ACAPS - CoronaNet Taxonomy Map

0 Introduction

This document maps the taxonomy used by the ACAPS COVID-19 Government Measures dataset to document government policies made in response to COVID-19 into the CoronaNet Research Project taxonomy. Each section maps the general area for which the taxonomy is mapped and each sub-section provides further detail as necessary. Following each explanation for how the mapping is conceptualized, there is R code for operationalizing this mapping. Please refer to the ACAPS ReadMe and the CoronaNet Codebook for more information on their respective taxonomies.

You can access (i) the original version of the ACAPS dataset, “acaps_covid19_government_measures_dataset_0.xlsx”, as well as (ii) the version which transforms this version of the CIHI dataset into the CoronaNet taxonomy, “acaps_coronanet_map.csv” (the rest of this document details how this transformation was implemented) from the CoronaNet pubic git repo.

1 Setup

To create replicate this taxonomy mapping exercise, users will need to load the following R packages and to read in the original ACAPS data as follows:

library(readr)
library(dplyr)
library(magrittr)
library(tidyr)
library(readxl)
library(here)
acaps = read_excel(here("data", "collaboration", "acaps", "acaps_covid19_government_measures_dataset_0.xlsx"), sheet = "Dataset")

2 Map Creation

The following code creates a map to translate the ACAPS data to the CoronaNet taxonomy. Where there is a straightforward one-to-one relationship between the two taxonomies, these are directly mapped in the below:

The ACAPS id variable allows each unique observation to be identifiable. This is conceptually the same as CoronaNet’s record_id variable. However, to make it clear when a policy originates from ACAPS and when it originates from CoronaNet, we encode this information into a new variable we name unique_id.
The ACAPS Comments variable is a close approximation to CoronaNet’s description variable. The main difference is that (at least in theory), CoronaNet’s description variable must always contain certain information (the policy initiator, the type of policy, the date the policy started, and if applicable: the geographic target of the policy, the demographic target of the policy and the end date of the policy) while there does not appear to be the same amount of information consistently captured in the ACAPS Comments variable. As such, it will likely be necessary to recode or back code for this information for observations in the ACAPS dataset that are not in the CoronaNet dataset.
The ACAPS DATE_IMPLEMENTED variable, which captures when a policy was implemented, is a direct match for CoronaNet’s date_start variable.
The ACAPS COUNTRY variable and ISO variable, which captures information on the initiating country, are direct matches for Coronanet’s country variable and ISO_A3 variable.
The ACAPS ADMIN_LEVEL_NAME variable, which captures information on whether a policy is applied toward a particular subnational government, is a close matche for Coronanet’s province variable. The difference between the two is that ADMIN_LEVEL_NAME includes information on both provinces/states as well as cities whereas the CoronaNet taxonomy captures this information separately in its province and city variables. Since these distinctions are not systematically separable in the ACAPS taxonomy, in this automated mapping, we map ADMIN_LEVEL_NAME to province while noting that downstream manual harmonization will need to make these distinctions.
The ACAPS LINK variable, which captures information on the URL link for the raw source of information on which the policy is based, is a direct match for CoronaNet’s link variable.
The ACAPS ENTRY_DATE variable, which captures information on when a policy was recorded, is a direct match for CoronaNet’s recorded_date variable.

acaps_coronanet_map <- data.frame(unique_id = acaps$ID, 
                                 entry_type = NA,
                                 correct_type= NA,
                                 update_type= NA,
                                 update_level= NA,
                                 description= acaps$COMMENTS,
                                 date_announced= NA,
                                 date_start= acaps$DATE_IMPLEMENTED,
                                 date_end= NA,
                                 country = acaps$COUNTRY,
                                 ISO_A3 = acaps$ISO,
                                 ISO_A2 = NA,
                                 init_country_level= NA,
                                 domestic_policy= NA,
                                 province = acaps$ADMIN_LEVEL_NAME,
                                 city= NA,
                                 type= NA,
                                 type_sub_cat= NA,
                                 type_text= NA,
                                 instituion_status= NA,
                                 target_country= NA,
                                 target_geog_level= NA,
                                 target_region= NA,
                                 target_province= NA,
                                 target_city= NA,
                                 target_other= NA,
                                 target_who_what= NA,
                                 target_who_gen = NA,
                                 target_direction= NA,
                                 travel_mechanism= NA,
                                 compliance= NA,
                                 enforcer= NA,
                                 index_high_est= NA,
                                 index_med_est= NA,
                                 index_low_est= NA,
                                 index_country_rank= NA,
                                 link = acaps$LINK,
                                 date_updated = NA,
                                 recorded_date = acaps$ENTRY_DATE)

2.1 Init Country Level

The init_country_level variable documents what level of government a policy originated from (e.g. national, provincial, municipal). Because the ACAPS taxonomy does not have a comparable variable, in our mapping of the ACAPS taxonomy:

We assume that if a policy takes an NA value in the province variable, which records what province a policy is applied to, that the policy originates from the national level.
Conversely, if the province variable is not empty, we assume that the policy originates from the provincial level.
Note that there may be some errors in coding here insofar as the ACAPS taxonomy captures both provinces and cities in the same variable in its taxonomy (see previous section for how we mapped ACAPS ADMIN_LEVEL_NAME for more information). As such, the init_country_level variable will likely need to be cleaned to take the value of Municipal or Other Governmental Level in manual harmonization downstream.

acaps_coronanet_map <- 
  acaps_coronanet_map %>%
  mutate(
    init_country_level = 
      case_when(
        !is.na(province) ~ 'Provincial',
        is.na(province) ~ 'National',
        TRUE ~ as.character(NA)
      )
  )

3 Policy Type

3.1. Category: Lockdown

3.1.1 Lockdown of the entire population.

The following code maps how ACAPS captures lockdown measures which apply to all people in a region to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY variable as Lockdown and the MEASURE as Full lockdown.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Lockdown and the target_who_gen as No special population targeted.

full_lockdown <- acaps %>%
          filter(CATEGORY == 'Lockdown' & MEASURE == 'Full lockdown') %>%
          mutate(type = 'Lockdown',
                 target_who_gen ='No special population targeted') %>%
          select(unique_id = ID, type, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, full_lockdown, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, full_lockdown, by = 'unique_id')

3.1.2 Lockdown of the partial population

The following code maps how ACAPS captures lockdown measures which apply to all people in a region to the CoronaNet taxonomy. Note that in the ACAPS taxonomy, the main difference between a full lockdown and a partial lockdown is whether stores related to alimentation continue to be open. In the CoronaNet taxonomy, these distinctions can be captured in its Restrictions and Regulations of Businesses policy type.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Lockdown and the MEASURE as Partial lockdown.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Lockdown and the target_who_gen as No special population targeted.

