{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Social Vulnerability Milan - Census Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Environment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### R Libraries\n", "Any required R libraries are imported into the kernal:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Load R libraries\n", "## none required" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Output directory" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# create the pipeline directory if it does not exist\n", "pipeline_dir <- file.path(\"../..\",\"2_pipeline\",\"Italy\",\"Milan\",\"1a_CensusData\",\"2021\")\n", "if(!dir.exists(pipeline_dir)){\n", " dir.create(pipeline_dir, recursive = TRUE)\n", " print(paste0(pipeline_dir, \" created\"))\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import the csv data\n", "Ireland census data from: https://www.cso.ie/en/census/census2022/census2022smallareapopulationstatistics" ] }, { "cell_type": "code", "execution_count": 3, "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 × 136
CODREGREGIONECODPROPROVINCIACODCOMCOMUNEPROCOMSEZ2011ASC_1LIV_CODASC_1LIV_NOME⋯CIT_1_EGYCIT_2_PHLCIT_3_CHNCIT_4_LKACIT_5_PERCIT_6_ROUCIT_7_BGDCIT_8_ECUCIT_9_MARCIT_10_UKR
<int><chr><int><chr><int><chr><int><dbl><int><chr>⋯<int><int><int><int><int><int><int><int><int><int>
13Lombardia15Milano146Milano151461.5146e+1115146001Centro Storico⋯0000000001
23Lombardia15Milano146Milano151461.5146e+1115146001Centro Storico⋯0400000000
33Lombardia15Milano146Milano151461.5146e+1115146001Centro Storico⋯0000000000
43Lombardia15Milano146Milano151461.5146e+1115146001Centro Storico⋯0000000000
53Lombardia15Milano146Milano151461.5146e+1115146001Centro Storico⋯0000000000
63Lombardia15Milano146Milano151461.5146e+1115146001Centro Storico⋯0000000000
\n" ], "text/latex": [ "A data.frame: 6 × 136\n", "\\begin{tabular}{r|lllllllllllllllllllll}\n", " & CODREG & REGIONE & CODPRO & PROVINCIA & CODCOM & COMUNE & PROCOM & SEZ2011 & ASC\\_1LIV\\_COD & ASC\\_1LIV\\_NOME & ⋯ & CIT\\_1\\_EGY & CIT\\_2\\_PHL & CIT\\_3\\_CHN & CIT\\_4\\_LKA & CIT\\_5\\_PER & CIT\\_6\\_ROU & CIT\\_7\\_BGD & CIT\\_8\\_ECU & CIT\\_9\\_MAR & CIT\\_10\\_UKR\\\\\n", " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 3 & Lombardia & 15 & Milano & 146 & Milano & 15146 & 1.5146e+11 & 15146001 & Centro Storico & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n", "\t2 & 3 & Lombardia & 15 & Milano & 146 & Milano & 15146 & 1.5146e+11 & 15146001 & Centro Storico & ⋯ & 0 & 4 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t3 & 3 & Lombardia & 15 & Milano & 146 & Milano & 15146 & 1.5146e+11 & 15146001 & Centro Storico & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t4 & 3 & Lombardia & 15 & Milano & 146 & Milano & 15146 & 1.5146e+11 & 15146001 & Centro Storico & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t5 & 3 & Lombardia & 15 & Milano & 146 & Milano & 15146 & 1.5146e+11 & 15146001 & Centro Storico & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t6 & 3 & Lombardia & 15 & Milano & 146 & Milano & 15146 & 1.5146e+11 & 15146001 & Centro Storico & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 136\n", "\n", "| | CODREG <int> | REGIONE <chr> | CODPRO <int> | PROVINCIA <chr> | CODCOM <int> | COMUNE <chr> | PROCOM <int> | SEZ2011 <dbl> | ASC_1LIV_COD <int> | ASC_1LIV_NOME <chr> | ⋯ ⋯ | CIT_1_EGY <int> | CIT_2_PHL <int> | CIT_3_CHN <int> | CIT_4_LKA <int> | CIT_5_PER <int> | CIT_6_ROU <int> | CIT_7_BGD <int> | CIT_8_ECU <int> | CIT_9_MAR <int> | CIT_10_UKR <int> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 3 | Lombardia | 15 | Milano | 146 | Milano | 15146 | 1.5146e+11 | 15146001 | Centro Storico | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n", "| 2 | 3 | Lombardia | 15 | Milano | 146 | Milano | 15146 | 1.5146e+11 | 15146001 | Centro Storico | ⋯ | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| 3 | 3 | Lombardia | 15 | Milano | 146 | Milano | 15146 | 1.5146e+11 | 15146001 | Centro Storico | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| 4 | 3 | Lombardia | 15 | Milano | 146 | Milano | 15146 | 1.5146e+11 | 15146001 | Centro Storico | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| 