Social Vulnerability Logroño - Census Data

Social Vulnerability Logroño - Census Data#

Environment#

R Libraries#

Any required R libraries are imported into the kernal:

# Load R libraries
## none required

Output directory#

# create the pipeline directory if it does not exist
pipeline_dir <- file.path("../..","2_pipeline","Spain","Logrono","1a_CensusData","2021")
if(!dir.exists(pipeline_dir)){
    dir.create(pipeline_dir, recursive = TRUE)
    print(paste0(pipeline_dir, " created"))
}

Load Data#

Import the csv data#

# Read the census data
# Current Spanish 2021 Census is a mixture of:
#  1) a static CSV or Excel downloaded from the INE Census website
#  2) extra data from missing indicators appended into this CSV or Excel
# in our case we read the CSV version
census_data <- read.csv("../../0_data/census/Spain/2021/Census_Data_2024-07-03_Census.csv", sep=",")
head(census_data)

census_homes_year_of_construction_data <- read.csv("../../0_data/census/Spain/2021/Censo_2021_custom_query__Family_homes__Year_of_construction__17_La_Rioja.csv", sep=",")
head(census_homes_year_of_construction_data)

census_household_structure_data <- read.csv("../../0_data/census/Spain/2021/Censo_2021_custom_query__People_living_in_family_homes__Household_structure__17_La_Rioja.csv", sep=",")
head(census_household_structure_data)

A data.frame: 6 × 48
	ccaa	CPRO	CMUN	dist	secc	t1_1	t2_1	t2_2	t3_1	t4_1	⋯	t22_3	t22_4	t22_5	c_ab0t4	c_ag0t4	c_amo75	c_afo75	c_ac5t9	fam_3plus	fam_bb_1970
	<int>	<int>	<int>	<int>	<int>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	17	26	1	1	1	236	0.41	0.59	50.97	0.10	⋯	18	13	4	3	3	20	18	10	NA	NA
2	17	26	2	1	1	1150	0.45	0.55	45.56	0.12	⋯	88	71	27	20	20	62	78	45	NA	NA
3	17	26	3	1	1	460	0.48	0.52	52.59	0.10	⋯	27	31	6	2	6	36	52	19	NA	NA
4	17	26	4	1	1	67	NA	NA	NA	NA	⋯	NA	NA	NA	1	0	6	2	0	NA	NA
5	17	26	5	1	1	1828	0.48	0.52	46.31	0.13	⋯	114	95	61	25	29	104	151	67	NA	NA
6	17	26	5	2	1	1682	0.49	0.51	39.04	0.21	⋯	136	128	42	64	48	60	87	105	NA	NA

A data.frame: 6 × 14
	home_year_id	home_total	before_1900	from_1900_to_1920	from_1921_to_1940	from_1941_to_1950	from_1951_to_1960	from_1961_a_1970	from_1971_to_1980	from_1981to_1990	from_1991_to_2000	from_2001_to_2010	from_2011_to_2020	not_recorded
	<dbl>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	2600201001	975	81	27	45	54	33	33	102	69	129	342	39	18
2	2600301001	768	21	90	171	120	63	90	54	45	21	36	18	39
3	2600501001	1581	48	81	57	27	48	57	156	147	129	57	36	735
4	2600502001	1011	9	6	9	24	33	42	39	57	111	579	30	78
5	2600601001	1854	36	54	42	57	72	123	189	246	195	525	51	264
6	2600701001	681	0	54	99	111	111	51	75	51	33	51	3	39

A data.frame: 6 × 13
	household_structure_id	household_structure_total	single_female_under_65	single_male_under_65	single_female_over_65	single_male_over_65	single_parent_with_a_child_under_25	single_parent_with_all_children_over_25	couple_without_children	couple_with_a_child_under_25	couple_with_all_children_over_25	couple_or_single_parent_with_a_child_under_25_years_and_more_person	other_household
	<dbl>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	2600201001	1149	33	72	42	36	60	39	162	333	78	144	150
2	2600501001	1686	33	90	51	48	78	57	267	516	99	261	189
3	2600502001	1683	60	87	27	24	147	30	210	651	99	174	174
4	2600601001	2511	66	120	78	48	192	78	402	891	183	282	174
5	2600701001	642	15	42	45	24	24	48	99	171	51	78	45
6	2600801001	1374	9	39	48	27	51	66	189	558	132	168	93

Prepare data#

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.

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.

