Social Vulnerability Ireland - Calculate Vulnerability

Environment

R Libraries

The relvant R libraries are imported in to the kernal:

# Load R libraries
if(!require("pacman"))
    install.packages("pacman")

p_load("sf", "tidyverse")

print("Loaded Packages:")
p_loaded()

Loading required package: pacman

[1] "Loaded Packages:"

1. 'lubridate'
2. 'forcats'
3. 'stringr'
4. 'dplyr'
5. 'purrr'
6. 'readr'
7. 'tidyr'
8. 'tibble'
9. 'ggplot2'
10. 'tidyverse'
11. 'sf'
12. 'pacman'

Output directory

# create the output directory if it does not exist
output_dir <- file.path("../..","3_outputs","Ireland","2022")
if(!dir.exists(output_dir)){
    dir.create(output_dir, recursive = TRUE)
    print(paste0(output_dir, " created"))
}

Set the GUID

GUID <- "SA_GUID__1"

Load Data

Import the data

# Load census data
census_indicator_data <- read.csv("../../2_pipeline/Ireland/1a_CensusData/2022/censusDataZ.csv")
colnames(census_indicator_data)[colnames(census_indicator_data) == "GUID"] = "SA_GUID__1"
head(census_indicator_data)

# Load Coperncius data: tree cover density (TCD) and imperviousness density (IMP)
tcd_indicator_data <- st_read("../../2_pipeline/Ireland/1b_Copernicus/2022/census_areas_TCD.geojson")
imd_indicator_data <- st_read("../../2_pipeline/Ireland/1b_Copernicus/2022/census_areas_IMD.geojson")

# Get the geospatial data from the TCS data (the IMD data also has same spatial data)
oa <- subset(tcd_indicator_data, select = c(GUID, 'geometry'))

# Load vulnerability mapping information from the config file
## This mapping information is used to help guide the amalgamation of the data.
## Weighting can be changed in this file, depending on the scenario.
## Scenario 1 (best case scenario): Weighting values 1 or -1:
##  where 1 means no change
##  or -1 means all the indicator values are multiplied by -1, resulting in an inverse indicator.
## Scenario 2: Weighting values 0.5 or -0.5:
##  For domains with just a single indicators or where there is a lack of information related to missing indicators. 
##  For these domains the weights are halved using a weight of 0.5, or -0.5 for an inverse indicator.
##  Therefore the influence of these indicators are reduced in half.
## Other scenarios are supported by using other decimal numbers if decided for a particular dataset.
indicator_mapping <- read.csv("config/vulnerabilityIndicatorMappings.csv", header=TRUE, sep=",", stringsAsFactors = FALSE, fileEncoding="UTF-8-BOM")

# Print up to 100 rows of vulnerabiltiy mapping config file
head(indicator_mapping,100)

A data.frame: 6 × 26

	SA_GUID__1	early_childhood	age_middle_to_oldest_old	poor_health	disability	one_parent_households	three_or_more_children_households	low_skilled_employment	farmers	unemployment	⋯	rented	journey_time	not_volunteers	primary_school_age	one_person_households	no_central_heating	private_water_supply	year_built	mobile_home	unoccupied_dwellings
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	4c07d11e-11d3-851d-e053-ca3ca8c0ca7f	-0.1079508	-0.4448091	-0.0867229	-0.4589362	-0.6992867	0.7971503	0.3821265	1.8818244	-0.83703083	⋯	-0.9475582	0.6458280	0.2721853	0.53543614	-0.7988385	-0.7784105	2.8290832	0.1247138	0.9507964	-0.2685905
2	4c07d11e-123a-851d-e053-ca3ca8c0ca7f	-0.2315741	0.7875206	0.1669895	0.5523710	-0.4125867	0.1677345	-0.3005233	1.0930868	-0.26121797	⋯	-0.9117719	0.3856925	-1.2744132	-0.09998755	-0.8885092	0.3283476	2.3937964	0.1865261	-0.1877926	-0.5759916
3	4c07d11e-14b1-851d-e053-ca3ca8c0ca7f	-0.3928420	-0.6320030	1.2441066	0.2624819	0.4618750	-0.3677598	-0.4182116	-0.4741963	0.06414964	⋯	-0.1088473	0.1347198	-0.4611731	0.29209620	-0.4097515	-0.2296817	-0.4744110	-0.5141473	-0.1877926	-0.5772738
4	bf640964-28f3-4ccf-a610-04685d80ea2e	0.7370521	0.3823155	0.3432288	-0.1651002	-0.5449551	-0.6223328	-0.7312063	0.1385892	-0.18265319	⋯	-0.7531121	-0.5219048	-0.7638791	0.28438525	-0.1349893	-0.3651274	0.6291462	0.1706238	0.3058362	-0.5006639
5	4c07d11d-f709-851d-e053-ca3ca8c0ca7f	-0.1666197	-0.3376567	0.1187018	-0.8724183	-0.3649398	0.2410678	-0.3254920	1.6827210	-0.28372469	⋯	-1.0212046	0.3517089	-0.4219533	0.36431183	-0.1519057	0.6567264	2.2075847	0.4902435	0.6692771	-0.3823238
6	4c07d11e-1237-851d-e053-ca3ca8c0ca7f	0.1772575	0.2407678	0.5828047	0.8390637	-0.4233032	0.4107411	0.7385225	1.5548942	0.45793149	⋯	-0.7493154	0.9712513	-0.9921427	0.24013874	-0.6550195	-0.2296817	0.3011058	0.7315812	0.4676137	-0.3881432

Reading layer `census_areas_TCD' from data source 
  `/Cities/2_pipeline/Ireland/1b_Copernicus/2022/census_areas_TCD.geojson' 
  using driver `GeoJSON'
Simple feature collection with 18919 features and 32 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 417471.5 ymin: 519663.7 xmax: 734481.1 ymax: 966896.3
Projected CRS: IRENET95 / Irish Transverse Mercator
Reading layer `census_areas_IMD' from data source 
  `/Cities/2_pipeline/Ireland/1b_Copernicus/2022/census_areas_IMD.geojson' 
  using driver `GeoJSON'
Simple feature collection with 18919 features and 32 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 417471.5 ymin: 519663.7 xmax: 734481.1 ymax: 966896.3
Projected CRS: IRENET95 / Irish Transverse Mercator

