nuts: Convert European Regional Data in R
2024-07-13
Key Features
:::float-image
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Efficient offline conversion of European regional data.
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Conversion between five NUTS versions: 2006, 2010, 2013, 2016, 2021.
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Conversion between three regional levels: NUTS-1, NUTS-2, NUTS-3.
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Ability to convert multiple NUTS versions at once when e.g. NUTS versions differ across countries and years. This scenario is common when working with data sourced from EUROSTAT.
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(Dasymetric) Spatial interpolation based on five weights (regional area size, 2011 and 2018 population size, 2012 and 2018 built-up area) built from granular [100m x 100m] geodata by the European Commission’s Joint Research Center (JRC).
NUTS Codes
The Nomenclature of Territorial Units for Statistics (NUTS) is a geocode standard for referencing the administrative divisions of European countries. A NUTS code starts with a two-letter combination indicating the country. The administrative subdivisions, or levels, are referred to with an additional number or a capital letter (NUTS-1). A second (NUTS-2) or third (NUTS-3) subdivision level is referred to with another digit each.
For example, the German district Northern Saxony (Nordsachsen) is located within the region Leipzig and the federate state Saxony.
- NUTS-1: States
- NUTS-2: States/Government Regions
- NUTS-3: Districts
Since administrative boundaries in Europe change for demographic, economic, political or other reasons, there are five different versions of the NUTS Nomenclature (2006, 2010, 2013, 2016, and 2021). The current version, effective from 1 January 2021, lists 104 regions at NUTS-1, 283 regions at NUTS-2, 1 345 regions at NUTS-3 level.
Spatial interpolation in a nutshell
When administrative units are restructured, regional data measured within old boundaries can be converted to the new boundaries under reasonable assumptions. The main task of this package is to use (dasymetric) spatial interpolation to accomplish this.
Let’s take the example of the German state Saxony in the figures below. Here, the NUTS-2 regions Leipzig (DED3 → DED5) and Chemnitz (DED1 → DED4) were reorganized. We are interested in the number of manure storage facilities in 2003 provided by EUROSTAT based on the 2006 NUTS version. A part of Leipzig was reassigned to Chemnitz (center plot), prompting us to recalculate the number of storage facilities in the 2010 version (right plot).
A simple approach is to redistribute manure storage facilities proportional to the transferred area, assuming equal distribution of manure storages across space. In a dasymetric approach, we could make use of built-up area, assuming that manure deposits are more likely to be found close to residential areas and economic sites. In our example, Leipzig lost about 7.7% ((\frac{5574}{72360})) of its built-up area. We re-calculate the number of manure storage facilities by computing 7.7% of Leipzig’s manure storages (\frac{5574}{72360} * 700 = 54), subtracting them from Leipzig and adding them to Chemnitz.
See the Section Spatial interpolation in detail for an in-depth description of the weighting procedure.
Usage
The package comes with three main functions:
-
nuts_classify() detects the NUTS version(s) and level(s) of a data set. Its output can be directly fed into the two other functions.
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nuts_convert_version() converts your data to a desired NUTS version (2006, 2010, 2013, 2016, 2021). This transformation works in any direction.
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nuts_aggregate() aggregates data to some upper-level NUTS code, i.e., it transforms NUTS-3 data to the NUTS-2 or NUTS-1 level (but not vice versa).
Workflow
The conversion can only be conducted after classifying the NUTS version(s) and level(s) of your data using the function nuts_classify(). This step ensures the validity and completeness of your NUTS codes before proceeding with the conversion.
Identifying NUTS version and level
The nuts_classify() function’s main purpose is to find the most suitable NUTS version and to identify the level of the data set. Below, you see an example using patent application data (per one million inhabitants) for Norway in 2012 at the NUTS-2 level. This data is again provided by EUROSTAT.
