iUOHDnXTTTf9j+pb5l3LNndR9SO1+0+x+1R1cnVW9dEV\ntL9r01G19XbU8IGmo1YrXb/7mmrczHBU9cM33XTTyw4e3NHd4B+ozj548OAH513IOt3rmmuu\nee+8i1incw8dOvShlj8yuxOcfcMNN1x/ww03fHLehazDbarbHzx48Ip5F7IO+6rzDh48+L55\nF7JOn3nddde9v6NfPO5Edzt8+PAnDx8+vJNPM799derBgwc/Mu9C1mF/dfeDBw/+3bwLWad7\nXXfddTv9f909Dh8+/JHDhw8fnnch6/SOeRew0xwZw0VzrmMz3Kfpvf1aJz6KdJumnuxuqT5r\nk+sCAAC2j4sauWi3H0F6Z/VL1dOqh1W/25Sgr2o6le5AdXZ1/+rx1adXz2m62zAAALAH7eYj\nSDWduvEd1Yc6+l6XGt5TfcOcagQAAObnovbIEaSa3ujPV8+r7td02t1ZTV17X199uHp79e55\nFQgAAGwPeyEgLTjSFITePu9CAACA7Wm33wcJAABgxQQkAACAQUACAAAYBCQAAIBBQAIAABgE\nJAAAgEFAAgAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAY\nBCQAAIBh/7wLYEtdXj1o3kUAAOwgP1798LyLYOsISHvL+6urqmfNu5A97rzq5dWF1SfmXMte\n97PV31fPnXMde93nVy/o/2/v3oPlrMsDjn/PkJAQriFgCJDQcKlRAVMsMBgGEUppMWptGcyU\nwbRV2+nUikrtzbaeItRRS71gncEKaJWhWKUtoCIUKUIRhFAuFhA0iYSbhBDCLfdz+sfzbHf3\nzW6yJzlnfyfn/X5mdnb3fX/77rO/dy/vs7/LC8cBQ4VjqbvLgFuBy0sHUnNvJg7ITykdiLga\neKJ0EOovE6R62QCsApaUDqTm1ub1fcAzJQMRa4Cf42eitN3zegkmSKW9TBwM+pkoaw6wGffD\neLCe2BeqEccgSZIkSVIyQZIkSZKkZIIkSZIkSckESZIkSZKSCZIkSZIkJRMkSZIkSUomSJIk\nSZKUTJAkSZIkKZkgSZIkSVKaVDoA9dWG0gEIiP0wDGwsHYjYgJ+L8WAD8XkYLh2I/EyME+6H\n8cN9UVPDeRksHIfG3vS8qLxDSwcgAPYH9iwdhBgA5pYOQgDMAnYrHYTYBTikdBACYDYwuXQQ\n6otBMi+yBaleVpcOQP9vaekABMDK0gEIiB+kZaWDEABPlQ5AAGwGflY6CAGwonQA6j/HIEmS\nJElSMkGSJEmSpGSCJEmSJEnJBEmSJEmSkgmSJEmSJCUTJEmSJElKJkiSJEmSlEyQJEmSJCmZ\nIEmSJElSMkGSJEmSpGSCJEmSJEnJBEmSJEmSkgmSJEmSJCUTJEmSJElKk0oHoCIGgIPy8hTw\nBLC5aET1NQU4LK+XAmvKhlNruwBvBF4BlhSOpW5+AZgFrAIeKRtK7c0H9gFuxd+FUnYB5gD7\nA48BT5cNp9ZmAHOBF4HlwPqi0aivhvMyWDgO9cdZwKM09/swkSC9p2RQNTQV+CSwlvZ98R/E\nwaL6ay5wG7EP7i4cS53MB+6h/TPwE+DNJYOqqd2BL9LcD3uUDae23g08Sftn4j7g5IIx1dEx\nwM2074dXgE8Rv9+amAZp7m8TpBpZROzrR4kv4ZOJxGhZLl9cLLL6uYKo82uAtwG/BlxCc//s\nWi602lkMvAD8D7ARE6R+mQWsBFYD7wMWAO8CVhAHIq8rF1rtHEe03K0k/iU3QSrjvUTdPwCc\nA5wC/CXwErAOeE250GrlcKLFaA3wF8R+OBO4hdg/l5cLTWNsEBOkWnqAaB4+qLL89cR74Ja+\nR1RP84j6vpno7tjq6lx3er+DqqkZRH1/jujmuA4TpH65iKj7t1aWz8/lV/U9ovp6ALgJOBC4\nHhOkEgaIlqNVxPdSq/cT++Tj/Q6qpi4m6vvMyvLdiH20nvi90MQzSOZFjkGqlz8l+jY/UVl+\nP/HP+d59j6ieNhP74nbyX4oWtwHvIA5UNPY2EC1415YOpIbeQYyBvK6y/F7gLmAh0ZK6oc9x\n1dGFwNeBodKB1Ng04BPAs0SS1Oq2vPZ3oT++AvyALX8X1gIPAqcSyZLjkSY4W5D0JuI98E+l\nAxGfI/bFiaUDqSlbkPpjL+J9Xk2OGr6Q64/sW0RqsAVp/PlNYp/8VelAam4GkcA+WjoQjZlB\nbEGqvROBfYE3AH9MtCL9TdGINA/4XWLQ+n8XjkUaSwfn9ZNd1jeWzwZ+NPbhSOPWNOCjwMs4\n9qWEI4lhCUcQYyUHiDHcmuBMkOrrOppd6q4E/oTo7qIy5hAz2G0CzmbLrnfSRDItr9d1Wb82\nr3fvQyzSeDWVmNDnKOJ3odo9XmPvAuDteftuYiKZH5YLR/3iiWInlpnAw5XLFV3Knk3MknMB\n0cXuf4mZ1DQ6PsaW++KELmWPBe4kzj1yapbV6BjJZ0L9symvu/1J11ju+CPV1auIiXwWAr9H\n/JGp/vs48E7gw8QY7tuBjxSNSH1hC9LEMsSWJ5R7rkvZb7Xc/jwxMPqrRJeWbv/qqncvsOW+\n6HSwt4joNvFT4odw+diGVTsj+UyofxqD0Kd3Wb9vXruvVEdHExME7AWcAdxYNpxauzMvAJ8m\nxuh9DLiBmExGE5iTNNTHPsB+XdZ9lXgfHNO/cGrvXcQB/PXAnoVjUXCShv4YIM7tck+X9dcQ\n30fV6Y419pykoayjiD8GlhLjUtV/0+g+Y+C7ic/Hh/oXjvpokMyL7GJXHwcQX7rdpjOemdd2\naemPM4DLiHFHC4mT0kl1MQx8j/in/ODKuj2Jk1gvYcvpjqWJbDbRWvQsceJku1uXsQRYRuc/\nLj1WqhFbkOqjcRboc2k/QelvEy0Zy3FcWj/sDTxDjPvarXAsamcLUv/8KvF9dA0xGB2ij/9l\nufycQnHVnS1I5XyXmKDk1aUDqbm/JT4DV9A+UczRwEpiDOURBeLS2BukmReZINXIHGAFsb8f\nJ04+tyzvryEma9DY+wBR5yuAO7pc/rpYdPVyDu31PkR0/WpdVm3h0Oj5B+Kz8AwxIP3xvP9l\n2v/E0dh5He3v9+eJffDDlmVvKxZdfcyn+Vvc7Xfh34pFVy+TiZa8YWA1cdLYB4iTvA8B55UL\nTWNsEM+DVEuPEf96LAaOJ+b2vxe4lJgowClE++N5ojVvazb2IxCxmfZJSb7foYxTro+dDxGD\nnRcBs4gk6Wo8EOynYdo/A/d2KLO5T7HU3bZ+F9b3JQptJFq4FwK/DhxCDFG4gWhV6jZ2UhOM\nLUiSJEmS6mwQJ2mQJEmSpHYmSJIkSZKUTJAkSZIkKZkgSZIkSVIyQZIkSZKkZIIkSZIkSckE\nSZIkSZKSCZIkSZIkJRMkSZIkSUomSJIkSZKUTJAkSZIkKZkgSZIkSVIyQZIkSZKkZIIkSZIk\nSckESZIkSZKSCZIkSZIkJRMkSZIkSUomSJIkSZKUTJAkSZIkKZkgSZIkSVIyQZIkSZKkZIIk\nSZIkSckESZIkSZKSCZIkSZIkJRMkSZIkSUomSJIkSZKUTJAkSZIkKZkgSZIkSVIyQZIkSZKk\nZIIkSZIkSckESZIkSZKSCZIkaSJ7E3BM6SAkSTsPEyRJ2rmdBLyhdBDj2E3AF1ruW1+SpK2a\nVDoASdIO+TZwP/DG0oHsJKwvSdJW2YIkSTu3l4AXSwexE7G+JElbZQuSJO3cqgf8rwX2B24B\n9gKOBpYBT1QedyhwALAGeBjY3GX7M4A5xB9qPwOeraw/MsvcAgwArwamA8uBp7YS9xEZZ7fn\nb30dAHOBV2UMT3fZ5nTgF4n6eAgY7lCm1wTpeGC3LuteBJb0sI2GbdVhwwAwj9hvy4BntrLN\nkdTfaLwPJKlWhvMyWDgOSdLI3Qt8ueX+vxDf6a8HVuftM1vWnww8SPO7fxhYBby/st1DgG8B\nQy3lhnLZ/i3lvpHrjqd5gN0oexUwtbLdhcDSyvM/B5xbKfeVXHcocCuwEdiUy/4d2KNS/u+y\nTGObjwK/lMvuaClXra9uflKJsfVydw+Ph97rEOAMIqlsfZ5rgVmVcr3W32i9DySpLgZpfh+a\nIEnSTuxQ4MCW+43E4j+J7/WTiBYCiIRhPXGAfxowGzgB+E4+5g9atnMzsA54L9GqMQ94H/AK\ncGNLucaB+FJgETCZaDG5JJdf1FJ2AZHk/Bg4HTiYOFC/K8v+fkvZS3PZEmAxkWjtmtsbBj7a\nUvZ3aCYuC4jWlT/K56kmSNX66mYOcHjlckU+z4U9PB56r8PjMs5Hgd8iJpE4j6irJTS7w4+k\n/kbrfSBJdTGICZIkTUhfIr7TL+2w7lrgZWBmZfluwONE96+GjcB/ddjGWcTB+y55v5Eg/X2l\n3CSii91qmt25b8iyR1XKTie6vi3r8Do+2aHsMPC9lmV3ES1XB1XKfjDL3sGOO5Vo/bmT3run\n91qHjVamuZVy/5jLT8r721N/O/o+kKS6GMQESZImpMaB8VsqyycDa4muY4s6XH6Qj5uT5ZcR\nB92nb+P5GgnSqR3WXZ3r5uXzrwMe6bKdRuvFIZXXcXKHsi8B9+XtKUSryv0dys1ldBKkGcTY\nnReAw0bwuF7qcBKxXx7osG4yzdaj7a2/HX0fSFJdDJJ5kZM0SNLE9Hjl/iyim9phwJVbedwB\nwGNEd61vANcTycFNxEH4NUQXsaoVHZY1JlOYmY+ZQnTF66TRajGb9haMJzuU3USz9eWAvF2d\nfIB8HaPhUqJb3mLgpy3LZ9KcRKJhCXB23u6lDg8k9kt1f0G0QDXMYvvqb0ffB5JUO07zLUkT\n08uV+5Pz+laiK1W3y11Z7kZivM4HiBnhziIOqFcAb+3wfOs6LBvK60ktz7+hS7yNZGBKl210\n09juxg7rGpMj7Ig/BN5OvPZ/7rD9pyuX51rW91KHjfg3bSOO7a2/HX0fSFLt2IIkSfWwKq8P\noHMy0+0xn83LVGICgYuBrxFdsNa0lJ3Oli0O++T18zQThxldnmvfSpy9akzZvXeHdTOIqbO3\n12uISSGWE4lS1Uo6dwFsta063Fa9NIxW/W3P+0CSasUWJEmqh+eJcSeHE7O8VZ1Gc5KDgSyz\ne8v6dcQsbhcT59WpThRwTIdtHkm0sjxCTNawjDgfT7WVA+DYfI6Htv1S2vycGBv0WrZMhk4Y\n4bZaTSFae3Ylusyt2XrxLfRah6uJBOxotjzv0mlEF72TGL36G8n7QJJqyQRJkurjS8SB+4U0\nx/BAJBLXAF/M+ycSSc35lccP0EyEqieB/SDt5/Z5C3EOnptptvJcTpy/6M8rj11MHKxfSUw/\nPVLfAfajvZVnD+AjbLuLXjefIOI/H7h9Ox4/kjq8nEikWqcunwZcQHTvW9pSbjTqr9f3gSTV\nlrPYSdLE0Zi97PAO6ybTnO3sIeKA+7vE+JfltM/QdmWWewT4OvCvNE+e+tmWco1Z7D5DjMG5\niphGej0x/qW1ZWkKkTANE2NgLiHG6QwRs7jt1+PreB74Ucv9eUQLzxAxY903M5aLiG5wd3bY\nxtYcntvaTNTD1zpcetFrHU4l6mOYqIfrMv4h4rxJDaNVfyN5H0hSXQySeZEtSJI0sfyYmFlt\nbYd1G4mWnXcSg/APIrpufRiYT/sMbWcDv0Gcx2ca0dXs28TJSs/tsO1PEScY3ZRlLwV+Gbin\npcx64FeIFo8VxAQGzxEtP8cCz/b4Om6jfRKBh4mTq36eSJQ252s6j5g57r7qBnrwfSIJmUWc\nkLV66UWvdbgOOAV4D/Ag0arzTeD4fE0No1V/I3kfSFIt2YIkSdpejRakXpMGSZLGo0FsQZIk\nSZKkdiZIkiRJkpRMkCRJO+JBYqzL9sw+J0nSuGOCJEnaEecTJ0tdWTgOSZJGhQmSJEmSJCUT\nJEmSJElKJkiSJEmSlEyQJEmSJCmZIEmSJElSMkGSJEmSpGSCJEmSJEnJBEmSJEmSkgmSJEmS\nJCUTJEmSJElKJkiSJEmSlEyQJEmSJCmZIEmSJElSMkGSJEmSpGSCJEmSJEnJBEmSJEmSkgmS\nJEmSJCUTJEmSJElKJkiSJEmSlEyQJEmSJCmZIEmSJElSMkGSJEmSpGSCJEmSJElpUsvtBcCf\nlQpEkiRJkgpZ0LgxAAwXDESSJEmSxg272EmSJElS+j+hiEI3RqgxogAAAABJRU5ErkJggg==", "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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gAAGAQkAACAQUACAAAYBCQAAIBBQAIAABgEJAAAgEFAAgAAGAQkAACAQUACAAAY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\nALBoOMQOAACg+n/ML/z0fdEyJwAAAABJRU5ErkJggg==", "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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2q6FtKRhVUHAADsCvslIM26NEeJAACAFez1ThoAAADWTUACAAAYBCQAAIBBQAIA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\nSZI0MOxiJ0mSJEnp/wF/ogs2L/T96QAAAABJRU5ErkJggg==", "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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zbdYa/PzE2hF7P8bpe91/Nr0m7L8Oa0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\nSAAAAEVAAgAAKAISAABAEZAAAACKgAQAAFAEJAAAgCIgAQAAFAE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"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": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 6
CCACPROCMUNCDISCSECsocial_vulnerability
<int><int><int><int><int><dbl[,1]>
11726 111-0.1413894
21726 1011-0.4444888
3172610011-0.6491854
4172610111-0.9789365
5172610211 0.4454417
6172610212 0.2948932
\n" ], "text/latex": [ "A data.frame: 6 × 6\n", "\\begin{tabular}{r|llllll}\n", " & CCA & CPRO & CMUN & CDIS & CSEC & social\\_vulnerability\\\\\n", " & & & & & & \\\\\n", "\\hline\n", "\t1 & 17 & 26 & 1 & 1 & 1 & -0.1413894\\\\\n", "\t2 & 17 & 26 & 10 & 1 & 1 & -0.4444888\\\\\n", "\t3 & 17 & 26 & 100 & 1 & 1 & -0.6491854\\\\\n", "\t4 & 17 & 26 & 101 & 1 & 1 & -0.9789365\\\\\n", "\t5 & 17 & 26 & 102 & 1 & 1 & 0.4454417\\\\\n", "\t6 & 17 & 26 & 102 & 1 & 2 & 0.2948932\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 6\n", "\n", "| | CCA <int> | CPRO <int> | CMUN <int> | CDIS <int> | CSEC <int> | social_vulnerability <dbl[,1]> |\n", "|---|---|---|---|---|---|---|\n", "| 1 | 17 | 26 | 1 | 1 | 1 | -0.1413894 |\n", "| 2 | 17 | 26 | 10 | 1 | 1 | -0.4444888 |\n", "| 3 | 17 | 26 | 100 | 1 | 1 | -0.6491854 |\n", "| 4 | 17 | 26 | 101 | 1 | 1 | -0.9789365 |\n", "| 5 | 17 | 26 | 102 | 1 | 1 | 0.4454417 |\n", "| 6 | 17 | 26 | 102 | 1 | 2 | 0.2948932 |\n", "\n" ], "text/plain": [ " CCA CPRO CMUN CDIS CSEC social_vulnerability\n", "1 17 26 1 1 1 -0.1413894 \n", "2 17 26 10 1 1 -0.4444888 \n", "3 17 26 100 1 1 -0.6491854 \n", "4 17 26 101 1 1 -0.9789365 \n", "5 17 26 102 1 1 0.4454417 \n", "6 17 26 102 1 2 0.2948932 " ] }, "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", "index_start = GUID_length+1\n", "index_end = ncol(domain_scores)\n", "vulnerability_scores$social_vulnerability <- rowSums(domain_scores[index_start:index_end], na.rm = TRUE)\n", "\n", "# generate z-scores with the scale function in order to standardise the vulnerability data\n", "vulnerability_scores <- vulnerability_scores %>% mutate_if(is.double, scale)\n", "\n", "# Print the first part of the vulnerability scores, which are now collated into one table\n", "head(vulnerability_scores)" ] }, { "cell_type": "code", "execution_count": 13, "id": "0a6ad33d-3988-4166-b078-9734c25f180d", "metadata": {}, "outputs": [ { "data": { "image/png": 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QHppOo9ba9wVHVO9fDqbdVLm57Dp5uex4lN51TdrXpkU0C6XfXYuVQKAADM1VoC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", "text/plain": [ "Plot with title “'Vulnerability' histogram”" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# Histogram visualisation of Vulnerability z-scores\n", "title <- paste(\"'Vulnerability' histogram\", sep = \"\")\n", "x_label <- paste(\"'Vulnerability' z-score\", sep = \"\")\n", "y_label <- paste(\"Count\", sep = \"\")\n", "hist(vulnerability_scores$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": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 38
