Characterizing European urban settlements from population data: a cartographic approach. Issue 5 (19th October 2016)

• Record Type: Journal Article
• Authors: Serrano Giné, D.; Russo, A.; Brandajs, F.; Pérez Albert, M.Y.
• Is Part Of: Cartography and geographic information science. Volume 43:Issue 5(2016)
• Page Start: 442
• Page End: 453
• Publication Date: 2016-10-19
• Subjects: Settlements -- urban shape -- population -- population density -- Europe; Cartography -- Periodicals; Geographic information systems -- Periodicals; 526
• DOI: 10.1080/15230406.2015.1076737
• Languages: English
• ISSNs: 1523-0406

Abstract

ABSTRACT: The cartographic identification and characterization of urban settlements is problematic, particularly in varied areas or in large geographical extensions. This is due to the divergence between morphological area structures and data attribution, which is generally available at the level of administrative units. Most existing experiences focus on small regions or use indirect data, from which only indirect information can be derived. This paper proposes an identification of urban settlements in the European Union space, which combines urban population and shape through geographic information system (GIS) techniques. For that purpose, 1 km 2 cells with associated population data are used. A procedure is developed establishing three different types of urban settlements, according to population and population density values: high-density urban clusters, small and medium-sized towns, and very small towns. A validation of this procedure is performed using direct survey from local experts in 10 selected study areas. The final results show that 78, 307 urban settlements can be identified: 88.17% of them are classified as "very small towns, " 10.75% as "small and medium-sized towns, " and 1.08% as high-density urban clusters. This exercise generates new information for the European Union and surrounding countries, and yields the basis for further research. All GIS operations are performed using vector layers, which is not usual in large-area regional studies.