A data.frame: 27 × 9

	domain	indicator	sensitivity	prepare	respond	recover	adaptive_capacity	enhanced_exposure	weight
	<chr>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	age	early_childhood	1	0	0	0	0	0	1
2	age	age_middle_to_oldest_old	1	0	0	0	0	0	1
3	health	poor_health	1	0	0	0	0	0	1
4	health	disability	1	0	0	0	0	0	1
5	income	one_parent_households	0	1	1	1	1	0	1
6	income	three_or_more_children_households	0	1	1	1	1	0	1
7	income	low_skilled_employment	0	1	1	1	1	0	1
8	income	farmers	0	1	1	1	1	0	1
9	income	unemployment	0	1	1	1	1	0	1
10	info_access_use	no_higher_education	0	1	1	1	1	0	1
11	info_access_use	speak_english	0	1	1	1	1	0	1
12	info_access_use	no_internet	0	1	1	1	1	0	1
13	local_knowledge	new_residents	0	1	1	0	1	0	1
14	local_knowledge	foreign_nationals	0	1	1	0	1	0	1
15	mobility	no_car	0	0	1	1	1	0	1
16	physical_access	journey_time	0	0	1	0	1	0	1
17	tenure	rented	0	1	0	0	1	0	1
18	social_network	not_volunteers	0	0	1	1	1	0	1
19	social_network	primary_school_age	0	0	1	1	1	0	-1
20	social_network	one_person_households	0	0	1	1	1	0	1
21	housing_characteristics	no_central_heating	0	0	0	1	1	0	1
22	housing_characteristics	private_water_supply	0	0	0	1	1	0	1
23	housing_characteristics	year_built	0	0	0	0	0	1	1
24	housing_characteristics	mobile_home	0	0	0	0	0	1	1
25	housing_characteristics	unoccupied_dwellings	0	0	0	0	0	1	1
26	physical_environment	impervious	0	0	0	0	0	1	1
27	physical_environment	tree_cover_density	0	0	0	0	0	1	1

Prepare Data

Combine data into a single indicator dataset

# combine census data with copernicus TCD and IMD data (without geospatial data to advoid duplication)
indicator_data <- merge(tcd_indicator_data, st_drop_geometry(census_indicator_data), by.x = GUID, by.y = GUID, all.x = TRUE)
indicator_data <- merge(imd_indicator_data, st_drop_geometry(indicator_data), by=GUID)

# drop the geometry
indicator_data <- st_drop_geometry(indicator_data)

# change the small area id column data type (GUID) to character string
indicator_data[GUID] <- lapply(indicator_data[GUID], as.character)

# trim the columns
indicator_data <- subset(indicator_data, select=c(names(census_indicator_data), 'tree_cover_density', 'impervious'))

# Set missing data fields to zero (0)
# Note: In Italian census the NA or empty data fields are areas with no population (e.g. parks, schools, etc.)
indicator_data[is.na(indicator_data)] <- 0

# Print the first part of the indicators, which are now collated into one table
head(indicator_data)

A data.frame: 6 × 28

	SA_GUID__1	early_childhood	age_middle_to_oldest_old	poor_health	disability	one_parent_households	three_or_more_children_households	low_skilled_employment	farmers	unemployment	⋯	not_volunteers	primary_school_age	one_person_households	no_central_heating	private_water_supply	year_built	mobile_home	unoccupied_dwellings	tree_cover_density	impervious
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	000581a3-4ebd-4a74-b5f8-0bd78cd7ede5	3.3880435	-0.8683184	-0.72494112	-0.92235833	-0.9537932	-0.28864382	-0.3191812	-0.5775435	-0.61650259	⋯	0.88627118	1.7138413	-1.4596182731	-0.7784105	-0.5390374	-0.8371139	-0.1877926	-0.2345322	0.7388976	-0.3062498
2	00275248-3d78-4fdb-8e5c-8ff1f76aebea	-1.2041476	1.0830212	0.27801178	-0.62611003	-0.5813707	0.24855343	-0.7175350	0.2814244	-0.23523246	⋯	-0.81754631	-0.5610249	-0.0009715101	-0.7784105	1.3835981	-0.4334056	-0.1877926	-0.3975997	0.1744364	-0.4046076
3	002b83e4-5cce-414a-8570-871a50ae0964	0.2940109	-0.6563918	0.07647292	0.06773408	0.3083236	-0.06192013	-0.7370951	-0.1126775	0.22240140	⋯	0.52365099	0.3585112	-0.2053615213	-0.1120970	-0.5390374	-0.7250643	-0.1877926	-0.5907831	0.3864675	0.2528562
4	005eec3f-d746-480f-ae2c-bc6f1151548d	-1.3337162	1.4547264	0.49313225	0.45603900	-1.0918818	-1.18351563	-1.9157382	-0.5775435	-0.07660151	⋯	-0.27505307	-1.3574765	-0.6525911498	-0.1318884	-0.3106055	-0.7827532	-0.1877926	-0.3274716	0.7008530	0.5785962
5	00b00ae4-229d-455d-84f1-d6face4876b1	1.7131001	-1.2060316	-0.32805634	-0.03080027	3.2416831	1.26642096	1.5271694	-0.5775435	0.75768133	⋯	1.59439615	2.9123134	-1.1159542610	-0.7784105	-0.5390374	-0.7424513	-0.1877926	-0.6509695	0.4060470	0.5467661
6	00df2b78-66c8-4009-89e0-0e14e15e4208	1.5084163	-0.8589597	-1.03118100	-1.02730679	-0.4959203	-0.57201990	0.4816479	-0.4599295	-0.97841257	⋯	-0.02986993	0.6262758	-0.9415702944	-0.7784105	-0.5390374	-0.6196710	-0.1877926	-0.4166064	0.7989094	-0.4272128

Weight the indicator datas

# Get the indicator weighting, previously loaded from the config file
indicator_weighting <- indicator_mapping %>% select('indicator', 'weight')
indicator_weighting <- indicator_weighting %>% spread(key = 'indicator', value = 'weight')

# Get the column names and weights
names <- names(indicator_weighting)
weights <- indicator_weighting[, names]

# Copy and rename the dataset
indicator_data_weighted <- indicator_data
head(indicator_data_weighted) 

# Multiply the indicators by the config file weighting
indicator_data_weighted[, names] <- sweep(indicator_data_weighted[, names], 2, unlist(weights[, names]), "*")
head(indicator_data_weighted)