CCACPROCMUNCDISCSECearly_childhood_boyearly_childhood_girlage_middle_to_oldest_old_maleage_middle_to_oldest_old_femaledisability⋯social_networkhousing_characteristicsphysical_environmentsensitivitypreparerespondrecoveradaptive_capacityenhanced_exposuresocial_vulnerability
<int><int><int><int><int><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]>
11726 111-0.19258164-0.1467916 0.48180824-0.10021618 1.2753218⋯0.36130070-0.9481277-0.8560700 1.1377171-0.3378133 0.184577960.1073270-0.15329655-1.00827077-0.1413894
21726 1011-0.36188692-0.3284478 1.32081073 0.44848018-1.3865567⋯0.30303248 0.0000000-0.2328068-0.9881374-0.4065854-0.334774370.8522391-0.26183757 0.04893220-0.4444888
3172610011 0.01083739-1.4002199 0.23798535-0.52201032-1.3865567⋯0.45418041 0.0000000-0.3822322-1.5969460-0.4065854-0.221404121.0122262-0.17316713-0.05159845-0.6491854
4172610111-1.36078806-1.4002199-0.22155305 0.07955613-1.3865567⋯0.78526637 0.0000000-1.5116073-1.8689267-0.4065854 0.026930721.3626742 0.02106336-0.81142134-0.9789365
5172610211 0.10257030-0.4581527 0.05129390 0.59299501 0.3880289⋯0.41782180 1.2637707-0.3792937 0.4071804-0.3116725 0.443547900.7414772-0.09187444 0.80062025 0.4454417
6172610212 0.64987009 0.6302157-0.04065368 0.64469364 0.3880289⋯0.06300444 0.7211874-0.3696338 0.7600790-0.3203095 0.331172140.3141994-0.30936550 0.44207919 0.2948932
\n" ], "text/latex": [ "A data.frame: 6 × 38\n", "\\begin{tabular}{r|lllllllllllllllllllll}\n", " & CCA & CPRO & CMUN & CDIS & CSEC & early\\_childhood\\_boy & early\\_childhood\\_girl & age\\_middle\\_to\\_oldest\\_old\\_male & age\\_middle\\_to\\_oldest\\_old\\_female & disability & ⋯ & social\\_network & housing\\_characteristics & physical\\_environment & sensitivity & prepare & respond & recover & adaptive\\_capacity & enhanced\\_exposure & social\\_vulnerability\\\\\n", " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 17 & 26 & 1 & 1 & 1 & -0.19258164 & -0.1467916 & 0.48180824 & -0.10021618 & 1.2753218 & ⋯ & 0.36130070 & -0.9481277 & -0.8560700 & 1.1377171 & -0.3378133 & 0.18457796 & 0.1073270 & -0.15329655 & -1.00827077 & -0.1413894\\\\\n", "\t2 & 17 & 26 & 10 & 1 & 1 & -0.36188692 & -0.3284478 & 1.32081073 & 0.44848018 & -1.3865567 & ⋯ & 0.30303248 & 0.0000000 & -0.2328068 & -0.9881374 & -0.4065854 & -0.33477437 & 0.8522391 & -0.26183757 & 0.04893220 & -0.4444888\\\\\n", "\t3 & 17 & 26 & 100 & 1 & 1 & 0.01083739 & -1.4002199 & 0.23798535 & -0.52201032 & -1.3865567 & ⋯ & 0.45418041 & 0.0000000 & -0.3822322 & -1.5969460 & -0.4065854 & -0.22140412 & 1.0122262 & -0.17316713 & -0.05159845 & -0.6491854\\\\\n", "\t4 & 17 & 26 & 101 & 1 & 1 & -1.36078806 & -1.4002199 & -0.22155305 & 0.07955613 & -1.3865567 & ⋯ & 0.78526637 & 0.0000000 & -1.5116073 & -1.8689267 & -0.4065854 & 0.02693072 & 1.3626742 & 0.02106336 & -0.81142134 & -0.9789365\\\\\n", "\t5 & 17 & 26 & 102 & 1 & 1 & 0.10257030 & -0.4581527 & 0.05129390 & 0.59299501 & 0.3880289 & ⋯ & 0.41782180 & 1.2637707 & -0.3792937 & 0.4071804 & -0.3116725 & 0.44354790 & 0.7414772 & -0.09187444 & 0.80062025 & 0.4454417\\\\\n", "\t6 & 17 & 26 & 102 & 1 & 2 & 0.64987009 & 0.6302157 & -0.04065368 & 0.64469364 & 0.3880289 & ⋯ & 0.06300444 & 0.7211874 & -0.3696338 & 0.7600790 & -0.3203095 & 0.33117214 & 0.3141994 & -0.30936550 & 0.44207919 & 0.2948932\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 38\n", "\n", "| | CCA <int> | CPRO <int> | CMUN <int> | CDIS <int> | CSEC <int> | early_childhood_boy <dbl> | early_childhood_girl <dbl> | age_middle_to_oldest_old_male <dbl> | age_middle_to_oldest_old_female <dbl> | disability <dbl> | ⋯ ⋯ | social_network <dbl> | housing_characteristics <dbl> | physical_environment <dbl> | sensitivity <dbl[,1]> | prepare <dbl[,1]> | respond <dbl[,1]> | recover <dbl[,1]> | adaptive_capacity <dbl[,1]> | enhanced_exposure <dbl[,1]> | social_vulnerability <dbl[,1]> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 17 | 26 | 1 | 1 | 1 | -0.19258164 | -0.1467916 | 0.48180824 | -0.10021618 | 1.2753218 | ⋯ | 0.36130070 | -0.9481277 | -0.8560700 | 1.1377171 | -0.3378133 | 0.18457796 | 0.1073270 | -0.15329655 | -1.00827077 | -0.1413894 |\n", "| 2 | 17 | 26 | 10 | 1 | 1 | -0.36188692 | -0.3284478 | 1.32081073 | 0.44848018 | -1.3865567 | ⋯ | 0.30303248 | 0.0000000 | -0.2328068 | -0.9881374 | -0.4065854 | -0.33477437 | 0.8522391 | -0.26183757 | 0.04893220 | -0.4444888 |\n", "| 3 | 17 | 26 | 100 | 1 | 1 | 0.01083739 | -1.4002199 | 0.23798535 | -0.52201032 | -1.3865567 | ⋯ | 0.45418041 | 0.0000000 | -0.3822322 | -1.5969460 | -0.4065854 | -0.22140412 | 1.0122262 | -0.17316713 | -0.05159845 | -0.6491854 |\n", "| 4 | 17 | 26 | 101 | 1 | 1 | -1.36078806 | -1.4002199 | -0.22155305 | 0.07955613 | -1.3865567 | ⋯ | 0.78526637 | 0.0000000 | -1.5116073 | -1.8689267 | -0.4065854 | 0.02693072 | 1.3626742 | 0.02106336 | -0.81142134 | -0.9789365 |\n", "| 5 | 17 | 26 | 102 | 1 | 1 | 0.10257030 | -0.4581527 | 0.05129390 | 0.59299501 | 0.3880289 | ⋯ | 0.41782180 | 1.2637707 | -0.3792937 | 0.4071804 | -0.3116725 | 0.44354790 | 0.7414772 | -0.09187444 | 0.80062025 | 0.4454417 |\n", "| 6 | 17 | 26 | 102 | 1 | 2 | 0.64987009 | 0.6302157 | -0.04065368 | 0.64469364 | 0.3880289 | ⋯ | 0.06300444 | 0.7211874 | -0.3696338 | 0.7600790 | -0.3203095 | 0.33117214 | 0.3141994 | -0.30936550 | 0.44207919 | 0.2948932 |\n", "\n" ], "text/plain": [ " CCA CPRO CMUN CDIS CSEC early_childhood_boy early_childhood_girl\n", "1 17 26 1 1 1 -0.19258164 -0.1467916 \n", "2 17 26 10 1 1 -0.36188692 -0.3284478 \n", "3 17 26 100 1 1 0.01083739 -1.4002199 \n", "4 17 26 101 1 1 -1.36078806 -1.4002199 \n", "5 17 26 102 1 1 0.10257030 -0.4581527 \n", "6 17 26 102 1 2 0.64987009 0.6302157 \n", " age_middle_to_oldest_old_male age_middle_to_oldest_old_female disability ⋯\n", "1 0.48180824 -0.10021618 1.2753218 ⋯\n", "2 1.32081073 0.44848018 -1.3865567 ⋯\n", "3 0.23798535 -0.52201032 -1.3865567 ⋯\n", "4 -0.22155305 0.07955613 -1.3865567 ⋯\n", "5 0.05129390 0.59299501 0.3880289 ⋯\n", "6 -0.04065368 0.64469364 0.3880289 ⋯\n", " social_network housing_characteristics physical_environment sensitivity\n", "1 0.36130070 -0.9481277 -0.8560700 1.1377171 \n", "2 0.30303248 0.0000000 -0.2328068 -0.9881374 \n", "3 0.45418041 0.0000000 -0.3822322 -1.5969460 \n", "4 0.78526637 0.0000000 -1.5116073 -1.8689267 \n", "5 0.41782180 1.2637707 -0.3792937 0.4071804 \n", "6 0.06300444 0.7211874 -0.3696338 0.7600790 \n", " prepare respond recover adaptive_capacity enhanced_exposure\n", "1 -0.3378133 0.18457796 0.1073270 -0.15329655 -1.00827077 \n", "2 -0.4065854 -0.33477437 0.8522391 -0.26183757 0.04893220 \n", "3 -0.4065854 -0.22140412 1.0122262 -0.17316713 -0.05159845 \n", "4 -0.4065854 0.02693072 1.3626742 0.02106336 -0.81142134 \n", "5 -0.3116725 0.44354790 0.7414772 -0.09187444 0.80062025 \n", "6 -0.3203095 0.33117214 0.3141994 -0.30936550 0.44207919 \n", " social_vulnerability\n", "1 -0.1413894 \n", "2 -0.4444888 \n", "3 -0.6491854 \n", "4 -0.9789365 \n", "5 0.4454417 \n", "6 0.2948932 " ] }, "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": [ "\n", "\n", "\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A matrix: 33 × 33 of type dbl
early_childhood_boyearly_childhood_girlage_middle_to_oldest_old_maleage_middle_to_oldest_old_femaledisabilityone_parent_householdsdependantsunemploymentattending_universityno_higher_education⋯social_networkhousing_characteristicsphysical_environmentsensitivitypreparerespondrecoveradaptive_capacityenhanced_exposuresocial_vulnerability
early_childhood_boy 1.00000000 0.759540053-0.44979947-0.372631631 0.23018526 0.33038590 0.67057589 0.35614054 0.23694332-0.36085632⋯-0.548326585 0.1050579664 0.35289264 0.41094687 0.34499285 0.020408818-0.42216457 0.12155807 0.29297386 0.330183124
