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Spatially Explicit Population Projections: The case of Copenhagen, Denmark

 3 years ago
source link: https://agile-giss.copernicus.org/articles/2/28/2021/
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AGILE GIScience Ser., 2, 28, 2021
https://doi.org/10.5194/agile-giss-2-28-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

04 Jun 2021

Spatially Explicit Population Projections: The case of Copenhagen, Denmark

Marina Georgatiand Carsten Keßler
  • Department of Planning, Aalborg University, Copenhagen, Denmark

Keywords: Migration, Spatial Projections, Population Change, Machine Learning, Convolution Neural Network

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

How to cite. Georgati, M. and Keßler, C.: Spatially Explicit Population Projections: The case of Copenhagen, Denmark, AGILE GIScience Ser., 2, 28, https://doi.org/10.5194/agile-giss-2-28-2021, 2021.

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