A data.frame: 6 × 28

	SA_GUID__1	early_childhood	age_middle_to_oldest_old	poor_health	disability	one_parent_households	three_or_more_children_households	low_skilled_employment	farmers	unemployment	⋯	not_volunteers	primary_school_age	one_person_households	no_central_heating	private_water_supply	year_built	mobile_home	unoccupied_dwellings	tree_cover_density	impervious
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	000581a3-4ebd-4a74-b5f8-0bd78cd7ede5	3.3880435	-0.8683184	-0.72494112	-0.92235833	-0.9537932	-0.28864382	-0.3191812	-0.5775435	-0.61650259	⋯	0.88627118	1.7138413	-1.4596182731	-0.7784105	-0.5390374	-0.8371139	-0.1877926	-0.2345322	0.7388976	-0.3062498
2	00275248-3d78-4fdb-8e5c-8ff1f76aebea	-1.2041476	1.0830212	0.27801178	-0.62611003	-0.5813707	0.24855343	-0.7175350	0.2814244	-0.23523246	⋯	-0.81754631	-0.5610249	-0.0009715101	-0.7784105	1.3835981	-0.4334056	-0.1877926	-0.3975997	0.1744364	-0.4046076
3	002b83e4-5cce-414a-8570-871a50ae0964	0.2940109	-0.6563918	0.07647292	0.06773408	0.3083236	-0.06192013	-0.7370951	-0.1126775	0.22240140	⋯	0.52365099	0.3585112	-0.2053615213	-0.1120970	-0.5390374	-0.7250643	-0.1877926	-0.5907831	0.3864675	0.2528562
4	005eec3f-d746-480f-ae2c-bc6f1151548d	-1.3337162	1.4547264	0.49313225	0.45603900	-1.0918818	-1.18351563	-1.9157382	-0.5775435	-0.07660151	⋯	-0.27505307	-1.3574765	-0.6525911498	-0.1318884	-0.3106055	-0.7827532	-0.1877926	-0.3274716	0.7008530	0.5785962
5	00b00ae4-229d-455d-84f1-d6face4876b1	1.7131001	-1.2060316	-0.32805634	-0.03080027	3.2416831	1.26642096	1.5271694	-0.5775435	0.75768133	⋯	1.59439615	2.9123134	-1.1159542610	-0.7784105	-0.5390374	-0.7424513	-0.1877926	-0.6509695	0.4060470	0.5467661
6	00df2b78-66c8-4009-89e0-0e14e15e4208	1.5084163	-0.8589597	-1.03118100	-1.02730679	-0.4959203	-0.57201990	0.4816479	-0.4599295	-0.97841257	⋯	-0.02986993	0.6262758	-0.9415702944	-0.7784105	-0.5390374	-0.6196710	-0.1877926	-0.4166064	0.7989094	-0.4272128

A data.frame: 6 × 28

	SA_GUID__1	early_childhood	age_middle_to_oldest_old	poor_health	disability	one_parent_households	three_or_more_children_households	low_skilled_employment	farmers	unemployment	⋯	not_volunteers	primary_school_age	one_person_households	no_central_heating	private_water_supply	year_built	mobile_home	unoccupied_dwellings	tree_cover_density	impervious
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	000581a3-4ebd-4a74-b5f8-0bd78cd7ede5	3.3880435	-0.8683184	-0.72494112	-0.92235833	-0.9537932	-0.28864382	-0.3191812	-0.5775435	-0.61650259	⋯	0.88627118	-1.7138413	-1.4596182731	-0.7784105	-0.5390374	-0.8371139	-0.1877926	-0.2345322	0.7388976	-0.3062498
2	00275248-3d78-4fdb-8e5c-8ff1f76aebea	-1.2041476	1.0830212	0.27801178	-0.62611003	-0.5813707	0.24855343	-0.7175350	0.2814244	-0.23523246	⋯	-0.81754631	0.5610249	-0.0009715101	-0.7784105	1.3835981	-0.4334056	-0.1877926	-0.3975997	0.1744364	-0.4046076
3	002b83e4-5cce-414a-8570-871a50ae0964	0.2940109	-0.6563918	0.07647292	0.06773408	0.3083236	-0.06192013	-0.7370951	-0.1126775	0.22240140	⋯	0.52365099	-0.3585112	-0.2053615213	-0.1120970	-0.5390374	-0.7250643	-0.1877926	-0.5907831	0.3864675	0.2528562
4	005eec3f-d746-480f-ae2c-bc6f1151548d	-1.3337162	1.4547264	0.49313225	0.45603900	-1.0918818	-1.18351563	-1.9157382	-0.5775435	-0.07660151	⋯	-0.27505307	1.3574765	-0.6525911498	-0.1318884	-0.3106055	-0.7827532	-0.1877926	-0.3274716	0.7008530	0.5785962
5	00b00ae4-229d-455d-84f1-d6face4876b1	1.7131001	-1.2060316	-0.32805634	-0.03080027	3.2416831	1.26642096	1.5271694	-0.5775435	0.75768133	⋯	1.59439615	-2.9123134	-1.1159542610	-0.7784105	-0.5390374	-0.7424513	-0.1877926	-0.6509695	0.4060470	0.5467661
6	00df2b78-66c8-4009-89e0-0e14e15e4208	1.5084163	-0.8589597	-1.03118100	-1.02730679	-0.4959203	-0.57201990	0.4816479	-0.4599295	-0.97841257	⋯	-0.02986993	-0.6262758	-0.9415702944	-0.7784105	-0.5390374	-0.6196710	-0.1877926	-0.4166064	0.7989094	-0.4272128

# Histogram visualisation of weighted indicators
indicator_columns <- colnames(indicator_data_weighted)[-1]
for( current_indicator_column in indicator_columns ) {
    indicator_filtered <- indicator_data_weighted[,current_indicator_column] 
    indicator_filtered[indicator_filtered == "NaN"] <- 0

    title <- paste("'", current_indicator_column, "' domain histogram", sep = "")
    x_label <- paste("'", current_indicator_column, "' z-score", sep = "")
    y_label <- paste("Count", sep = "")
    hist(indicator_filtered, breaks="FD", col="grey", labels = FALSE, main=title, xlab=x_label, ylab=y_label)
    box("figure", lwd = 4)
}

Process social vulnerability scores

Calculate domain scores

# Get the domains and their associated indicator ID
domain_indicators <- indicator_mapping %>% select('domain', 'indicator')

# Get a vector/array of the unique domain names
unique_domains <- unique(domain_indicators$domain)

# Initialise the domain score dataset with the GUID
domain_scores <- indicator_data_weighted %>% select(all_of(GUID))

# Loop through each domain
for (current_domain in unique_domains) {
    # Identify which indicators are used within this domain (current_domain)
    current_domain_info <- domain_indicators %>% filter(domain == current_domain)