However, we have added customized code to recode the type variable to take on values other than Lockdown based on keywords in their textual description. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

partial_lockdown <- acaps %>%
          filter(CATEGORY == 'Lockdown' & MEASURE == 'Partial lockdown') %>%
          mutate(type =
                   case_when(
                     grepl("urfew", COMMENTS) ~ 'Curfew',
                     TRUE ~ 'Lockdown'
                   ),
                 target_who_gen ='No special population targeted') %>%
          select(unique_id = ID, type, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, partial_lockdown, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, partial_lockdown, by = 'unique_id')

3.1.3 Lockdown for a specific population

The following code maps how ACAPS captures lockdown measures which apply to refugees or other minorities to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Lockdown and the MEASURE as Lockdown of refugee/IDP camps or other minorities.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Lockdown and the target_who_gen is Asylum/refugee seekers

refugee_lockdown <- acaps %>%
          filter(CATEGORY == 'Lockdown' & MEASURE == 'Lockdown of refugee/idp camps or other minorities') %>%
          mutate(type =
                   case_when(
                     grepl("urfew", COMMENTS) ~ 'Curfew',
                     TRUE ~ 'Lockdown'
                   ),
                 target_who_gen ='Asylum/refugee seekers') %>%
          select(unique_id = ID, type, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, refugee_lockdown, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, refugee_lockdown, by = 'unique_id')

3.2 Governance and Socio-economic measures

3.2.1 State of Emergency

The following code maps how ACAPS captures declarations of emergency which applies to all people in a region to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Governance and Socio-economic measures and the MEASURE as State of emergency declared.
The CoronaNet taxonomy aims to capture such policies by coding the type variable a Declaration of Emergency and the target_who_gen is No special population targeted.

However, we have added customized code to recode the type variable to take on values other than Declaration of Emergency based on keywords in their textual description. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

declar_emergency <- acaps %>%
          filter(CATEGORY == 'Governance and socio-economic measures' & MEASURE == 'State of emergency declared') %>%
          mutate(type =
                   case_when(
                     grepl("curfew put", COMMENTS) ~ 'Curfew',
                     grepl("continuing curfew", COMMENTS) ~ 'Curfew',
                     TRUE ~ 'Declaration of Emergency'
                   ),
                 target_who_gen ='No special population targeted') %>%
          select(unique_id = ID, type, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, declar_emergency, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, declar_emergency, by = 'unique_id')

3.2.2 Limitation of international commerce

The following code maps how ACAPS captures whether authorities limit the import or export of either food or health items to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Governance and Socio-economic measures and the MEASURE as Limit product imports/exports.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Other Policy Not Listed Above and the target_who_gen is No special population targeted.

However, we have added customized code to recode the type variable to take on values other than Other Policy Not Listed Above based on keywords in their textual description. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

limit_commerce <- acaps %>%
           filter(CATEGORY == 'Governance and socio-economic measures' & MEASURE == 'Limit product imports/exports') %>%
           mutate(type =
                    case_when(
                      grepl("bars|restaurants", COMMENTS) ~ "Restriction and Regulation of Businesses",
                      grepl("Event|event", COMMENTS) ~ "Restrictions of Mass Gatherings",
                      grepl("disinfect|medic|mask|drug|Health|health", COMMENTS) ~ "Health Resources",
                      grepl("uarantine", COMMENTS) ~ "Quarantine",
                      grepl("education|school|School", COMMENTS) ~ "Closure and Regulation of Schools",
                      grepl("Port Authorities", COMMENTS) ~ "External Border Restrictions",
                      TRUE ~ "Other Policy Not Listed Above"
                    ),
                  target_who_gen ='No special population targeted') %>%
       select(unique_id = ID, type, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, limit_commerce, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, limit_commerce, by = 'unique_id')

3.2.3 Emergency administrative structures activated or established

The following code maps how ACAPS captures whether emergency administrative measures have been put in place to coordinate, decide or monitor COVID-19 measures to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Governance and Socio-economic measures and the MEASURE as Emergency administrative structures activated or established.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as New Task Force, Bureau or Administrative Configuration and the target_who_gen as No special population targeted.

However, we have added customized code to recode the type variable to take on values other than New Task Force, Bureau or Administrative Configuration based on keywords in their textual description. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

governance_emergency <- acaps %>%
           filter(CATEGORY == 'Governance and socio-economic measures' & MEASURE == 'Emergency administrative structures activated or established') %>%
           mutate(type =
                    case_when(
                      grepl("Interinstitutional Unit", COMMENTS) ~ 'New Task Force, Bureau or Administrative Configuration',
                      grepl("bars|restaurants", COMMENTS) ~ "Restriction and Regulation of Businesses",
                      grepl("uarantine", COMMENTS) ~ "Quarantine",
                      grepl("education|school|School", COMMENTS) ~ "Closure and Regulation of Schools",
                      grepl("urfew", COMMENTS) ~ 'Curfew',
                      TRUE ~ "New Task Force, Bureau or Administrative Configuration"
                    ),
                  target_who_gen ='No special population targeted') %>%
       select(unique_id = ID, type, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, governance_emergency, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, governance_emergency, by = 'unique_id')

3.2.4 Military Deployment

The following code maps how ACAPS captures whether the military has been deployed to support medical operations and ensure compliance with the measures of the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Governance and Socio-economic measures and the MEASURE as Military deployment.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as New Task Force, Bureau or Administrative Configuration, the type_sub_cat variable as Other Administrative Configuration and the target_who_gen as No special population targeted.

However, we have added customized code to recode policies based on keywords in their textual description. This strategy is applied to recode the type and target_who_gen variables. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

military_deployment <- acaps %>%
           filter(CATEGORY == 'Governance and socio-economic measures' & MEASURE == 'Military deployment') %>%
           mutate(type =
                   case_when(
                     grepl("urfew", COMMENTS) ~ 'Curfew',
                     TRUE ~ 'New Task Force, Bureau or Administrative Configuration'
                   ),
                 target_who_gen =
                   case_when(
                     grepl("homeless", COMMENTS) ~ 'Homeless population',
                     TRUE ~ 'No special population targeted'
                   ),
                 type_sub_cat = 'Other Administrative Configuration') %>%
           select(unique_id = ID, type, target_who_gen, type_sub_cat)

Following this, the object which incorporates the mapping of the relevant subset of the data, military_deployment, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, military_deployment, by = 'unique_id')

3.2.5 Economic Measures

The following code maps how ACAPS captures whether authorities have taken economic measures to mitigate the impact of other restrictions on the economy and to society to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Governance and Socio-economic measures and the MEASURE as Economic measures.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Other and the target_who_gen is No special population targeted.

However, we have added customized codeto recode the target_who_gen variable to take on values other than No special population targeted based on keywords in their textual description. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

other <- acaps %>%
          filter(CATEGORY == 'Governance and socio-economic measures' & MEASURE == 'Economic measures') %>%
          mutate(type = 'Other Policy Not Listed Above',
                 target_who_gen =
                   case_when(
                     grepl("efugee", COMMENTS) ~ 'Asylum/refugee seekers',
                     TRUE ~ 'No special population targeted'
                   )) %>%
          select(unique_id = ID, type, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, other, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, other, by = 'unique_id')

3.3 Movement restrictions

3.3.1 Curfews

The following code maps how ACAPS captures the curfew measures which apply to all people in a region to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Movement restrictions and the MEASURE as Curfew.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Curfew and the target_who_gen as No special population targeted.

curfew <- acaps %>%
          filter(CATEGORY == 'Movement restrictions' & MEASURE == 'Curfews') %>%
          mutate(type = 'Curfew',
                 target_who_gen ='No special population targeted') %>%
          select(unique_id = ID, type, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, curfew, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, curfew, by = 'unique_id')

3.3.2 Border Checks

The following code maps how ACAPS captures border checks measures, such as checking for identification documents, into CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Movement restrictions and the MEASURE as Border checks.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as External Border Restrictions, the type_sub_cat variable as Travel History Form, and the target_who_gen as No special population targeted.

border_checks <- acaps %>%
           filter(CATEGORY == 'Movement restrictions' & MEASURE == 'Border checks') %>%
           mutate(type = 'External Border Restrictions',
                  target_who_gen ='No special population targeted',
                  type_sub_cat = 'Travel History Form') %>%
           select(unique_id = ID, type, target_who_gen, type_sub_cat)

Following this, the object which incorporates the mapping of the relevant subset of the data, border_checks, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, border_checks, by = 'unique_id')

3.3.3 Border Closure

The following code maps how ACAPS captures border closure measures policies into CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Movement restrictions and the MEASURE as Border closure.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as External Border Restrictions and the target_who_gen as No special population targeted.
Where possible, we also map the mechanism of travel into CoronaNet’s travel_mechanism variable by extracting keywords from the ACAPS COMMENTS variable.