5 | 3 | Lombardia | 15 | Milano | 146 | Milano | 15146 | 1.5146e+11 | 15146001 | Centro Storico | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| 6 | 3 | Lombardia | 15 | Milano | 146 | Milano | 15146 | 1.5146e+11 | 15146001 | Centro Storico | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "\n" ], "text/plain": [ " CODREG REGIONE CODPRO PROVINCIA CODCOM COMUNE PROCOM SEZ2011 \n", "1 3 Lombardia 15 Milano 146 Milano 15146 1.5146e+11\n", "2 3 Lombardia 15 Milano 146 Milano 15146 1.5146e+11\n", "3 3 Lombardia 15 Milano 146 Milano 15146 1.5146e+11\n", "4 3 Lombardia 15 Milano 146 Milano 15146 1.5146e+11\n", "5 3 Lombardia 15 Milano 146 Milano 15146 1.5146e+11\n", "6 3 Lombardia 15 Milano 146 Milano 15146 1.5146e+11\n", " ASC_1LIV_COD ASC_1LIV_NOME ⋯ CIT_1_EGY CIT_2_PHL CIT_3_CHN CIT_4_LKA\n", "1 15146001 Centro Storico ⋯ 0 0 0 0 \n", "2 15146001 Centro Storico ⋯ 0 4 0 0 \n", "3 15146001 Centro Storico ⋯ 0 0 0 0 \n", "4 15146001 Centro Storico ⋯ 0 0 0 0 \n", "5 15146001 Centro Storico ⋯ 0 0 0 0 \n", "6 15146001 Centro Storico ⋯ 0 0 0 0 \n", " CIT_5_PER CIT_6_ROU CIT_7_BGD CIT_8_ECU CIT_9_MAR CIT_10_UKR\n", "1 0 0 0 0 0 1 \n", "2 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 \n", "4 0 0 0 0 0 0 \n", "5 0 0 0 0 0 0 \n", "6 0 0 0 0 0 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Read the census data\n", "census_data <- read.csv('../../0_data/census/Italy/Milan/2021/Milano_indicatori_2021_sezioni.csv', sep=\",\")\n", "head(census_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare data\n", "We only require a subset of the census data for our purposes. We therefore need to extract the relevant data, then combine these to create our vulnerability indicators.\n", "\n", "In addition, the raw data is not suitable for use within the vulnerabiltiy assessment. It needs to be normalised based on the number of people/households within each small area. Therefore, the data is converted to percentages based on the total persons/households within each small area." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Supporting data" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "#### Code that uniquely identifies the census area" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "5740" ], "text/latex": [ "5740" ], "text/markdown": [ "5740" ], "text/plain": [ "[1] 5740" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "identifier <- c('SEZ2011')\n", "census_area_id <- census_data[, identifier, drop = FALSE]\n", "\n", "# number of rows in the census data\n", "head( nrow(census_data) )" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "#### Population total" ] }, { "cell_type": "code", "execution_count": 5, "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 × 1
population_total
<int>
113
216
317
411
5 1
6 6
\n" ], "text/latex": [ "A data.frame: 6 × 1\n", "\\begin{tabular}{r|l}\n", " & population\\_total\\\\\n", " & \\\\\n", "\\hline\n", "\t1 & 13\\\\\n", "\t2 & 16\\\\\n", "\t3 & 17\\\\\n", "\t4 & 11\\\\\n", "\t5 & 1\\\\\n", "\t6 & 6\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 1\n", "\n", "| | population_total <int> |\n", "|---|---|\n", "| 1 | 13 |\n", "| 2 | 16 |\n", "| 3 | 17 |\n", "| 4 | 11 |\n", "| 5 | 1 |\n", "| 6 | 6 |\n", "\n" ], "text/plain": [ " population_total\n", "1 13 \n", "2 16 \n", "3 17 \n", "4 11 \n", "5 1 \n", "6 6 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "population_total <- census_data[, 'P1', drop = FALSE]\n", "names(population_total)[1] <- 'population_total'\n", "head(population_total)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "#### Households / families total" ] }, { "cell_type": "code", "execution_count": 6, "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 × 1
households_total
<int>
1 9
2 7
317
4 7
5 1
6 3