    # Count the number of indicators in this domain
    domain_indicator_count <- length(current_domain_info$indicator)

    # Get a vector/array of the indicators used by this domain, and add the GUID column name
    current_domain_indicators <- current_domain_info$indicator
    current_domain_indicators <- (c(GUID, current_domain_indicators))

    # filter the dataset to only use the indicators in the domain
    current_domain_data <- indicator_data_weighted[current_domain_indicators]

    # Calculate the internal weight distribution for the indicators within this domain,
    # using an equal weight distribution across this domain
    internal_domain_weight <- 1.0 / domain_indicator_count

    # Internally weight the data for this domain
    current_domain_data_weighted <- current_domain_data %>% mutate_if(is.numeric, function(x) {x*internal_domain_weight})

    # Sum each data row to get the total score for the domain
    current_domain_data_weighted[, current_domain] <- rowSums(current_domain_data_weighted[2:(domain_indicator_count+1)], na.rm = TRUE)

    # Add the current domain score to the overall results
    domain_indicator_score <- current_domain_data_weighted %>% select(all_of(GUID), all_of(current_domain))
    domain_scores <- merge(domain_scores, domain_indicator_score, by=GUID)
}

# Print the first part of the domain z-scores, which are now collated into one table
head(domain_scores)

A data.frame: 6 × 12

	SA_GUID__1	age	health	income	info_access_use	local_knowledge	mobility	physical_access	tenure	social_network	housing_characteristics	physical_environment
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	000581a3-4ebd-4a74-b5f8-0bd78cd7ede5	1.25986257	-0.8236497	-0.55113286	-0.3552132	0.5916092	-0.80430587	1.0782176	-0.6291412	-0.76239614	-0.51537733	0.2163239
2	00275248-3d78-4fdb-8e5c-8ff1f76aebea	-0.06056320	-0.1740491	-0.20083207	-0.1481675	-0.5988715	-0.65728630	-0.4619480	-1.0319325	-0.08583099	-0.08272206	-0.1150856
3	002b83e4-5cce-414a-8570-871a50ae0964	-0.18119048	0.0721035	-0.07619354	0.3552067	2.0026374	-0.07351769	2.0566182	0.3733581	-0.01340725	-0.43095487	0.3196619
4	005eec3f-d746-480f-ae2c-bc6f1151548d	0.06050512	0.4745856	-0.96905613	-0.5608664	3.0503211	1.07997948	-0.7677528	1.1891164	0.14327742	-0.34810226	0.6397246
5	00b00ae4-229d-455d-84f1-d6face4876b1	0.25353426	-0.1794283	1.24308226	-0.6141542	-0.5494787	-0.02238299	-0.5840908	2.1279286	-0.81129050	-0.57973227	0.4764065
6	00df2b78-66c8-4009-89e0-0e14e15e4208	0.32472828	-1.0292439	-0.40492687	-0.4923798	0.4801259	-0.83850016	3.0768631	-0.6391705	-0.53257202	-0.50830359	0.1858483

# Histogram visualisation of domain z-scores
for( current_domain in unique_domains ) {
    domain_scores_filtered <- domain_scores[,current_domain] 
    domain_scores_filtered[domain_scores_filtered == "NaN"] <- 0

    title <- paste("'", current_domain, "' domain histogram", sep = "")
    x_label <- paste("'", current_domain, "' z-score", sep = "")
    y_label <- paste("Count", sep = "")
    hist(domain_scores_filtered, breaks="FD", col="grey", labels=FALSE, main=title, xlab=x_label, ylab=y_label)
    box("figure", lwd = 4)
}

Calculate dimension scores

Need to collate the domains into the dimensions

# Create a vector/array of the dimension names
dimensions <- c('sensitivity', 'prepare', 'respond', 'recover', 'adaptive_capacity', 'enhanced_exposure')

# Get the dimension and their associated indicator ID
dimension_indicators <- indicator_mapping %>% select(c('domain', all_of(dimensions)))
head(dimension_indicators)

# Initialise the dimensions score dataset with the GUID
dimension_scores <- indicator_data_weighted %>% select(all_of(GUID))

# loop through each of the dimensions and:
for (current_dimension in dimensions){
    # Identify which indicators are used within this dimension (current_dimension)
    # Then select the indicators marked with value 1, which means that the indicator is part of the dimension
    current_dimension_info <- dimension_indicators %>% select(c('domain', all_of(current_dimension)))
    current_dimension_info <- current_dimension_info %>% filter(dimension_indicators[, current_dimension] == 1)

    # Get a array/vector of the unique domains in this dimension
    current_dimension_domains <- unique(current_dimension_info$domain)

    # Count the number of domains in this dimension
    dimension_domain_count <- length(current_dimension_domains)

    # Filter the domain scores dataset to only use the domains in the dimension, and add the GUID column name
    current_dimension_data <- domain_scores %>% select(c(all_of(GUID), all_of(current_dimension_domains)))  

    # Sum each data row to get the total score for the dimension
    current_dimension_data[, current_dimension] <- rowSums(current_dimension_data[2:(dimension_domain_count+1)], na.rm = TRUE)

    # Add the current dimension score to the overall results
    dimension_indicator_score <- current_dimension_data %>% select(all_of(GUID), all_of(current_dimension))
    dimension_scores <- merge(dimension_scores, dimension_indicator_score, by=GUID)  
}

# generate z-scores with the scale function in order to standardise the dimension data
dimension_scores <- dimension_scores %>% mutate_if(is.numeric, scale)

# Print the first part of the dimension scores, which are now collated into one table
head(dimension_scores)

A data.frame: 6 × 7

	domain	sensitivity	prepare	respond	recover	adaptive_capacity	enhanced_exposure
	<chr>	<int>	<int>	<int>	<int>	<int>	<int>
1	age	1	0	0	0	0	0
2	age	1	0	0	0	0	0
3	health	1	0	0	0	0	0
4	health	1	0	0	0	0	0
5	income	0	1	1	1	1	0
6	income	0	1	1	1	1	0

A data.frame: 6 × 7

	SA_GUID__1	sensitivity	prepare	respond	recover	adaptive_capacity	enhanced_exposure
	<chr>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>
1	000581a3-4ebd-4a74-b5f8-0bd78cd7ede5	0.38290372	-0.4856123	-0.3377139	-1.3729086	-0.61316276	-0.3497016
2	00275248-3d78-4fdb-8e5c-8ff1f76aebea	-0.20594059	-1.0185818	-0.9052009	-0.5397312	-1.02866162	-0.2313086
3	002b83e4-5cce-414a-8570-871a50ae0964	-0.09575557	1.3659655	1.7874752	-0.1097374	1.32022239	-0.1301417
4	005eec3f-d746-480f-ae2c-bc6f1151548d	0.46969785	1.3940082	0.8307672	-0.3008060	0.88678611	0.3410119
5	00b00ae4-229d-455d-84f1-d6face4876b1	0.06504954	1.1356657	-0.5626938	-0.3603958	0.06607222	-0.1208251
6	00df2b78-66c8-4009-89e0-0e14e15e4208	-0.61841747	-0.5434782	0.5417954	-1.2756320	0.04443066	-0.3770668

