Settlement Grid - JRC
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) 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.
 The DEGURBA classification schema is a people-based definition of cities and settlements: it operates using as main input a 1 kmÂ² 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 1500 inhabitants per kmÂ² or a density of built-up surface > 50%, and a minimum total resident population of 50000. The 'LDC' are continuous grid cells with a density of at least 300 inhabitants per kmÂ² and a minimum total population of 5000. 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)
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.
1000m spatial resolution
1975, 1990, 2000, 2015