While in theory, we could have further specified that the target_who_what variable to be set to All Foreign Nationals, we found that there were substantial miscodings in the ACAPS data, which warranted a broader mapping. Moreover, we have added customized code to recode the variables based on keywords in their textual description. This strategy is applied to recode typetypes and target_who_gen variables. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

border_closure <- acaps %>%
           filter(CATEGORY == 'Movement restrictions' & MEASURE == 'Border closure') %>%
           mutate(type =
                   case_when(
                     grepl("urfew", COMMENTS) ~ 'Curfew',
                     TRUE ~ 'External Border Restrictions'
                   ),
                  target_who_gen =
                    case_when(
                     grepl("efugee", COMMENTS) ~ 'Asylum/refugee seekers',
                     grepl("migrant", COMMENTS) ~ 'Other population not specified above',
                     TRUE ~ 'No special population targeted'),
                  travel_mechanism =
                    case_when(
                      grepl('flight|air', COMMENTS)~ "Flights",
                      grepl('rain|rains', COMMENTS)~ "Trains",
                      grepl('sea ports|boat|boats|sea ports|ship|ships|vessels', COMMENTS)~ "Seaports",
                      grepl('cruis', COMMENTS)~ "Cruises",
                      TRUE ~ as.character(NA)
                    )
                  ) %>%
    select(unique_id = ID, type, target_who_gen,travel_mechanism)

Following this, the object which incorporates the mapping of the relevant subset of the data, border_closure, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, border_closure, by = 'unique_id')

3.3.4 Full Border Closure

The following code maps how ACAPS captures full border closure measures policies into CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Movement restrictions and the MEASURE as Complete border closure.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as External Border Restrictions, the type_sub_cat variable as Total border crossing ban, and the target_who_gen as No special population targeted.

full_closure <- acaps %>%
           filter(CATEGORY == 'Movement restrictions' & MEASURE == 'Complete border closure') %>%
           mutate(type = 'External Border Restrictions',
                  target_who_gen ='No special population targeted',
                  type_sub_cat = 'Total border crossing ban') %>%
           select(unique_id = ID, type, target_who_gen, type_sub_cat)

Following this, the object which incorporates the mapping of the relevant subset of the data, full_closure, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, full_closure, by = 'unique_id')

3.3.5 International Flights suspension

The following code maps how ACAPS captures suspending the operation of international flight policies into CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Movement restrictions and the MEASURE as International flights suspension.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as External Border Restrictions, the type_sub_cat variable as Total border crossing ban, the travel_mechanism variable as Flights, and the target_who_gen as No special population targeted.

However, we have added customized code to recode this policy into mechanisms of travel other than Flights based on keywords in their textual description. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

international_closure <- acaps %>%
           filter(CATEGORY == 'Movement restrictions' & MEASURE == 'International flights suspension') %>%
           mutate(type = 'External Border Restrictions',
                  target_who_gen ='No special population targeted',
                  type_sub_cat = 'Total border crossing ban',
                  travel_mechanism =
                    case_when(
                      grepl('train ', COMMENTS)~ "Trains",
                      TRUE ~ "Flights"
                    )
                  ) %>%
    select(unique_id = ID, type, target_who_gen, type_sub_cat, travel_mechanism)

Following this, the object which incorporates the mapping of the relevant subset of the data, international_closure, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, international_closure, by = 'unique_id')

3.3.6 Visa restrictions

The following code maps how ACAPS captures visa restriction measures policies into CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Movement restrictions and the MEASURE as Visa restrictions.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as External Border Restrictions, the type_sub_cat variable as Visa restrictions, and the target_who_gen is No special population targeted.

However, we have added customized code to recode the target_who_gen variable to take on values other than No special population targeted based on keywords in their textual description. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

visa_restrictions <- acaps %>%
           filter(CATEGORY == 'Movement restrictions' & MEASURE == 'Visa restrictions') %>%
           mutate(type = 'External Border Restrictions',
                  target_who_gen =
                    case_when(
                     grepl("migrant", COMMENTS) ~ 'Other population not specified above',
                     TRUE ~ 'No special population targeted'),
                  type_sub_cat = 'Visa restrictions') %>%
      select(unique_id = ID, type, target_who_gen,type_sub_cat)

Following this, the object which incorporates the mapping of the relevant subset of the data, visa_restrictions, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, visa_restrictions, by = 'unique_id')

3.3.7 Additional requirements upon arrival

The following code maps how ACAPS captures the movement restrictions measures which apply to all people in a region to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Movement restrictions and the MEASURE as Additional health/documents requirements upon arrival.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as External Border Restrictions, the type_sub_cat variable as Health Certificates, and the target_who_gen as No special population targeted.

additional_requirements <- acaps %>%
           filter(CATEGORY == 'Movement restrictions' & MEASURE == 'Additional health/documents requirements upon arrival') %>%
           mutate(type = 'External Border Restrictions',
                  target_who_gen ='No special population targeted',
                  type_sub_cat = 'Health Certificates') %>%
           select(unique_id = ID, type, target_who_gen, type_sub_cat)

Following this, the object which incorporates the mapping of the relevant subset of the data, additional_requirements, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, additional_requirements, by = 'unique_id')

3.3.8 Surveillance and monitoring

The following code maps how ACAPS captures the health monitoring measures which apply to all people in a region to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Movement restrictions and the MEASURE as Surveillance and monitoring.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Health Monitoring and the target_who_gen as No special population targeted.

However, we have added customized code to recode the type variable to take on values other than Health Monitoring based on keywords in their textual description. This strategy is also applied to recode target_who_gen. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

health_monitoring <- acaps %>%
          filter(CATEGORY == 'Movement restrictions' & MEASURE == 'Surveillance and monitoring') %>%
          mutate(type =
                   case_when(
                     grepl("uarantine", COMMENTS) ~ "Quarantine",
                     grepl("travel|border|countr|irport", COMMENTS) ~  'External Border Restrictions',
                     grepl("warehouse|grocery|shop|business|estaurant|bar", COMMENTS) ~ "Restriction and Regulation of Businesses",
                     grepl("mask", COMMENTS) ~ 'Social Distancing',
                     grepl("Church|mass gath", COMMENTS) ~'Restrictions of Mass Gatherings',
                     grepl("team", COMMENTS) ~'New Task Force, Bureau or Administrative Configuration',
                     grepl("Emergency|emergency", COMMENTS) ~ 'Declaration of Emergency',
                     grepl("econ", COMMENTS) ~ 'Other Policy Not Listed Above',
                     grepl("remote medical consultations", COMMENTS) ~ "Health Resources",
                     grepl("rumour|rumor", COMMENTS) ~ "Anti-Disinformation Measures",
                     grepl("Regulations Repealed", COMMENTS) ~ "Curfew",
                     grepl("stricter curfew|curfew overturned", COMMENTS) ~ "Curfew",
                     TRUE ~ 'Health Monitoring'
                   ),
                 target_who_gen =case_when(
                     grepl("migrant", COMMENTS) ~ 'Other population not specified above',
                     TRUE ~ 'No special population targeted')) %>%
     select(unique_id = ID, type, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, health_monitoring, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, health_monitoring, by = 'unique_id')

3.3.9 Movement restrictions (Checkpoint Deployment)

The following code maps how ACAPS captures policies that deploy checkpoints within a country to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Movement restrictions and the MEASURE as Checkpoints within the country.
The CoronaNet taxonomy aims to capture such policies by coding the type variable asInternal Border Restrictions, the type_sub_cat variable as Health Certificates, and the target_who_gen is No special population targeted.