# Histogram visualisation of dimension z-scores
for( current_dimension in dimensions ){
    dimension_scores_filtered <- dimension_scores[,current_dimension] 
    dimension_scores_filtered[dimension_scores_filtered == "NaN"] <- 0
   
    title <- paste("'", current_dimension, "' dimension histogram", sep = "")
    x_label <- paste("'", current_dimension, "' z-score", sep = "")
    y_label <- paste("Count", sep = "")
    hist(dimension_scores_filtered, breaks="FD", col="grey", labels=FALSE, main=title, xlab=x_label, ylab=y_label)
    box("figure", lwd = 4)
}

Calculate vulnerability score

# Initialise the vulnerability score dataset with the GUID
vulnerability_scores <- domain_scores %>% select(all_of(GUID))

#sum the domains to create a total overall score of vulnerability
vulnerability_scores$social_vulnerability <- rowSums(domain_scores[2:(ncol(domain_scores))], na.rm = TRUE)

# generate z-scores with the scale function in order to standardise the vulnerability data
vulnerability_scores <- vulnerability_scores %>% mutate_if(is.numeric, scale)

# Print the first part of the vulnerability scores, which are now collated into one table
head(vulnerability_scores)

A data.frame: 6 × 2

	SA_GUID__1	social_vulnerability
	<chr>	<dbl[,1]>
1	000581a3-4ebd-4a74-b5f8-0bd78cd7ede5	-0.33398100
2	00275248-3d78-4fdb-8e5c-8ff1f76aebea	-0.93275401
3	002b83e4-5cce-414a-8570-871a50ae0964	1.13569837
4	005eec3f-d746-480f-ae2c-bc6f1151548d	1.02930797
5	00b00ae4-229d-455d-84f1-d6face4876b1	0.19607516
6	00df2b78-66c8-4009-89e0-0e14e15e4208	-0.09735021

# Histogram visualisation of Vulnerability z-scores
title <- paste("'Vulnerability' histogram", sep = "")
x_label <- paste("'Vulnerability' z-score", sep = "")
y_label <- paste("Count", sep = "")
hist(vulnerability_scores$social_vulnerability, breaks="FD", col="grey", labels=FALSE, main=title, xlab=x_label, ylab=y_label)
box("figure", lwd = 4)

# Merge all the indicators, domains, dimensions, and total vulnerability into one dataset
output_dataset <- merge(indicator_data_weighted, domain_scores, by=GUID)
output_dataset <- merge(output_dataset, dimension_scores, by=GUID)
output_dataset <- merge(output_dataset, vulnerability_scores, by=GUID)

head(output_dataset)

A data.frame: 6 × 46

	SA_GUID__1	early_childhood	age_middle_to_oldest_old	poor_health	disability	one_parent_households	three_or_more_children_households	low_skilled_employment	farmers	unemployment	⋯	social_network	housing_characteristics	physical_environment	sensitivity	prepare	respond	recover	adaptive_capacity	enhanced_exposure	social_vulnerability
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<dbl>	<dbl>	<dbl>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>
1	000581a3-4ebd-4a74-b5f8-0bd78cd7ede5	3.3880435	-0.8683184	-0.72494112	-0.92235833	-0.9537932	-0.28864382	-0.3191812	-0.5775435	-0.61650259	⋯	-0.76239614	-0.51537733	0.2163239	0.38290372	-0.4856123	-0.3377139	-1.3729086	-0.61316276	-0.3497016	-0.33398100
2	00275248-3d78-4fdb-8e5c-8ff1f76aebea	-1.2041476	1.0830212	0.27801178	-0.62611003	-0.5813707	0.24855343	-0.7175350	0.2814244	-0.23523246	⋯	-0.08583099	-0.08272206	-0.1150856	-0.20594059	-1.0185818	-0.9052009	-0.5397312	-1.02866162	-0.2313086	-0.93275401
3	002b83e4-5cce-414a-8570-871a50ae0964	0.2940109	-0.6563918	0.07647292	0.06773408	0.3083236	-0.06192013	-0.7370951	-0.1126775	0.22240140	⋯	-0.01340725	-0.43095487	0.3196619	-0.09575557	1.3659655	1.7874752	-0.1097374	1.32022239	-0.1301417	1.13569837
4	005eec3f-d746-480f-ae2c-bc6f1151548d	-1.3337162	1.4547264	0.49313225	0.45603900	-1.0918818	-1.18351563	-1.9157382	-0.5775435	-0.07660151	⋯	0.14327742	-0.34810226	0.6397246	0.46969785	1.3940082	0.8307672	-0.3008060	0.88678611	0.3410119	1.02930797
5	00b00ae4-229d-455d-84f1-d6face4876b1	1.7131001	-1.2060316	-0.32805634	-0.03080027	3.2416831	1.26642096	1.5271694	-0.5775435	0.75768133	⋯	-0.81129050	-0.57973227	0.4764065	0.06504954	1.1356657	-0.5626938	-0.3603958	0.06607222	-0.1208251	0.19607516
6	00df2b78-66c8-4009-89e0-0e14e15e4208	1.5084163	-0.8589597	-1.03118100	-1.02730679	-0.4959203	-0.57201990	0.4816479	-0.4599295	-0.97841257	⋯	-0.53257202	-0.50830359	0.1858483	-0.61841747	-0.5434782	0.5417954	-1.2756320	0.04443066	-0.3770668	-0.09735021

Correlations

# check the correlations
correlation <- cor(output_dataset %>% select(-c(all_of(GUID))), use="pairwise.complete.obs")
correlation