early_childhood_girl 0.75954005 1.000000000-0.46707295-0.365530974 0.26364051 0.33986362 0.69435577 0.36004951 0.29591717-0.39610178⋯-0.596400846 0.0911557556 0.38809959 0.43829722 0.35423522 0.003914884-0.47404722 0.10950479 0.30735942 0.339651432
age_middle_to_oldest_old_male-0.44979947-0.467072951 1.00000000 0.554816775-0.29036166-0.45598571-0.62497380-0.34357715-0.42706479 0.43854968⋯ 0.560920526-0.0976966424-0.36515398-0.11579601-0.32214094 0.065933061 0.44983077-0.09107455-0.29644999-0.216741127
age_middle_to_oldest_old_female-0.37263163-0.365530974 0.55481678 1.000000000-0.06347671-0.45546676-0.45061486-0.01977655-0.24756632 0.18899810⋯ 0.579507014 0.0464677389-0.01493317 0.12447682-0.16591374 0.204713854 0.38051051 0.08636588 0.01859601 0.094105996
disability 0.23018526 0.263640512-0.29036166-0.063476710 1.00000000-0.04926880 0.44524799 0.66041158 0.24108336-0.35998489⋯-0.104345540 0.1140625145 0.31311428 0.91574394 0.24743845 0.269148367-0.12279689 0.21968497 0.27264011 0.541205238
one_parent_households 0.33038590 0.339863620-0.45598571-0.455466763-0.04926880 1.00000000 0.44098288 0.08855721 0.41209063-0.20215971⋯-0.633596005 0.2369424946 0.42875198-0.09694488 0.50291888-0.072330693-0.24962760 0.25320804 0.42248985 0.283327135
dependants 0.67057589 0.694355771-0.62497380-0.450614861 0.44524799 0.44098288 1.00000000 0.56720146 0.55171806-0.66446988⋯-0.756633822 0.0829294936 0.51345917 0.45794746 0.38170650-0.050928458-0.66309316 0.06572621 0.38370731 0.357862941
unemployment 0.35614054 0.360049506-0.34357715-0.019776546 0.66041158 0.08855721 0.56720146 1.00000000 0.33617823-0.41956319⋯-0.201583893 0.2543291753 0.52983989 0.66236418 0.51846520 0.465082702-0.08601945 0.46813351 0.49867787 0.682300003
attending_university 0.23694332 0.295917170-0.42706479-0.247566321 0.24108336 0.41209063 0.55171806 0.33617823 1.00000000-0.80231522⋯-0.444542590-0.0998329821 0.45902384 0.18194667 0.08596680-0.311683928-0.61881770-0.11091324 0.23712302 0.110056131
no_higher_education-0.36085632-0.396101779 0.43854968 0.188998102-0.35998489-0.20215971-0.66446988-0.41956319-0.80231522 1.00000000⋯ 0.359087857 0.1816427966-0.46586422-0.34713213 0.08891632 0.439654954 0.80803001 0.26081266-0.19176042-0.069422773
foreign_nationals 0.42604982 0.453075887-0.33707442-0.086558180 0.47484750 0.13940285 0.55855872 0.70132633 0.16908401-0.17722844⋯-0.344220134 0.3930076266 0.47504867 0.52088805 0.76986598 0.706907540-0.06089854 0.67454294 0.54754029 0.761204213
rented 0.19893150 0.200527443-0.26872461-0.138932327 0.01728583 0.58359421 0.19886232 0.19663693 0.25030330-0.15646775⋯-0.249652474 0.2445553823 0.54975446 0.01348079 0.68961198 0.128269631-0.06156210 0.63113802 0.50565219 0.549539325
primary_school_age-0.65679028-0.690484814 0.55898237 0.504557789-0.28672716-0.40852694-0.84126926-0.35945994-0.38544019 0.50117885⋯ 0.839691152-0.0764614131-0.39566986-0.31645101-0.31384459 0.191164019 0.70772433 0.04573707-0.30332716-0.217873743
one_person_households-0.22156559-0.267393210 0.35142193 0.443686124 0.13659002-0.64327666-0.37761112 0.04895747-0.34213141 0.06656413⋯ 0.799861106-0.0008684821-0.25593992 0.18857068-0.31411959 0.180859976 0.40075833 0.02717408-0.16662814 0.003512541
year_built 0.10505797 0.091155756-0.09769664 0.046467739 0.11406251 0.23694249 0.08292949 0.25432918-0.09983298 0.18164280⋯-0.049408896 1.0000000000 0.26356151 0.13299007 0.49313913 0.486775885 0.27094304 0.51053624 0.77979729 0.650035629
tree_cover_density 0.38502113 0.414558106-0.31956017-0.025669031 0.29130747 0.29541420 0.53298471 0.42060960 0.34500674-0.38288149⋯-0.428694837 0.1803864548 0.87274526 0.35824276 0.36450898 0.087039689-0.34561834 0.19752132 0.67620326 0.520913738
impervious 0.20875349 0.239541111-0.30448896 0.001689509 0.24169082 0.44708573 0.33428776 0.49015701 0.44640218-0.41631699⋯-0.247471063 0.2761895237 0.83655012 0.24602585 0.55573889 0.131326510-0.20208813 0.48739139 0.71103367 0.645888772
age 0.51441045 0.508826242 0.35018899 0.448288787 0.07684377-0.13240437 0.15883005 0.19368368-0.07782264-0.07103765⋯-0.002360354 0.0795870159 0.19811287 0.47094367 0.11592014 0.161919237-0.03615852 0.12425339 0.17701967 0.300376066
health 0.23018526 0.263640512-0.29036166-0.063476710 1.00000000-0.04926880 0.44524799 0.66041158 0.24108336-0.35998489⋯-0.104345540 0.1140625145 0.31311428 0.91574394 0.24743845 0.269148367-0.12279689 0.21968497 0.27264011 0.541205238
income 0.53809505 0.570891186-0.62267021-0.389151337 0.44841072 0.63584339 0.86707123 0.68303775 0.77851407-0.71256967⋯-0.680471207 0.1555872409 0.65093009 0.41823692 0.49628699 0.012749347-0.54883841 0.22454098 0.51715347 0.484368003
info_access_use-0.36085632-0.396101779 0.43854968 0.188998102-0.35998489-0.20215971-0.66446988-0.41956319-0.80231522 1.00000000⋯ 0.359087857 0.1816427966-0.46586422-0.34713213 0.08891632 0.439654954 0.80803001 0.26081266-0.19176042-0.069422773
local_knowledge 0.42604982 0.453075887-0.33707442-0.086558180 0.47484750 0.13940285 0.55855872 0.70132633 0.16908401-0.17722844⋯-0.344220134 0.3930076266 0.47504867 0.52088805 0.76986598 0.706907540-0.06089854 0.67454294 0.54754029 0.761204213
tenure 0.19893150 0.200527443-0.26872461-0.138932327 0.01728583 0.58359421 0.19886232 0.19663693 0.25030330-0.15646775⋯-0.249652474 0.2445553823 0.54975446 0.01348079 0.68961198 0.128269631-0.06156210 0.63113802 0.50565219 0.549539325
social_network-0.54832658-0.596400846 0.56092053 0.579507014-0.10434554-0.63359601-0.75663382-0.20158389-0.44454259 0.35908786⋯ 1.000000000-0.0494088961-0.40120270-0.09327390-0.38253686 0.226944393 0.68461214 0.04498125-0.29044958-0.137323581
housing_characteristics 0.10505797 0.091155756-0.09769664 0.046467739 0.11406251 0.23694249 0.08292949 0.25432918-0.09983298 0.18164280⋯-0.049408896 1.0000000000 0.26356151 0.13299007 0.49313913 0.486775885 0.27094304 0.51053624 0.77979729 0.650035629
physical_environment 0.35289264 0.388099588-0.36515398-0.014933172 0.31311428 0.42875198 0.51345917 0.52983989 0.45902384-0.46586422⋯-0.401202696 0.2635615089 1.00000000 0.35686743 0.53128199 0.126104958-0.32487143 0.39044819 0.80942177 0.677580930
sensitivity 0.41094687 0.438297216-0.11579601 0.124476818 0.91574394-0.09694488 0.45794746 0.66236418 0.18194667-0.34713213⋯-0.093273903 0.1329900657 0.35686743 1.00000000 0.26563892 0.303382929-0.12321838 0.24444113 0.31255713 0.599884974
prepare 0.34499285 0.354235222-0.32214094-0.165913736 0.24743845 0.50291888 0.38170650 0.51846520 0.08596680 0.08891632⋯-0.382536861 0.4931391345 0.53128199 0.26563892 1.00000000 0.690091341 0.15896290 0.90579806 0.64498971 0.835927532
respond 0.02040882 0.003914884 0.06593306 0.204713854 0.26914837-0.07233069-0.05092846 0.46508270-0.31168393 0.43965495⋯ 0.226944393 0.4867758850 0.12610496 0.30338293 0.69009134 1.000000000 0.66294354 0.85021883 0.37816498 0.702116874
recover-0.42216457-0.474047225 0.44983077 0.380510515-0.12279689-0.24962760-0.66309316-0.08601945-0.61881770 0.80803001⋯ 0.684612142 0.2709430415-0.32487143-0.12321838 0.15896290 0.662943544 1.00000000 0.48583097-0.04589749 0.185101540
adaptive_capacity 0.12155807 0.109504790-0.09107455 0.086365882 0.21968497 0.25320804 0.06572621 0.46813351-0.11091324 0.26081266⋯ 0.044981247 0.5105362367 0.39044819 0.24444113 0.90579806 0.850218830 0.48583097 1.00000000 0.56418215 0.840851042
enhanced_exposure 0.29297386 0.307359416-0.29644999 0.018596006 0.27264011 0.42248985 0.38370731 0.49867787 0.23712302-0.19176042⋯-0.290449581 0.7797972928 0.80942177 0.31255713 0.64498971 0.378164977-0.04589749 0.56418215 1.00000000 0.835445536
social_vulnerability 0.33018312 0.339651432-0.21674113 0.094105996 0.54120524 0.28332714 0.35786294 0.68230000 0.11005613-0.06942277⋯-0.137323581 0.6500356288 0.67758093 0.59988497 0.83592753 0.702116874 0.18510154 0.84085104 0.83544554 1.000000000
