Built-up area and built-up area change in countries and regions
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Built-up area: Joint Research Centre & Pesaresi et al. (2016) Global Human Settlement Built-up Layer (GHS_BUILT_LDSMT_GLOBE_R2015B_3857_38_v1_0)

Population: UN (2017) World Population Prospects

Political and administrative boundaries: FAO (2015) Global Administrative Unit Layers (GAUL)

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March 2018
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Built-up area and built-up area change

Methodology: http://dx.doi.org/10.1787/72a9e331-en

The Global Human Settlement built-up layers map the extent and change over time of built-up areas. It is one product of an ongoing larger framework that produces spatial information about the human presence on the planet.

"Built-up" is defined as the presence of buildings (roofed structures). This definition largely excludes other parts of urban environments and the human footprint such as paved surfaces (roads, parking lots), commercial and industrial sites (ports, landfills, quarries, runways) and urban green spaces (parks, gardens). Consequently, such built-up area may be quite different from other urban area data that use alternative definitions.

The denominator used to calculate percentages is total land area which is the area of the country excluding inland water.

GHSL provides some advantages over CCI-LC (the Land Coverand Land Cover Change indicators published alongside) for information on built-up area and urban expansion. It has a longer time series for some areas (from 1975) and a much higher resolution (38m) which makes it more suitable for studying changes in smaller areas like cities and for studying the changing urban structure.

There are some important limitations: data from the 1975-1990 epoch underestimates the built-up areas as detected in the successive epochs because the earlier satellite-borne sensors were inferior to later sensors and coverage of the world was less comprehensive. Furthmore the data may not be reliable for small regions or areas where fewer observations are available (e.g. cloudy areas).

Note

These indicators are calculated by intersecting political, administrative or functional urban area boundaries with raster datasets using GIS software. They provide accessible tabular statistics of the underlying datasets for a variety of geographic output areas that can be used immediately without performing the spatial analysis that would otherwise be required.

Maps of the underlying geographic data set/s used can be viewed in a web browser at the above links. Prospective users are encouraged to examine the underlying data for their area of interest and familiarise themselves with the methodology used in their production so as to better understand what the data show and what kinds of conclusions they can be used to support. Irrespective of the underyling data, all earth-observation derived statistics come with caveats such as scale dependence, limitations associated with classification of continuous phenomena into discrete classes, and uneven geographical and temporal accuracy.

For more details on the methodology see: 

Methodology: http://dx.doi.org/10.1787/72a9e331-en

Built-up area and built-up area change in countries and regionsContact person/organisation

env.stat@oecd.org

Data source(s) used

Built-up area: Joint Research Centre & Pesaresi et al. (2016) Global Human Settlement Built-up Layer (GHS_BUILT_LDSMT_GLOBE_R2015B_3857_38_v1_0)

Population: UN (2017) World Population Prospects

Political and administrative boundaries: FAO (2015) Global Administrative Unit Layers (GAUL)

Date last updated
March 2018
Key statistical concept

Built-up area and built-up area change

Methodology: http://dx.doi.org/10.1787/72a9e331-en

The Global Human Settlement built-up layers map the extent and change over time of built-up areas. It is one product of an ongoing larger framework that produces spatial information about the human presence on the planet.

"Built-up" is defined as the presence of buildings (roofed structures). This definition largely excludes other parts of urban environments and the human footprint such as paved surfaces (roads, parking lots), commercial and industrial sites (ports, landfills, quarries, runways) and urban green spaces (parks, gardens). Consequently, such built-up area may be quite different from other urban area data that use alternative definitions.

The denominator used to calculate percentages is total land area which is the area of the country excluding inland water.

GHSL provides some advantages over CCI-LC (the Land Coverand Land Cover Change indicators published alongside) for information on built-up area and urban expansion. It has a longer time series for some areas (from 1975) and a much higher resolution (38m) which makes it more suitable for studying changes in smaller areas like cities and for studying the changing urban structure.

There are some important limitations: data from the 1975-1990 epoch underestimates the built-up areas as detected in the successive epochs because the earlier satellite-borne sensors were inferior to later sensors and coverage of the world was less comprehensive. Furthmore the data may not be reliable for small regions or areas where fewer observations are available (e.g. cloudy areas).

Note

These indicators are calculated by intersecting political, administrative or functional urban area boundaries with raster datasets using GIS software. They provide accessible tabular statistics of the underlying datasets for a variety of geographic output areas that can be used immediately without performing the spatial analysis that would otherwise be required.

Maps of the underlying geographic data set/s used can be viewed in a web browser at the above links. Prospective users are encouraged to examine the underlying data for their area of interest and familiarise themselves with the methodology used in their production so as to better understand what the data show and what kinds of conclusions they can be used to support. Irrespective of the underyling data, all earth-observation derived statistics come with caveats such as scale dependence, limitations associated with classification of continuous phenomena into discrete classes, and uneven geographical and temporal accuracy.

For more details on the methodology see: 

Methodology: http://dx.doi.org/10.1787/72a9e331-en