A matrix: 45 × 45 of type dbl

	early_childhood	age_middle_to_oldest_old	poor_health	disability	one_parent_households	three_or_more_children_households	low_skilled_employment	farmers	unemployment	no_higher_education	⋯	social_network	housing_characteristics	physical_environment	sensitivity	prepare	respond	recover	adaptive_capacity	enhanced_exposure	social_vulnerability
early_childhood	1.000000000	-0.32493451	-0.19669611	-0.348783559	0.09850766	0.25303355	0.013435867	-0.01978601	-0.13004664	-0.137898158	⋯	-0.38366160	-0.160256869	-0.03372868	0.056874631	0.00745299	-0.13050168	-0.26465066	-0.12476835	-0.13656575	-0.092781282
age_middle_to_oldest_old	-0.324934515	1.00000000	0.30653419	0.524380117	-0.17156982	-0.21339372	-0.018174118	0.11130965	0.07775926	0.196145051	⋯	0.26164119	0.169576750	-0.13170866	0.660967764	-0.26899725	-0.10851529	0.13857486	-0.15313015	-0.01957992	0.040267361
poor_health	-0.196696114	0.30653419	1.00000000	0.635159460	0.30466708	-0.20580461	0.291697215	-0.13013724	0.57211583	0.424979686	⋯	0.42639629	0.022511126	0.15267300	0.765871577	0.29258876	0.35238297	0.51649964	0.33806981	0.16419232	0.534897413
disability	-0.348783559	0.52438012	0.63515946	1.000000000	0.26578634	-0.26676366	0.279103306	-0.12151826	0.55274473	0.435642179	⋯	0.47151022	0.042917765	0.10336068	0.794732682	0.17260065	0.27259948	0.49373907	0.25145006	0.12903530	0.461766118
one_parent_households	0.098507657	-0.17156982	0.30466708	0.265786342	1.00000000	0.13073343	0.399706920	-0.22809509	0.52557353	0.268872631	⋯	0.03932941	-0.253554724	0.20588712	0.218302790	0.40820193	0.24661411	0.28460567	0.27320175	0.03805277	0.332412186
three_or_more_children_households	0.253033549	-0.21339372	-0.20580461	-0.266763657	0.13073343	1.00000000	0.120114454	0.27576238	-0.12234336	-0.066259317	⋯	-0.68726343	-0.075504006	-0.31261976	-0.190010345	-0.27880835	-0.39096576	-0.39137609	-0.41701881	-0.35520161	-0.464972587
low_skilled_employment	0.013435867	-0.01817412	0.29169722	0.279103306	0.39970692	0.12011445	1.000000000	0.06854214	0.44466332	0.385397713	⋯	0.07614730	-0.027756007	-0.04957132	0.248442188	0.25359005	0.23286477	0.35800972	0.18890973	-0.06651743	0.217004201
farmers	-0.019786008	0.11130965	-0.13013724	-0.121518259	-0.22809509	0.27576238	0.068542137	1.00000000	-0.09975730	0.097044899	⋯	-0.24591427	0.504804657	-0.53228034	-0.070281154	-0.23117977	-0.16727776	0.01664975	-0.17952180	-0.19571665	-0.282746200
unemployment	-0.130046641	0.07775926	0.57211583	0.552744730	0.52557353	-0.12234336	0.444663319	-0.09975730	1.00000000	0.560757353	⋯	0.42159647	0.043276112	0.14323937	0.470747553	0.52914364	0.49626060	0.67751217	0.50210064	0.16835665	0.580520974
no_higher_education	-0.137898158	0.19614505	0.42497969	0.435642179	0.26887263	-0.06625932	0.385397713	0.09704490	0.56075735	1.000000000	⋯	0.29491114	0.151364799	-0.03472561	0.403287370	0.32913634	0.37864876	0.57496105	0.33645163	0.06372239	0.386552633
speak_english	0.159789220	-0.25558916	0.04837885	-0.098596519	0.11246126	-0.16309966	0.091922308	-0.28731064	0.13838698	-0.050461791	⋯	0.13908692	-0.140883234	0.28951633	-0.064086473	0.62692745	0.49581042	0.26247788	0.50071430	0.19279833	0.453890551
no_internet	-0.171955862	0.34737657	0.35435344	0.403307921	0.04603465	-0.10769893	0.396781083	0.40227118	0.44235813	0.506317270	⋯	0.33097211	0.422236585	-0.23707766	0.409525528	0.20552774	0.31968299	0.62414926	0.29067519	0.04031925	0.307150723
new_residents	-0.051379223	-0.15971228	-0.10907521	-0.116649178	-0.21942293	-0.35028773	-0.324276498	-0.19301376	-0.14388092	-0.183981741	⋯	0.23901731	0.037697828	0.18031885	-0.191716536	0.45319577	0.43205921	0.12879105	0.44553145	0.20109990	0.347594200
foreign_nationals	0.089204917	-0.34831679	-0.08502597	-0.243047847	-0.05245160	-0.36431346	-0.208958688	-0.35112423	-0.04082757	-0.174546953	⋯	0.25145621	-0.087764849	0.35614831	-0.257713163	0.65661147	0.53908801	0.20154501	0.58794853	0.29241963	0.482867295
no_car	-0.225941633	-0.01883762	0.38646681	0.349463767	0.22544232	-0.52259886	0.046124863	-0.35333484	0.44095634	0.237776379	⋯	0.70498011	0.053488925	0.51586944	0.215564107	0.68895359	0.77101618	0.78780492	0.80994503	0.54021233	0.838259521
rented	0.002567175	-0.32199244	0.22335071	0.126685685	0.42133306	-0.35310108	0.061563474	-0.45121952	0.39073362	0.084570979	⋯	0.45635738	-0.187597533	0.46911229	0.013435087	0.86540369	0.64376464	0.50093892	0.76420116	0.33867084	0.731306447
journey_time	0.158255605	-0.21205543	-0.17667633	-0.222821823	-0.03706221	0.23763496	0.025850812	0.11678757	-0.17818191	-0.088076691	⋯	-0.28522486	-0.045655661	-0.08990361	-0.198950445	-0.09560752	0.22052776	-0.23087296	0.10578340	-0.11761271	0.008771085
not_volunteers	0.108404034	-0.21304424	0.25229599	0.128253747	0.39590010	-0.18634699	0.207617082	-0.39555772	0.35920035	0.158925312	⋯	0.50891924	-0.290180669	0.43997269	0.121095455	0.45026808	0.44654439	0.37008743	0.44186793	0.24385282	0.492609731
primary_school_age	-0.582863517	0.37135841	0.23178782	0.365188402	-0.21914478	-0.61570082	-0.103842573	-0.05264041	0.14261364	0.145501555	⋯	0.73178786	0.216055278	0.12681477	0.169181539	0.10760710	0.33282560	0.43452917	0.32385574	0.26384884	0.342381580
one_person_households	-0.289474835	0.36267025	0.36494585	0.445420560	-0.09845405	-0.56641225	0.047846714	-0.04144683	0.33765654	0.282795242	⋯	0.75045960	0.321140599	0.11646612	0.387791315	0.34092200	0.48009915	0.67081769	0.50630006	0.32157576	0.553804415
no_central_heating	-0.142289862	0.03779754	0.09161714	0.089734980	-0.07580625	-0.22561659	-0.027559381	0.11521648	0.12626793	0.090393332	⋯	0.27721567	0.589072386	0.02141389	0.033733420	0.22104280	0.28103287	0.43420592	0.36415663	0.40145124	0.312820361
private_water_supply	0.001127354	0.05584319	-0.14674819	-0.159032326	-0.23398375	0.30658908	0.061452795	0.71896521	-0.13401411	0.042351139	⋯	-0.28998956	0.553849147	-0.52246046	-0.109201576	-0.27089731	-0.20519093	-0.03451958	-0.19622269	-0.15441491	-0.305736645
