nuts: Convert European Regional Data in R
Moritz Hennicke
Werner Krause
2024-12-03
Source:vignettes/nuts.Rmd
nuts.RmdKey Features
Efficient offline conversion of European regional data.
Conversion between five NUTS versions: 2006, 2010, 2013, 2016, 2021.
Conversion between three regional levels: NUTS-1, NUTS-2, NUTS-3.
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.
(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.1 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
- DED: Saxony
- NUTS-2: States/Government Regions
- DED5: Leipzig
- NUTS-3: Districts
- DED53: Northern Saxony
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 level2.
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.
Holdings with Manure Storage Facilities; BU = Built-up area in square meters; Sources: Shapefiles and data are from EUROSTAT; Created using the sf package.
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.nuts_convert_version()converts your data to a desired NUTS version (2006, 2010, 2013, 2016, 2021). This transformation works in any direction.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.
Sequential workflow to convert regional NUTS data
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$...) or
retrieved using the three helper functions nuts_get_data(),
nuts_get_version(), and
nuts_get_missing().
- The first item gives the original data set
augmented with the columns
from_version,from_level, andcountry, indicating the NUTS version that best suits the data. All functions of the package always group NUTS codes across country names which are automatically generated from the provided NUTS codes.
Below, you see that all data entries correspond to the 2016 NUTS version.
# pat_classified$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_varsargument).
# 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.
Converting patent data between versions; Sources: Shapefiles and data are from EUROSTAT; Created using the sf package.
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 58.9Converting 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 104The 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.
Aggregating patents from NUTS 3 to NUTS 2 and NUTS 1; Sources: Shapefiles and data are from EUROSTAT; Created using the sf package.
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.3 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 SloveniaThe 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 NAUsers 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 0Using 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 4.02Multiple 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 1When 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.## # A tibble: 2 × 3
## # Groups: country [2]
## country to_version n
## <chr> <dbl> <int>
## 1 Germany 2021 38
## 2 Italy 2021 21Spatial 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.
Norwegian NUTS-2 regions with boundary changes; Sources: Shapefiles from EUROSTAT; Created using the sf package.
The changes between the two versions can be summarized as follows:
- Boundary changes of regions with continued NUTS codes
-
NO02ceases a small area to the newNO08 -
NO06makes small area gains fromNO05
- Changes to regions with discontinued NUTS codes
-
NO01is absorbed byNO08 -
NO03is split up betweenNO08andNO09 -
NO04divides intoNO0AandNO09 -
NO05largely becomes the newNO0A, and gives a small area toNO06
Spatial interpolation and conversion tables
To keep track of these changes, the nuts package uses
two data sets:
- Stocks: data(
all_nuts_codes) contains all historical NUTS codes by NUTS version and country - Flows: data(
cross_walks) contains the conversion tables between NUTS versions
They are based on data provided by the JRC. Both data sets can also be used by the user manually to explore specific conversion patterns more closely.
For Norway going from version 2016 to 2021 at NUTS level 2, the
cross_walks can be easily subset as follows:
no_walks <- cross_walks %>%
filter(nchar(from_code) == 4,
from_version == 2016,
to_version == 2021,
grepl("^NO", from_code))Which results in the following conversion table:
| from_code | to_code | from_version | to_version | level | country | areaKm | pop18 | pop11 | artif_surf18 | artif_surf12 |
|---|---|---|---|---|---|---|---|---|---|---|
| NO01 | NO08 | 2016 | 2021 | 2 | Norway | 5365.0 | 1268387.7 | 1131221.0 | 58104 | 55927 |
| NO02 | NO02 | 2016 | 2021 | 2 | Norway | 52072.3 | 370392.3 | 362070.7 | 60625 | 54887 |
| NO02 | NO08 | 2016 | 2021 | 2 | Norway | 517.6 | 15843.5 | 15019.0 | 1952 | 1813 |
| NO03 | NO08 | 2016 | 2021 | 2 | Norway | 19123.5 | 575350.1 | 535560.5 | 66876 | 62509 |
| NO03 | NO09 | 2016 | 2021 | 2 | Norway | 17414.4 | 403640.4 | 385648.8 | 50076 | 47799 |
| NO04 | NO09 | 2016 | 2021 | 2 | Norway | 16360.8 | 292218.9 | 272016.3 | 44779 | 42346 |
| NO04 | NO0A | 2016 | 2021 | 2 | Norway | 9326.0 | 451949.5 | 416975.4 | 39112 | 36432 |
| NO05 | NO06 | 2016 | 2021 | 2 | Norway | 931.8 | 3510.2 | 3625.9 | 869 | 832 |
| NO05 | NO0A | 2016 | 2021 | 2 | Norway | 47902.2 | 837246.8 | 790090.1 | 99951 | 94757 |
| NO06 | NO06 | 2016 | 2021 | 2 | Norway | 41029.0 | 447774.0 | 417827.7 | 47291 | 43630 |
| NO07 | NO07 | 2016 | 2021 | 2 | Norway | 112453.1 | 452720.0 | 437265.2 | 81907 | 79098 |
In addition to tracing the evolution of NUTS codes, the table
contains flows of area, population and artificial
surfaces between regions and versions. These flows were computed by the
JRC with granular [100m x 100m] geographic data. The
ggalluvial plot below visualizes the flows of area size
between the NUTS-2 regions mapped above.
