Territorial typologies manual - degree of urbanisation


This article forms part of Eurostat’s methodology manual on territorial typologies.

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The degree of urbanisation classifies local administrative units (LAUs) as cities, towns and suburbs or rural areas based on a combination of geographical contiguity and population density, measured by minimum population thresholds applied to 1 km² population grid cells; each LAU belongs exclusively to one of these three classes.

Full article

Classes for the typology and their conditions

Details of the typology

The degree of urbanisation is a classification based on the following three categories:

  • cities, otherwise referred to as densely populated areas — code 1;
  • towns and suburbs, otherwise referred to as intermediate density areas — code 2;
  • rural areas, otherwise referred to as thinly populated areas — code 3.

Urban areas refers to an aggregate composed of information covering cities as well as towns and suburbs (in other words, densely populated areas and intermediate density areas).

Methodology for the typology

The basis for the degree of urbanisation classification is data for 1 km² population grid cells. Each cell has the same shape and surface area, thereby avoiding distortions caused by using units varying in size. This is a considerable advantage when compared with alternative approaches such as those based on the use of population data for local administrative units (such as municipalities).

The use of relatively small (1 km²) and uniform grid cells means that the basic concept for the degree of urbanisation looks inside larger local administrative units to detect the presence of individual rural areas, towns and suburbs, or cities, providing more accurate data for the three categories when aggregated to produce national data. Note that to have a population grid covering all of the EU Member States, it was necessary to employ a ‘top-down’ approach (or a disaggregation grid) for those Member States which did not dispose of a 1 km² grid; such an approach is based on disaggregating population data for local administrative units according to land use or land cover information. In some other cases, Member States use a hybrid approach to manage situations where the coverage of the population grid is incomplete. More information pertaining to population grids as a basis for developing territorial typologies is provided in the introductory chapter.

Step 1: classifying grid cells

Groups of 1 km² population grid cells are plotted in relation to their neighbouring cells to identify:

  • rural grid cells: all grid cells outside of urban clusters/centres;
  • urban clusters (or moderate-density clusters): a cluster of contiguous grid cells of 1 km² (in other words, grid cells that share a common border including grid cells that only touch diagonally at corners) with a population density of at least 300 inhabitants per km² and a minimum population of at least 5 000 inhabitants;
  • urban centres (or high-density clusters): a cluster of non-diagonal contiguous grid cells (in other words, excluding those cells with only touching corners) with a population density of at least 1 500 inhabitants per km² and collectively at least 50 000 inhabitants after gap-filling.

For a more detailed explanation of how grid cells are classified to the various cluster types (including the gap-filling process), see Chapter 1.

Figure 1: Schematic overview of the degree or urbanisation classification
Source: Directorate-General Regional and Urban Policy, based on data from Eurostat, JRC, national statistical authorities

Step 2: classifying local administrative units according to the degree of urbanisation

Once all grid cells have been classified and urban centres, urban clusters and rural grid cells identified, the next step concerns overlaying these results onto local administrative units (LAUs), as follows:

  • cities (densely populated areas) — where at least 50 % of the population lives in one or more urban centres (code 1);
  • towns and suburbs (intermediate density areas) — where less than 50 % of the population lives in an urban centre, but at least 50 % of the population lives in an urban cluster (code 2);
  • rural areas (thinly populated areas) — where more than 50 % of the population lives in rural grid cells (code 3).

Note that once this second step has been completed, then each LAU should be classified to one and only one class/category. However, in order to classify LAUs based on the population grid, the LAUs have to be transformed into a raster as well, which can lead to some situations which require an ad-hoc solution (see further adjustments below). For more information on LAUs, see the section on Building blocks for typologies in the introductory chapter.

Figure 2: More than one urban centre needed to define a city — an example for Haarlemmermeer
Source: Eurostat (based on GEOSTAT population grid from 2011 and LAU 2016)

Figure 2 shows that when classifying LAUs as cities, it may be necessary to consider more than one urban centre. In this example, there were 65 593 people living in the urban centre of Haarlemmermeer in the Netherlands, which equated to just 46 % of the total population of the LAU for Haarlemmermeer (below the threshold of 50 % that is required to identify a city). Nevertheless, as shown in the example, there were two adjacent LAUs — Amsterdam and Haarlem — and their urban centres spill over into Haarlemmermeer. Aggregating the total population of the three urban centres that are located within the boundaries of Haarlemmermeer results in the share of those living in urban centres rising to some 54 % of the total population; as such, Haarlemmermeer is classified as a city within the degree of urbanisation.