year_built	-0.207242519	0.17554207	0.06408561	0.111494130	-0.22330381	-0.21114257	-0.087738295	0.22166419	0.02703836	0.081079787	⋯	0.26318819	0.620950841	-0.03434341	0.063147973	0.01462106	0.16448416	0.37855146	0.21251976	0.36738596	0.185202760
mobile_home	-0.002882623	0.01460049	0.02558688	0.006689724	-0.03103467	0.05752729	0.026925013	0.12928285	0.05310555	0.088576191	⋯	-0.01959576	0.442263639	-0.12155848	0.019308979	-0.04667923	-0.02275203	0.10294588	0.02668735	0.16648826	0.001256837
unoccupied_dwellings	-0.091314689	0.18455898	0.02763047	0.069645093	-0.13614674	-0.13588649	-0.049737523	0.20905626	0.04712357	0.115643499	⋯	0.11177671	0.555694679	-0.16749821	0.083618308	0.03907978	0.06995558	0.22347137	0.12525194	0.19471828	0.090953053
tree_cover_density	0.034488254	-0.07166947	0.10003601	0.072430433	0.18333969	-0.08450058	0.057292604	-0.30875578	0.10882391	0.003406082	⋯	0.11393401	-0.238307149	0.83825268	0.059376078	0.12521936	0.13556357	0.06591669	0.12328634	0.66775824	0.299616504
impervious	-0.091034560	-0.14914081	0.15592109	0.100854300	0.16183117	-0.43960813	-0.140399181	-0.58361507	0.13131766	-0.061623752	⋯	0.46135537	-0.262154110	0.83825268	0.007285676	0.42179354	0.44156161	0.30034761	0.46939893	0.65235633	0.567816655
age	0.580975682	0.58097568	0.09452898	0.151122124	-0.06287885	0.03411488	-0.004077839	0.07876719	-0.04499963	0.050128513	⋯	-0.10501335	0.008020888	-0.14237889	0.617790397	-0.22509054	-0.20570308	-0.10850350	-0.23916535	-0.13438228	-0.045194594
health	-0.301636240	0.45947425	0.90420116	0.904201156	0.31544608	-0.26131810	0.315638018	-0.13915902	0.62201898	0.475901772	⋯	0.49651923	0.036180495	0.14158004	0.862974046	0.25723779	0.34559923	0.55863604	0.32598934	0.16214734	0.551129317
income	0.075916346	-0.07553580	0.29380663	0.250336170	0.64505548	0.49554535	0.717456839	0.35871273	0.61691992	0.439651774	⋯	-0.13977031	0.067508516	-0.19245359	0.238988854	0.24030818	0.14734169	0.33364262	0.12975823	-0.14504558	0.134891693
info_access_use	-0.078409030	0.15044432	0.43250507	0.386854037	0.22331894	-0.17612469	0.456743892	0.11076648	0.59647221	0.760734303	⋯	0.39972040	0.226098780	0.00926576	0.391228997	0.60697954	0.62398411	0.76372504	0.58933978	0.15511165	0.599660894
local_knowledge	0.021418562	-0.28766827	-0.10990858	-0.203676181	-0.15394725	-0.40463844	-0.301941075	-0.30811472	-0.10459002	-0.203014621	⋯	0.27772755	-0.028350136	0.30377116	-0.254486744	0.62842138	0.54990602	0.18705072	0.58520155	0.27945233	0.470243606
mobility	-0.225941633	-0.01883762	0.38646681	0.349463767	0.22544232	-0.52259886	0.046124863	-0.35333484	0.44095634	0.237776379	⋯	0.70498011	0.053488925	0.51586944	0.215564107	0.68895359	0.77101618	0.78780492	0.80994503	0.54021233	0.838259521
physical_access	0.158255605	-0.21205543	-0.17667633	-0.222821823	-0.03706221	0.23763496	0.025850812	0.11678757	-0.17818191	-0.088076691	⋯	-0.28522486	-0.045655661	-0.08990361	-0.198950445	-0.09560752	0.22052776	-0.23087296	0.10578340	-0.11761271	0.008771085
tenure	0.002567175	-0.32199244	0.22335071	0.126685685	0.42133306	-0.35310108	0.061563474	-0.45121952	0.39073362	0.084570979	⋯	0.45635738	-0.187597533	0.46911229	0.013435087	0.86540369	0.64376464	0.50093892	0.76420116	0.33867084	0.731306447
social_network	-0.383661598	0.26164119	0.42639629	0.471510222	0.03932941	-0.68726343	0.076147297	-0.24591427	0.42159647	0.294911138	⋯	1.00000000	0.124046433	0.34314795	0.340532929	0.45139215	0.63252801	0.74098527	0.63883230	0.41647779	0.697477175
housing_characteristics	-0.160256869	0.16957675	0.02251113	0.042917765	-0.25355472	-0.07550401	-0.027756007	0.50480466	0.04327611	0.151364799	⋯	0.12404643	1.000000000	-0.29851456	0.032806901	-0.01550888	0.10410836	0.39997203	0.19276815	0.35325440	0.103010067
physical_environment	-0.033728676	-0.13170866	0.15267300	0.103360679	0.20588712	-0.31261976	-0.049571316	-0.53228034	0.14323937	-0.034725609	⋯	0.34314795	-0.298514559	1.00000000	0.039762327	0.32628163	0.34424297	0.21846891	0.35352423	0.78742043	0.517405536
sensitivity	0.056874631	0.66096776	0.76587158	0.794732682	0.21830279	-0.19001035	0.248442188	-0.07028115	0.47074755	0.403287370	⋯	0.34053293	0.032806901	0.03976233	1.000000000	0.08937894	0.16939852	0.38805513	0.13676922	0.06016465	0.414383023
prepare	0.007452990	-0.26899725	0.29258876	0.172600648	0.40820193	-0.27880835	0.253590047	-0.23117977	0.52914364	0.329136336	⋯	0.45139215	-0.015508877	0.32628163	0.089378944	1.00000000	0.82877495	0.69063013	0.89027582	0.30981126	0.826010881
respond	-0.130501679	-0.10851529	0.35238297	0.272599478	0.24661411	-0.39096576	0.232864767	-0.16727776	0.49626060	0.378648760	⋯	0.63252801	0.104108361	0.34424297	0.169398515	0.82877495	1.00000000	0.79468845	0.96950374	0.40467386	0.918295723
recover	-0.264650656	0.13857486	0.51649964	0.493739070	0.28460567	-0.39137609	0.358009724	0.01664975	0.67751217	0.574961054	⋯	0.74098527	0.399972035	0.21846891	0.388055130	0.69063013	0.79468845	1.00000000	0.82225951	0.47247540	0.834733672
adaptive_capacity	-0.124768352	-0.15313015	0.33806981	0.251450062	0.27320175	-0.41701881	0.188909731	-0.17952180	0.50210064	0.336451630	⋯	0.63883230	0.192768146	0.35352423	0.136769221	0.89027582	0.96950374	0.82225951	1.00000000	0.47103374	0.935696312
enhanced_exposure	-0.136565749	-0.01957992	0.16419232	0.129035302	0.03805277	-0.35520161	-0.066517431	-0.19571665	0.16835665	0.063722393	⋯	0.41647779	0.353254399	0.78742043	0.060164653	0.30981126	0.40467386	0.47247540	0.47103374	1.00000000	0.573701986
social_vulnerability	-0.092781282	0.04026736	0.53489741	0.461766118	0.33241219	-0.46497259	0.217004201	-0.28274620	0.58052097	0.386552633	⋯	0.69747717	0.103010067	0.51740554	0.414383023	0.82601088	0.91829572	0.83473367	0.93569631	0.57370199	1.000000000