However, we have added customized code to recode the type variable to take on values other than Internal Border Restrictions based on keywords in their textual description. The same strategy is applied to recode type_sub_cat. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

checkpoints_deployment <- acaps %>%
           filter(CATEGORY == 'Movement restrictions' & MEASURE == 'Checkpoints within the country') %>%
           mutate(type =
                    case_when(
                      grepl("mall|shop", COMMENTS) ~ "Restriction and Regulation of Businesses",
                      grepl("irport", COMMENTS) ~  'External Border Restrictions',
                      grepl("uarantine", COMMENTS) ~ "Quarantine",
                      TRUE ~ 'Internal Border Restrictions'),
                  target_who_gen ='No special population targeted',
                  type_sub_cat =
                    case_when(
                      grepl("temp|health screening", COMMENTS)~ "Health Screenings (e.g. temperature checks)",
                      TRUE ~  'Health Certificates'
                    )
                   )%>%
  select(unique_id = ID, type, target_who_gen, type_sub_cat)

Following this, the object which incorporates the mapping of the relevant subset of the data, checkpoints_deployment, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, checkpoints_deployment, by = 'unique_id')

3.3.10 Domestic Travel Restrictions

The following code maps how ACAPS captures policies that limit the movement of people within a country the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Movement restrictions and the MEASURE as Domestic travel restrictions.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Internal Border Restrictions and the target_who_gen is No special population targeted.
Where possible, we also map the mechanism of travel into CoronaNet’s travel_mechanism variable and the type_sub_cat by extracting keywords from the ACAPS COMMENTS variable.

However, we have added customized code to recode the type variable to take on values other than Internal Border Restrictions based on keywords in their textual description. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

domestic_restrictions <- acaps %>%
           filter(CATEGORY == 'Movement restrictions' & MEASURE == 'Domestic travel restrictions') %>%
           mutate(type = case_when(
               grepl("car|traffic|taxi|vehicle", COMMENTS) ~ "Social Distancing",
               grepl("ordone sanit", COMMENTS) ~ "Lockdown",
               grepl("mall|shop", COMMENTS) ~ "Restriction and Regulation of Businesses",
               grepl('urfew', COMMENTS) ~ 'Curfew',
               grepl("uarantine", COMMENTS) ~ "Quarantine",
               TRUE ~ 'Internal Border Restrictions'),
            type_sub_cat = case_when(
               grepl('car', COMMENTS) ~ 'Regulating private vehicles in public circulation',
               TRUE ~ as.character(NA)
             ),
             travel_mechanism = case_when(
               grepl('flight', COMMENTS)~ "Flights",
               grepl('train|Train', COMMENTS)~ "Trains",
               grepl('bus', COMMENTS)~ "Buses",
               grepl('ship', COMMENTS)~ "Seaports",
               TRUE ~ as.character(NA)
             ),
                  target_who_gen ='No special population targeted' )%>%
     select(unique_id = ID, type, type_sub_cat, travel_mechanism, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, domestic_restrictions, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, domestic_restrictions, by = 'unique_id')

3.4 Public Health Measures

3.4.1 Health screenings

The following code maps how ACAPS captures health screenings in airports and border crossings to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Public Health Measures and the MEASURE as Health screenings in airports and border crossings.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as External Border Restrictions, the type_sub_cat variable as Health Screenings (e.g. temperature checks), and the target_who_gen is No special population targeted.
Where possible, we also map the mechanism of travel into CoronaNet’s travel_mechanism variable by extracting keywords from the ACAPS COMMENTS variable.

However, we have added customized code to recode policies to type_sub_cat to values other than Health Screenings (e.g. temperature checks) based on keywords in their textual description. The same strategy is applied to recode target_who_gen. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

health_screenings <- acaps %>%
           filter(CATEGORY == 'Public health measures' & MEASURE == 'Health screenings in airports and border crossings') %>%
           mutate(type = 'External Border Restrictions',
                  target_who_gen =
                    case_when(
                      grepl("migrant", COMMENTS) ~ 'Other population not specified above',
                     TRUE ~ 'No special population targeted'),
                  type_sub_cat =
                    case_when(
                      grepl("form|question|history", COMMENTS)~ "Travel History Form (e.g. documents where traveler has recently been)",
                      grepl("test", COMMENTS)~ "Negative COVID-19 Test",
                      TRUE ~ 'Health Screenings (e.g. temperature checks)'
                    ),
                  travel_mechanism = case_when(
               grepl('flight|irport|Flight', COMMENTS)~ "Flights",
               grepl('train|Train', COMMENTS)~ "Trains",
               grepl('bus', COMMENTS)~ "Buses",
               grepl('ship', COMMENTS)~ "Seaports",
               TRUE ~ as.character(NA)
             )
                   ) %>%
       select(unique_id = ID, type, type_sub_cat, travel_mechanism, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, health_screenings, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, health_screenings, by = 'unique_id')

3.4.2 Strengthening the public health system

The following code maps how ACAPS captures policies that seek to strengthen the public health system to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Public Health Measures and the MEASURE as Strengthening the public health system.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Health Resources and the target_who_gen as Essential workers.
Where possible, we also map the policy sub type into CoronaNet’s type_sub_cat variable by extracting keywords from the ACAPS COMMENTS variable.

However, we have added customized code to recode the type variable to take on values other than Health Resources based on keywords in their textual description. The same strategy is applied to recode target_who_gen. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

