Data Characteristics
Data Characteristics Date last updated Date last updated Concepts & Classifications Concepts & Classifications Key statistical concept Key statistical conceptSource
- Built-up statistics are calculated using Florczyk et al. (2019) 30m resolution multitemporal built-up grid (GHSBUILTLDSMTGLOBER2018A385730V20)
- Population used for per capita values is taken from UN (2019) World Population Prospects.
- For political and administrative boundaries, OECD (2020) territorial grid units are used where available, for the remaining countries, the FAO (2015) Global Administrative Unit Layers (GAUL 2014) are used (see the Boundary definition section below for details). The OECD (2020) Functional Urban Area definition is used for cities.
Built-up area and built-up area change
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 land area used as the denominator is the sum of the 'Land, never built-up', 'Built-up from 2000 to 2014', 'Built-up from 1990 to 2000', 'Built-up from 1975 to 1990', and 'Built-up before 1975' values from the built-up grid (i.e. it excludes inland water bodies, and nodata areas).
Notes and limitations
GHSL built-up statistics are usually preferable to the urban or artificial classes of multi-class land cover datasets like CCI-LC (published alongside) for information on built-up area and urban expansion because it has a long time series for some areas (from 1975) and a very high resolution (30m) 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
built-up areas compared to successive epochs because the earlier satellite-borne sensors were inferior to later sensors, observations less frequent and coverage less comprehensive. Furthermore the data may not be reliable for small regions or areas where fewer observations are available (e.g. cloudy areas).
Areas estimated by pixel-counting built-up area grids are highly scale dependent so country totals calculated from a 30m grid (e.g. here) will be different from those calculated from a 250m or 1km resolution grid, even when the underlying estimation method is otherwise the same.
Known issues
- Built-up area in Ponta Delgada (FUA PT007L1) missing in GHSBUILTLDSMTGLOBER2018A385730V20 so the estimates for that FUA are incorrect.
Note on GIS-derived indicators
These indicators are calculated by intersecting political, administrative or functional urban area boundaries with raster datasets. They provide accessible tabular statistics of the underlying datasets for a variety of geographic output areas that can be used 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.
Boundary definition
- Large regions correspond to OECD territorial level 2 (TL2) or GAUL level 1 (GAUL 1).
- Small regions correspond to OECD territorial level 3 (TL3) or GAUL level 2 (GAUL 2).
The boundary unit codes indicate the source and year of the definition used. OECDXXXX2020 means that boundary comes from the OECD's reference areas as of 2020, and the unique ID in the source dataset is XXXX. Similarly GAUL1XXXX2015 means the boundaries come from GAUL 1 (2015) and the source GAUL id is XXXX. FUAs all come from the same dataset and simply use the unique ID from the source.
Background, context and method
Source
- Built-up statistics are calculated using Florczyk et al. (2019) 30m resolution multitemporal built-up grid (GHSBUILTLDSMTGLOBER2018A385730V20)
- Population used for per capita values is taken from UN (2019) World Population Prospects.
- For political and administrative boundaries, OECD (2020) territorial grid units are used where available, for the remaining countries, the FAO (2015) Global Administrative Unit Layers (GAUL 2014) are used (see the Boundary definition section below for details). The OECD (2020) Functional Urban Area definition is used for cities.
Built-up area and built-up area change
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 land area used as the denominator is the sum of the 'Land, never built-up', 'Built-up from 2000 to 2014', 'Built-up from 1990 to 2000', 'Built-up from 1975 to 1990', and 'Built-up before 1975' values from the built-up grid (i.e. it excludes inland water bodies, and nodata areas).
Notes and limitations
GHSL built-up statistics are usually preferable to the urban or artificial classes of multi-class land cover datasets like CCI-LC (published alongside) for information on built-up area and urban expansion because it has a long time series for some areas (from 1975) and a very high resolution (30m) 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
built-up areas compared to successive epochs because the earlier satellite-borne sensors were inferior to later sensors, observations less frequent and coverage less comprehensive. Furthermore the data may not be reliable for small regions or areas where fewer observations are available (e.g. cloudy areas).
Areas estimated by pixel-counting built-up area grids are highly scale dependent so country totals calculated from a 30m grid (e.g. here) will be different from those calculated from a 250m or 1km resolution grid, even when the underlying estimation method is otherwise the same.
Known issues
- Built-up area in Ponta Delgada (FUA PT007L1) missing in GHSBUILTLDSMTGLOBER2018A385730V20 so the estimates for that FUA are incorrect.
Note on GIS-derived indicators
These indicators are calculated by intersecting political, administrative or functional urban area boundaries with raster datasets. They provide accessible tabular statistics of the underlying datasets for a variety of geographic output areas that can be used 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.
Boundary definition
- Large regions correspond to OECD territorial level 2 (TL2) or GAUL level 1 (GAUL 1).
- Small regions correspond to OECD territorial level 3 (TL3) or GAUL level 2 (GAUL 2).
The boundary unit codes indicate the source and year of the definition used. OECDXXXX2020 means that boundary comes from the OECD's reference areas as of 2020, and the unique ID in the source dataset is XXXX. Similarly GAUL1XXXX2015 means the boundaries come from GAUL 1 (2015) and the source GAUL id is XXXX. FUAs all come from the same dataset and simply use the unique ID from the source.