Disaggregation of population estimates at high spatial resolution with machine learning

Original language	English
Supervisors	Keßler, Carsten, Principal supervisor Hansen, Henning Sten, Co-supervisor
Publisher	Aalborg Universitetsforlag
Electronic ISBNs	978-87-7573-598-3
DOIs	https://doi.org/10.54337/aau679682906
Publication status	Published - 2023

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Projects

• 1 Finished

Projects per year

• FUME: Future Migration Scenarios for Europe

  Hansen, H. S. (PI), Keßler, C. (CoI), Georgati, M. (Project Participant), Kveladze, I. (Project Participant) & Diop, L. E. N. (Project Participant)

  01/12/2019 → 31/05/2023

  Project: Research

  • Migration Pattern 100%
  • visualization method 75%
  • Use Case 50%
  • Migration Network 50%
  • Temporal Variation 50%

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Georgati, M. (2023). Disaggregation of population estimates at high spatial resolution with machine learning. Aalborg Universitetsforlag. https://doi.org/10.54337/aau679682906

Georgati, Marina. / Disaggregation of population estimates at high spatial resolution with machine learning. Aalborg Universitetsforlag, 2023.

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title = "Disaggregation of population estimates at high spatial resolution with machine learning",

author = "Marina Georgati",

year = "2023",

doi = "10.54337/aau679682906",

language = "English",

series = "PhD Series, Technical Faculty of IT and Design, Aalborg University",

publisher = "Aalborg Universitetsforlag",

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Georgati, M 2023, 'Disaggregation of population estimates at high spatial resolution with machine learning'. https://doi.org/10.54337/aau679682906

Disaggregation of population estimates at high spatial resolution with machine learning. / Georgati, Marina.
Aalborg Universitetsforlag, 2023.

Research output: PhD thesis

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AU - Georgati, Marina

PY - 2023

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DO - 10.54337/aau679682906

M3 - PhD thesis

T3 - PhD Series, Technical Faculty of IT and Design, Aalborg University

PB - Aalborg Universitetsforlag

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Georgati M. Disaggregation of population estimates at high spatial resolution with machine learning. Aalborg Universitetsforlag, 2023. doi: 10.54337/aau679682906