\n" ], "text/latex": [ "A data.frame: 6 × 1\n", "\\begin{tabular}{r|l}\n", " & households\\_total\\\\\n", " & \\\\\n", "\\hline\n", "\t1 & 9\\\\\n", "\t2 & 7\\\\\n", "\t3 & 17\\\\\n", "\t4 & 7\\\\\n", "\t5 & 1\\\\\n", "\t6 & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 1\n", "\n", "| | households_total <int> |\n", "|---|---|\n", "| 1 | 9 |\n", "| 2 | 7 |\n", "| 3 | 17 |\n", "| 4 | 7 |\n", "| 5 | 1 |\n", "| 6 | 3 |\n", "\n" ], "text/plain": [ " households_total\n", "1 9 \n", "2 7 \n", "3 17 \n", "4 7 \n", "5 1 \n", "6 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "households_total <- census_data[, 'PF1', drop = FALSE]\n", "names(households_total)[1] <- 'households_total'\n", "head(households_total)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Domain data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Age domain" ] }, { "cell_type": "code", "execution_count": 7, "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 × 4
early_childhood_boy_pctearly_childhood_girl_pctage_middle_to_oldest_old_male_pctage_middle_to_oldest_old_female_pct
<dbl><dbl><dbl><dbl>
100 15.384620.00
200 6.250006.25
300 23.529410.00
400 18.181820.00
500100.000000.00
600 0.000000.00
\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r|llll}\n", " & early\\_childhood\\_boy\\_pct & early\\_childhood\\_girl\\_pct & age\\_middle\\_to\\_oldest\\_old\\_male\\_pct & age\\_middle\\_to\\_oldest\\_old\\_female\\_pct\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t1 & 0 & 0 & 15.38462 & 0.00\\\\\n", "\t2 & 0 & 0 & 6.25000 & 6.25\\\\\n", "\t3 & 0 & 0 & 23.52941 & 0.00\\\\\n", "\t4 & 0 & 0 & 18.18182 & 0.00\\\\\n", "\t5 & 0 & 0 & 100.00000 & 0.00\\\\\n", "\t6 & 0 & 0 & 0.00000 & 0.00\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "| | early_childhood_boy_pct <dbl> | early_childhood_girl_pct <dbl> | age_middle_to_oldest_old_male_pct <dbl> | age_middle_to_oldest_old_female_pct <dbl> |\n", "|---|---|---|---|---|\n", "| 1 | 0 | 0 | 15.38462 | 0.00 |\n", "| 2 | 0 | 0 | 6.25000 | 6.25 |\n", "| 3 | 0 | 0 | 23.52941 | 0.00 |\n", "| 4 | 0 | 0 | 18.18182 | 0.00 |\n", "| 5 | 0 | 0 | 100.00000 | 0.00 |\n", "| 6 | 0 | 0 | 0.00000 | 0.00 |\n", "\n" ], "text/plain": [ " early_childhood_boy_pct early_childhood_girl_pct\n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "5 0 0 \n", "6 0 0 \n", " age_middle_to_oldest_old_male_pct age_middle_to_oldest_old_female_pct\n", "1 15.38462 0.00 \n", "2 6.25000 6.25 \n", "3 23.52941 0.00 \n", "4 18.18182 0.00 \n", "5 100.00000 0.00 \n", "6 0.00000 0.00 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Domain:Age ###\n", "\n", "# Age - early childhood boy (under 5 years old)\n", "early_childhood_boy_fields <- c(\n", " 'P30' # Age 0 to 4 boys\n", ")\n", "early_childhood_boy_data <- census_data[, early_childhood_boy_fields, drop = FALSE]\n", "early_childhood_boy <- rowSums(early_childhood_boy_data, na.rm=TRUE)\n", "early_childhood_boy_pct <- (early_childhood_boy / population_total) * 100.0\n", "names(early_childhood_boy_pct)[1] <- 'early_childhood_boy_pct'\n", "\n", "# Age - early childhood girl (under 5 years old)\n", "early_childhood_girl_fields <- c(\n", " 'P67' # Age 0 to 4 girls\n", ")\n", "early_childhood_girl_data <- census_data[, early_childhood_girl_fields, drop = FALSE]\n", "early_childhood_girl <- rowSums(early_childhood_girl_data, na.rm=TRUE)\n", "early_childhood_girl_pct <- (early_childhood_girl / population_total) * 100.0\n", "names(early_childhood_girl_pct)[1] <- 'early_childhood_girl_pct'\n", "\n", "# Age - middle to oldest old male (75+ years old)\n", "age_middle_to_oldest_old_male_fields <- c(\n", " 'P45' # Age 75+ males\n", ")\n", "age_middle_to_oldest_old_male_data <- census_data[, age_middle_to_oldest_old_male_fields, drop = FALSE]\n", "age_middle_to_oldest_old_male <- rowSums(age_middle_to_oldest_old_male_data, na.rm=TRUE)\n", "age_middle_to_oldest_old_male_pct <- (age_middle_to_oldest_old_male / population_total) * 100.0\n", "names(age_middle_to_oldest_old_male_pct)[1] <- 'age_middle_to_oldest_old_male_pct'\n", "\n", "# Age - middle to oldest old male (75+ years old)\n", "age_middle_to_oldest_old_female_fields <- c(\n", " 'P82' # Age 75+ females\n", ")\n", "age_middle_to_oldest_old_female_data <- census_data[, age_middle_to_oldest_old_female_fields, drop = FALSE]\n", "age_middle_to_oldest_old_female <- rowSums(age_middle_to_oldest_old_female_data, na.rm=TRUE)\n", "age_middle_to_oldest_old_female_pct <- (age_middle_to_oldest_old_female / population_total) * 100.0\n", "names(age_middle_to_oldest_old_female_pct)[1] <- 'age_middle_to_oldest_old_female_pct'\n", "\n", "# Combine all these indicators into an array for this domain\n", "age_domain_pct <- cbind(early_childhood_boy_pct,\n", " early_childhood_girl_pct,\n", " age_middle_to_oldest_old_male_pct,\n", " age_middle_to_oldest_old_female_pct)\n", "\n", "# Print the first six rows of the data to visually check it looks OK\n", "head(age_domain_pct)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Income domain" ] }, { "cell_type": "code", "execution_count": 8, "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 × 2