# Load packages
library(nuts)
library(dplyr)
library(stringr)
# Loading and subsetting Eurostat data
data(patents, package = "nuts")
pat_n2 <- patents %>%
filter(nchar(geo) == 4) # NUTS-2 values
pat_n2_mhab_12_no <- pat_n2 %>%
filter(unit == "P_MHAB") %>% # Patents per one million inhabitants
filter(time == 2012) %>% # 2012
filter(str_detect(geo, "^NO")) %>% # Norway
dplyr::select(-unit)
# Classifying the Data
pat_classified <- nuts_classify(
data = pat_n2_mhab_12_no,
nuts_code = "geo"
)
##
## ── Classifying version of NUTS codes ─────────────────────────────────
## Within groups defined by country:
## ✔ All NUTS codes can be identified and classified.
## ✔ Unique NUTS version classified.
## ✔ No missing NUTS codes.
The function returns a list with three items. These items can be called directly from the output object (data$...) or retrieved using the three helper functions nuts_get_data(), nuts_get_version(), and nuts_get_missing().
- The first item gives the original data set augmented with the columns
from_version, from_level, and country, indicating the NUTS version that best suits the data. All functions of the package always group NUTS codes across country names which are automatically generated from the provided NUTS codes.
Below, you see that all data entries correspond to the 2016 NUTS version.
# pat_classified$data # Call list item directly or...
nuts_get_data(pat_classified) # ...use helper function
## # A tibble: 7 × 6
## from_code from_version from_level country time values
## <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 NO01 2016 2 Norway 2012 125.
## 2 NO02 2016 2 Norway 2012 13.2
## 3 NO03 2016 2 Norway 2012 57.4
## 4 NO04 2016 2 Norway 2012 110.
## 5 NO05 2016 2 Norway 2012 48.9
## 6 NO06 2016 2 Norway 2012 145.
## 7 NO07 2016 2 Norway 2012 16.5
- The second item provides an overview of the share of matching NUTS codes for each of the five existing NUTS versions. The overlap is computed within country and possibly additional groups (if provided via the
group_vars argument).
# pat_classified$versions_data # Call list item directly or...
nuts_get_version(pat_classified) # ...use helper function
## # A tibble: 5 × 3
## from_version country overlap_perc
## <chr> <chr> <dbl>
## 1 2016 Norway 100
## 2 2013 Norway 100
## 3 2010 Norway 100
## 4 2006 Norway 100
## 5 2021 Norway 42.9
- The third item gives all NUTS codes that are missing across groups. Such missing codes might lead to conversion errors and are, by default, omitted from all conversion procedures. In our example, no NUTS codes are missing.
# pat_classified$missing_data # Call list item directly or...
nuts_get_missing(pat_classified) # ...use helper function
## # A tibble: 0 × 4
## # ℹ 4 variables: from_code <chr>, from_version <chr>,
## # from_level <dbl>, country <chr>
Converting data between NUTS versions
Once the NUTS version and level of the original data are identified, you can easily convert the data to any other NUTS version. Here is an example of transforming the 2013 Norwegian data to the 2021 NUTS version. Between 2016 and 2021, the number of NUTS-2 regions in Norway decreased by one as the borders of six regions were transformed. The maps below show the affected regions.
We provide the classified NUTS data, specify the target NUTS version for data transformation, and supply the variable containing the values to be interpolated. It is important to indicate the variable type in the named input-vector since the interpolation approaches differ for absolute and relative values.
# Converting Data to 2021 NUTS version
pat_converted <- nuts_convert_version(
data = pat_classified,
to_version = "2021",
variables = c("values" = "relative")
)
##
## ── Converting version of NUTS codes ──────────────────────────────────
## Within groups defined by country:
## ℹ Converting NUTS codes in 1 version 2016 to version 2021.
## ✔ All NUTS codes can be converted.
## ✔ Version is unique.
## ✔ No missing NUTS codes.
The output below displays the corresponding data frames based on the original and converted NUTS codes. The original data set comprises of seven observations, whereas the converted data set contains six. The regions NO01, NO03, NO04, and NO05 are lost, while NO08, NO09, and NO0A are now listed.
