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https://doi.org/10.5281/zenodo...
Dataset . 2023
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FUME Local population projections in destination cities

Authors: Georgati, Marina; Keßler, Carsten;

FUME Local population projections in destination cities

Abstract

FUME data on projected distributions of migrants at local level between 2030 and 2050. The dataset contains a folder of data for each destination city as a gridded dataset at 100m resolution in GeoTIFF format. The examined destination cities are: Amsterdam, Copenhagen, Krakow and Rome. The dataset is provided as 100m grid cells based on the Eurostat GISCO grid of the 2021 NUTS version, using ETRS89 Lambert Azimuthal Equal-Area (EPSG: 3035) as coordinate system. The file names consist of the projected year, the corresponding scenario, and the reference migrant group. The projections have been performed for the years 2030, 2040 and 2050. The investigated scenarios are the following: • benchmark (bs), • baseline (bs), • Rising East (re), • EU Recovery (eur), • Intensifying Global Competition (igc), and • War (war). The migration background is derived from data about the Region of Origin (RoO) for migrants in Copenhagen and Amsterdam, and from Region of Citizenship (CoC) for migrants in Krakow and Rome. The case study of Copenhagen covers the two central NUTS3 areas (DK011, DK012) and the groups presented are the following: • total population (totalpop), • native population (DNK), • Eastern EU European migrants (EU_East), • Western EU Europeans migrants (EU_West), • Non-EU European migrants (EurNonEU), • migrants from Turkey (Turkey), • the MENAP countries (MENAP; excluding Turkey), • other non-Western (OthNonWest), and • other Western countries (OthWestern). The case study of Amsterdam covers one NUTS3 area (NL329) and the presented groups are the following: • total population (totalpop), • native population (NLD), • Eastern EU European migrants (EU East), • Western EU European migrants (EU West), • migrants from Turkey and Morocco (Turkey + Morocco), • migrants from the Middle East and Africa (Middle East + Africa), • migrants from the former colonies (Former Colonies), and • migrants from the rest of the world (Other Europe etc). The case study of Krakow covers the Municipality of Krakow, and the presented groups are the following: • total population (totalpop), • native population (POL), • EU/EFTA European migrants (EU), • non-EU European migrants (Europe_nonEU), and • migrants from the rest of the world (Other). The case of Rome covers the Municipality of Rome, and the presented groups are the following: • total population (totalpop), • native population (ITA), • migrants from Romania (ROU), • Philippines (PHL), • Bangladesh (BGD), • the EU (EU; excluding Romania), • Africa (Africa), • Asia (Asia; excluding Philippines and Bangladesh) and • America (America).

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Keywords

machine learning, migration, local projections

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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