Supporting data#

Code that uniquely identifies the census area#

identifier <- c('ccaa', 'CPRO', 'CMUN', 'dist', 'secc')
census_area_id <- census_data[, identifier, drop = FALSE]

Merge Census datasets#

# number of rows in the census data - base
head( nrow(census_data) )

# number of rows in the census data - family homes: year of construction
#                                   - family homes: household structure
# Note: information from some census sections has been suppressed to protect statistical confidentiality
head( nrow(census_homes_year_of_construction_data) )
head( nrow(census_household_structure_data) )

# construct the identifier - family homes: year of construction
census_homes_year_of_construction_data$ccaa = as.integer(17)
census_homes_year_of_construction_data$CPRO = as.integer(substr(census_homes_year_of_construction_data$home_year_id, 1, 2))
census_homes_year_of_construction_data$CMUN = as.integer(substr(census_homes_year_of_construction_data$home_year_id, 3, 5))
census_homes_year_of_construction_data$dist = as.integer(substr(census_homes_year_of_construction_data$home_year_id, 6, 7))
census_homes_year_of_construction_data$secc = as.integer(substr(census_homes_year_of_construction_data$home_year_id, 8, 10))

# construct the identifier - family homes: household structure
census_household_structure_data$ccaa = as.integer(17)
census_household_structure_data$CPRO = as.integer(substr(census_household_structure_data$household_structure_id, 1, 2))
census_household_structure_data$CMUN = as.integer(substr(census_household_structure_data$household_structure_id, 3, 5))
census_household_structure_data$dist = as.integer(substr(census_household_structure_data$household_structure_id, 6, 7))
census_household_structure_data$secc = as.integer(substr(census_household_structure_data$household_structure_id, 8, 10))

# merge using the identifier
census_data <- merge(census_data, census_homes_year_of_construction_data, by=identifier, all.x=TRUE)
census_data <- merge(census_data, census_household_structure_data, by=identifier, all.x=TRUE)

head(census_data)

343

202

209

A data.frame: 6 × 75
	ccaa	CPRO	CMUN	dist	secc	t1_1	t2_1	t2_2	t3_1	t4_1	⋯	single_male_under_65	single_female_over_65	single_male_over_65	single_parent_with_a_child_under_25	single_parent_with_all_children_over_25	couple_without_children	couple_with_a_child_under_25	couple_with_all_children_over_25	couple_or_single_parent_with_a_child_under_25_years_and_more_person	other_household
	<int>	<int>	<int>	<int>	<int>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	17	26	1	1	1	236	0.41	0.59	50.97	0.10	⋯	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	17	26	2	1	1	1150	0.45	0.55	45.56	0.12	⋯	72	42	36	60	39	162	333	78	144	150
3	17	26	3	1	1	460	0.48	0.52	52.59	0.10	⋯	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	17	26	4	1	1	67	NA	NA	NA	NA	⋯	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
5	17	26	5	1	1	1828	0.48	0.52	46.31	0.13	⋯	90	51	48	78	57	267	516	99	261	189
6	17	26	5	2	1	1682	0.49	0.51	39.04	0.21	⋯	87	27	24	147	30	210	651	99	174	174

Totals#

Population total#

population_total <- census_data[, 't1_1', drop = FALSE]
names(population_total)[1] <- 'population_total'
head(population_total)

A data.frame: 6 × 1
	population_total
	<int>
1	236
2	1150
3	460
4	67
5	1828
6	1682

Dwellings total#

dwellings_total <- census_data[, 't18_1', drop = FALSE]
names(dwellings_total)[1] <- 'dwellings_total'
head(dwellings_total)

A data.frame: 6 × 1
	dwellings_total
	<int>
1	284
2	976
3	768
4	NA
5	1581
6	1010

Households total#

households_total <- census_data[, 't21_1', drop = FALSE]
names(households_total)[1] <- 'households_total'
head(households_total)

A data.frame: 6 × 1
	households_total
	<int>
1	112
2	495
3	215
4	NA
5	697
6	673

Domain data#

Age domain#

### Domain:Age ###

# Age - early childhood boy (under 5 years old)
early_childhood_boy_fields <- c(
    'c_ab0t4' # Age 0 to 4 boys
)
early_childhood_boy_data <- census_data[, early_childhood_boy_fields, drop = FALSE]
early_childhood_boy <- rowSums(early_childhood_boy_data, na.rm=TRUE)
early_childhood_boy_pct <- (early_childhood_boy / population_total) * 100.0
names(early_childhood_boy_pct)[1] <- 'early_childhood_boy_pct'