dependants_pctunemployment_pct
<dbl><dbl>
1 7.69230815.384615
212.50000031.250000
3 0.00000017.647059
4 9.090909 9.090909
5 0.000000 0.000000
6 0.000000 0.000000
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & dependants\\_pct & unemployment\\_pct\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & 7.692308 & 15.384615\\\\\n", "\t2 & 12.500000 & 31.250000\\\\\n", "\t3 & 0.000000 & 17.647059\\\\\n", "\t4 & 9.090909 & 9.090909\\\\\n", "\t5 & 0.000000 & 0.000000\\\\\n", "\t6 & 0.000000 & 0.000000\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | dependants_pct <dbl> | unemployment_pct <dbl> |\n", "|---|---|---|\n", "| 1 | 7.692308 | 15.384615 |\n", "| 2 | 12.500000 | 31.250000 |\n", "| 3 | 0.000000 | 17.647059 |\n", "| 4 | 9.090909 | 9.090909 |\n", "| 5 | 0.000000 | 0.000000 |\n", "| 6 | 0.000000 | 0.000000 |\n", "\n" ], "text/plain": [ " dependants_pct unemployment_pct\n", "1 7.692308 15.384615 \n", "2 12.500000 31.250000 \n", "3 0.000000 17.647059 \n", "4 9.090909 9.090909 \n", "5 0.000000 0.000000 \n", "6 0.000000 0.000000 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Domain:Income ###\n", "\n", "# Dependants - using under 15 years old for the 2022 Italian census\n", "dependants_fields <- c(\n", " 'P14',\n", " 'P15',\n", " 'P16')\n", "dependants_data <- census_data[, dependants_fields, drop = FALSE]\n", "dependants <- rowSums(dependants_data, na.rm=TRUE)\n", "dependants_pct <- (dependants / population_total) * 100.0\n", "names(dependants_pct)[1] <- 'dependants_pct'\n", "\n", "# Unemployment\n", "## Calculate total poluation for ages 15-64.\n", "## Note: P101 = total employed people aged 15-64.\n", "## Note: Sum of P17, P18, P19, P20, P21, P22, P23, P24, P25 and P26 is total poluation for ages 15-64.\n", "## Note: to calculate unemployed we use: P101 - total poluation for ages 15-64.\n", "## Note: this unemployed figure might also indicate students not working\n", "population_15to64_fields <- c(\n", " 'P17',\n", " 'P18',\n", " 'P19',\n", " 'P20',\n", " 'P21',\n", " 'P22',\n", " 'P23',\n", " 'P24',\n", " 'P25',\n", " 'P26')\n", "population_15to64_data <- census_data[, population_15to64_fields, drop = FALSE]\n", "population_15to64 <- rowSums(population_15to64_data, na.rm=TRUE)\n", "unemployment <- population_15to64 - census_data$P101\n", "unemployment_pct <- (unemployment / population_total) * 100.0\n", "names(unemployment_pct)[1] <- 'unemployment_pct'\n", "\n", "# Combine all these indicators into an array for this domain\n", "income_domain_pct <- cbind(dependants_pct,\n", " unemployment_pct)\n", "\n", "# Print the first six rows of the data to visually check it looks OK\n", "head(income_domain_pct)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Information Access/Use domain" ] }, { "cell_type": "code", "execution_count": 9, "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 × 1
no_higher_education_pct
<dbl>
1 7.692308
218.750000
3 0.000000
4 0.000000
5 0.000000
6 0.000000
\n" ], "text/latex": [ "A data.frame: 6 × 1\n", "\\begin{tabular}{r|l}\n", " & no\\_higher\\_education\\_pct\\\\\n", " & \\\\\n", "\\hline\n", "\t1 & 7.692308\\\\\n", "\t2 & 18.750000\\\\\n", "\t3 & 0.000000\\\\\n", "\t4 & 0.000000\\\\\n", "\t5 & 0.000000\\\\\n", "\t6 & 0.000000\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 1\n", "\n", "| | no_higher_education_pct <dbl> |\n", "|---|---|\n", "| 1 | 7.692308 |\n", "| 2 | 18.750000 |\n", "| 3 | 0.000000 |\n", "| 4 | 0.000000 |\n", "| 5 | 0.000000 |\n", "| 6 | 0.000000 |\n", "\n" ], "text/plain": [ " no_higher_education_pct\n", "1 7.692308 \n", "2 18.750000 \n", "3 0.000000 \n", "4 0.000000 \n", "5 0.000000 \n", "6 0.000000 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Domain:Information