pat_n2_mhab_12_no
## # A tibble: 7 × 3
## geo time values
## <chr> <dbl> <dbl>
## 1 NO01 2012 125.
## 2 NO02 2012 13.2
## 3 NO03 2012 57.4
## 4 NO04 2012 110.
## 5 NO05 2012 48.9
## 6 NO06 2012 145.
## 7 NO07 2012 16.5
pat_converted
## # A tibble: 6 × 4
## to_code to_version country values
## <chr> <chr> <chr> <dbl>
## 1 NO02 2021 Norway 13.2
## 2 NO06 2021 Norway 143.
## 3 NO07 2021 Norway 16.5
## 4 NO08 2021 Norway 71.0
## 5 NO09 2021 Norway 83.0
## 6 NO0A 2021 Norway 58.9
Converting multiple variables simultaneously
You can also convert multiple variables at once. Below, we add the number of patent applications per 1000 inhabitants as a second variable:
# Converting Multiple Variables
pat_n2_mhab_12_no %>%
mutate(values_per_thous = values * 1000) %>%
nuts_classify(
data = .,
nuts_code = "geo"
) %>%
nuts_convert_version(
data = .,
to_version = "2021",
variables = c("values" = "relative",
"values_per_thous" = "relative")
)
##
## ── Classifying version of NUTS codes ─────────────────────────────────
## Within groups defined by country:
## ✔ All NUTS codes can be identified and classified.
## ✔ Unique NUTS version classified.
## ✔ No missing NUTS codes.
##
## ── Converting version of NUTS codes ──────────────────────────────────
## Within groups defined by country:
## ℹ Converting NUTS codes in 1 version 2016 to version 2021.
## ✔ All NUTS codes can be converted.
## ✔ Version is unique.
## ✔ No missing NUTS codes.
## # A tibble: 6 × 5
## to_code to_version country values values_per_thous
## <chr> <chr> <chr> <dbl> <dbl>
## 1 NO02 2021 Norway 13.2 13239
## 2 NO06 2021 Norway 143. 143106.
## 3 NO07 2021 Norway 16.5 16463
## 4 NO08 2021 Norway 71.0 71037.
## 5 NO09 2021 Norway 83.0 82964.
## 6 NO0A 2021 Norway 58.9 58904.
Converting grouped data
Longitudinal regional data, as commonly supplied by EUROSTAT, often comes with varying NUTS versions across countries and years (and other dimensions). It is possible to harmonize data across such groups with the group_vars argument in nuts_classify(). Below, we transform data within country and year groups for Sweden, Slovenia, and Croatia to the 2021 NUTS version.
# Classifying grouped data (time)
pat_n2_mhab_sesihr <- pat_n2 %>%
filter(unit == "P_MHAB") %>%
filter(str_detect(geo, "^SE|^SI|^HR"))
pat_classified <- nuts_classify(nuts_code = "geo", data = pat_n2_mhab_sesihr,
group_vars = "time")
##
## ── Classifying version of NUTS codes ─────────────────────────────────
## Within groups defined by country and time:
## ✔ All NUTS codes can be identified and classified.
## ✔ Unique NUTS version classified.
## ✔ No missing NUTS codes.
Note that the detected best-fitting NUTS versions differ across countries:
nuts_get_data(pat_classified) %>%
group_by(country, from_version) %>%
tally()
## # A tibble: 3 × 3
## # Groups: country [3]
## country from_version n
## <chr> <chr> <int>
## 1 Croatia 2016 24
## 2 Slovenia 2010 26
## 3 Sweden 2021 104
The grouping is stored and passed on to the conversion function:
# Converting grouped data (Time)
pat_converted <- nuts_convert_version(
data = pat_classified,
to_version = "2021",
variables = c("values" = "relative")
)
##
## ── Converting version of NUTS codes ──────────────────────────────────
## Within groups defined by country and time:
## ℹ Converting NUTS codes in 3 versions 2016, 2021, and 2010 to version
## 2021.
## ✔ All NUTS codes can be converted.
## ✔ Version is unique.
## ✔ No missing NUTS codes.
Conveniently, the group argument can also be used to transform higher dimensional data. Below, we include two indicators for patent applications to convert data that varies at the indicator-year-country-NUTS code level.
# Classifying and converting multi-group data
pat_n2_mhabmact_12_sesihr <- pat_n2 %>%
filter(unit %in% c("P_MHAB", "P_MACT")) %>%
filter(str_detect(geo, "^SE|^SI|^HR"))
pat_converted <- pat_n2_mhabmact_12_sesihr %>%
nuts_classify(
data = .,
nuts_code = "geo",
group_vars = c("time", "unit")
) %>%
nuts_convert_version(
data = .,
to_version = "2021",
variables = c("values" = "relative")
)