# Age - early childhood girl (under 5 years old)
early_childhood_girl_fields <- c(
    'c_ag0t4' # Age 0 to 4 girls
)
early_childhood_girl_data <- census_data[, early_childhood_girl_fields, drop = FALSE]
early_childhood_girl <- rowSums(early_childhood_girl_data, na.rm=TRUE)
early_childhood_girl_pct <- (early_childhood_girl / population_total) * 100.0
names(early_childhood_girl_pct)[1] <- 'early_childhood_girl_pct'

# Age - middle to oldest old male (75+ years old)
age_middle_to_oldest_old_male_fields <- c(
    'c_amo75' # Age 75+ males
)
age_middle_to_oldest_old_male_data <- census_data[, age_middle_to_oldest_old_male_fields, drop = FALSE]
age_middle_to_oldest_old_male <- rowSums(age_middle_to_oldest_old_male_data, na.rm=TRUE)
age_middle_to_oldest_old_male_pct <- (age_middle_to_oldest_old_male / population_total) * 100.0
names(age_middle_to_oldest_old_male_pct)[1] <- 'age_middle_to_oldest_old_male_pct'

# Age - middle to oldest old male (75+ years old)
age_middle_to_oldest_old_female_fields <- c(
    'c_afo75' # Age 75+ females
)
age_middle_to_oldest_old_female_data <- census_data[, age_middle_to_oldest_old_female_fields, drop = FALSE]
age_middle_to_oldest_old_female <- rowSums(age_middle_to_oldest_old_female_data, na.rm=TRUE)
age_middle_to_oldest_old_female_pct <- (age_middle_to_oldest_old_female / population_total) * 100.0
names(age_middle_to_oldest_old_female_pct)[1] <- 'age_middle_to_oldest_old_female_pct'

# Combine all these indicators into an array for this domain
age_domain_pct <- cbind(early_childhood_boy_pct,
                        early_childhood_girl_pct,
                        age_middle_to_oldest_old_male_pct,
                        age_middle_to_oldest_old_female_pct)

# Print the first six rows of the data to visually check it looks OK
head(age_domain_pct)

A data.frame: 6 × 4
	early_childhood_boy_pct	early_childhood_girl_pct	age_middle_to_oldest_old_male_pct	age_middle_to_oldest_old_female_pct
	<dbl>	<dbl>	<dbl>	<dbl>
1	1.2711864	1.271186	8.474576	7.627119
2	1.7391304	1.739130	5.391304	6.782609
3	0.4347826	1.304348	7.826087	11.304348
4	1.4925373	0.000000	8.955224	2.985075
5	1.3676149	1.586433	5.689278	8.260394
6	3.8049941	2.853746	3.567182	5.172414

Health domain#

### Domain:Health ###

# Health - people with a disability preventing work (already a percentage)
disability_fields <- c(
    't13_1'
)
disability_data <- census_data[, disability_fields, drop = FALSE]
disability <- rowSums(disability_data, na.rm=TRUE)
disability_pct <- disability

# Combine all these indicators into an array for this domain
health_domain_pct <- cbind(disability_pct)

# Print the first six rows of the data to visually check it looks OK
head(health_domain_pct)

A matrix: 6 × 1 of type dbl
disability_pct
0.03
0.02
0.01
0.00
0.03
0.02

Income domain#

### Domain:Income ###

# One parent households
one_parent_households_fields <- c(
    'single_parent_with_a_child_under_25'
)
one_parent_households_data <- census_data[, one_parent_households_fields, drop = FALSE]
one_parent_households <- rowSums(one_parent_households_data, na.rm=TRUE)
one_parent_households_pct <- (one_parent_households / households_total) * 100.0
names(one_parent_households_pct)[1] <- 'one_parent_households_pct'

# Dependants under 16 years of age (already a percentage)
dependants_fields <- c(
    't4_1'
)
dependants_data <- census_data[, dependants_fields, drop = FALSE]
dependants <- rowSums(dependants_data, na.rm=TRUE)
dependants_pct <- dependants

# Unemployment (already a percentage)
unemployment_fields <- c(
    't10_1'
)
unemployment_data <- census_data[, unemployment_fields, drop = FALSE]
unemployment <- rowSums(unemployment_data, na.rm=TRUE)
unemployment_pct <- unemployment

# population attending university (already a percentage)
attending_university_fields <- c(
    't8_1'
)
attending_university_data <- census_data[, attending_university_fields, drop = FALSE]
attending_university <- rowSums(attending_university_data, na.rm=TRUE)
attending_university_pct <- attending_university

# Combine all these indicators into an array for this domain
income_domain_pct <- cbind(one_parent_households_pct,
                           dependants_pct,
                           unemployment_pct,
                           attending_university_pct)

# Print the first six rows of the data to visually check it looks OK
head(income_domain_pct)

A data.frame: 6 × 4
	one_parent_households_pct	dependants_pct	unemployment_pct	attending_university_pct
	<dbl>	<dbl>	<dbl>	<dbl>
1	0.00000	0.10	0.04	0.05
2	12.12121	0.12	0.16	0.03
3	0.00000	0.10	0.08	0.04
4	NA	0.00	0.00	0.00
5	11.19082	0.13	0.18	0.02
6	21.84250	0.21	0.10	0.03

Information Access/Use domain#

### Domain:Information Access/Use ###

# No higher education (already a percentage)
no_higher_education_fields <- c(
    't9_1' # need to invert
)
no_higher_education_data <- census_data[, no_higher_education_fields, drop = FALSE]
no_higher_education <- rowSums(no_higher_education_data, na.rm=TRUE)
no_higher_education_pct <- (1.0-no_higher_education) # invert

# Combine all these indicators into an array for this domain
info_domain_pct <- cbind(no_higher_education_pct)

# Print the first six rows of the data to visually check it looks OK
head(info_domain_pct)

A matrix: 6 × 1 of type dbl
no_higher_education_pct
0.77
0.80
0.79
1.00
0.80
0.75

Local knowledge domain#

### Domain:Local Knowledge ###

# Foreign nationals (already a percentage)
foreign_nationals_fields <- c(
    't5_1'
)
foreign_nationals_data <- census_data[, foreign_nationals_fields, drop = FALSE]
foreign_nationals <- rowSums(foreign_nationals_data, na.rm=TRUE)
foreign_nationals_pct <- foreign_nationals

# Combine all these indicators into an array for this domain
local_knowledge_domain_pct <- cbind(foreign_nationals_pct)

# Print the first six rows of the data to visually check it looks OK
head(local_knowledge_domain_pct)

A matrix: 6 × 1 of type dbl
foreign_nationals_pct
0.11
0.16
0.13
0.00
0.15
0.11

Physical access domain#

###  Domain:Tenure ###

# Households renting
rented_fields <- c(
    't20_2'
)
rented_data <- census_data[, rented_fields, drop = FALSE]
rented <- rowSums(rented_data, na.rm=TRUE)
rented_pct <- (rented / dwellings_total) * 100.0
names(rented_pct)[1] <- 'rented_pct'

# Combine all these indicators into an array for this domain
tenure_domain_pct <- cbind(rented_pct)

# Print the first six rows of the data to visually check it looks OK
head(tenure_domain_pct)

A data.frame: 6 × 1
	rented_pct
	<dbl>
1	4.577465
2	4.610656
3	1.822917
4	NA
5	4.111322
6	5.247525

Social Network domain data#

### Domain:Social Network ###

# Primary school age children
primary_school_age_fields <- c(
    'c_ac5t9'
)
primary_school_age_data <- census_data[, primary_school_age_fields, drop = FALSE]
primary_school_age <- rowSums(primary_school_age_data, na.rm=TRUE)
primary_school_age_pct <- (primary_school_age / population_total) * 100.0
names(primary_school_age_pct)[1] <- 'primary_school_age_pct'

# One person households
one_person_households_fields<- c(
    't22_1'
)
one_person_households_data <- census_data[, one_person_households_fields, drop = FALSE]
one_person_households <- rowSums(one_person_households_data, na.rm=TRUE)
one_person_households_pct <- (one_person_households / households_total) * 100.0
names(one_person_households_pct)[1] <- 'one_person_households_pct'

# Combine all these indicators into an array for this domain
social_network_domain_pct <- cbind(primary_school_age_pct,
                                   one_person_households_pct)

# Print the first six rows of the data to visually check it looks OK
head(social_network_domain_pct)

A data.frame: 6 × 2
	primary_school_age_pct	one_person_households_pct
	<dbl>	<dbl>
1	4.237288	41.07143
2	3.913043	36.76768
3	4.130435	38.60465
4	0.000000	NA
5	3.665208	31.99426
6	6.242568	29.12333

