Spatially Explicit Population Projections: The case of Copenhagen, Denmark

Abstract. Cities expand rapidly with international migration significantly contributing to urban growth and population change. However, cities miss out on a great opportunity of reclaiming valuable knowledge future distribution due the lack established tools methodologies project where it is more likely for people specific socio-demographic groups set up home. The present work suggests that spatially explicit projections can play significant role as tool planning managing diversity creatively, especially when combination social, demographic topographic data utilized. Machine learning techniques have demonstrated capabilities capture relationships among this plethora features estimate distribution. We flexible, ML-based methodology high-resolution gridded by characteristics, specifically region origin, capital Copenhagen, Denmark, combining various input layers.