##
## ── Classifying version of NUTS codes ─────────────────────────────────
## Within groups defined by country, time, and unit:
## ✔ All NUTS codes can be identified and classified.
## ✔ Unique NUTS version classified.
## ✔ No missing NUTS codes.
##
## ── Converting version of NUTS codes ──────────────────────────────────
## Within groups defined by country, time, and unit:
## ℹ Converting NUTS codes in 3 versions 2016, 2021, and 2010 to version
## 2021.
## ✔ All NUTS codes can be converted.
## ✔ Version is unique.
## ✔ No missing NUTS codes.
Converting data between NUTS levels
The nuts_aggregate() function facilitates the aggregation of data from lower NUTS levels to higher ones using spatial weights. This enables users to summarize variables upward from the NUTS-3 level to NUTS-2 or NUTS-1 levels. It is important to note that this function does not support disaggregation since this comes with strong assumptions about the spatial distribution of a variable’s values.
In the following example, we illustrate how to aggregate the total number of patent applications in Sweden from NUTS-3 to higher levels. The functions below return a warning concerning non-identifiable NUTS codes. See Non-identified NUTS codes for further information.
data("patents", package = "nuts")
# Aggregating data from NUTS-3 to NUTS-2 and NUTS-1
pat_n3 <- patents %>%
filter(nchar(geo) == 5)
pat_n3_nr_12_se <- pat_n3 %>%
filter(unit %in% c("NR")) %>%
filter(time == 2012) %>%
filter(str_detect(geo, "^SE"))
pat_classified <- nuts_classify(
data = pat_n3_nr_12_se,
nuts_code = "geo"
)
##
## ── Classifying version of NUTS codes ─────────────────────────────────
## Within groups defined by country:
## ! These NUTS codes cannot be identified or classified: SEXXX and
## SEZZZ.
## ✔ Unique NUTS version classified.
## ✔ No missing NUTS codes.
pat_level2 <- nuts_aggregate(
data = pat_classified,
to_level = 2,
variables = c("values" = "absolute")
)
##
## ── Aggregating level of NUTS codes ───────────────────────────────────
## Within groups defined by country:
## ℹ Aggregate from NUTS regional level 3 to 2.
## ✖ These NUTS codes cannot be converted and are dropped: SEXXX and
## SEZZZ.
## ✔ Version is unique.
## ✔ No missing NUTS codes.
pat_level1 <- nuts_aggregate(
data = pat_classified,
to_level = 1,
variables = c("values" = "absolute")
)
##
## ── Aggregating level of NUTS codes ───────────────────────────────────
## Within groups defined by country:
## ℹ Aggregate from NUTS regional level 3 to 1.
## ✖ These NUTS codes cannot be converted and are dropped: SEXXX and
## SEZZZ.
## ✔ Version is unique.
## ✔ No missing NUTS codes.
Inconsistent versions and levels
Non-identified NUTS codes
If the input data contains NUTS codes that cannot be identified in any NUTS version, the output of classify_nuts lists all of these codes. All conversion procedures (nuts_convert_version() and nuts_aggregate()) will work as expected while ignoring values for these regions.
The example below classifies 2012 patent data from Denmark. The original EUROSTAT data contains the codes DKZZZ and DKXXX, which are not part of the conversion matrices. Codes ending with the letter Z refer to “Extra-Regio” territories. These codes collect statistics for territories that cannot be attached to a certain region. Codes ending with the letter X refer to observations with unknown regions.
pat_n3.nr.12.dk <- pat_n3 %>%
filter(unit %in% c("NR")) %>%
filter(time == 2012) %>%
filter(str_detect(geo, "^DK"))
pat_classified <- nuts_classify(
data = pat_n3.nr.12.dk,
nuts_code = "geo"
)
##
## ── Classifying version of NUTS codes ─────────────────────────────────
## Within groups defined by country:
## ! These NUTS codes cannot be identified or classified: DKXXX and
## DKZZZ.
## ✔ Unique NUTS version classified.
## ✔ No missing NUTS codes.
Missing NUTS codes
nuts_classify() also checks whether the NUTS codes provided are complete (or values of a variable that the user wants to convert are missing for a region). Missing values in the input data will, by default, result in missing values for all affected transformed regions in the output data.