Further adjustments

Adjusting the results for cities

As the typologies for the degree or urbanisation and for functional urban areas (cities and their commuting zones) share a common definition of cities, any changes that may be made to the classification of cities should be adopted for both typologies (using the same rules). More information on adjustments that might be made when classifying cities is provided in Chapter 3 (under the heading Further adjustments), while the relationships between these typologies (and the related typology of metropolitan regions — NUTS level 3 regions where at least half of the population lives in a functional urban area composed of at least 250 000 inhabitants; see Chapter 6 for more information) is shown in Figure 3.

Figure 3: Three typologies which are joined together by a common definition for cities

Local administrative units with no population in the raster equivalent

A number of LAUs do not have any population for their raster equivalent. When calculating their degree of urbanisation, these LAUs are not assigned any population as they are too small (smaller than one grid cell); as such, they are given no initial classification. These LAUs with no population in the raster equivalent are classified according to their surrounding cluster; they were found to be exclusively in high-density clusters (urban centres). An example is provided for Dublin in Ireland (see Figure 4).

Figure 4: Local administrative units with no population in the raster equivalent — an example for Dublin
Source: Eurostat (based on GEOSTAT population grid from 2011 and LAU 2016)

Border effects

Thinly populated LAUs that are classified as intermediate density areas or densely populated areas may be classified incorrectly if rural grid cells cover most of their territory. Those LAUs with a total population of less than 5 000 inhabitants and with 90 % or more of their area composed of rural grid cells could be reclassified as thinly populated areas; this adjustment is optional. An example is provided for Maincy in France (LAU code FR77269), see Figure 5: based on the population grid, it has a population of 4 575 inhabitants, with some 2 941 of these living in a high-density cluster. However, as its overall population is less than 5 000 inhabitants and just 7.3 % of Maincy’s total area of 10 km² is covered by this cluster, it is reclassified as a rural area.

In a similar vein, small LAUs classified as rural areas may be classified incorrectly due to the coarse resolution of the population grid compared with the small size of some LAUs. Those LAUs with an area of less than 5 km² and with more than 30 % of their surface area covered by non-rural grid cells could be reclassified as intermediate density areas or densely populated areas according to the respective shares of these clusters; this adjustment is also optional.

Figure 5: Local administrative units reclassified due to border effects — an example for Maincy
Source: Eurostat (based on GEOSTAT population grid from 2011 and LAU 2016)

Links to other spatial concepts/typologies

The degree of urbanisation classification provides streamlined and harmonised definitions for a number of similar but not identical spatial concepts, for example, all urban centres with at least 50 000 inhabitants — cities — are included in the city statistics data collection exercise (see Chapter 3 for more information), while rural areas identified by the degree of urbanisation and predominantly rural regions (from the urban-rural typology; see Chapter 5 for more information) are both based on the share of population living in rural grid cells.

Results

Map 1 provides an overview of the final classification for the degree of urbanisation by LAU.

For all EU Member States, EFTA countries and some candidate countries a list of their LAUs with their degree of urbanisation category is available on Eurostat’s classification server, RAMON.

Map 1: Degree of urbanisation for local administrative units (LAU)
Source: Eurostat, JRC and European Commission Directorate-General for Regional Policy

Changes to the typology over time

Historical developments

Urban and rural developments are central concepts used by a wide range of policymakers, researchers, national administrations and international organisations. While these terms may be readily understood by the general public, a clear statistical definition at an international level has proved elusive.

The degree of urbanisation classification was originally introduced in 1991, distinguishing between densely, intermediate and thinly populated areas. It was based on information for numbers of inhabitants, population density and the contiguity of local administrative units at level 2 (LAU2), otherwise referred to as municipalities. As LAU2s varied considerably in terms of their size/area, the results were compromised in terms of comparability; this was especially the case for EU Member States characterised by relatively large or relatively small LAUs. Note also that the original classification for the degree of urbanisation was based on different population density thresholds to those currently employed: for example, densely populated areas had a lower threshold of 500 inhabitants per km², which led to many smaller towns and some suburbs being classified within this category.