Access/Use ###\n", "\n", "# No higher education\n", "### Note: calculation test1 for P83 (total population age 9+) = P86 + P87 + P88 + P89 + P90 (without qualification + all primary to highest education levels)\n", "### Note: calculation test2 for P83 (total population age 9+) = P16 + P17+ P18 + P19 + P20 + P21 + P22 + P23 + P24 + P25 + P26 + P27 + P28 + P29 (age 10+)\n", "### Note: using without qualification (P86) + primary/elementary schools qualification only (P87) \n", "no_higher_education_fields <- c(\n", " 'P86',\n", " 'P87'\n", ")\n", "no_higher_education_data <- census_data[, no_higher_education_fields, drop = FALSE]\n", "no_higher_education <- rowSums(no_higher_education_data, na.rm=TRUE)\n", "no_higher_education_pct <- (no_higher_education / population_total) * 100.0\n", "names(no_higher_education_pct)[1] <- 'no_higher_education_pct'\n", "\n", "# Combine all these indicators into an array for this domain\n", "info_domain_pct <- cbind(no_higher_education_pct)\n", "\n", "# Print the first six rows of the data to visually check it looks OK\n", "head(info_domain_pct)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Local knowledge domain" ] }, { "cell_type": "code", "execution_count": 10, "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 × 1
foreign_nationals_pct
<dbl>
123.07692
231.25000
3 0.00000
418.18182
5 0.00000
666.66667
\n" ], "text/latex": [ "A data.frame: 6 × 1\n", "\\begin{tabular}{r|l}\n", " & foreign\\_nationals\\_pct\\\\\n", " & \\\\\n", "\\hline\n", "\t1 & 23.07692\\\\\n", "\t2 & 31.25000\\\\\n", "\t3 & 0.00000\\\\\n", "\t4 & 18.18182\\\\\n", "\t5 & 0.00000\\\\\n", "\t6 & 66.66667\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 1\n", "\n", "| | foreign_nationals_pct <dbl> |\n", "|---|---|\n", "| 1 | 23.07692 |\n", "| 2 | 31.25000 |\n", "| 3 | 0.00000 |\n", "| 4 | 18.18182 |\n", "| 5 | 0.00000 |\n", "| 6 | 66.66667 |\n", "\n" ], "text/plain": [ " foreign_nationals_pct\n", "1 23.07692 \n", "2 31.25000 \n", "3 0.00000 \n", "4 18.18182 \n", "5 0.00000 \n", "6 66.66667 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Domain:Local Knowledge ###\n", "\n", "# Foreign nationals (already a percentage)\n", "foreign_nationals_fields <- c(\n", " 'ST1'\n", ")\n", "foreign_nationals_data <- census_data[, foreign_nationals_fields, drop = FALSE]\n", "foreign_nationals <- rowSums(foreign_nationals_data, na.rm=TRUE)\n", "foreign_nationals_pct <- (foreign_nationals / population_total) * 100.0\n", "names(foreign_nationals_pct)[1] <- 'foreign_nationals_pct'\n", "\n", "# Combine all these indicators into an array for this domain\n", "local_knowledge_domain_pct <- cbind(foreign_nationals_pct)\n", "\n", "# Print the first six rows of the data to visually check it looks OK\n", "head(local_knowledge_domain_pct)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Social Network domain data" ] }, { "cell_type": "code", "execution_count": 11, "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 × 2
primary_school_age_pctone_person_households_pct
<dbl><dbl>
10.000000 66.66667
20.000000 42.85714
30.000000100.00000
49.090909 57.14286
50.000000100.00000
60.000000 66.66667
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & primary\\_school\\_age\\_pct & one\\_person\\_households\\_pct\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & 0.000000 & 66.66667\\\\\n", "\t2 & 0.000000 & 42.85714\\\\\n", "\t3 & 0.000000 & 100.00000\\\\\n", "\t4 & 9.090909 & 57.14286\\\\\n", "\t5 & 0.000000 & 100.00000\\\\\n", "\t6 & 0.000000 & 66.66667\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | primary_school_age_pct <dbl> | one_person_households_pct <dbl> |\n", "|---|---|---|\n", "| 1 | 0.000000 | 66.66667 |\n", "| 2 | 0.000000 | 42.85714 |\n", "| 3 | 0.000000 | 100.00000 |\n", "| 4 | 9.090909 | 57.14286 |\n", "| 5 | 0.000000 | 100.00000 |\n", "| 6 | 0.000000 | 66.66667 |\n", "\n" ], "text/plain": [ " primary_school_age_pct one_person_households_pct\n", "1 0.000000 66.66667 \n", "2 0.000000 42.85714 \n", "3 0.000000 100.00000 \n", "4 9.090909 57.14286 \n", "5 0.000000 100.00000 \n", "6 0.000000 66.66667 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Domain:Social Network ###\n", "\n", "# Primary school age