Settlement Grid - JRC


Settlement Grid

Represented classes

Inhabited areas

RUR (rural grid cells)

LDC (low density clusters)

HDC (high density clusters) 


The GHS-SMOD is the rural-urban Settlement classification MODel adopted by the GHSL. It is the representation of the degree of urbanization (DEGURBA)[1] concept into the GHSL data scenario. Each grid in the GHS-SMOD has been generated by integrating the GHSL built-up areas and GHSL population grids data for reference epochs: 1975, 1990, 2000, 2015.

[1] The DEGURBA classification schema is a people-based definition of cities and settlements: it operates using as main input a 1 km² grid cell accounting for population at a given point in time. The DEGURBA discriminates the population grid cells in three main classes: 'urban centers' (cities), 'urban clusters' (towns and suburbs), and 'rural grid cells'. (base). These class abstractions translate to 'high density clusters (HDC)', 'low density clusters (LDC)', and 'rural grid cells (RUR)', respectively, in the GHS-SMOD implementation. The 'HDC' differ from the DEGURBA 'urban centers' in that they account for the over-fragmentation of cities in regions with large low-density residential development by integrating the built-up layer. In the GHS-SMOD representation, the 'HDC' are the spatial generalization of contiguous population grid cells (4-connectivity, gap-filling) with a density of at least 1,500 inhabitants per km² or a density of built-up surface > 50%, and a minimum total resident population of 50,000. The 'LDC' are continuous grid cells with a density of at least 300 inhabitants per km² and a minimum total population of 5,000. The 'RUR' are grid cells outside 'HDC' and 'LDC' with population > 0 and < 300. Everything else is classified as inhabited areas where population = 0. This dataset was produced in the World Mollweide projection (EPSG:54009).

European Commission's Joint Research Centre (JRC)

Source data

The European Commission's Joint Research Centre (JRC)

GHS Settlement Grid (LDS)


The GHSL relies on the design and implementation of new spatial data mining technologies allowing to automatically process and extract analytics and knowledge from large amount of heterogeneous data including: global, fine-scale satellite image data streams, census data, and crowd sources or volunteered geographic information sources.

Spatial resolution

Global coverage

1,000 m spatial resolution 

Temporal resolution

Reference epochs: 

1975, 1990, 2000, 2015