In 2011, the OECD together with the European Commission’s Directorates-General for Regional and Urban Policy, Eurostat, Agriculture and Rural Development and the Joint Research Centre (JRC) started working on revising the degree of urbanisation classification. As a result the methodology has been improved see: A harmonised definition of cities and rural areas: the new degree of urbanisation; WP 01/2014. The refinement of the methodology also provided an opportunity to harmonise several similar but not identical spatial concepts.

Changes over time that impact on the classification

The degree of urbanisation classification should be updated to reflect any changes to the underlying sources of information that are used in the compilation of this classification. As such, the classification may be updated to reflect: changes to LAU boundaries or changes to population distributions for 1 km² grid cells. The frequency of such updates varies according to the source of information.

Changes to the degree of urbanisation classification resulting from a revision of population distributions for 1 km² grid cells are less common and these may be expected every 10 years, when new census data becomes available. The next major update of the population grid is foreseen to take place for the 2021 reference year.

Annual updates of the degree of urbanisation classification should be made to reflect changes to LAU boundaries. These modifications can be implemented in two ways: applying the degree of urbanisation methodology as described above for the new layer of LAUs; or estimating the degree of urbanisation based on changes to LAU boundaries. The first approach is more labour intensive, while the second is particularly suitable if boundary changes for LAUs are relatively small or consist principally of merging LAUs, especially if these have the same degree of urbanisation.

Updating the degree of urbanisation to reflect changes in LAU boundaries

LAU boundaries may change over time in three different ways: LAUs can merge, they may undergo a boundary shift, or they may be split. The most common change for LAUs within the EU in recent years has been for two or more units to be merged; boundary shifts have been less common, while splitting units apart has been rare.

Case 1: LAU mergers

Merging two LAUs with different degrees of urbanisation may be resolved by giving precedence to the more densely populated unit: when merging LAUs composed of a city and a town or suburb, reclassify the new LAU as a city; when merging LAUs composed of a town or suburb and a rural area, reclassify the new LAU as a town or suburb. Such a process may be further refined by taking into account the relative population sizes of the two LAUs.

Case 1a: LAU mergers involving the same degree of urbanisation

The degree of urbanisation is additive, meaning that if two LAUs classified as thinly populated areas are subsequently merged into a single LAU then they will remain a thinly populated area; this is also true for the other degrees of urbanisation.

Case 1b: LAU mergers involving a densely populated area

The degree or urbanisation methodology specifies that each high-density cluster should have at least 75 % of its population covered by densely populated LAUs. It also foresees a method to match densely populated areas with the geographic areas of administrative or political functions and links the degree of urbanisation to the city data collection exercise. This means that any merger involving an LAU that has been previously classified as a densely populated area should result in the newly merged LAU also being classified as a densely populated area.

Case 1c: LAU mergers involving thinly populated and intermediate density areas

These mergers can be addressed in two simple ways: using the population of the urban cluster or using the population of the LAUs.

In the first case, if the population of the relevant urban cluster(s) is available then add the population inhabiting the urban cluster for each of the LAUs and divide this by the total population of the new LAU to determine the new degree of urbanisation. If more than 50 % of the population of the new LAU lives in an urban cluster, the new LAU should be classified as an intermediate density area. If the population share is less than 50 %, then the new LAU should be classified as a thinly populated area.

In the second case, if the population living in the urban cluster cannot be identified, then the degree of urbanisation may be determined based on the population distribution between the LAUs. If more than 50 % of the population of the new LAU comes from thinly populated LAUs, the new LAU should be classified as thinly populated. If more than 50 % of the population of the new LAU comes from intermediate density LAUs, the new LAU should be classified as intermediate density.

Case 2: LAU boundary shifts

Whereas mergers can be dealt with using simple methods, boundary shifts cannot always be as reliably addressed. Indeed, in some rare cases, boundary shifts between LAUs that have the same degree of urbanisation can lead to a change in classification. Such complexity means that a simple rule of thumb is often the preferred and most efficient approach.