\n" ], "text/latex": [ "A matrix: 33 × 33 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 & disability & one\\_parent\\_households & dependants & unemployment & attending\\_university & no\\_higher\\_education & ⋯ & social\\_network & housing\\_characteristics & physical\\_environment & sensitivity & prepare & respond & recover & adaptive\\_capacity & enhanced\\_exposure & social\\_vulnerability\\\\\n", "\\hline\n", "\tearly\\_childhood\\_boy & 1.00000000 & 0.759540053 & -0.44979947 & -0.372631631 & 0.23018526 & 0.33038590 & 0.67057589 & 0.35614054 & 0.23694332 & -0.36085632 & ⋯ & -0.548326585 & 0.1050579664 & 0.35289264 & 0.41094687 & 0.34499285 & 0.020408818 & -0.42216457 & 0.12155807 & 0.29297386 & 0.330183124\\\\\n", "\tearly\\_childhood\\_girl & 0.75954005 & 1.000000000 & -0.46707295 & -0.365530974 & 0.26364051 & 0.33986362 & 0.69435577 & 0.36004951 & 0.29591717 & -0.39610178 & ⋯ & -0.596400846 & 0.0911557556 & 0.38809959 & 0.43829722 & 0.35423522 & 0.003914884 & -0.47404722 & 0.10950479 & 0.30735942 & 0.339651432\\\\\n", "\tage\\_middle\\_to\\_oldest\\_old\\_male & -0.44979947 & -0.467072951 & 1.00000000 & 0.554816775 & -0.29036166 & -0.45598571 & -0.62497380 & -0.34357715 & -0.42706479 & 0.43854968 & ⋯ & 0.560920526 & -0.0976966424 & -0.36515398 & -0.11579601 & -0.32214094 & 0.065933061 & 0.44983077 & -0.09107455 & -0.29644999 & -0.216741127\\\\\n", "\tage\\_middle\\_to\\_oldest\\_old\\_female & -0.37263163 & -0.365530974 & 0.55481678 & 1.000000000 & -0.06347671 & -0.45546676 & -0.45061486 & -0.01977655 & -0.24756632 & 0.18899810 & ⋯ & 0.579507014 & 0.0464677389 & -0.01493317 & 0.12447682 & -0.16591374 & 0.204713854 & 0.38051051 & 0.08636588 & 0.01859601 & 0.094105996\\\\\n", "\tdisability & 0.23018526 & 0.263640512 & -0.29036166 & -0.063476710 & 1.00000000 & -0.04926880 & 0.44524799 & 0.66041158 & 0.24108336 & -0.35998489 & ⋯ & -0.104345540 & 0.1140625145 & 0.31311428 & 0.91574394 & 0.24743845 & 0.269148367 & -0.12279689 & 0.21968497 & 0.27264011 & 0.541205238\\\\\n", "\tone\\_parent\\_households & 0.33038590 & 0.339863620 & -0.45598571 & -0.455466763 & -0.04926880 & 1.00000000 & 0.44098288 & 0.08855721 & 0.41209063 & -0.20215971 & ⋯ & -0.633596005 & 0.2369424946 & 0.42875198 & -0.09694488 & 0.50291888 & -0.072330693 & -0.24962760 & 0.25320804 & 0.42248985 & 0.283327135\\\\\n", "\tdependants & 0.67057589 & 0.694355771 & -0.62497380 & -0.450614861 & 0.44524799 & 0.44098288 & 1.00000000 & 0.56720146 & 0.55171806 & -0.66446988 & ⋯ & -0.756633822 & 0.0829294936 & 0.51345917 & 0.45794746 & 0.38170650 & -0.050928458 & -0.66309316 & 0.06572621 & 0.38370731 & 0.357862941\\\\\n", "\tunemployment & 0.35614054 & 0.360049506 & -0.34357715 & -0.019776546 & 0.66041158 & 0.08855721 & 0.56720146 & 1.00000000 & 0.33617823 & -0.41956319 & ⋯ & -0.201583893 & 0.2543291753 & 0.52983989 & 0.66236418 & 0.51846520 & 0.465082702 & -0.08601945 & 0.46813351 & 0.49867787 & 0.682300003\\\\\n", "\tattending\\_university & 0.23694332 & 0.295917170 & -0.42706479 & -0.247566321 & 0.24108336 & 0.41209063 & 0.55171806 & 0.33617823 & 1.00000000 & -0.80231522 & ⋯ & -0.444542590 & -0.0998329821 & 0.45902384 & 0.18194667 & 0.08596680 & -0.311683928 & -0.61881770 & -0.11091324 & 0.23712302 & 0.110056131\\\\\n", "\tno\\_higher\\_education & -0.36085632 & -0.396101779 & 0.43854968 & 0.188998102 & -0.35998489 & -0.20215971 & -0.66446988 & -0.41956319 & -0.80231522 & 1.00000000 & ⋯ & 0.359087857 & 0.1816427966 & -0.46586422 & -0.34713213 & 0.08891632 & 0.439654954 & 0.80803001 & 0.26081266 & -0.19176042 & -0.069422773\\\\\n", "\tforeign\\_nationals & 0.42604982 & 0.453075887 & -0.33707442 & -0.086558180 & 0.47484750 & 0.13940285 & 0.55855872 & 0.70132633 & 0.16908401 & -0.17722844 & ⋯ & -0.344220134 & 0.3930076266 & 0.47504867 & 0.52088805 & 0.76986598 & 0.706907540 & -0.06089854 & 0.67454294 & 0.54754029 & 0.761204213\\\\\n", "\trented & 0.19893150 & 0.200527443 & -0.26872461 & -0.138932327 & 0.01728583 & 0.58359421 & 0.19886232 & 0.19663693 & 0.25030330 & -0.15646775 & ⋯ & -0.249652474 & 0.2445553823 & 0.54975446 & 0.01348079 & 0.68961198 & 0.128269631 & -0.06156210 & 0.63113802 & 0.50565219 & 0.549539325\\\\\n", "\tprimary\\_school\\_age & -0.65679028 & -0.690484814 & 0.55898237 & 0.504557789 & -0.28672716 & -0.40852694 & -0.84126926 & -0.35945994 & -0.38544019 & 0.50117885 & ⋯ & 0.839691152 & -0.0764614131 & -0.39566986 & -0.31645101 & -0.31384459 & 0.191164019 & 0.70772433 & 0.04573707 & -0.30332716 & -0.217873743\\\\\n", "\tone\\_person\\_households & -0.22156559 & -0.267393210 & 0.35142193 & 0.443686124 & 0.13659002 & -0.64327666 & -0.37761112 & 0.04895747 & -0.34213141 & 0.06656413 & ⋯ & 0.799861106 & -0.0008684821 & -0.25593992 & 0.18857068 & -0.31411959 & 0.180859976 & 0.40075833 & 0.02717408 & -0.16662814 & 0.003512541\\\\\n", "\tyear\\_built & 0.10505797 & 0.091155756 & -0.09769664 & 0.046467739 & 0.11406251 & 0.23694249 & 0.08292949 & 0.25432918 & -0.09983298 & 0.18164280 & ⋯ & -0.049408896 & 1.0000000000 & 0.26356151 & 0.13299007 & 0.49313913 & 0.486775885 & 0.27094304 & 0.51053624 & 0.77979729 & 0.650035629\\\\\n", "\ttree\\_cover\\_density & 0.38502113 & 0.414558106 & -0.31956017 & -0.025669031 & 0.29130747 & 0.29541420 & 0.53298471 & 0.42060960 & 0.34500674 & -0.38288149 & ⋯ & -0.428694837 & 0.1803864548 & 0.87274526 & 0.35824276 & 0.36450898 & 0.087039689 & -0.34561834 & 0.19752132 & 0.67620326 & 0.520913738\\\\\n", "\timpervious & 0.20875349 & 0.239541111 & -0.30448896 & 0.001689509 & 0.24169082 & 0.44708573 & 0.33428776 & 0.49015701 & 0.44640218 & -0.41631699 & ⋯ & -0.247471063 & 0.2761895237 & 0.83655012 & 0.24602585 & 0.55573889 & 0.131326510 & -0.20208813 & 0.48739139 & 0.71103367 & 0.645888772\\\\\n", "\tage & 0.51441045 & 0.508826242 & 0.35018899 & 0.448288787 & 0.07684377 & -0.13240437 & 0.15883005 & 0.19368368 & -0.07782264 & -0.07103765 & ⋯ & -0.002360354 & 0.0795870159 & 0.19811287 & 0.47094367 & 0.11592014 & 0.161919237 & -0.03615852 & 0.12425339 & 0.17701967 & 0.300376066\\\\\n", "\thealth & 0.23018526 & 0.263640512 & -0.29036166 & -0.063476710 & 1.00000000 & -0.04926880 & 0.44524799 & 0.66041158 & 0.24108336 & -0.35998489 & ⋯ & -0.104345540 & 0.1140625145 & 0.31311428 & 0.91574394 & 0.24743845 & 0.269148367 & -0.12279689 & 0.21968497 & 0.27264011 & 0.541205238\\\\\n", "\tincome & 0.53809505 & 0.570891186 & -0.62267021 & -0.389151337 & 0.44841072 & 0.63584339 & 0.86707123 & 0.68303775 & 0.77851407 & -0.71256967 & ⋯ & -0.680471207 & 0.1555872409 & 0.65093009 & 0.41823692 & 0.49628699 & 0.012749347 & -0.54883841 & 0.22454098 & 0.51715347 & 0.484368003\\\\\n", "\tinfo\\_access\\_use & -0.36085632 & -0.396101779 & 0.43854968 & 0.188998102 & -0.35998489 & -0.20215971 & -0.66446988 & -0.41956319 & -0.80231522 & 1.00000000 & ⋯ & 0.359087857 & 0.1816427966 & -0.46586422 & -0.34713213 & 0.08891632 & 0.439654954 & 0.80803001 & 0.26081266 & -0.19176042 & -0.069422773\\\\\n", "\tlocal\\_knowledge & 0.42604982 & 0.453075887 & -0.33707442 & -0.086558180 & 0.47484750 & 0.13940285 & 0.55855872 & 0.70132633 & 0.16908401 & -0.17722844 & ⋯ & -0.344220134 & 0.3930076266 & 0.47504867 & 0.52088805 & 0.76986598 & 0.706907540 & -0.06089854 & 0.67454294 & 0.54754029 & 0.761204213\\\\\n", "\ttenure & 0.19893150 & 0.200527443 & -0.26872461 & -0.138932327 & 0.01728583 & 0.58359421 & 0.19886232 & 0.19663693 & 0.25030330 & -0.15646775 & ⋯ & -0.249652474 & 0.2445553823 & 0.54975446 & 0.01348079 & 0.68961198 & 0.128269631 & -0.06156210 & 0.63113802 & 0.50565219 & 0.549539325\\\\\n", "\tsocial\\_network & -0.54832658 & -0.596400846 & 0.56092053 & 0.579507014 & -0.10434554 & -0.63359601 & -0.75663382 & -0.20158389 & -0.44454259 & 0.35908786 & ⋯ & 1.000000000 & -0.0494088961 & -0.40120270 & -0.09327390 & -0.38253686 & 0.226944393 & 0.68461214 & 0.04498125 & -0.29044958 & -0.137323581\\\\\n", "\thousing\\_characteristics & 0.10505797 & 0.091155756 & -0.09769664 & 0.046467739 & 0.11406251 & 0.23694249 & 0.08292949 & 0.25432918 & -0.09983298 & 0.18164280 & ⋯ & -0.049408896 & 1.0000000000 & 0.26356151 & 0.13299007 & 0.49313913 & 0.486775885 & 0.27094304 & 0.51053624 & 0.77979729 & 0.650035629\\\\\n", "\tphysical\\_environment & 0.35289264 & 0.388099588 & -0.36515398 & -0.014933172 & 0.31311428 & 0.42875198 & 0.51345917 & 0.52983989 & 0.45902384 & -0.46586422 & ⋯ & -0.401202696 & 0.2635615089 & 1.00000000 & 0.35686743 & 0.53128199 & 0.126104958 & -0.32487143 & 0.39044819 & 0.80942177 & 0.677580930\\\\\n", "\tsensitivity & 0.41094687 & 0.438297216 & -0.11579601 & 0.124476818 & 0.91574394 & -0.09694488 & 0.45794746 & 0.66236418 & 0.18194667 & -0.34713213 & ⋯ & -0.093273903 & 0.1329900657 & 0.35686743 & 1.00000000 & 0.26563892 & 0.303382929 & -0.12321838 & 0.24444113 & 0.31255713 & 0.599884974\\\\\n", "\tprepare & 0.34499285 & 0.354235222 & -0.32214094 & -0.165913736 & 0.24743845 & 0.50291888 & 0.38170650 & 0.51846520 & 0.08596680 & 0.08891632 & ⋯ & -0.382536861 & 0.4931391345 & 0.53128199 & 0.26563892 & 1.00000000 & 0.690091341 & 0.15896290 & 0.90579806 & 0.64498971 & 0.835927532\\\\\n", "\trespond & 0.02040882 & 0.003914884 & 0.06593306 & 0.204713854 & 0.26914837 & -0.07233069 & -0.05092846 & 0.46508270 & -0.31168393 & 0.43965495 & ⋯ & 0.226944393 & 0.4867758850 & 0.12610496 & 0.30338293 & 0.69009134 & 1.000000000 & 0.66294354 & 0.85021883 & 0.37816498 & 0.702116874\\\\\n", "\trecover & -0.42216457 & -0.474047225 & 0.44983077 & 0.380510515 & -0.12279689 & -0.24962760 & -0.66309316 & -0.08601945 & -0.61881770 & 0.80803001 & ⋯ & 0.684612142 & 0.2709430415 & -0.32487143 & -0.12321838 & 0.15896290 & 0.662943544 & 1.00000000 & 0.48583097 & -0.04589749 & 0.185101540\\\\\n", "\tadaptive\\_capacity & 0.12155807 & 0.109504790 & -0.09107455 & 0.086365882 & 0.21968497 & 0.25320804 & 0.06572621 & 0.46813351 & -0.11091324 & 0.26081266 & ⋯ & 0.044981247 & 0.5105362367 & 0.39044819 & 0.24444113 & 0.90579806 & 0.850218830 & 0.48583097 & 1.00000000 & 0.56418215 & 0.840851042\\\\\n", "\tenhanced\\_exposure & 0.29297386 & 0.307359416 & -0.29644999 & 0.018596006 & 0.27264011 & 0.42248985 & 0.38370731 & 0.49867787 & 0.23712302 & -0.19176042 & ⋯ & -0.290449581 & 0.7797972928 & 0.80942177 & 0.31255713 & 0.64498971 & 0.378164977 & -0.04589749 & 0.56418215 & 1.00000000 & 0.835445536\\\\\n", "\tsocial\\_vulnerability & 0.33018312 & 0.339651432 & -0.21674113 & 0.094105996 & 0.54120524 & 0.28332714 & 0.35786294 & 0.68230000 & 0.11005613 & -0.06942277 & ⋯ & -0.137323581 & 0.6500356288 & 0.67758093 & 0.59988497 & 0.83592753 & 0.702116874 & 0.18510154 & 0.84085104 & 0.83544554 & 1.000000000\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 33 × 33 of type dbl\n", "\n", "| | early_childhood_boy | early_childhood_girl | age_middle_to_oldest_old_male | age_middle_to_oldest_old_female | disability | one_parent_households | dependants | unemployment | attending_university | no_higher_education | ⋯ | social_network | housing_characteristics | physical_environment | sensitivity | prepare | respond | recover | adaptive_capacity | enhanced_exposure | social_vulnerability |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| early_childhood_boy | 1.00000000 | 0.759540053 | -0.44979947 | -0.372631631 | 0.23018526 | 0.33038590 | 0.67057589 | 0.35614054 | 0.23694332 | -0.36085632 | ⋯ | -0.548326585 | 0.1050579664 | 0.35289264 | 0.41094687 | 0.34499285 | 0.020408818 | -0.42216457 | 0.12155807 | 0.29297386 | 0.330183124 |\n", "| early_childhood_girl | 0.75954005 | 1.000000000 | -0.46707295 | -0.365530974 | 0.26364051 | 0.33986362 | 0.69435577 | 0.36004951 | 0.29591717 | -0.39610178 | ⋯ | -0.596400846 | 0.0911557556 | 0.38809959 | 0.43829722 | 0.35423522 | 0.003914884 | -0.47404722 | 0.10950479 | 0.30735942 | 0.339651432 |\n", "| age_middle_to_oldest_old_male | -0.44979947 | -0.467072951 | 1.00000000 | 0.554816775 | -0.29036166 | -0.45598571 | -0.62497380 | -0.34357715 | -0.42706479 | 0.43854968 | ⋯ | 0.560920526 | -0.0976966424 | -0.36515398 | -0.11579601 | -0.32214094 | 0.065933061 | 0.44983077 | -0.09107455 | -0.29644999 | -0.216741127 |\n", "| age_middle_to_oldest_old_female | -0.37263163 | -0.365530974 | 0.55481678 | 1.000000000 | -0.06347671 | -0.45546676 | -0.45061486 | -0.01977655 | -0.24756632 | 0.18899810 | ⋯ | 0.579507014 | 0.0464677389 | -0.01493317 | 0.12447682 | -0.16591374 | 0.204713854 | 0.38051051 | 0.08636588 | 0.01859601 | 0.094105996 |\n", "| disability | 0.23018526 | 0.263640512 | -0.29036166 | -0.063476710 | 1.00000000 | -0.04926880 | 0.44524799 | 0.66041158 | 0.24108336 | -0.35998489 | ⋯ | -0.104345540 | 0.1140625145 | 0.31311428 | 0.91574394 | 0.24743845 | 0.269148367 | -0.12279689 | 0.21968497 | 0.27264011 | 0.541205238 |\n", "| one_parent_households | 0.33038590 | 0.339863620 | -0.45598571 | -0.455466763 | -0.04926880 | 1.00000000 | 0.44098288 | 0.08855721 | 0.41209063 | -0.20215971 | ⋯ | -0.633596005 | 0.2369424946 | 0.42875198 | -0.09694488 | 0.50291888 | -0.072330693 | -0.24962760 | 0.25320804 | 0.42248985 | 0.283327135 |\n", "| dependants | 0.67057589 | 0.694355771 | -0.62497380 | -0.450614861 | 0.44524799 | 0.44098288 | 1.00000000 | 0.56720146 | 0.55171806 | -0.66446988 | ⋯ | -0.756633822 | 0.0829294936 | 0.51345917 | 0.45794746 | 0.38170650 | -0.050928458 | -0.66309316 | 0.06572621 | 0.38370731 | 0.357862941 |\n", "| unemployment | 0.35614054 | 0.360049506 | -0.34357715 | -0.019776546 | 0.66041158 | 0.08855721 | 0.56720146 | 1.00000000 | 0.33617823 | -0.41956319 | ⋯ | -0.201583893 | 0.2543291753 | 0.52983989 | 0.66236418 | 0.51846520 | 0.465082702 | -0.08601945 | 0.46813351 | 0.49867787 | 0.682300003 |\n", "| attending_university | 0.23694332 | 0.295917170 | -0.42706479 | -0.247566321 | 0.24108336 | 0.41209063 | 0.55171806 | 0.33617823 | 1.00000000 | -0.80231522 | ⋯ | -0.444542590 | -0.0998329821 | 0.45902384 | 0.18194667 | 0.08596680 | -0.311683928 | -0.61881770 | -0.11091324 | 0.23712302 | 0.110056131 |\n", "| no_higher_education | -0.36085632 | -0.396101779 | 0.43854968 | 0.188998102 | -0.35998489 | -0.20215971 | -0.66446988 | -0.41956319 | -0.80231522 | 1.00000000 | ⋯ | 0.359087857 | 0.1816427966 | -0.46586422 | -0.34713213 | 0.08891632 | 0.439654954 | 0.80803001 | 0.26081266 | -0.19176042 | -0.069422773 |\n", "| foreign_nationals | 0.42604982 | 0.453075887 | -0.33707442 | -0.086558180 | 0.47484750 | 0.13940285 | 0.55855872 | 0.70132633 | 0.16908401 | -0.17722844 | ⋯ | -0.344220134 | 0.3930076266 | 0.47504867 | 0.52088805 | 0.76986598 | 0.706907540 | -0.06089854 | 0.67454294 | 0.54754029 | 0.761204213 |\n", "| rented | 0.19893150 | 0.200527443 | -0.26872461 | -0.138932327 | 0.01728583 | 0.58359421 | 0.19886232 | 0.19663693 | 0.25030330 | -0.15646775 | ⋯ | -0.249652474 | 0.2445553823 | 0.54975446 | 0.01348079 | 0.68961198 | 0.128269631 | -0.06156210 | 0.63113802 | 0.50565219 | 0.549539325 |\n", "| primary_school_age | -0.65679028 | -0.690484814 | 0.55898237 | 0.504557789 | -0.28672716 | -0.40852694 | -0.84126926 | -0.35945994 | -0.38544019 | 0.50117885 | ⋯ | 0.839691152 | -0.0764614131 | -0.39566986 | -0.31645101 | -0.31384459 | 0.191164019 | 0.70772433 | 0.04573707 | -0.30332716 | -0.217873743 |\n", "| one_person_households | -0.22156559 | -0.267393210 | 0.35142193 | 0.443686124 | 0.13659002 | -0.64327666 | -0.37761112 | 0.04895747 | -0.34213141 | 0.06656413 | ⋯ | 0.799861106 | -0.0008684821 | -0.25593992 | 0.18857068 | -0.31411959 | 0.180859976 | 0.40075833 | 0.02717408 | -0.16662814 | 0.003512541 |\n", "| year_built | 0.10505797 | 0.091155756 | -0.09769664 | 0.046467739 | 0.11406251 | 0.23694249 | 0.08292949 | 0.25432918 | -0.09983298 | 0.18164280 | ⋯ | -0.049408896 | 1.0000000000 | 0.26356151 | 0.13299007 | 0.49313913 | 0.486775885 | 0.27094304 | 0.51053624 | 0.77979729 | 0.650035629 |\n", "| tree_cover_density | 0.38502113 | 0.414558106 | -0.31956017 | -0.025669031 | 0.29130747 | 0.29541420 | 0.53298471 | 0.42060960 | 0.34500674 | -0.38288149 | ⋯ | -0.428694837 | 0.1803864548 | 0.87274526 | 0.35824276 | 0.36450898 | 0.087039689 | -0.34561834 | 0.19752132 | 0.67620326 | 0.520913738 |\n", "| impervious | 0.20875349 | 0.239541111 | -0.30448896 | 0.001689509 | 0.24169082 | 0.44708573 | 0.33428776 | 0.49015701 | 0.44640218 | -0.41631699 | ⋯ | -0.247471063 | 0.2761895237 | 0.83655012 | 0.24602585 | 0.55573889 | 0.131326510 | -0.20208813 | 0.48739139 | 0.71103367 | 0.645888772 |\n", "| age | 0.51441045 | 0.508826242 | 0.35018899 | 0.448288787 | 0.07684377 | -0.13240437 | 0.15883005 | 0.19368368 | -0.07782264 | -0.07103765 | ⋯ | -0.002360354 | 0.0795870159 | 0.19811287 | 0.47094367 | 0.11592014 | 0.161919237 | -0.03615852 | 0.12425339 | 0.17701967 | 0.300376066 |\n", "| health | 0.23018526 | 0.263640512 | -0.29036166 | -0.063476710 | 1.00000000 | -0.04926880 | 0.44524799 | 0.66041158 | 0.24108336 | -0.35998489 | ⋯ | -0.104345540 | 0.1140625145 | 0.31311428 | 0.91574394 | 0.24743845 | 0.269148367 | -0.12279689 | 0.21968497 | 0.27264011 | 0.541205238 |\n", "| income | 0.53809505 | 0.570891186 | -0.62267021 | -0.389151337 | 0.44841072 | 0.63584339 | 0.86707123 | 0.68303775 | 0.77851407 | -0.71256967 | ⋯ | -0.680471207 | 0.1555872409 | 0.65093009 | 0.41823692 | 0.49628699 | 0.012749347 | -0.54883841 | 0.22454098 | 0.51715347 | 0.484368003 |\n", "| info_access_use | -0.36085632 | -0.396101779 | 0.43854968 | 0.188998102 | -0.35998489 | -0.20215971 | -0.66446988 | -0.41956319 | -0.80231522 | 1.00000000 | ⋯ | 0.359087857 | 0.1816427966 | -0.46586422 | -0.34713213 | 0.08891632 | 0.439654954 | 0.80803001 | 0.26081266 | -0.19176042 | -0.069422773 |\n", "| local_knowledge | 0.42604982 | 0.453075887 | -0.33707442 | -0.086558180 | 0.47484750 | 0.13940285 | 0.55855872 | 0.70132633 | 0.16908401 | -0.17722844 | ⋯ | -0.344220134 | 0.3930076266 | 0.47504867 | 0.52088805 | 0.76986598 | 0.706907540 | -0.06089854 | 0.67454294 | 0.54754029 | 0.761204213 |\n", "| tenure | 0.19893150 | 0.200527443 | -0.26872461 | -0.138932327 | 0.01728583 | 0.58359421 | 0.19886232 | 0.19663693 | 0.25030330 | -0.15646775 | ⋯ | -0.249652474 | 0.2445553823 | 0.54975446 | 0.01348079 | 0.68961198 | 0.128269631 | -0.06156210 | 0.63113802 | 0.50565219 | 0.549539325 |\n", "| social_network | -0.54832658 | -0.596400846 | 0.56092053 | 0.579507014 | -0.10434554 | -0.63359601 | -0.75663382 | -0.20158389 | -0.44454259 | 0.35908786 | ⋯ | 1.000000000 | -0.0494088961 | -0.40120270 | -0.09327390 | -0.38253686 | 0.226944393 | 0.68461214 | 0.04498125 | -0.29044958 | -0.137323581 |\n", "| housing_characteristics | 0.10505797 | 0.091155756 | -0.09769664 | 0.046467739 | 0.11406251 | 0.23694249 | 0.08292949 | 0.25432918 | -0.09983298 | 0.18164280 | ⋯ | -0.049408896 | 1.0000000000 | 0.26356151 | 0.13299007 | 0.49313913 | 0.486775885 | 