public_health_system <- acaps %>%
           filter(CATEGORY == 'Public health measures' & MEASURE == 'Strengthening the public health system') %>%
           mutate(type =
           case_when(
             grepl("task force", COMMENTS) ~ " New Task Force, Bureau or Administrative Configuration",
             grepl("COVAX|Astra|COVID-19 vaccine|COVID-19 Vacc|Corona vaccine|Pfizer|coronavirus vaccine|Covid-19 vaccine|NZ Govt vaccine", COMMENTS) ~ "COVID-19 Vaccines",
             grepl("water|repatriation", COMMENTS) ~ "Other Policy Not Listed Above",
             grepl("Elderly|ursing home", COMMENTS) ~ "Social Distancing",
             grepl("Government to release contact tracing", COMMENTS) ~ 'Health Monitoring',
             grepl("Prohibition of health workers to travel abroad", COMMENTS) ~ "External Border Restrictions",
             TRUE ~ 'Health Resources',
           ),
         target_who_gen =
           case_when(
             grepl("efugee", COMMENTS) ~ "Asylum/refugee seekers",
             grepl("migrant", COMMENTS) ~ 'Other population not specified above',
             TRUE ~ 'No special population targeted',
         ),
         type_sub_cat = case_when(
          type == 'Health Resources' & grepl("insurance", COMMENTS) ~ "Health Insurance",
          type == 'Health Resources' & grepl("flu", COMMENTS) ~ "Non COVID-19 Vaccines",
          type == 'Health Resources' & grepl("sanitizer", COMMENTS) ~ "Hand Sanitizer",
          type == 'Health Resources' & grepl("ospital|hotel| bed", COMMENTS) ~ "Masks",
          type == 'Health Resources' & grepl("center", COMMENTS) ~ "Temporary Medical Centers",
          type == 'Health Resources' &  grepl("ppe|Protective Great|rotective gear|PPE|personal protective equipment", COMMENTS) ~ "Masks",
          type == 'Health Resources' &  grepl("mask|Mask", COMMENTS) ~ "Hospitals",
          type == 'Health Resources' &  grepl("drug|medicine pack", COMMENTS) ~ "Medicine/Drugs",
          type == 'Health Resources' &  grepl("lab|Lab|diagnostic|research", COMMENTS) ~ "Health Research Facilities",
          type == 'Health Resources' &  grepl("doctor|Doctor", COMMENTS) ~ "Doctors",
          type == 'Health Resources' &  grepl("Nurse|nurse", COMMENTS) ~ "nurse",
          type == 'Health Resources' &   grepl("ventilator|espirator", COMMENTS) ~ "Ventilators",
          type == 'Health Resources' &   grepl("uarantine|isolation", COMMENTS) ~ "Temporary Quarantine Centers",
          type == 'Health Resources' &   grepl("paramedic|contact tracer", COMMENTS) ~ "Other Heath Staff",
          type == 'Health Resources' &   grepl("technician|retired professional|retired health|elemedicine|ele-hotline|hotline|medicine student|medical student|medical professional|professional|staff capacity", COMMENTS) ~ "Unspecified Health Staff",
          type == 'Health Resources' &   grepl("olulnteer", COMMENTS) ~ "Health Volunteers",
          type == 'Health Resources' &   grepl("medical goods|biosafety materials|edical Supply", COMMENTS) ~ "Unspecified Health Materials",
          type == 'Health Resources' &   grepl("test|Test|TEST", COMMENTS) ~ "Test Kits",
           TRUE ~ as.character(NA)
         )
  )%>%
  select(unique_id = ID, type, target_who_gen, type_sub_cat)

Following this, the object which incorporates the mapping of the relevant subset of the data, public_health_system, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, public_health_system, by = 'unique_id')

3.4.3 Medical tests

The following code maps how ACAPS captures policies in which governments oblige people to take health checks not related to COVID-19 to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Public Health Measures and the MEASURE as Obligatory medical tests not related to COVID-19.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Health Testing, the type_sub_cat variable as Other health testing, and the target_who_gen as No special population targeted.

medical_test <- acaps %>%
           filter(CATEGORY == 'Public health measures' & MEASURE == 'Obligatory medical tests not related to COVID-19') %>%
           mutate(type = 'Health Testing',
                  target_who_gen ='No special population targeted',
                  type_sub_cat = 'Other health testing') %>%
           select(unique_id = ID, target_who_gen, type_sub_cat)

Following this, the object which incorporates the mapping of the relevant subset of the data, medical_test, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, medical_test, by = 'unique_id')

3.4.4 Mass population testing

The following code maps how ACAPS captures mass screening of the population of a country or region to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Public Health Measures and the MEASURE as Mass population testing.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Health Testing, the type_sub_cat variable as Health testing of entire population under the government’s jurisdiction, and the target_who_gen is No special population targeted.

However, we have added customized code to recode the type variable to take on values other than Health Testing based on keywords in their textual description. This strategy is applied to recode type_sub_cat and target_who_gen as well. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

mass_testing <- acaps %>%
           filter(CATEGORY == 'Public health measures' & MEASURE == 'Mass population testing') %>%
           mutate(type =
           case_when(
             grepl("teacher|school", COMMENTS) ~ "Closure and Regulation of Schools",
             TRUE ~   'Health Testing'
           ),
          target_who_gen =
             case_when(
             grepl("teacher|school", COMMENTS) ~ "NA",
             grepl("migrant", COMMENTS) ~ 'Migrant workers',
             TRUE ~   'No special population targeted'
           ),
          type_sub_cat =
             case_when(
             grepl("teacher|school", COMMENTS) ~ "NA",
             TRUE ~   "Health testing of entire population under the government's jurisdiction"
           ))%>%
  select(unique_id = ID, type, target_who_gen, type_sub_cat)

Following this, the object which incorporates the mapping of the relevant subset of the data, mass_testing, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, mass_testing, by = 'unique_id')

3.4.5 Testing policy

The following code maps how ACAPS captures policies in which tests are conducted to identify people infected with COVID-19 in the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Public Health Measures and the MEASURE as Testing policy.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Health Testing and the target_who_gen as No special population targeted.
Where possible, we also map the policy sub type into CoronaNet’s type_sub_cat variable by extracting keywords from the ACAPS COMMENTS variable.

However, we have added customized code to recode policies based on keywords in their textual description. This strategy is applied to recode the type_sub_cat and target_who_gen variables. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

testing_policy <- acaps %>%
           filter(CATEGORY == 'Public health measures' & MEASURE == 'Testing policy') %>%
  mutate(type =
           case_when(
             grepl("travel|border|country|diplomat|departure|passenger|destination|airport|arriv|Country|ferry", COMMENTS) ~ "External Border Restrictions",
             grepl("shipment|allocat|test kit|Test kit|Reception of", COMMENTS) ~ "Health Resources",
             grepl("upermarket|grocery|shop|business| bar|restaurant", COMMENTS) ~ "Restriction and Regulation of Businesses",
             grepl("disinfect", COMMENTS) ~ "Hygiene",
             grepl("teacher|school", COMMENTS) ~ " Closure and Regulation of Schools",
             TRUE ~   'Health Testing'
           ),
         type_sub_cat = case_when(
           type == "External Border Restrictions" ~ "Negative COVID-19 Test",
           type == 'Health Resources' ~ 'Test Kits',
           grepl("upermarket|grocery", COMMENTS) ~ "Supermarkets/grocery stores",
           TRUE ~ as.character(NA)
         ),
         target_who_gen =
           case_when(
             grepl("efugee", COMMENTS) ~ "Asylum/refugee seekers",
             grepl("migrant", COMMENTS) ~ 'Migrant workers',
             TRUE ~ 'No special population targeted')) %>%
  select(unique_id = ID, type, type_sub_cat, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, testing_policy, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, testing_policy, by = 'unique_id')

3.4.6 General recommendations

The following code maps how ACAPS captures the public health messages to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Public Health Measures and the MEASURE as General recommendations.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Public Awareness Measures, the compliance variable as Recommended/Voluntary but no penalties, and the target_who_gen as No special population targeted.