The example with Slovenia below illustrates this case.
pat_n3_nr_12_si <- pat_n3 %>%
filter(unit %in% c("NR")) %>%
filter(time == 2012) %>%
filter(str_detect(geo, "^SI"))
pat_classified <- nuts_classify(
data = pat_n3_nr_12_si,
nuts_code = "geo"
)
##
## ── Classifying version of NUTS codes ─────────────────────────────────
## Within groups defined by country:
## ! These NUTS codes cannot be identified or classified: SIXXX and
## SIZZZ.
## ✔ Unique NUTS version classified.
## ✖ Missing NUTS codes detected. See the tibble 'missing_data' in the
## output.
nuts_classify() returns a warning that NUTS codes are missing in the input data. These codes can be inspected by calling nuts_get_missing(pat_classified).
nuts_get_missing(pat_classified)
## # A tibble: 2 × 4
## from_code from_version from_level country
## <chr> <chr> <dbl> <chr>
## 1 SI011 2010 3 Slovenia
## 2 SI016 2010 3 Slovenia
The resulting conversion returns three missing values as the source code SI011 transformed into SI031 and the region SI016 was split into SI036 and SI037.
nuts_convert_version(
data = pat_classified,
to_version = "2021",
variables = c("values" = "absolute")
) %>%
filter(is.na(values))
##
## ── Converting version of NUTS codes ──────────────────────────────────
## Within groups defined by country:
## ℹ Converting NUTS codes in 1 version 2010 to version 2021.
## ✖ These NUTS codes cannot be converted and are dropped: SIXXX and
## SIZZZ.
## ✔ Version is unique.
## ✖ Missing NUTS codes in data. No values are calculated for regions
## associated with missing NUTS codes. Ensure that the input data is
## complete.
## # A tibble: 3 × 4
## to_code to_version country values
## <chr> <chr> <chr> <dbl>
## 1 SI031 2021 Slovenia NA
## 2 SI036 2021 Slovenia NA
## 3 SI037 2021 Slovenia NA
Users have the option missing_weights_pct to investigate the consequences of missing values in the converted data. Setting the argument to TRUE returns a variable that indicates the percentage of missing weights due to missing NUTS codes (or missing values in the variable). The data frame below shows three regions that could not be computed due to missing data. Values in region SI036 could not be computed since 97.9% of the weights are missing. Values for region SI037 are missing as well even though only 0.8% of its population-weighted area is missing.
nuts_convert_version(
data = pat_classified,
to_version = "2021",
weight = "pop18",
variables = c("values" = "absolute"),
missing_weights_pct = TRUE
) %>%
arrange(desc(values_na_w))
##
## ── Converting version of NUTS codes ──────────────────────────────────
## Within groups defined by country:
## ℹ Converting NUTS codes in 1 version 2010 to version 2021.
## ✖ These NUTS codes cannot be converted and are dropped: SIXXX and
## SIZZZ.
## ✔ Version is unique.
## ✖ Missing NUTS codes in data. No values are calculated for regions
## associated with missing NUTS codes. Ensure that the input data is
## complete.
## # A tibble: 12 × 5
## to_code to_version country values values_na_w
## <chr> <chr> <chr> <dbl> <dbl>
## 1 SI031 2021 Slovenia NA 100
## 2 SI036 2021 Slovenia NA 97.9
## 3 SI037 2021 Slovenia NA 0.802
## 4 SI032 2021 Slovenia 7.84 0
## 5 SI033 2021 Slovenia 3.22 0
## 6 SI034 2021 Slovenia 15.2 0
## 7 SI035 2021 Slovenia 6.99 0
## 8 SI038 2021 Slovenia 1.25 0
## 9 SI041 2021 Slovenia 42.1 0
## 10 SI042 2021 Slovenia 6.56 0
## 11 SI043 2021 Slovenia 7.22 0
## 12 SI044 2021 Slovenia 3.3 0
Using the the share of missing weights in combination with the option missing_rm, the nuts package allows to recover some of the missing regions approximately. We can achieve this by setting missing_rm to TRUE, effectively assuming 0 for missing values. In the next step we remove regions with a high share of missing weights from the output data again. The data frame below shows that values for SI037 could still be used assuming 0 patents for 0.8% of the missing population-weighted area to construct the region.