0.27094304 | 0.51053624 | 0.77979729 | 0.650035629 |\n", "| physical_environment | 0.35289264 | 0.388099588 | -0.36515398 | -0.014933172 | 0.31311428 | 0.42875198 | 0.51345917 | 0.52983989 | 0.45902384 | -0.46586422 | ⋯ | -0.401202696 | 0.2635615089 | 1.00000000 | 0.35686743 | 0.53128199 | 0.126104958 | -0.32487143 | 0.39044819 | 0.80942177 | 0.677580930 |\n", "| sensitivity | 0.41094687 | 0.438297216 | -0.11579601 | 0.124476818 | 0.91574394 | -0.09694488 | 0.45794746 | 0.66236418 | 0.18194667 | -0.34713213 | ⋯ | -0.093273903 | 0.1329900657 | 0.35686743 | 1.00000000 | 0.26563892 | 0.303382929 | -0.12321838 | 0.24444113 | 0.31255713 | 0.599884974 |\n", "| prepare | 0.34499285 | 0.354235222 | -0.32214094 | -0.165913736 | 0.24743845 | 0.50291888 | 0.38170650 | 0.51846520 | 0.08596680 | 0.08891632 | ⋯ | -0.382536861 | 0.4931391345 | 0.53128199 | 0.26563892 | 1.00000000 | 0.690091341 | 0.15896290 | 0.90579806 | 0.64498971 | 0.835927532 |\n", "| respond | 0.02040882 | 0.003914884 | 0.06593306 | 0.204713854 | 0.26914837 | -0.07233069 | -0.05092846 | 0.46508270 | -0.31168393 | 0.43965495 | ⋯ | 0.226944393 | 0.4867758850 | 0.12610496 | 0.30338293 | 0.69009134 | 1.000000000 | 0.66294354 | 0.85021883 | 0.37816498 | 0.702116874 |\n", "| recover | -0.42216457 | -0.474047225 | 0.44983077 | 0.380510515 | -0.12279689 | -0.24962760 | -0.66309316 | -0.08601945 | -0.61881770 | 0.80803001 | ⋯ | 0.684612142 | 0.2709430415 | -0.32487143 | -0.12321838 | 0.15896290 | 0.662943544 | 1.00000000 | 0.48583097 | -0.04589749 | 0.185101540 |\n", "| adaptive_capacity | 0.12155807 | 0.109504790 | -0.09107455 | 0.086365882 | 0.21968497 | 0.25320804 | 0.06572621 | 0.46813351 | -0.11091324 | 0.26081266 | ⋯ | 0.044981247 | 0.5105362367 | 0.39044819 | 0.24444113 | 0.90579806 | 0.850218830 | 0.48583097 | 1.00000000 | 0.56418215 | 0.840851042 |\n", "| enhanced_exposure | 0.29297386 | 0.307359416 | -0.29644999 | 0.018596006 | 0.27264011 | 0.42248985 | 0.38370731 | 0.49867787 | 0.23712302 | -0.19176042 | ⋯ | -0.290449581 | 0.7797972928 | 0.80942177 | 0.31255713 | 0.64498971 | 0.378164977 | -0.04589749 | 0.56418215 | 1.00000000 | 0.835445536 |\n", "| social_vulnerability | 0.33018312 | 0.339651432 | -0.21674113 | 0.094105996 | 0.54120524 | 0.28332714 | 0.35786294 | 0.68230000 | 0.11005613 | -0.06942277 | ⋯ | -0.137323581 | 0.6500356288 | 0.67758093 | 0.59988497 | 0.83592753 | 0.702116874 | 0.18510154 | 0.84085104 | 0.83544554 | 1.000000000 |\n", "\n" ], "text/plain": [ " early_childhood_boy early_childhood_girl\n", "early_childhood_boy 1.00000000 0.759540053 \n", "early_childhood_girl 0.75954005 1.000000000 \n", "age_middle_to_oldest_old_male -0.44979947 -0.467072951 \n", "age_middle_to_oldest_old_female -0.37263163 -0.365530974 \n", "disability 0.23018526 0.263640512 \n", "one_parent_households 0.33038590 0.339863620 \n", "dependants 0.67057589 0.694355771 \n", "unemployment 0.35614054 0.360049506 \n", "attending_university 0.23694332 0.295917170 \n", "no_higher_education -0.36085632 -0.396101779 \n", "foreign_nationals 0.42604982 0.453075887 \n", "rented 0.19893150 0.200527443 \n", "primary_school_age -0.65679028 -0.690484814 \n", "one_person_households -0.22156559 -0.267393210 \n", "year_built 0.10505797 0.091155756 \n", "tree_cover_density 0.38502113 0.414558106 \n", "impervious 0.20875349 0.239541111 \n", "age 0.51441045 0.508826242 \n", "health 0.23018526 0.263640512 \n", "income 0.53809505 0.570891186 \n", "info_access_use -0.36085632 -0.396101779 \n", "local_knowledge 0.42604982 0.453075887 \n", "tenure 0.19893150 0.200527443 \n", "social_network -0.54832658 -0.596400846 \n", "housing_characteristics 0.10505797 0.091155756 \n", "physical_environment 0.35289264 0.388099588 \n", "sensitivity 0.41094687 0.438297216 \n", "prepare 0.34499285 0.354235222 \n", "respond 0.02040882 0.003914884 \n", "recover -0.42216457 -0.474047225 \n", "adaptive_capacity 0.12155807 0.109504790 \n", "enhanced_exposure 0.29297386 0.307359416 \n", "social_vulnerability 0.33018312 0.339651432 \n", " age_middle_to_oldest_old_male\n", "early_childhood_boy -0.44979947 \n", "early_childhood_girl -0.46707295 \n", "age_middle_to_oldest_old_male 1.00000000 \n", "age_middle_to_oldest_old_female 0.55481678 \n", "disability -0.29036166 \n", "one_parent_households -0.45598571 \n", "dependants -0.62497380 \n", "unemployment -0.34357715 \n", "attending_university -0.42706479 \n", "no_higher_education 0.43854968 \n", "foreign_nationals -0.33707442 \n", "rented -0.26872461 \n", "primary_school_age 0.55898237 \n", "one_person_households 0.35142193 \n", "year_built -0.09769664 \n", "tree_cover_density -0.31956017 \n", "impervious -0.30448896 \n", "age 0.35018899 \n", "health -0.29036166 \n", "income -0.62267021 \n", "info_access_use 0.43854968 \n", "local_knowledge -0.33707442 \n", "tenure -0.26872461 \n", "social_network 0.56092053 \n", "housing_characteristics -0.09769664 \n", "physical_environment -0.36515398 \n", "sensitivity -0.11579601 \n", "prepare -0.32214094 \n", "respond 0.06593306 \n", "recover 0.44983077 \n", "adaptive_capacity -0.09107455 \n", "enhanced_exposure -0.29644999 \n", "social_vulnerability -0.21674113 \n", " age_middle_to_oldest_old_female disability \n", "early_childhood_boy -0.372631631 0.23018526\n", "early_childhood_girl -0.365530974 0.26364051\n", "age_middle_to_oldest_old_male 0.554816775 -0.29036166\n", "age_middle_to_oldest_old_female 1.000000000 -0.06347671\n", "disability -0.063476710 1.00000000\n", "one_parent_households -0.455466763 -0.04926880\n", "dependants -0.450614861 0.44524799\n", "unemployment -0.019776546 0.66041158\n", "attending_university -0.247566321 0.24108336\n", "no_higher_education 0.188998102 -0.35998489\n", "foreign_nationals -0.086558180 0.47484750\n", "rented -0.138932327 0.01728583\n", "primary_school_age 0.504557789 -0.28672716\n", "one_person_households 0.443686124 0.13659002\n", "year_built 0.046467739 0.11406251\n", "tree_cover_density -0.025669031 0.29130747\n", "impervious 0.001689509 0.24169082\n", "age 0.448288787 0.07684377\n", "health -0.063476710 1.00000000\n", "income -0.389151337 0.44841072\n", "info_access_use 0.188998102 -0.35998489\n", "local_knowledge -0.086558180 0.47484750\n", "tenure -0.138932327 0.01728583\n", "social_network 0.579507014 -0.10434554\n", "housing_characteristics 0.046467739 0.11406251\n", "physical_environment -0.014933172 0.31311428\n", "sensitivity 0.124476818 0.91574394\n", "prepare -0.165913736 0.24743845\n", "respond 0.204713854 0.26914837\n", "recover 0.380510515 -0.12279689\n", "adaptive_capacity 0.086365882 0.21968497\n", "enhanced_exposure 0.018596006 0.27264011\n", "social_vulnerability 0.094105996 0.54120524\n", " one_parent_households dependants unemployment\n", "early_childhood_boy 0.33038590 0.67057589 0.35614054 \n", "early_childhood_girl 0.33986362 0.69435577 0.36004951 \n", "age_middle_to_oldest_old_male -0.45598571 -0.62497380 -0.34357715 \n", "age_middle_to_oldest_old_female -0.45546676 -0.45061486 -0.01977655 \n", "disability -0.04926880 0.44524799 0.66041158 \n", "one_parent_households 1.00000000 0.44098288 0.08855721 \n", "dependants 0.44098288 1.00000000 0.56720146 \n", "unemployment 0.08855721 0.56720146 1.00000000 \n", "attending_university 0.41209063 0.55171806 0.33617823 \n", "no_higher_education -0.20215971 -0.66446988 -0.41956319 \n", "foreign_nationals 0.13940285 0.55855872 0.70132633 \n", "rented 0.58359421 0.19886232 0.19663693 \n", "primary_school_age -0.40852694 -0.84126926 -0.35945994 \n", "one_person_households -0.64327666 -0.37761112 0.04895747 \n", "year_built 0.23694249 0.08292949 0.25432918 \n", "tree_cover_density 0.29541420 0.53298471 0.42060960 \n", "impervious 0.44708573 0.33428776 0.49015701 \n", "age -0.13240437 0.15883005 0.19368368 \n", "health -0.04926880 0.44524799 0.66041158 \n", "income 0.63584339 0.86707123 0.68303775 \n", "info_access_use -0.20215971 -0.66446988 -0.41956319 \n", "local_knowledge 0.13940285 0.55855872 0.70132633 \n", "tenure 0.58359421 0.19886232 0.19663693 \n", "social_network -0.63359601 -0.75663382 -0.20158389 \n", "housing_characteristics 0.23694249 0.08292949 0.25432918 \n", "physical_environment 0.42875198 0.51345917 0.52983989 \n", "sensitivity -0.09694488 0.45794746 0.66236418 \n", "prepare 0.50291888 0.38170650 0.51846520 \n", "respond -0.07233069 -0.05092846 0.46508270 \n", "recover -0.24962760 -0.66309316 -0.08601945 \n", "adaptive_capacity 0.25320804 0.06572621 0.46813351 \n", "enhanced_exposure 0.42248985 0.38370731 0.49867787 \n", "social_vulnerability 0.28332714 0.35786294 0.68230000 \n", " attending_university no_higher_education ⋯\n", "early_childhood_boy 0.23694332 -0.36085632 ⋯\n", "early_childhood_girl 0.29591717 -0.39610178 ⋯\n", "age_middle_to_oldest_old_male -0.42706479 0.43854968 ⋯\n", "age_middle_to_oldest_old_female -0.24756632 0.18899810 ⋯\n", "disability 