A simple rule may be established whereby if an LAU loses less than 25 % of its previous population or gains less than 50 % of its population due to boundary shifts, then the degree of urbanisation does not change. This rule of thumb is likely to cover 90 % of all boundary shifts and ensures continuity. If this is not the case, then further investigation is required, as described below:

Case 2a: changes in the degree of urbanisation from boundary shifts are excluded

For each LAU, the share of population in the three different types of population grids cells is known. For example, if as the result of a boundary shift the population of an LAU that has 100 % of its population in rural grid cells shrinks, then it will remain a thinly populated area. Equally, if a boundary shift for an LAU that has 100 % of its population in rural grid cells rises, then the new LAU would need to more than double its population before it could (potentially) become an intermediate density area. As a result, if the boundary shift leads to a change in population that is too small to tip the population share of the revised LAU below 50 % of the relevant grid cells, it keeps the same degree of urbanisation.

Case 2b: changes in the degree of urbanisation from boundary shifts are unlikely (but cannot be excluded)

If the boundary shift leads to a change in population that is theoretically sufficient to the tip the population share of the revised LAU below or above 50 %, but the shift is between LAUs with the same degree of urbanisation, then the same degree of urbanisation should be kept.

Case 2c: changes in the degree of urbanisation from boundary shifts are likely

In some cases, changes in the degree of urbanisation are likely. Take for example, if a city were to gain part of a suburb as a result of a boundary shift. The city (a densely populated area) gains a small number of additional inhabitants (which does not have an impact on its degree of urbanisation). The suburb loses some of its population (that is reclassified to the city). As a result, the population in the revised LAU covered by the suburb may have less than 50 % of its population living in an urban cluster in which case it should subsequently be reclassified as a thinly populated area.

Case 3: splitting LAUs

This type of change is relatively rare. Therefore, the main recommendation is one of continuity; in other words, maintain the same degree of urbanisation. If an LAU is split, the new LAUs should have the same degree of urbanisation as the old LAU. If there are concerns, that the new LAUs may have different urban structures, the same approaches as described for boundary shifts can be used.

Future developments

At the time of writing, a 2021 population and housing census implementing regulation is in the process of being adopted by the European Commission. It includes an article for 1 km² population grid statistics: as well as information for annual counts of populations, it also foresees more detailed analyses, including population by sex, population by age, number of employed persons, population by place of birth, population by usual place of residence one year prior to the census.

Eurostat are also discussing post-2021 census developments with national statistical authorities. It is possible that from the mid-2020s onwards, the ESS will agree to produce annual counts of populations (based on usual place of residence) for a 1 km² grid, with data to be made available within 12 months of the end of the reference period.

Further information

Glossary entry:

Degree of urbanisation

Detailed methodology:

A harmonised definition of cities and rural areas: the new degree of urbanisation (WP 01/2014), European Commission, Directorate-General for Regional and Urban Policy

Dedicated section:

Background information for the degree of urbanisation

Correspondence for local administrative units:

Correspondence table for LAUs and the degree of urbanisation

Published indicators

A variety of different statistical surveys collect data for LAUs and this information may be used to calculate data for the three different degrees or urbanisation. This process involves aggregating the data for all cities within a territory (for example a Member State, or the EU as a whole) into one value, and doing the same for all towns and suburbs and for all rural areas. Indeed, the classification provides a means for accessing a much broader range of data from a number of different surveys, including the EU’s labour force survey (LFS) and EU statistics on income and living conditions (EU-SILC) and tourism statistics; see below for more details relating to the available data.

Visualisation tools:

Eurostat publishes data on the degree of urbanisation through Regions and cities illustrated.

Database:

Eurostat’s website provides information for over 100 indicators by degree of urbanisation. These statistics are available for the following statistical domains: health, education, living conditions and welfare, the labour market, tourism, and the digital economy and society. They are available here.

Examples

Figure 6: Employment rate, by degree of urbanisation, 2017
(% share of population aged 20-64)
Source: Eurostat (lfst_r_ergau)


Figure 7: Proportion of people using online telephone or video calls, by degree of urbanisation, 2017
(% share of people aged 16-74; based on frequency of use during the three months prior to the survey)
Source: Eurostat (isoc_ci_ac_i)


Figure 8: Proportion of people at risk of poverty or social exclusion, by degree of urbanisation, 2016
(% share of population)
Source: Eurostat (ilc_peps13) and (ilc_peps01)
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