nuts_convert_version(
data = pat_classified,
to_version = "2021",
weight = "pop18",
variables = c("values" = "absolute"),
missing_weights_pct = TRUE,
missing_rm = TRUE
) %>%
filter(to_code %in% c("SI031", "SI036", "SI037")) %>%
mutate(values_imp = ifelse(values_na_w < 1, values, NA))
##
## ── Converting version of NUTS codes ──────────────────────────────────
## Within groups defined by country:
## ℹ Converting NUTS codes in 1 version 2010 to version 2021.
## ✖ These NUTS codes cannot be converted and are dropped: SIXXX and
## SIZZZ.
## ✔ Version is unique.
## ✖ Missing NUTS codes in data. No values are calculated for regions
## associated with missing NUTS codes. Ensure that the input data is
## complete.
## # A tibble: 3 × 6
## to_code to_version country values values_na_w values_imp
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 SI031 2021 Slovenia 0 100 NA
## 2 SI036 2021 Slovenia 0.331 97.9 NA
## 3 SI037 2021 Slovenia 4.02 0.802 4.02
Multiple NUTS levels within groups
The package does not allow for the conversion of multiple NUTS levels at once. The classification function will throw an error in this case. The conversion needs to be conducted for every level separately.
patents %>%
filter(nchar(geo) %in% c(4, 5), grepl("^EL", geo)) %>%
distinct(geo, .keep_all = T) %>%
nuts_classify(nuts_code = "geo", data = .)
## Error in `nuts_classify()`:
## ! Data contains NUTS codes from multiple levels (2 and 3).
Multiple NUTS versions within groups
Converting multiple NUTS versions within groups might lead to erroneous spatial interpolations since overlaps between regions of different versions are possible.
The example below illustrates this problem. We classify German and Italian manure storage facility data from EUROSTAT without specifying group_vars. Instead, we keep all unique NUTS codes to artificially create a data set containing different NUTS versions. nuts_classify() returns a warning and by inspecting the identified versions, we see that there are mixed versions within groups (the countries).
man_deit <- manure %>%
filter(grepl("^DE|^IT", geo)) %>%
filter(nchar(geo) == 4, ) %>%
distinct(geo, .keep_all = T) %>%
nuts_classify(nuts_code = "geo", data = .)
##
## ── Classifying version of NUTS codes ─────────────────────────────────
## Within groups defined by country:
## ! These NUTS codes cannot be identified or classified: DEZZ.
## ✖ Multiple NUTS versions classified. See the tibble 'versions_data'
## in the output.
## ✖ Missing NUTS codes detected. See the tibble 'missing_data' in the
## output.
nuts_get_data(man_deit) %>%
group_by(country, from_version) %>%
tally()
## # A tibble: 5 × 3
## # Groups: country [3]
## country from_version n
## <chr> <chr> <int>
## 1 Germany 2006 38
## 2 Germany 2021 3
## 3 Italy 2006 9
## 4 Italy 2021 21
## 5 <NA> <NA> 1
When proceeding to the conversion with either nuts_convert_version() or nuts_aggregate(), both functions will throw an error. For convenience, we added the option multiple_versions that subsets the supplied data to the dominant version within groups when specified with most_frequent. Hence, all codes from other, non-dominant versions are discarded.
Once we convert this data set, all NUTS regions unrecognized according to the 2006 (Germany) and 2021 (Italy) version are dropped automatically.
man_deit_converted <- nuts_convert_version(
data = man_deit,
to_version = 2021,
variables = c("values" = "relative"),
multiple_versions = "most_frequent"
)
##
## ── Converting version of NUTS codes ──────────────────────────────────
## Within groups defined by country:
## ℹ Converting NUTS codes in 2 versions 2006 and 2021 to version 2021.
## ✖ These NUTS codes cannot be converted and are dropped: DEZZ.
## ! Choosing most frequent version within group and dropping 12 rows.
## ✖ Missing NUTS codes in data. No values are calculated for regions
## associated with missing NUTS codes. Ensure that the input data is
## complete.
man_deit_converted %>%
group_by(country, to_version) %>%
tally()
## # A tibble: 2 × 3
## # Groups: country [2]
## country to_version n
## <chr> <dbl> <int>
## 1 Germany 2021 38
## 2 Italy 2021 21
Spatial interpolation in detail
This section describes the spatial interpolation procedure. We first cover the logic of conversion tables and then explain the methods used in the package for converting versions and levels.
Changes in administrative boundaries
Below, Norwegian NUTS-2 regions for the versions 2016 and 2021 are shown. All regions apart from Norway’s most Northern region have been reorganized in this period.