0.24108336 -0.35998489 ⋯\n", "one_parent_households 0.41209063 -0.20215971 ⋯\n", "dependants 0.55171806 -0.66446988 ⋯\n", "unemployment 0.33617823 -0.41956319 ⋯\n", "attending_university 1.00000000 -0.80231522 ⋯\n", "no_higher_education -0.80231522 1.00000000 ⋯\n", "foreign_nationals 0.16908401 -0.17722844 ⋯\n", "rented 0.25030330 -0.15646775 ⋯\n", "primary_school_age -0.38544019 0.50117885 ⋯\n", "one_person_households -0.34213141 0.06656413 ⋯\n", "year_built -0.09983298 0.18164280 ⋯\n", "tree_cover_density 0.34500674 -0.38288149 ⋯\n", "impervious 0.44640218 -0.41631699 ⋯\n", "age -0.07782264 -0.07103765 ⋯\n", "health 0.24108336 -0.35998489 ⋯\n", "income 0.77851407 -0.71256967 ⋯\n", "info_access_use -0.80231522 1.00000000 ⋯\n", "local_knowledge 0.16908401 -0.17722844 ⋯\n", "tenure 0.25030330 -0.15646775 ⋯\n", "social_network -0.44454259 0.35908786 ⋯\n", "housing_characteristics -0.09983298 0.18164280 ⋯\n", "physical_environment 0.45902384 -0.46586422 ⋯\n", "sensitivity 0.18194667 -0.34713213 ⋯\n", "prepare 0.08596680 0.08891632 ⋯\n", "respond -0.31168393 0.43965495 ⋯\n", "recover -0.61881770 0.80803001 ⋯\n", "adaptive_capacity -0.11091324 0.26081266 ⋯\n", "enhanced_exposure 0.23712302 -0.19176042 ⋯\n", "social_vulnerability 0.11005613 -0.06942277 ⋯\n", " social_network housing_characteristics\n", "early_childhood_boy -0.548326585 0.1050579664 \n", "early_childhood_girl -0.596400846 0.0911557556 \n", "age_middle_to_oldest_old_male 0.560920526 -0.0976966424 \n", "age_middle_to_oldest_old_female 0.579507014 0.0464677389 \n", "disability -0.104345540 0.1140625145 \n", "one_parent_households -0.633596005 0.2369424946 \n", "dependants -0.756633822 0.0829294936 \n", "unemployment -0.201583893 0.2543291753 \n", "attending_university -0.444542590 -0.0998329821 \n", "no_higher_education 0.359087857 0.1816427966 \n", "foreign_nationals -0.344220134 0.3930076266 \n", "rented -0.249652474 0.2445553823 \n", "primary_school_age 0.839691152 -0.0764614131 \n", "one_person_households 0.799861106 -0.0008684821 \n", "year_built -0.049408896 1.0000000000 \n", "tree_cover_density -0.428694837 0.1803864548 \n", "impervious -0.247471063 0.2761895237 \n", "age -0.002360354 0.0795870159 \n", "health -0.104345540 0.1140625145 \n", "income -0.680471207 0.1555872409 \n", "info_access_use 0.359087857 0.1816427966 \n", "local_knowledge -0.344220134 0.3930076266 \n", "tenure -0.249652474 0.2445553823 \n", "social_network 1.000000000 -0.0494088961 \n", "housing_characteristics -0.049408896 1.0000000000 \n", "physical_environment -0.401202696 0.2635615089 \n", "sensitivity -0.093273903 0.1329900657 \n", "prepare -0.382536861 0.4931391345 \n", "respond 0.226944393 0.4867758850 \n", "recover 0.684612142 0.2709430415 \n", "adaptive_capacity 0.044981247 0.5105362367 \n", "enhanced_exposure -0.290449581 0.7797972928 \n", "social_vulnerability -0.137323581 0.6500356288 \n", " physical_environment sensitivity prepare \n", "early_childhood_boy 0.35289264 0.41094687 0.34499285\n", "early_childhood_girl 0.38809959 0.43829722 0.35423522\n", "age_middle_to_oldest_old_male -0.36515398 -0.11579601 -0.32214094\n", "age_middle_to_oldest_old_female -0.01493317 0.12447682 -0.16591374\n", "disability 0.31311428 0.91574394 0.24743845\n", "one_parent_households 0.42875198 -0.09694488 0.50291888\n", "dependants 0.51345917 0.45794746 0.38170650\n", "unemployment 0.52983989 0.66236418 0.51846520\n", "attending_university 0.45902384 0.18194667 0.08596680\n", "no_higher_education -0.46586422 -0.34713213 0.08891632\n", "foreign_nationals 0.47504867 0.52088805 0.76986598\n", "rented 0.54975446 0.01348079 0.68961198\n", "primary_school_age -0.39566986 -0.31645101 -0.31384459\n", "one_person_households -0.25593992 0.18857068 -0.31411959\n", "year_built 0.26356151 0.13299007 0.49313913\n", "tree_cover_density 0.87274526 0.35824276 0.36450898\n", "impervious 0.83655012 0.24602585 0.55573889\n", "age 0.19811287 0.47094367 0.11592014\n", "health 0.31311428 0.91574394 0.24743845\n", "income 0.65093009 0.41823692 0.49628699\n", "info_access_use -0.46586422 -0.34713213 0.08891632\n", "local_knowledge 0.47504867 0.52088805 0.76986598\n", "tenure 0.54975446 0.01348079 0.68961198\n", "social_network -0.40120270 -0.09327390 -0.38253686\n", "housing_characteristics 0.26356151 0.13299007 0.49313913\n", "physical_environment 1.00000000 0.35686743 0.53128199\n", "sensitivity 0.35686743 1.00000000 0.26563892\n", "prepare 0.53128199 0.26563892 1.00000000\n", "respond 0.12610496 0.30338293 0.69009134\n", "recover -0.32487143 -0.12321838 0.15896290\n", "adaptive_capacity 0.39044819 0.24444113 0.90579806\n", "enhanced_exposure 0.80942177 0.31255713 0.64498971\n", "social_vulnerability 0.67758093 0.59988497 0.83592753\n", " respond recover adaptive_capacity\n", "early_childhood_boy 0.020408818 -0.42216457 0.12155807 \n", "early_childhood_girl 0.003914884 -0.47404722 0.10950479 \n", "age_middle_to_oldest_old_male 0.065933061 0.44983077 -0.09107455 \n", "age_middle_to_oldest_old_female 0.204713854 0.38051051 0.08636588 \n", "disability 0.269148367 -0.12279689 0.21968497 \n", "one_parent_households -0.072330693 -0.24962760 0.25320804 \n", "dependants -0.050928458 -0.66309316 0.06572621 \n", "unemployment 0.465082702 -0.08601945 0.46813351 \n", "attending_university -0.311683928 -0.61881770 -0.11091324 \n", "no_higher_education 0.439654954 0.80803001 0.26081266 \n", "foreign_nationals 0.706907540 -0.06089854 0.67454294 \n", "rented 0.128269631 -0.06156210 0.63113802 \n", "primary_school_age 0.191164019 0.70772433 0.04573707 \n", "one_person_households 0.180859976 0.40075833 0.02717408 \n", "year_built 0.486775885 0.27094304 0.51053624 \n", "tree_cover_density 0.087039689 -0.34561834 0.19752132 \n", "impervious 0.131326510 -0.20208813 0.48739139 \n", "age 0.161919237 -0.03615852 0.12425339 \n", "health 0.269148367 -0.12279689 0.21968497 \n", "income 0.012749347 -0.54883841 0.22454098 \n", "info_access_use 0.439654954 0.80803001 0.26081266 \n", "local_knowledge 0.706907540 -0.06089854 0.67454294 \n", "tenure 0.128269631 -0.06156210 0.63113802 \n", "social_network 0.226944393 0.68461214 0.04498125 \n", "housing_characteristics 0.486775885 0.27094304 0.51053624 \n", "physical_environment 0.126104958 -0.32487143 0.39044819 \n", "sensitivity 0.303382929 -0.12321838 0.24444113 \n", "prepare 0.690091341 0.15896290 0.90579806 \n", "respond 1.000000000 0.66294354 0.85021883 \n", "recover 0.662943544 1.00000000 0.48583097 \n", "adaptive_capacity 0.850218830 0.48583097 1.00000000 \n", "enhanced_exposure 0.378164977 -0.04589749 0.56418215 \n", "social_vulnerability 0.702116874 0.18510154 0.84085104 \n", " enhanced_exposure social_vulnerability\n", "early_childhood_boy 0.29297386 0.330183124 \n", "early_childhood_girl 0.30735942 0.339651432 \n", "age_middle_to_oldest_old_male -0.29644999 -0.216741127 \n", "age_middle_to_oldest_old_female 0.01859601 0.094105996 \n", "disability 0.27264011 0.541205238 \n", "one_parent_households 0.42248985 0.283327135 \n", "dependants 0.38370731 0.357862941 \n", "unemployment 0.49867787 0.682300003 \n", "attending_university 0.23712302 0.110056131 \n", "no_higher_education -0.19176042 -0.069422773 \n", "foreign_nationals 0.54754029 0.761204213 \n", "rented 0.50565219 0.549539325 \n", "primary_school_age -0.30332716 -0.217873743 \n", "one_person_households -0.16662814 0.003512541 \n", "year_built 0.77979729 0.650035629 \n", "tree_cover_density 0.67620326 0.520913738 \n", "impervious 0.71103367 0.645888772 \n", "age 0.17701967 0.300376066 \n", "health 0.27264011 0.541205238 \n", "income 0.51715347 0.484368003 \n", "info_access_use -0.19176042 -0.069422773 \n", "local_knowledge 0.54754029 0.761204213 \n", "tenure 0.50565219 0.549539325 \n", "social_network -0.29044958 -0.137323581 \n", "housing_characteristics 0.77979729 0.650035629 \n", "physical_environment 0.80942177 0.677580930 \n", "sensitivity 0.31255713 0.599884974 \n", "prepare 0.64498971 0.835927532 \n", "respond 0.37816498 0.702116874 \n", "recover -0.04589749 0.185101540 \n", "adaptive_capacity 0.56418215 0.840851042 \n", "enhanced_exposure 1.00000000 0.835445536 \n", "social_vulnerability 0.83544554 1.000000000 " ] }, "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": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 39
CCACPROCMUNCDISCSECearly_childhood_boyearly_childhood_girlage_middle_to_oldest_old_maleage_middle_to_oldest_old_femaledisability⋯housing_characteristicsphysical_environmentsensitivitypreparerespondrecoveradaptive_capacityenhanced_exposuresocial_vulnerabilitygeometry
<int><int><int><int><int><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]><MULTIPOLYGON [m]>
11726 111-0.19258164-0.1467916 0.48180824-0.10021618 1.2753218⋯-0.9481277-0.8560700 1.1377171-0.3378133 0.184577960.1073270-0.15329655-1.00827077-0.1413894MULTIPOLYGON (((526123.5 47...