However, we have added customized code to recode the type variable to take on values other than Public Awareness Measures based on keywords in their textual description. This strategy is also applied to recode compliance and target_who_gen variables. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

general_recommendations <- acaps %>%
          filter(CATEGORY == 'Public health measures' & MEASURE == 'General recommendations') %>%
          mutate(type = case_when(
            grepl('stay at home|stay home', COMMENTS) ~ 'Lockdown',
            grepl('travel|Travel|trip|countr|abroad|not to fly', COMMENTS) ~ 'External Border Restrictions',
            grepl('education|school|day care', COMMENTS) ~ 'Closure and Regulation of Schools',
            grepl('hygiene|cleaning of various|disinfection', COMMENTS) ~ 'Hygiene',
            grepl('unauthorized letter|cyber', COMMENTS) ~ ' Anti-Disinformation Measures',
            grepl('uarantine|self-isolat', COMMENTS) ~ 'Quarantine',
            grepl('mask|aged care|visits|physical distance| meter|Visit|public transport', COMMENTS) ~ 'Social Distancing',
            grepl('usiness|employ|entitie|worker|companies|work|Employ|Work|ustomer|market|shopping|grocery', COMMENTS) ~ 'Restriction and Regulation of Businesses',
            grepl('gatherings|Gathering|mass event|Church|church|political demo', COMMENTS) ~ ' Restrictions of Mass Gatherings',
            grepl('beach|civil serv|swimming', COMMENTS) ~ 'Restriction and Regulation of Government Services',
            TRUE ~ 'Public Awareness Measures'
            ),
           compliance = case_when(
             grepl("fine", COMMENTS) ~ 'Mandatory With Fines',
             TRUE ~    'Recommended/Voluntary but no penalties'
            ),
           target_who_gen =
             case_when(
             grepl("abuse", COMMENTS) ~ "Domestically abused people",
             grepl("migrant", COMMENTS) ~ 'Migrant workers',
             grepl("homeless", COMMENTS) ~ 'Homeless population',
             TRUE ~ 'No special population targeted'))%>%
  select(unique_id = ID, type, compliance, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, general_recommendations, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, general_recommendations, by = 'unique_id')

3.4.7 Other public health measures enforced

The following code maps how ACAPS captures miscellaneous public health measures to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Public Health Measures and the MEASURE as Other public health measures enforce.
This policy can be mapped into multiple different options in the CoronaNet type variable. We can however map information from these policies types in the target_who_gen as No special population targeted.
Where possible, we also map relevant information into the CoronaNet’s type and travel_mechansim variable by extracting keywords from the ACAPS COMMENTS variable.

However, we have added customized code to recode policies in the target_who_gen variable based on keywords in their textual description. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

measures_enforce <- acaps %>%
           filter(CATEGORY == 'Public health measures' & MEASURE == 'Other public health measures enforced') %>%
           mutate(type = case_when(
                grepl('stay at home|stay home|between 18:00|Full lockdown|lockdown', COMMENTS) ~ 'Lockdown',
                grepl('travel|Travel|trip|countr|abroad|border|passenger|international|entry|airport|Airp|flight|cruise|Flight|air|trans|Tankers|ship|ships|Vessels', COMMENTS) ~ 'External Border Restrictions',
                grepl('education|school|day care|child|student', COMMENTS) ~ 'Closure and Regulation of Schools',
                grepl('unauthorized letter|cyber', COMMENTS) ~ 'Anti-Disinformation Measures',
                grepl('uarantine|self-isolat', COMMENTS) ~ 'Quarantine',
                grepl('mask|aged care|visits|physical disance| meter|-meter|Visit|public transport|visitation|adult strides|Fewer train|spread|distancing|handshake|kiss', COMMENTS) ~ 'Social Distancing',
                grepl('cigarette|Retail|Store|dining|bank|office|store|usiness|employ|entitie|worker|companies|work|Employ|Work|ustomer|market|shopping|Grocer|grocer|otel|estaurant|Ski|ski|Grocery|shop|alcohol|liquor|Tobac|tobac|catering|Catering|cinema|guest|establishment|bar|bars|tourism|Slaughterhouses|Vehicle occupancy|gyms', COMMENTS) ~ 'Restriction and Regulation of Businesses',
                grepl('desinfect|Desinfect|Cleaning|Sanit|Steril|hygiene|cleaning of various|disinfec|Sanitiz|Disinf|Hygie|saniti', COMMENTS) ~ 'Hygiene',
                grepl('gatherings|Gathering|mass event|Church|church|political demo|worship|Worship|relig|event|venue|mosqu|demonstrations|sport', COMMENTS) ~ 'Restrictions of Mass Gatherings',
                grepl('emergency|extends', COMMENTS) ~ 'Declaration of Emergency',
                grepl('beach|civil serv|swimming|park|elect|post|library|public building|license|public serv|public therm|constitution|museum|justicial system', COMMENTS) ~ 'Restriction and Regulation of Government Services',
                grepl("hospital|PPE|pharmac|Pharm|Hospital|surgery|Chloro|hand sanitiz|flu|omeless|pneumo|Flu|public drinking|birth", COMMENTS) ~ "Health Resources",
                grepl("guid|Guid|list|Toolbox|toolbox", COMMENTS) ~'Public Awareness Measures',
                grepl('test|sanitation', COMMENTS) ~ 'Health Testing',
                grepl('counsel|new health technology|budget', COMMENTS) ~ 'Health Resources',
                grepl('Red|contact trac|surveillance|investigate|control order|Mitigation|mitigation|indicator|monitor|monitoring|tracing', COMMENTS) ~ 'Health Monitoring',
                grepl('expert group', COMMENTS) ~ 'New Task Force, Bureau or Administrative Configuration',
                grepl("food|Relief|ban hunting|labour pretec|price-controlled|cigarrettes|army|labour|Hackathon", COMMENTS) ~"Other Policy Not Listed Above",
                grepl('COVID-19 Vaccine|COVID-19 vaccine|COVID 19 Vaccine|vaccination|vaccine', COMMENTS) ~ 'COVID-19 Vaccines',
                TRUE ~ as.character(NA),
              ),
            target_who_gen =
             case_when(
                 grepl("abuse", COMMENTS) ~ "Domestically abused people",
                 grepl("migrant", COMMENTS) ~ 'Migrant workers',
                 grepl("homeless", COMMENTS) ~ 'Homeless population',
                 grepl("refugee", COMMENTS) ~ 'Asylum/refugee seekers',
                 TRUE ~ 'No special population targeted'),
            travel_mechanism = case_when(
               grepl('flight|irport|Flight', COMMENTS)~ "Flights",
               grepl('train|Train', COMMENTS)~ "Trains",
               grepl('bus |buses', COMMENTS)~ "Buses",
               grepl('ship', COMMENTS)~ "Seaports",
               TRUE ~ as.character(NA)
             ))%>%
          select(unique_id = ID, type, target_who_gen, travel_mechanism)

Following this, the object which incorporates the mapping of the relevant subset of the data, measures_enforce, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, measures_enforce, by = 'unique_id')

3.4.8 Funeral and Burial Regulations

The following code maps how ACAPS captures policies on funeral and burial regulations to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Public Health Measures and the MEASURE as Amendments to funeral and burial regulations.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Hygiene and the target_who_gen is No special population targeted.