children\n", "primary_school_age_fields <- c(\n", " 'P15' # Primary School Age Children (Age 5-9) (Indicator P15)\n", ")\n", "primary_school_age_data <- census_data[, primary_school_age_fields, drop = FALSE]\n", "primary_school_age <- rowSums(primary_school_age_data, na.rm=TRUE)\n", "primary_school_age_pct <- (primary_school_age / population_total) * 100.0\n", "names(primary_school_age_pct)[1] <- 'primary_school_age_pct'\n", "\n", "# One person households\n", "one_person_households_fields<- c(\n", " 'PF3' # Households with one person (Indicator PF3)\n", ")\n", "one_person_households_data <- census_data[, one_person_households_fields, drop = FALSE]\n", "one_person_households <- rowSums(one_person_households_data, na.rm=TRUE)\n", "one_person_households_pct <- (one_person_households / households_total) * 100.0\n", "names(one_person_households_pct)[1] <- 'one_person_households_pct'\n", "\n", "# Combine all these indicators into an array for this domain\n", "social_network_domain_pct <- cbind(primary_school_age_pct,\n", " one_person_households_pct)\n", "\n", "# Print the first six rows of the data to visually check it looks OK\n", "head(social_network_domain_pct)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Combine all data into one table" ] }, { "cell_type": "code", "execution_count": 12, "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 × 11
SEZ2011early_childhood_boy_pctearly_childhood_girl_pctage_middle_to_oldest_old_male_pctage_middle_to_oldest_old_female_pctdependants_pctunemployment_pctno_higher_education_pctforeign_nationals_pctprimary_school_age_pctone_person_households_pct
<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
11.5146e+1100 15.384620.00 7.69230815.384615 7.69230823.076920.000000 66.66667
21.5146e+1100 6.250006.2512.50000031.25000018.75000031.250000.000000 42.85714
31.5146e+1100 23.529410.00 0.00000017.647059 0.000000 0.000000.000000100.00000
41.5146e+1100 18.181820.00 9.090909 9.090909 0.00000018.181829.090909 57.14286
51.5146e+1100100.000000.00 0.000000 0.000000 0.000000 0.000000.000000100.00000
61.5146e+1100 0.000000.00 0.000000 0.000000 0.00000066.666670.000000 66.66667
\n" ], "text/latex": [ "A data.frame: 6 × 11\n", "\\begin{tabular}{r|lllllllllll}\n", " & SEZ2011 & early\\_childhood\\_boy\\_pct & early\\_childhood\\_girl\\_pct & age\\_middle\\_to\\_oldest\\_old\\_male\\_pct & age\\_middle\\_to\\_oldest\\_old\\_female\\_pct & dependants\\_pct & unemployment\\_pct & no\\_higher\\_education\\_pct & foreign\\_nationals\\_pct & primary\\_school\\_age\\_pct & one\\_person\\_households\\_pct\\\\\n", " & & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 1.5146e+11 & 0 & 0 & 15.38462 & 0.00 & 7.692308 & 15.384615 & 7.692308 & 23.07692 & 0.000000 & 66.66667\\\\\n", "\t2 & 1.5146e+11 & 0 & 0 & 6.25000 & 6.25 & 12.500000 & 31.250000 & 18.750000 & 31.25000 & 0.000000 & 42.85714\\\\\n", "\t3 & 1.5146e+11 & 0 & 0 & 23.52941 & 0.00 & 0.000000 & 17.647059 & 0.000000 & 0.00000 & 0.000000 & 100.00000\\\\\n", "\t4 & 1.5146e+11 & 0 & 0 & 18.18182 & 0.00 & 9.090909 & 9.090909 & 0.000000 & 18.18182 & 9.090909 & 57.14286\\\\\n", "\t5 & 1.5146e+11 & 0 & 0 & 100.00000 & 0.00 & 0.000000 & 0.000000 & 0.000000 & 0.00000 & 0.000000 & 100.00000\\\\\n", "\t6 & 1.5146e+11 & 0 & 0 & 0.00000 & 0.00 & 0.000000 & 0.000000 & 0.000000 & 66.66667 & 0.000000 & 66.66667\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 11\n", "\n", "| | SEZ2011 <dbl> | early_childhood_boy_pct <dbl> | early_childhood_girl_pct <dbl> | age_middle_to_oldest_old_male_pct <dbl> | age_middle_to_oldest_old_female_pct <dbl> | dependants_pct <dbl> | unemployment_pct <dbl> | no_higher_education_pct <dbl> | foreign_nationals_pct <dbl> | primary_school_age_pct <dbl> | one_person_households_pct <dbl> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 1.5146e+11 | 0 | 0 | 15.38462 | 0.00 | 7.692308 | 15.384615 | 7.692308 | 23.07692 | 0.000000 | 66.66667 |\n", "| 2 | 1.5146e+11 | 0 | 0 | 6.25000 | 6.25 | 12.500000 | 31.250000 | 18.750000 | 31.25000 | 0.000000 | 42.85714 |\n", "| 3 | 1.5146e+11 | 0 | 0 | 23.52941 | 0.00 | 0.000000 | 17.647059 | 0.000000 | 0.00000 | 0.000000 | 100.00000 |\n", "| 4 | 1.5146e+11 | 0 | 0 | 18.18182 | 0.00 | 9.090909 | 9.090909 | 0.000000 | 18.18182 | 9.090909 | 57.14286 |\n", "| 5 | 1.5146e+11 | 0 | 0 | 100.00000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 100.00000 |\n", "| 6 | 1.5146e+11 | 0 | 0 | 0.00000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 66.66667 | 0.000000 | 66.66667 |\n", "\n" ], "text/plain": [ " SEZ2011 early_childhood_boy_pct early_childhood_girl_pct\n", "1 1.5146e+11 0 0 \n", "2 1.5146e+11 0 0 \n", "3 1.5146e+11 0 0 \n", "4 1.5146e+11 0 0 \n", "5 1.5146e+11 0 0 \n", "6 1.5146e+11 0 0 \n", " age_middle_to_oldest_old_male_pct age_middle_to_oldest_old_female_pct\n", "1 15.38462 0.00 \n", "2 6.25000 6.25 \n", "3 23.52941 0.00 \n", "4 18.18182 0.00 \n", "5 100.00000 0.00 \n", "6 0.00000 0.00 \n", " dependants_pct unemployment_pct no_higher_education_pct foreign_nationals_pct\n", "1 7.692308 15.384615 7.692308 23.07692 \n", "2 12.500000 31.250000 18.750000 31.25000 \n", "3 0.000000 17.647059 0.000000 0.00000 \n", "4 9.090909 9.090909 0.000000 18.18182 \n", "5 0.000000 0.000000 0.000000 0.00000 \n", "6 0.000000 0.000000 0.000000 66.66667 \n", " primary_school_age_pct one_person_households_pct\n", "1 0.000000 66.66667 \n", "2 0.000000 42.85714 \n", "3 0.000000 100.00000 \n", "4 9.090909 57.14286 \n", "5 0.000000 100.00000 \n", "6 0.000000 66.66667 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Combine all data into one table\n", "indicator_domains_pct <- cbind(census_area_id,\n", " age_domain_pct,\n", " income_domain_pct,\n", " info_domain_pct,\n", " local_knowledge_domain_pct,\n", " social_network_domain_pct)\n", "head(indicator_domains_pct)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate Z-Score\n", "The raw data is not suitable for use within the vulnerabiltiy assessment. It needs to be standardised. Therefore, the data is converted to z-scores. Z-scores are:\n", "\n", ">\"A statistical measurement of a score's relationship to the mean (average value) in a group of scores. A Z-score of 0 means the score is the same as the mean (average value). A Z-score can be positive or negative, indicating whether it is above or below the mean and by how many standard deviations. Z-score standardisation represents the deviation of a raw score from its mean in standard deviation units.\" (Kazmierczak et al., 2015)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate the Z-score" ] }, { "cell_type": "code", "execution_count": 13, "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 × 11
SEZ2011early_childhood_boyearly_childhood_girlage_middle_to_oldest_old_maleage_middle_to_oldest_old_femaledependantsunemploymentno_higher_educationforeign_nationalsprimary_school_ageone_person_households
<dbl><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]><dbl[,1]>
11.5146e+11-1.230411-1.064144 2.1196292-1.3062152-0.7801407-0.5060886-0.6075338 0.284861877-1.458348 0.9333249
21.5146e+11-1.230411-1.064144 0.2302043-0.3189938 0.1197864 1.3419462 0.8849880 0.768744204-1.458348-0.7026211
31.5146e+11-1.230411-1.064144 3.8043177-1.3062152-2.2200240-0.2425543-1.6458099-1.081394102-1.458348 3.2236494
41.5146e+11-1.230411-1.064144 2.6982091-1.3062152-0.5183437-1.2391934-1.6458099-0.004949997 1.865648 0.2789465
51.5146e+11-1.230411-1.06414419.6216703-1.3062152-2.2200240-2.2981224-1.6458099-1.081394102-1.458348 3.2236494
61.5146e+11-1.230411-1.064144-1.0625601-1.3062152-2.2200240-2.2981224-1.6458099 2.865567617-1.458348 0.9333249
\n" ], "text/latex": [ "A data.frame: 6 × 11\n", "\\begin{tabular}{r|lllllllllll}\n", " & SEZ2011 & early\\_childhood\\_boy & early\\_childhood\\_girl & age\\_middle\\_to\\_oldest\\_old\\_male & age\\_middle\\_to\\_oldest\\_old\\_female & dependants & unemployment & no\\_higher\\_education & foreign\\_nationals & primary\\_school\\_age & one\\_person\\_households\\\\\n", " & & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 1.5146e+11 & -1.230411 & -1.064144 & 2.1196292 & -1.3062152 & -0.7801407 & -0.5060886 & -0.6075338 & 0.284861877 & -1.458348 & 0.9333249\\\\\n", "\t2 & 1.5146e+11 & -1.230411 & -1.064144 & 0.2302043 & -0.3189938 & 0.1197864 & 1.3419462 & 0.8849880 & 0.768744204 & -1.458348 & -0.7026211\\\\\n", "\t3 & 1.5146e+11 & -1.230411 & -1.064144 & 3.8043177 & -1.3062152 & -2.2200240 & -0.2425543 & -1.6458099 & -1.081394102 & -1.458348 & 3.2236494\\\\\n", "\t4 & 1.5146e+11 & -1.230411 & -1.064144 & 2.6982091 & -1.3062152 & -0.5183437 & -1.2391934 & -1.6458099 & -0.004949997 & 1.865648 & 0.2789465\\\\\n", "\t5 & 1.5146e+11 & -1.230411 & -1.064144 & 19.6216703 & -1.3062152 & -2.2200240 & -2.2981224 & -1.6458099 & -1.081394102 & -1.458348 & 3.2236494\\\\\n", "\t6 & 1.5146e+11 & -1.230411 & -1.064144 & -1.0625601 & -1.3062152 & -2.2200240 & -2.2981224 & -1.6458099 & 2.865567617 & -1.458348 & 0.9333249\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 11\n", "\n", "| | SEZ2011 <dbl> | early_childhood_boy <dbl[,1]> | early_childhood_girl <dbl[,1]> | age_middle_to_oldest_old_male <dbl[,1]> | age_middle_to_oldest_old_female <dbl[,1]> | dependants <dbl[,1]> | unemployment <dbl[,1]> | no_higher_education <dbl[,1]> | foreign_nationals <dbl[,1]> | primary_school_age <dbl[,1]> | one_person_households <dbl[,1]> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 1.5146e+11 | -1.230411 | -1.064144 | 2.1196292 | -1.3062152 | -0.7801407 | -0.5060886 | -0.6075338 | 0.284861877 | -1.458348 | 0.9333249 |\n", "| 2 | 1.5146e+11 | -1.230411 | -1.064144 | 0.2302043 | -0.3189938 | 0.1197864 | 1.3419462 | 0.8849880 | 0.768744204 | -1.458348 | -0.7026211 |\n", "| 3 | 1.5146e+11 | -1.230411 | -1.064144 | 3.8043177 | -1.3062152 | -2.2200240 | -0.2425543 | -1.6458099 | -1.081394102 | -1.458348 | 3.2236494 |\n", "| 4 | 1.5146e+11 | -1.230411 | -1.064144 | 2.6982091 | -1.3062152 | -0.5183437 | -1.2391934 | -1.6458099 | -0.004949997 | 1.865648 | 0.2789465 |\n", "| 5 | 1.5146e+11 | -1.230411 | -1.064144 | 19.6216703 | -1.3062152 | -2.2200240 | -2.2981224 | -1.6458099 | -1.081394102 | -1.458348 | 3.2236494 |\n", "| 6 | 1.5146e+11 | -1.230411 | -1.064144 | -1.0625601 | -1.3062152 | -2.2200240 | -2.2981224 | -1.6458099 | 2.865567617 | -1.458348 | 0.9333249 |\n", "\n" ], "text/plain": [ " SEZ2011 early_childhood_boy early_childhood_girl\n", "1 1.5146e+11 -1.230411 -1.064144 \n", "2 1.5146e+11 -1.230411 -1.064144 \n", "3 1.5146e+11 -1.230411 -1.064144 \n", "4 1.5146e+11 -1.230411 -1.064144 \n", "5 1.5146e+11 -1.230411 -1.064144 \n", "6 1.5146e+11 -1.230411 -1.064144 \n", " age_middle_to_oldest_old_male age_middle_to_oldest_old_female dependants\n", "1 2.1196292 -1.3062152 -0.7801407\n", "2 0.2302043 -0.3189938 0.1197864\n", "3 3.8043177 -1.3062152 -2.2200240\n", "4 2.6982091 -1.3062152 -0.5183437\n", "5 19.6216703 -1.3062152 -2.2200240\n", "6 -1.0625601 -1.3062152 -2.2200240\n", " unemployment no_higher_education foreign_nationals primary_school_age\n", "1 -0.5060886 -0.6075338 0.284861877 -1.458348 \n", "2 1.3419462 0.8849880 0.768744204 -1.458348 \n", "3 -0.2425543 -1.6458099 -1.081394102 -1.458348 \n", "4 -1.2391934 -1.6458099 -0.004949997 1.865648 \n", "5 -2.2981224 -1.6458099 -1.081394102 -1.458348 \n", "6 -2.2981224 -1.6458099 2.865567617 -1.458348 \n", " one_person_households\n", "1 0.9333249 \n", "2 -0.7026211 \n", "3 3.2236494 \n", "4 0.2789465 \n", "5 3.2236494 \n", "6 0.9333249 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Copy the data\n", "indicator_z_scores <- indicator_domains_pct\n", "\n", "# Get the number of columns in the data\n", "num_cols = ncol(indicator_z_scores)\n", "\n", "# Calculate the z scores for each of the relevant columns - starting at the 2nd column\n", "for(col in names(indicator_z_scores)[2:num_cols]) {\n", " # rename column (remove the '_pct' in the name)\n", " new_col_name <- gsub(\"_pct\", \"\", col)\n", " indicator_z_scores[new_col_name] = scale(indicator_z_scores[col])\n", "}\n", "\n", "# Remove the original data to leave only the area identifier and the z scores\n", "indicator_z_scores <- indicator_z_scores[-c(2:num_cols)]\n", "\n", "head(indicator_z_scores)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Output the Z-score data" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Output the z-score data as a csv file\n", "output_file <- file.path(pipeline_dir, \"censusDataZ.csv\")\n", "write.csv(indicator_z_scores, output_file, row.names = FALSE)" ] }, { "cell_type": "markdown", "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" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }