Territorial typologies

From Statistics Explained

Traditionally, typologies of territory were determined by population size and density of local administrative units at level 2 (LAU level 2), such as communes, municipalities or local authorities. The new typologies that are described here use a population grid, which is a more accurate basis to characterise areas and regions. This article provides a short overview of the typologies, including definitions, terminology and some basic statistical data.

These typologies start by classifying grid cells of 1 km² to a typology of clusters according to their similarities in terms of population size and density: each grid cell is classified to one type of cluster only. Areas (LAU level 2) or regions (NUTS level 3) can then be classified to area or regional typologies based on the population share in different types of clusters: again, each LAU level 2 area or NUTS level 3 region is classified to one type only. In each of these various typologies (of clusters, areas or regions) the whole geographical territory of the European Union (EU) is covered without any overlaps or omission.

The area typology applied to LAU level 2 is primarily used in surveys such as the labour force survey (LFS) and the survey on income and living conditions (SILC); the regional typology applied to the NUTS level 3 regions is mainly used to monitor rural development.

Map 1: Type of clusters (1) - Source: Eurostat, JRC, EFGS, REGIO-GIS
Map 2: Degree of urbanisation for LAU level 2 areas (1) - Source: Eurostat (agr_r_accts)
Map 3: Urban-rural typology for NUTS level 3 regions (1) - Source: Eurostat (agr_r_accts)
Figure 1: Contiguous grid cells
Table 1: Summary regarding the names of the different typologies and items - Source: Eurostat, JRC, EFGS, REGIO-GIS
Table 2: Share of population using different typologies (1)
(% of population) - Source: Eurostat, JRC, EFGS, REGIO-GIS
Table 3: Share of land area using different typologies (1)
(% of land area) - Source: Eurostat, JRC, EFGS, REGIO-GIS

Typologies

Cluster types

The typology of clusters classifies 1 km² grid cells (and clusters thereof), splitting them into three types. The criteria used are the population density in the individual grid cells and the combined population level of clusters, where clusters are made up of contiguous cells (in other words, neighbouring or adjoining cells); see later for a more detailed explanation of contiguous cells and the so-called gap-filling technique used for high-density clusters. The three types of grid cells or clusters in the typology are the following.

  • High-density clusters/city centres/urban centres: clusters of contiguous grid cells of 1 km² with a density of at least 1 500 inhabitants per km² and a minimum population of 50 000 after gap-filling.
  • Urban clusters: clusters of contiguous grid cells of 1 km²with a density of at least 300 inhabitants per km² and a minimum population of 5 000.
  • Rural grid cells: grid cells outside high-density clusters and urban clusters.

Contiguous cells and filling gaps in the cluster typology

To determine population size, the grid cells need to be grouped in clusters. The methods presented here use three different rules for contiguity to create clusters. These three rules are explained below.

  1. Contiguous including diagonals — used for urban clusters If the central square (grid cell) in Figure 1 is above the density threshold, it will be grouped with each of the other surrounding eight grid cells that exceed the density threshold.
  2. Contiguous excluding diagonals — used for high-density clusters If the central square in Figure 1 is above the density threshold, it will be grouped with each of the four cells directly above, below or next to the central square that also exceed the density threshold. This means that cells numbered 2, 4, 5 and 7 can be included in the same cluster. Cells with number 1, 3, 6 and 8 cannot as they have a diagonal connection.
  3. The majority rule or gap-filling — used for high-density clusters

The goal for the high-density clusters is to identify urban centres without any gaps. Therefore, enclaves need to be filled. If the central square in Figure 1 is not, in its own right, a part of a high-density cluster, it will be added to a high-density cluster if five or more of the eight surrounding cells (therefore including diagonals) belong to a single high-density cluster. This rule is applied iteratively until no more cells can be added.

Degree of urbanisation typology for LAU level 2 areas — an area typology

Depending on the share of the population living in the different types of cluster, LAU level 2 areas are classified into three degrees of urbanisation.

  • Densely-populated areas/cities/large urban areas: at least 50 % of the population lives in high-density clusters [1].
  • Intermediate density areas/towns and suburbs/small urban areas: less than 50 % of the population lives in rural grid cells and less than 50 % lives in high-density clusters.
  • Thinly-populated areas/rural areas: more than 50 % of the population lives in rural grid cells.

Urban-rural typology for NUTS level 3 regions — a regional typology

Depending on the share of the rural population (in other words, the share of the population living in rural grid cells), the NUTS level 3 regions are classified into the following three groups.

  • Predominantly urban regions/urban regions: the rural population is less than 20 % of the total population.
  • Intermediate regions: the rural population is between 20 % and 50 % of the total population.
  • Predominantly rural regions/rural regions: the rural population is 50 % or more of the total population.

In a last step, the size of the cities in the region is considered.

  • A region classified as predominantly rural by the criteria above becomes intermediate if it contains a city of more than 200 000 inhabitants representing at least 25 % of the regional population. 
  • A region classified as intermediate by the criteria above becomes predominantly urban if it contains a city of more than 500 000 inhabitants representing at least 25 % of the regional population.

Summary table: names and alternative names

The names of typologies and items may differ according to context, users or means of dissemination. Table 1 gives a summary of the vocabulary used as well as the geographical scale.

Main statistical findings

Share of population by type of territory

Although these typologies show similar patterns, the use of different typologies may produce rather different figures. Thus, as Table 2 shows, around 33 % of the EU-27 population lived in rural grid cells, 28 % in thinly populated areas and 23 % in predominantly rural regions.

Moreover, the variability between the figures is more pronounced at the national level than for the EU as a whole. As Table 2 illustrates, 35 % of the Bulgarian population lived in high-density clusters, 43 % in densely populated areas and 16 % in predominantly urban regions.

Share of land area by type of territory

The data produced using these different typologies present a broader range in terms of surface area than in terms of the population. As Table 3 shows, 3 % of the EU-27’s land area was covered by urban clusters, 13 % by intermediate density areas and 39 % by intermediate regions. Again, there is greater variability at the national level than for the EU as a whole, as Table 3 clearly shows.

Data sources and availability

These typologies classify different territories, defined at different geographical scales, namely grid cells, LAU 2 areas or NUTS level 3 regions. However, the analysis of the statistical data using these typologies may be disseminated at a higher geographical level. Hence, the proportion of EU-27 land area classified as composed of intermediate regions is an indicator for the EU based on a regional typology. A similar indicator could also be disseminated at national, NUTS level 1, NUTS level 2 and NUTS level 3 levels. However, in some cases statistical data using these typologies can only be calculated and disseminated for the EU as a whole or at the national level. This is mainly to do with representativeness, confidentiality and reliability of the indicator. Some surveys, for example SILC, can provide reliable statistics by degree of urbanisation for thinly populated areas at the national level, but not at NUTS level 3.

Context

The European Commission has introduced typologies based on population size and density to monitor situations and trends in urban and rural areas and regions. The Treaty on European Union (also called the Treaty of Maastricht) specifically mentions that particular attention should be paid to rural areas and rural regions.

The Lisbon Treaty included territorial cohesion alongside economic and social cohesion as an objective for the EU. This new concept was presented in a ‘Green Paper on territorial cohesion — Turning territorial diversity into strength’ (COM(2008) 616) and the debate has been summarised in the ‘Sixth progress report on economic and social cohesion’ in 2009. The report ‘Investing in Europe's future — Fifth cohesion report on economic, social and territorial cohesion’ explains the main issues related to territorial cohesion and how these could be transposed into policy proposals. One of the main issues related to territorial cohesion is the need for data on different territorial levels, particularly for lower geographical levels. The classification of the degree of urbanisation provides a unique insight into trends at the local level, and highlights the differences between urban and rural areas.

See also

Further Eurostat information

Publications

Dedicated section

City statistics - Urban Audit

Methodology / Metadata

  • A file with all the classifications can be found here.
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Source data for tables and graphs on this page (MS Excel)

Other information

External links

Notes

  1. In addition, each high-density cluster should have at least 75 % of its population in densely-populated LAU level 2 areas. This also ensures that all high-density clusters are represented by at least one densely-populated LAU level 2, even when this high-density cluster represents less than 50 % of the population of that LAU level 2.
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