However, we have added customized code to recode the type variable to take on values other than Hygiene based on keywords in their textual description. The same strategy is applied to recode sub-policy type and target_who_gen. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

funeral_regulations <- acaps %>%
           filter(CATEGORY == 'Public health measures' & MEASURE == 'Amendments to funeral and burial regulations') %>%
           mutate(type =
           case_when(
            grepl("funeral|Funeral", COMMENTS) ~ 'Restrictions of Mass Gatherings',
            TRUE ~ 'Hygiene'
           ),
           target_who_gen ='Other workers where the distinction between essential and non-essential is not explicitly made',
           type_sub_cat =
           case_when(
             grepl("funeral|Funeral", COMMENTS) ~ 'Funerals and mourning rituals',
             TRUE ~ 'Burial Procedure'
           ),
           ) %>%
  select(unique_id = ID, type, target_who_gen, type_sub_cat)

Following this, the object which incorporates the mapping of the relevant subset of the data, funeral_regulations, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, funeral_regulations, by = 'unique_id')

3.4.9 Awareness campaigns

The following code maps how ACAPS captures public health awareness campaigns to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Public Health Measures and the MEASURE as Awareness campaigns.
The CoronaNet taxonomy aims to capture such policies by coding the type variable asPublic Awareness Measures and the target_who_gen as No special population targeted.

awareness_campaigns <- acaps %>%
           filter(CATEGORY == 'Public health measures' & MEASURE == 'Awareness campaigns') %>%
           mutate(type = 'Public Awareness Measures',
                  target_who_gen =
                    case_when(
                       grepl("efugee", COMMENTS) ~ "Asylum/refugee seekers",
                       grepl("migrant", COMMENTS) ~ 'Migrant workers',
                       TRUE ~ 'No special population targeted'))%>%
    select(unique_id = ID, type, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, awareness_campaigns, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, awareness_campaigns, by = 'unique_id')

3.4.10 Psychological assistance and medical social work

The following code maps how ACAPS captures polices that provide psychological assistance to those affected by COVID-19 and their families to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Public Health Measures and the MEASURE as Psychological assistance and medical social work.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Public Awareness Measures and the target_who_gen as No special population targeted.

psychological_assistance <- acaps %>%
           filter(CATEGORY == 'Public health measures' & MEASURE == 'Psychological assistance and medical social work') %>%
           mutate(type = 'Public Awareness Measures',
                  target_who_gen =
                    case_when(
                       grepl("abuse", COMMENTS) ~ "Domestically abused people",
                       grepl("prisone", COMMENTS) ~ 'Prisoners',
                       TRUE ~ 'No special population targeted'),
                  type_sub_cat = 'Disseminating information related to COVID-19 to the public that is reliable and factually accurate') %>%
    select(unique_id = ID, type, target_who_gen,type_sub_cat)

Following this, the object which incorporates the mapping of the relevant subset of the data, psychological_assistance, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, psychological_assistance, by = 'unique_id')

3.4.11 Introduction of isolation and quarantine policies

The following code maps how ACAPS captures the quarantine measures which apply to all people in a region to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Public Health Measures and the MEASURE as Isolation and quarantine policies.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Quarantine and the target_who_gen is No special population targeted.
Where possible, we also map relevant information on the policy sub type into the CoronaNet’s type_sub_cat by extracting keywords from the ACAPS COMMENTS variable.

However, we have added customized code to recode the type variable to take on values other than Quarantine based on keywords in their textual description. This strategy is also applied to recode the target_who_gen variable. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

quarantine <- acaps %>%
  filter(CATEGORY == 'Public health measures' & MEASURE == 'Isolation and quarantine policies') %>%
mutate( type =
          case_when(
            grepl("daycare|day care", COMMENTS) ~ "Closure and Regulation of Schools",
            grepl("retirement|residential homes|Hospital bars kids|Elderly|one visitor|aged 70|elderly|nursing homes|residential care|care institu|care home|care facil|Homecare|care centre|Care insti|old people|visits for various public|Social welfare|visiting ban", COMMENTS) ~ "Social Distancing",
            grepl("Stay-home|Stay-Home|people are not allowed to move out of their houses|24 hours curfew|24h curfew|stay-at-home|stay at home|leave home between 1100", COMMENTS) ~ "Lockdown",
            grepl("curfew", COMMENTS) ~ "Curfew",
            grepl("vaccine development", COMMENTS) ~ "COVID-19 Vaccines",
            grepl("employ", COMMENTS) ~ "Restriction and Regulation of Businesses",
            TRUE ~ "Quarantine"
          ),
        target_who_gen =
                    case_when(
                       grepl("nigrant", COMMENTS) ~ "Migrant workers",
                       TRUE ~ 'No special population targeted'),
        type_sub_cat = case_when(
          type == 'Social Distancing' & grepl("retirement|residential homes|Elderly|one visitor|aged 70|elderly|nursing homes|residential care|care institu|care home|care facil|Homecare|care centre|Care insti|old people|visits for various public|Social welfare|visiting ban", COMMENTS) ~ 'Restrictions on visiting nursing homes/long term care facilities',
          type == 'Social Distancing' & grepl("Hospital bars kids", COMMENTS) ~ 'Restrictions on visiting hospitals',
          type == 'Social Distancing' & grepl("care centre|Care insti|old people|visits for various public|Social welfare", COMMENTS) ~ 'Restrictions on visiting other group residential facilities (e.g. orphanages, group homes)',
          TRUE ~ as.character(NA)
         )
        ) %>%
  select(type, unique_id = ID,target_who_gen, type_sub_cat)

Following this, the object which incorporates the mapping of the relevant subset of the data, quarantine, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, quarantine, by = 'unique_id')

3.4.12 Requirement to wear protective gear in public

The following code maps how ACAPS captures policies that require people to wear masks or gloves to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Public Health Measures and the MEASURE as Requirement to wear protective gear in public.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Social Distancing and the target_who_gen is No special population targeted.
Where possible, we also map relevant information on the policy sub type into the CoronaNet’s type_sub_cat by extracting keywords from the ACAPS COMMENTS variable.

However, we have added customized code to recode the type variable to take on values other than Social Distancing based on keywords in their textual description. The same strategy is applied to recode sub-policy type. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

masks <- acaps %>%
  filter(CATEGORY == 'Public health measures' & MEASURE == 'Requirement to wear protective gear in public') %>%
  mutate( type =
             case_when(
               grepl("store|shops|estaurant| bar|ustomer|profession|office|compani", COMMENTS) ~ "Restriction and Regulation of Businesses",
               grepl("school", COMMENTS) ~ "Closure and Regulation of Schools",
               grepl("uarantine", COMMENTS) ~ "Quarantine",
               grepl("roups of up to|indoor events", COMMENTS) ~ "Restrictions of Mass Gatherings",
               TRUE ~ "Social Distancing"
             ),
         target_who_gen ='No special population targeted',
         type_sub_cat =
           case_when(
             grepl("estaurant", COMMENTS) ~ "Restaurant",
             grepl(" bar", COMMENTS) ~ "Bars",
             grepl("public transport", COMMENTS) ~ "Other",
             grepl("public areas|public spaces|everywhere|public place|PUBLIC SPACE", COMMENTS) ~ "All public spaces / everywhere",
             TRUE ~ as.character(NA)
           )
         ) %>%
  select(type,type_sub_cat, unique_id = ID, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, masks, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, masks, by = 'unique_id')

3.5 Social Distancing

3.5.1 Schools closure

The following code maps how ACAPS captures school closure policies to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Social Distancing and the MEASURE as Schools closure.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as Closure and Regulation of Schools and the target_who_gen as No special population targeted.

reg_schools <- acaps %>%
          filter(CATEGORY == 'Social distancing' & MEASURE == 'Schools closure') %>%
          mutate(type = 'Closure and Regulation of Schools',
                 target_who_gen ='No special population targeted') %>%
          select(unique_id = ID, type, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, reg_schools, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, reg_schools, by = 'unique_id')

3.5.2 Closures of businesses and public services

The following code maps how ACAPS captures the restriction and regulations of business or government services, which applies to all people in a region to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Social Distancing and the MEASURE is Closures of businesses and public services.
The CoronaNet taxonomy aims to capture such policies by coding the type variable asRestriction and Regulations of Government Services and the target_who_gen is No special population targeted.