Alluvial plot illustrating area size flows; Created using the ggalluvial package.
To illustrate the main idea, the map below showcases
population densities across NUTS-2 regions. As
population is not uniformly distributed across space, weighting regions
dependent on their area size comes with strong assumptions. For
instance, region NO01 in version 2016, that contains the
city of Oslo, makes a relatively modest geographical contribution to the
new region NO08, but significantly bolsters the population
of the latter. Assuming that the variable to be converted is correlated
with population across space, the conversion can thus be refined using
population weights to account for flows between different versions.
Spatial distribution of population and boundary changes; Sources: Shapefiles and population raster from EUROSTAT; Created using the sf and the terra packages.
Conversion methods
The following subsections describe the method used to convert absolute and relative values between versions and levels.
Conversion of absolute values between versions
In this example, we transform absolute values, the
number of patent applications (NR) in Norway, from
version 2016 to 2021, utilizing spatial interpolation
based on the population distribution in 2018.
The conversion employs the cross_walks table, which
includes population flow data (expressed in thousands) between two
NUTS-2 regions from the source version to the target version. The
function joins the variable of interest, NR, which varies
across the departing NUTS-2 codes (from_code). The function
initially calculates a weight (w) equal to
the population flow’s share of the total population in the departing
region in version 2016 (from_code):
| from_code | to_code | from_version | to_version | NR | pop18 | w |
|---|---|---|---|---|---|---|
| NO01 | NO08 | 2016 | 2021 | 146 | 1268 | 1268/(1268) = 1 |
| NO02 | NO02 | 2016 | 2021 | 5 | 370 | 370/(370 + 15) = 0.96 |
| NO02 | NO08 | 2016 | 2021 | 5 | 15 | 15/(370 + 15) = 0.04 |
| NO03 | NO08 | 2016 | 2021 | 54 | 575 | 575/(575 + 403) = 0.59 |
| NO03 | NO09 | 2016 | 2021 | 54 | 403 | 403/(575 + 403) = 0.41 |
| NO04 | NO09 | 2016 | 2021 | 80 | 292 | 292/(292 + 451) = 0.39 |
| NO04 | NO0A | 2016 | 2021 | 80 | 451 | 451/(292 + 451) = 0.61 |
| NO05 | NO06 | 2016 | 2021 | 41 | 3 | 3/(3 + 837) = 0 |
| NO05 | NO0A | 2016 | 2021 | 41 | 837 | 837/(3 + 837) = 1 |
| NO06 | NO06 | 2016 | 2021 | 62 | 447 | 447/(447) = 1 |
| NO07 | NO07 | 2016 | 2021 | 7 | 452 | 452/(452) = 1 |
To obtain the number of patent applications at the desired 2021
version, the function summarizes the data for the new NUTS regions in
version 2021 (to_code) by taking the
population-weighted sum of all flows.
| to_code | to_version | NR |
|---|---|---|
| NO02 | 2021 | 5 x 0.96 = 4.8 |
| NO06 | 2021 | 41 x 0 + 62 x 1 = 62 |
| NO07 | 2021 | 7 x 1 = 7 |
| NO08 | 2021 | 146 x 1 + 5 x 0.04 + 54 x 0.59 = 178.06 |
| NO09 | 2021 | 54 x 0.41 + 80 x 0.39 = 53.34 |
| NO0A | 2021 | 80 x 0.61 + 41 x 1 = 89.8 |
Conversion of relative values between versions
To convert relative values, such as the number of
patent applications per 1000 inhabitants,
nuts_convert_version() departs again from the conversion
table seen above. We focus on the variable P_MHAB, patent
applications per one million inhabitants. The function summarizes these
relative values by computing the weighted average with
respect to 2018 population flows.