Housing Characteristics domain#

### Domain: Housing Characteristics ####

# Indicator: Persons residing in family homes - year built pre 1970
#    'fam_bb_1970'
year_built_fields <- c(
    'before_1900',
    'from_1900_to_1920',
    'from_1921_to_1940',
    'from_1941_to_1950',
    'from_1951_to_1960',
    'from_1961_a_1970'
)
year_built_data <- census_data[, year_built_fields, drop = FALSE]
year_built <- rowSums(year_built_data, na.rm=TRUE)
year_built_pct <- (year_built / dwellings_total) * 100.0
names(year_built_pct)[1] <- 'year_built_pct'
#year_built_pct[is.na(year_built_pct)] <- 0

# Combine all these indicators into an array for this domain
housing_characteristics_domain_pct <- cbind(year_built_pct)

# Print the first six rows of the data to visually check it looks OK
head(housing_characteristics_domain_pct)

A data.frame: 6 × 1
	year_built_pct
	<dbl>
1	0.00000
2	27.97131
3	72.26562
4	NA
5	20.11385
6	12.17822

Combine all data into one table#

# Combine all data into one table
indicator_domains_pct <- cbind(census_area_id,
                               age_domain_pct,
                               health_domain_pct,
                               income_domain_pct,
                               info_domain_pct,
                               local_knowledge_domain_pct,
                               tenure_domain_pct,
                               social_network_domain_pct,
                               housing_characteristics_domain_pct)
head(indicator_domains_pct)

A data.frame: 6 × 20
	ccaa	CPRO	CMUN	dist	secc	early_childhood_boy_pct	early_childhood_girl_pct	age_middle_to_oldest_old_male_pct	age_middle_to_oldest_old_female_pct	disability_pct	one_parent_households_pct	dependants_pct	unemployment_pct	attending_university_pct	no_higher_education_pct	foreign_nationals_pct	rented_pct	primary_school_age_pct	one_person_households_pct	year_built_pct
	<int>	<int>	<int>	<int>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	17	26	1	1	1	1.2711864	1.271186	8.474576	7.627119	0.03	0.00000	0.10	0.04	0.05	0.77	0.11	4.577465	4.237288	41.07143	0.00000
2	17	26	2	1	1	1.7391304	1.739130	5.391304	6.782609	0.02	12.12121	0.12	0.16	0.03	0.80	0.16	4.610656	3.913043	36.76768	27.97131
3	17	26	3	1	1	0.4347826	1.304348	7.826087	11.304348	0.01	0.00000	0.10	0.08	0.04	0.79	0.13	1.822917	4.130435	38.60465	72.26562
4	17	26	4	1	1	1.4925373	0.000000	8.955224	2.985075	0.00	NA	0.00	0.00	0.00	1.00	0.00	NA	0.000000	NA	NA
5	17	26	5	1	1	1.3676149	1.586433	5.689278	8.260394	0.03	11.19082	0.13	0.18	0.02	0.80	0.15	4.111322	3.665208	31.99426	20.11385
6	17	26	5	2	1	3.8049941	2.853746	3.567182	5.172414	0.02	21.84250	0.21	0.10	0.03	0.75	0.11	5.247525	6.242568	29.12333	12.17822

Calculate Z-Score#

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:

“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)

Calculate the Z-score#

# Copy the data
indicator_z_scores <- indicator_domains_pct

# Get the number of columns in the data
num_cols = ncol(indicator_z_scores)

# Calculate the z scores for each of the relevant columns - starting at the 2nd column
for(col in names(indicator_z_scores)[6:num_cols]) {
  # rename column (remove the '_pct' in the name)
  new_col_name <- gsub("_pct", "", col)
  indicator_z_scores[new_col_name] = scale(indicator_z_scores[col])
}

# Remove the original data to leave only the area identifier and the z scores
indicator_z_scores <- indicator_z_scores[-c(6:num_cols)]

head(indicator_z_scores)

A data.frame: 6 × 20
	ccaa	CPRO	CMUN	dist	secc	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
	<int>	<int>	<int>	<int>	<int>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>	<dbl[,1]>
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
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
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
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
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
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

Output the Z-score data#

# Output the z-score data as a csv file
output_file <- file.path(pipeline_dir, "censusDataZ.csv")
write.csv(indicator_z_scores, output_file, row.names = FALSE)

END