However, we have added customized code to recode the type variable to take on values other than Restriction and Regulation of Businesses based on keywords in their textual description. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

reg_gov_services <- acaps %>%
          filter(CATEGORY == 'Social distancing' & MEASURE == 'Closure of businesses and public services') %>%
          mutate(type =
           case_when(
             grepl("judic|overnment inst|overnmental service|overnmental insti|State Employ|public facil|Park|park|Eiffel|governance|Court|Cultural|overnment off|public off|jury|Indoor swimming|Public space|librar|assport|ublic square|public service|pool|park|ublic serv|amping|ublic admin|museum|beach|ublic access|court|Museum|Librar|Elect|elect", COMMENTS) ~'Restriction and Regulation of Government Services',
             grepl("School|educa|SCHOOL|Kinder|school|nurser|Educat|daycare", COMMENTS) ~'Closure and Regulation of Schools',
             grepl("prayer|relgion|church|Church|event|eligious", COMMENTS) ~'Restrictions of Mass Gatherings',
             grepl("ublic transport|Hospital|visits to geriatric|isiting restri", COMMENTS) ~'Social Distancing',
             grepl("urgery|entist|detnal", COMMENTS) ~'Health Resources',
             grepl("Emergency|emergency", COMMENTS) ~'Declaration of Emergency',
             grepl("erry serv", COMMENTS) ~'External Border Restrictions',
               TRUE ~       'Restriction and Regulation of Businesses',
           ),
         target_who_gen ='No special population targeted') %>%
  select(unique_id = ID, type, target_who_gen)

Following this, the object which incorporates the mapping of the relevant subset of the data, reg_gov_services, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, reg_gov_services, by = 'unique_id')

3.5.3 Restriction of Mass Gatherings of Prisoners

The following code maps how ACAPS captures prison policies undertaken to mitigate the spread of COVID-19 to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Social Distancing and the MEASURE as Changes in prison policies.
The CoronaNet taxonomy aims to capture such policies by coding the type variable asRestriction of Mass Gatherings, the type_sub_cat as Prison population reduced, the target_who_gen as Prisoners.

mass_gathering <- acaps %>%
           filter(CATEGORY == 'Social distancing' & MEASURE == 'Changes in prison-related policies') %>%
           mutate(type = 'Restriction of Mass Gatherings',
                  target_who_gen ='Prisoners',
                  type_sub_cat = 'Prison population reduced')%>%
          select(unique_id = ID, type, target_who_gen, type_sub_cat)

Following this, the object which incorporates the mapping of the relevant subset of the data, mass_gathering, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, mass_gathering, by = 'unique_id')

3.5.4 Restriction of Mass Gatherings of General Public

The following code maps how ACAPS captures the restrictions of mass gathering measures which apply to all people in a region to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Social Distancing and the MEASURE as Limit public gatherings.
The CoronaNet taxonomy aims to capture such policies by coding the type variable asRestriction of Mass Gatherings, the type_sub_cat variable as All/Unspecified mass gatherings, and the target_who_gen as No special population targeted.

However, we have added customized code to recode the type variable to take on values other than Restrictions of Mass Gatherings based on keywords in their textual description. This strategy is also applied to recode the type_sub_cat variable. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

mass_gathering_general <- acaps %>%
           filter(CATEGORY == 'Social distancing' & MEASURE == 'Limit public gatherings') %>%
  mutate(type =
           case_when(
             grepl("market|store|vocational|business|workpla|Food Service|Bar|restar|bar", COMMENTS) ~ 'Restriction and Regulation of Businesses',
             grepl("hildcare|school", COMMENTS) ~ ' Closure and Regulation of Schools',
             grepl("ublic transpor|old people|elderly|cemet|etirement|ospital", COMMENTS) ~ 'Social Distancing',
             TRUE ~ 'Restrictions of Mass Gatherings'
           ),
         target_who_gen ='No special population targeted',
         type_sub_cat = case_when(
          type =='Restrictions of Mass Gatherings'&  grepl("Funeral|funeral|burial", COMMENTS) ~ "Funerals and mourning rituals",
          type =='Restrictions of Mass Gatherings'&  grepl("Wedding|wedding|marriage", COMMENTS) ~ "Weddings and marriage rituals",
          type =='Restrictions of Mass Gatherings'&  grepl("Church|Relig|religious|mosque", COMMENTS) ~ "Attendance at religious services restricted (e.g. mosque/church closings)",
          type =='Restrictions of Mass Gatherings'& TRUE ~ "All/Unspecified mass gatherings"
         )) %>%
  select(unique_id = ID, type, target_who_gen, type_sub_cat)

Following this, the object which incorporates the mapping of the relevant subset of the data, mass_gathering_general, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, mass_gathering_general, by = 'unique_id')

3.6 Humanitarian Exemptions

The following code maps how ACAPS captures humanitarian exemptions measures to the CoronaNet taxonomy.

The ACAPS taxonomy aims to capture such policies by coding the CATEGORY as Humanitarian exemption and the MEASURE as Humanitarian exemptions.
The CoronaNet taxonomy aims to capture such policies by coding the type variable as External Border Restrictions and the target_who_gen as No special population targeted.
Where possible, we also map relevant information on the travel mechanism into the CoronaNet’s travel_mechanism by extracting keywords from the ACAPS COMMENTS variable.

However, we have added customized code to recode the target_who_gen variable to take on values other than No special population targeted based on keywords in their textual description. This was necessary as some policies are miscoded in the original ACAPS taxonomy and this code helps minimize the chance that these miscodings are mapped into the CoronaNet taxonomy. The keywords were identified based on manual inspection of the ACAPS data.

humanitarian_exemp <- acaps %>%
           filter(CATEGORY == 'Humanitarian exemption' & MEASURE == 'Humanitarian exemptions') %>%
           mutate(type = 'External Border Restrictions',
                  target_who_gen =
                    case_when(
                     grepl("efugee", COMMENTS) ~ 'Asylum/refugee seekers',
                     grepl("migrant", COMMENTS) ~ 'Migrant workers',
                     TRUE ~ 'No special population targeted'),
                  travel_mechanism =
                    case_when(
                      grepl('flight|air', COMMENTS)~ "Flights",
                      grepl('rain|rains', COMMENTS)~ "Trains",
                      grepl('sea ports|boat|boats|sea ports|ship|ships|vessels', COMMENTS)~ "Seaports",
                      grepl('cruis', COMMENTS)~ "Cruises",
                      TRUE ~ as.character(NA))
                  ) %>%
    select(unique_id = ID, type, target_who_gen,travel_mechanism)

Following this, the object which incorporates the mapping of the relevant subset of the data, humanitarian_exep, is incorporated into the main object which records this mapping, acaps_coronanet_map, using the unique_id variable to match the corresponding records.

acaps_coronanet_map <- rows_update(acaps_coronanet_map, humanitarian_exemp, by = 'unique_id')

4 Final Map Adjustments

Finally, once all the variables that are possible to harmonize from the ACAPS dataset to CoronaNet taxonomy have been identified, the results are exported in an .rds and .csv format, to be consolidated together with the other external databases to be harmonised. The final consolidated dataset is then processed for manual harmonisation into the CoronaNet Research Project dataset.

write.csv(acaps_coronanet_map,    "acaps_coronanet_map.csv")
saveRDS(acaps_coronanet_map, "acaps_coronanet_map.rds")