Add geometry

# add st_drop_geometry
output_dataset_geom <- merge(output_dataset, oa, by.x=GUID, by.y=GUID, all.x = TRUE)
head(output_dataset_geom)

A data.frame: 6 × 47

	SA_GUID__1	early_childhood	age_middle_to_oldest_old	poor_health	disability	one_parent_households	three_or_more_children_households	low_skilled_employment	farmers	unemployment	⋯	housing_characteristics	physical_environment	sensitivity	prepare	respond	recover	adaptive_capacity	enhanced_exposure	social_vulnerability	geometry
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<dbl>	<dbl>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<MULTIPOLYGON [m]>
1	000581a3-4ebd-4a74-b5f8-0bd78cd7ede5	3.3880435	-0.8683184	-0.72494112	-0.92235833	-0.9537932	-0.28864382	-0.3191812	-0.5775435	-0.61650259	⋯	-0.51537733	0.2163239	0.38290372	-0.4856123	-0.3377139	-1.3729086	-0.61316276	-0.3497016	-0.33398100	MULTIPOLYGON (((676048.9 72...
2	00275248-3d78-4fdb-8e5c-8ff1f76aebea	-1.2041476	1.0830212	0.27801178	-0.62611003	-0.5813707	0.24855343	-0.7175350	0.2814244	-0.23523246	⋯	-0.08272206	-0.1150856	-0.20594059	-1.0185818	-0.9052009	-0.5397312	-1.02866162	-0.2313086	-0.93275401	MULTIPOLYGON (((637187.3 72...
3	002b83e4-5cce-414a-8570-871a50ae0964	0.2940109	-0.6563918	0.07647292	0.06773408	0.3083236	-0.06192013	-0.7370951	-0.1126775	0.22240140	⋯	-0.43095487	0.3196619	-0.09575557	1.3659655	1.7874752	-0.1097374	1.32022239	-0.1301417	1.13569837	MULTIPOLYGON (((645674.5 69...
4	005eec3f-d746-480f-ae2c-bc6f1151548d	-1.3337162	1.4547264	0.49313225	0.45603900	-1.0918818	-1.18351563	-1.9157382	-0.5775435	-0.07660151	⋯	-0.34810226	0.6397246	0.46969785	1.3940082	0.8307672	-0.3008060	0.88678611	0.3410119	1.02930797	MULTIPOLYGON (((716705.1 72...
5	00b00ae4-229d-455d-84f1-d6face4876b1	1.7131001	-1.2060316	-0.32805634	-0.03080027	3.2416831	1.26642096	1.5271694	-0.5775435	0.75768133	⋯	-0.57973227	0.4764065	0.06504954	1.1356657	-0.5626938	-0.3603958	0.06607222	-0.1208251	0.19607516	MULTIPOLYGON (((697195.3 79...
6	00df2b78-66c8-4009-89e0-0e14e15e4208	1.5084163	-0.8589597	-1.03118100	-1.02730679	-0.4959203	-0.57201990	0.4816479	-0.4599295	-0.97841257	⋯	-0.50830359	0.1858483	-0.61841747	-0.5434782	0.5417954	-1.2756320	0.04443066	-0.3770668	-0.09735021	MULTIPOLYGON (((662960.4 71...

Export

# CSV
write.csv(output_dataset, file.path(output_dir, "social_vulnerability_index_ireland_2022.csv"), row.names = FALSE)

# GeoJSON
st_write(output_dataset_geom, file.path(output_dir, "social_vulnerability_index_ireland_2022.geojson"), delete_dsn=TRUE)

# Shapefile
st_write(output_dataset_geom, file.path(output_dir, "social_vulnerability_index_ireland_2022.shp"), append = FALSE)

Deleting source `../../3_outputs/Ireland/2022/social_vulnerability_index_ireland_2022.geojson' using driver `GeoJSON'
Writing layer `social_vulnerability_index_ireland_2022' to data source 
  `../../3_outputs/Ireland/2022/social_vulnerability_index_ireland_2022.geojson' using driver `GeoJSON'
Writing 18919 features with 46 fields and geometry type Multi Polygon.

Warning message in abbreviate_shapefile_names(obj):
“Field names abbreviated for ESRI Shapefile driver”

Deleting layer `social_vulnerability_index_ireland_2022' using driver `ESRI Shapefile'
Writing layer `social_vulnerability_index_ireland_2022' to data source 
  `../../3_outputs/Ireland/2022/social_vulnerability_index_ireland_2022.shp' using driver `ESRI Shapefile'
Writing 18919 features with 46 fields and geometry type Multi Polygon.

END