| to_code | to_version | P_MHAB |
|---|---|---|
| NO02 | 2021 | (370 x 13)/(370) = 13 |
| NO06 | 2021 | (3 x 48 + 447 x 145)/(3 + 447) = 144 |
| NO07 | 2021 | (452 x 16)/(452) = 16 |
| NO08 | 2021 | (1268 x 125 + 15 x 13 + 575 x 57)/(1268 + 15 + 575) = 103 |
| NO09 | 2021 | (403 x 57 + 292 x 110)/(403 + 292) = 79 |
| NO0A | 2021 | (451 x 110 + 837 x 48)/(451 + 837) = 70 |
Conversion of absolute values between NUTS levels
The function nuts_aggregate() aggregates from lower to
higher order levels, e.g. from NUTS-3 to NUTS-2. Since
higher order regions are perfectly split into lower order regions in the
NUTS system, the function takes simply the sum of the values in case of
absolute variables.
Conversion of relative values between NUTS levels
Relative values are aggregated in
nuts_aggregate() by computing the weighted mean of all
lower order regional levels. To convert, for example,
the number of patent applications per one million inhabitants from
NUTS-3 to NUTS-2, the function adds the population size in 2018.
| nuts_3 | nuts_2 | pop18 | P_MHAB |
|---|---|---|---|
| NO011 | NO01 | 662 | 145 |
| NO012 | NO01 | 606 | 102 |
| NO021 | NO02 | 196 | 7 |
| NO022 | NO02 | 188 | 18 |
| NO031 | NO03 | 289 | 34 |
| NO032 | NO03 | 279 | 45 |
| NO033 | NO03 | 239 | 106 |
| NO034 | NO03 | 169 | 45 |
| NO041 | NO04 | 113 | 43 |
| NO042 | NO04 | 178 | 50 |
| NO043 | NO04 | 451 | 150 |
| NO051 | NO05 | 495 | 24 |
| NO052 | NO05 | 102 | 58 |
| NO053 | NO05 | 241 | 91 |
| NO061 | NO06 | 307 | 208 |
| NO062 | NO06 | 139 | 3 |
| NO071 | NO07 | 225 | 10 |
| NO072 | NO07 | 154 | 33 |
The number of patent applications at the NUTS-2 level is computed by the weighted average using NUTS-3 population numbers.
| nuts_2 | P_MHAB |
|---|---|
| NO01 | (662 x 145 + 606 x 102)/(662 + 606) = 124 |
| NO02 | (196 x 7 + 188 x 18)/(196 + 188) = 12 |
| NO03 | (289 x 34 + 279 x 45 + 239 x 106 + 169 x 45)/(289 + 279 + 239 + 169) = 56 |
| NO04 | (113 x 43 + 178 x 50 + 451 x 150)/(113 + 178 + 451) = 109 |
| NO05 | (495 x 24 + 102 x 58 + 241 x 91)/(495 + 102 + 241) = 47 |
| NO06 | (307 x 208 + 139 x 3)/(307 + 139) = 144 |
| NO07 | (225 x 10 + 154 x 33)/(225 + 154) = 19 |
Citation
Please support the development of open science and data by citing the JRC and us in your work:
Joint Research Centre (2022) NUTS converter. https://urban.jrc.ec.europa.eu/tools/nuts-converter
Hennicke M, Krause W (2024). nuts: Convert European Regional Data. doi:10.5281/zenodo.10573056 https://doi.org/10.5281/zenodo.10573056, R package version 1.1.0, https://docs.ropensci.org/nuts/.
Bibtex Users:
@Manual{,
title = {NUTS converter},
author = {Joint Research Centre},
year = {2022},
url = {https://urban.jrc.ec.europa.eu/tools/nuts-converter},
}
@Manual{,
title = {nuts: Convert European Regional Data},
author = {Moritz Hennicke and Werner Krause},
year = {2024},
note = {R package version 1.1.0},
url = {https://docs.ropensci.org/nuts/},
doi = {10.5281/zenodo.10573056},
}