21726 1011-0.36188692-0.3284478 1.32081073 0.44848018-1.3865567⋯ 0.0000000-0.2328068-0.9881374-0.4065854-0.334774370.8522391-0.26183757 0.04893220-0.4444888MULTIPOLYGON (((528744.7 46...
3172610011 0.01083739-1.4002199 0.23798535-0.52201032-1.3865567⋯ 0.0000000-0.3822322-1.5969460-0.4065854-0.221404121.0122262-0.17316713-0.05159845-0.6491854MULTIPOLYGON (((575382.6 46...
4172610111-1.36078806-1.4002199-0.22155305 0.07955613-1.3865567⋯ 0.0000000-1.5116073-1.8689267-0.4065854 0.026930721.3626742 0.02106336-0.81142134-0.9789365MULTIPOLYGON (((540334.4 46...
5172610211 0.10257030-0.4581527 0.05129390 0.59299501 0.3880289⋯ 1.2637707-0.3792937 0.4071804-0.3116725 0.443547900.7414772-0.09187444 0.80062025 0.4454417MULTIPOLYGON (((521986.4 46...
6172610212 0.64987009 0.6302157-0.04065368 0.64469364 0.3880289⋯ 0.7211874-0.3696338 0.7600790-0.3203095 0.331172140.3141994-0.30936550 0.44207919 0.2948932MULTIPOLYGON (((519949.8 47...
\n" ], "text/latex": [ "A data.frame: 6 × 39\n", "\\begin{tabular}{r|lllllllllllllllllllll}\n", " & CCA & CPRO & CMUN & CDIS & CSEC & early\\_childhood\\_boy & early\\_childhood\\_girl & age\\_middle\\_to\\_oldest\\_old\\_male & age\\_middle\\_to\\_oldest\\_old\\_female & disability & ⋯ & housing\\_characteristics & physical\\_environment & sensitivity & prepare & respond & recover & adaptive\\_capacity & enhanced\\_exposure & social\\_vulnerability & geometry\\\\\n", " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 17 & 26 & 1 & 1 & 1 & -0.19258164 & -0.1467916 & 0.48180824 & -0.10021618 & 1.2753218 & ⋯ & -0.9481277 & -0.8560700 & 1.1377171 & -0.3378133 & 0.18457796 & 0.1073270 & -0.15329655 & -1.00827077 & -0.1413894 & MULTIPOLYGON (((526123.5 47...\\\\\n", "\t2 & 17 & 26 & 10 & 1 & 1 & -0.36188692 & -0.3284478 & 1.32081073 & 0.44848018 & -1.3865567 & ⋯ & 0.0000000 & -0.2328068 & -0.9881374 & -0.4065854 & -0.33477437 & 0.8522391 & -0.26183757 & 0.04893220 & -0.4444888 & MULTIPOLYGON (((528744.7 46...\\\\\n", "\t3 & 17 & 26 & 100 & 1 & 1 & 0.01083739 & -1.4002199 & 0.23798535 & -0.52201032 & -1.3865567 & ⋯ & 0.0000000 & -0.3822322 & -1.5969460 & -0.4065854 & -0.22140412 & 1.0122262 & -0.17316713 & -0.05159845 & -0.6491854 & MULTIPOLYGON (((575382.6 46...\\\\\n", "\t4 & 17 & 26 & 101 & 1 & 1 & -1.36078806 & -1.4002199 & -0.22155305 & 0.07955613 & -1.3865567 & ⋯ & 0.0000000 & -1.5116073 & -1.8689267 & -0.4065854 & 0.02693072 & 1.3626742 & 0.02106336 & -0.81142134 & -0.9789365 & MULTIPOLYGON (((540334.4 46...\\\\\n", "\t5 & 17 & 26 & 102 & 1 & 1 & 0.10257030 & -0.4581527 & 0.05129390 & 0.59299501 & 0.3880289 & ⋯ & 1.2637707 & -0.3792937 & 0.4071804 & -0.3116725 & 0.44354790 & 0.7414772 & -0.09187444 & 0.80062025 & 0.4454417 & MULTIPOLYGON (((521986.4 46...\\\\\n", "\t6 & 17 & 26 & 102 & 1 & 2 & 0.64987009 & 0.6302157 & -0.04065368 & 0.64469364 & 0.3880289 & ⋯ & 0.7211874 & -0.3696338 & 0.7600790 & -0.3203095 & 0.33117214 & 0.3141994 & -0.30936550 & 0.44207919 & 0.2948932 & MULTIPOLYGON (((519949.8 47...\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 39\n", "\n", "| | CCA <int> | CPRO <int> | CMUN <int> | CDIS <int> | CSEC <int> | early_childhood_boy <dbl> | early_childhood_girl <dbl> | age_middle_to_oldest_old_male <dbl> | age_middle_to_oldest_old_female <dbl> | disability <dbl> | ⋯ ⋯ | housing_characteristics <dbl> | physical_environment <dbl> | sensitivity <dbl[,1]> | prepare <dbl[,1]> | respond <dbl[,1]> | recover <dbl[,1]> | adaptive_capacity <dbl[,1]> | enhanced_exposure <dbl[,1]> | social_vulnerability <dbl[,1]> | geometry <MULTIPOLYGON [m]> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 17 | 26 | 1 | 1 | 1 | -0.19258164 | -0.1467916 | 0.48180824 | -0.10021618 | 1.2753218 | ⋯ | -0.9481277 | -0.8560700 | 1.1377171 | -0.3378133 | 0.18457796 | 0.1073270 | -0.15329655 | -1.00827077 | -0.1413894 | MULTIPOLYGON (((526123.5 47... |\n", "| 2 | 17 | 26 | 10 | 1 | 1 | -0.36188692 | -0.3284478 | 1.32081073 | 0.44848018 | -1.3865567 | ⋯ | 0.0000000 | -0.2328068 | -0.9881374 | -0.4065854 | -0.33477437 | 0.8522391 | -0.26183757 | 0.04893220 | -0.4444888 | MULTIPOLYGON (((528744.7 46... |\n", "| 3 | 17 | 26 | 100 | 1 | 1 | 0.01083739 | -1.4002199 | 0.23798535 | -0.52201032 | -1.3865567 | ⋯ | 0.0000000 | -0.3822322 | -1.5969460 | -0.4065854 | -0.22140412 | 1.0122262 | -0.17316713 | -0.05159845 | -0.6491854 | MULTIPOLYGON (((575382.6 46... |\n", "| 4 | 17 | 26 | 101 | 1 | 1 | -1.36078806 | -1.4002199 | -0.22155305 | 0.07955613 | -1.3865567 | ⋯ | 0.0000000 | -1.5116073 | -1.8689267 | -0.4065854 | 0.02693072 | 1.3626742 | 0.02106336 | -0.81142134 | -0.9789365 | MULTIPOLYGON (((540334.4 46... |\n", "| 5 | 17 | 26 | 102 | 1 | 1 | 0.10257030 | -0.4581527 | 0.05129390 | 0.59299501 | 0.3880289 | ⋯ | 1.2637707 | -0.3792937 | 0.4071804 | -0.3116725 | 0.44354790 | 0.7414772 | -0.09187444 | 0.80062025 | 0.4454417 | MULTIPOLYGON (((521986.4 46... |\n", "| 6 | 17 | 26 | 102 | 1 | 2 | 0.64987009 | 0.6302157 | -0.04065368 | 0.64469364 | 0.3880289 | ⋯ | 0.7211874 | -0.3696338 | 0.7600790 | -0.3203095 | 0.33117214 | 0.3141994 | -0.30936550 | 0.44207919 | 0.2948932 | MULTIPOLYGON (((519949.8 47... |\n", "\n" ], "text/plain": [ " CCA CPRO CMUN CDIS CSEC early_childhood_boy early_childhood_girl\n", "1 17 26 1 1 1 -0.19258164 -0.1467916 \n", "2 17 26 10 1 1 -0.36188692 -0.3284478 \n", "3 17 26 100 1 1 0.01083739 -1.4002199 \n", "4 17 26 101 1 1 -1.36078806 -1.4002199 \n", "5 17 26 102 1 1 0.10257030 -0.4581527 \n", "6 17 26 102 1 2 0.64987009 0.6302157 \n", " age_middle_to_oldest_old_male age_middle_to_oldest_old_female disability ⋯\n", "1 0.48180824 -0.10021618 1.2753218 ⋯\n", "2 1.32081073 0.44848018 -1.3865567 ⋯\n", "3 0.23798535 -0.52201032 -1.3865567 ⋯\n", "4 -0.22155305 0.07955613 -1.3865567 ⋯\n", "5 0.05129390 0.59299501 0.3880289 ⋯\n", "6 -0.04065368 0.64469364 0.3880289 ⋯\n", " housing_characteristics physical_environment sensitivity prepare \n", "1 -0.9481277 -0.8560700 1.1377171 -0.3378133\n", "2 0.0000000 -0.2328068 -0.9881374 -0.4065854\n", "3 0.0000000 -0.3822322 -1.5969460 -0.4065854\n", "4 0.0000000 -1.5116073 -1.8689267 -0.4065854\n", "5 1.2637707 -0.3792937 0.4071804 -0.3116725\n", "6 0.7211874 -0.3696338 0.7600790 -0.3203095\n", " respond recover adaptive_capacity enhanced_exposure\n", "1 0.18457796 0.1073270 -0.15329655 -1.00827077 \n", "2 -0.33477437 0.8522391 -0.26183757 0.04893220 \n", "3 -0.22140412 1.0122262 -0.17316713 -0.05159845 \n", "4 0.02693072 1.3626742 0.02106336 -0.81142134 \n", "5 0.44354790 0.7414772 -0.09187444 0.80062025 \n", "6 0.33117214 0.3141994 -0.30936550 0.44207919 \n", " social_vulnerability geometry \n", "1 -0.1413894 MULTIPOLYGON (((526123.5 47...\n", "2 -0.4444888 MULTIPOLYGON (((528744.7 46...\n", "3 -0.6491854 MULTIPOLYGON (((575382.6 46...\n", "4 -0.9789365 MULTIPOLYGON (((540334.4 46...\n", "5 0.4454417 MULTIPOLYGON (((521986.4 46...\n", "6 0.2948932 MULTIPOLYGON (((519949.8 47..." ] }, "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/Spain/Logrono/2021/social_vulnerability_index_logrono_2021.geojson' using driver `GeoJSON'\n", "Writing layer `social_vulnerability_index_logrono_2021' to data source \n", " `../../3_outputs/Spain/Logrono/2021/social_vulnerability_index_logrono_2021.geojson' using driver `GeoJSON'\n", "Writing 343 features with 38 fields and geometry type Multi 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_logrono_2021' using driver `ESRI Shapefile'\n", "Writing layer `social_vulnerability_index_logrono_2021' to data source \n", " `../../3_outputs/Spain/Logrono/2021/social_vulnerability_index_logrono_2021.shp' using driver `ESRI Shapefile'\n", "Writing 343 features with 38 fields and geometry type Multi Polygon.\n" ] } ], "source": [ "# CSV\n", "write.csv(output_dataset, file.path(output_dir, \"social_vulnerability_index_logrono_2021.csv\"), row.names = FALSE)\n", "\n", "# GeoJSON\n", "st_write(output_dataset_geom, file.path(output_dir, \"social_vulnerability_index_logrono_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_logrono_2021.shp\"), append = FALSE)" ] }, { "cell_type": "markdown", "id": "b0ad01c2-b342-4f7f-b9d9-c03ccb0ed11b", "metadata": {}, "source": [ "**END**" ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.3.3" } }, "nbformat": 4, "nbformat_minor": 5 }