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Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates

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Listed:
  • Franz Schug
  • David Frantz
  • Sebastian van der Linden
  • Patrick Hostert

Abstract

Gridded population data is widely used to map fine scale population patterns and dynamics to understand associated human-environmental processes for global change research, disaster risk assessment and other domains. This study mapped gridded population across Germany using weighting layers from building density, building height (both from previous studies) and building type datasets, all created from freely available, temporally and globally consistent Copernicus Sentinel-1 and Sentinel-2 data. We first produced and validated a nation-wide dataset of predominant residential and non-residential building types. We then examined the impact of different weighting layers from density, type and height on top-down dasymetric mapping quality across scales. We finally performed a nation-wide bottom-up population estimate based on the three datasets. We found that integrating building types into dasymetric mapping is helpful at fine scale, as population is not redistributed to non-residential areas. Building density improved the overall quality of population estimates at all scales compared to using a binary building layer. Most importantly, we found that the combined use of density and height, i.e. volume, considerably increased mapping quality in general and with regard to regional discrepancy by largely eliminating systematic underestimation in dense agglomerations and overestimation in rural areas. We also found that building density, type and volume, together with living floor area per capita, are suitable to produce accurate large-area bottom-up population estimates.

Suggested Citation

  • Franz Schug & David Frantz & Sebastian van der Linden & Patrick Hostert, 2021. "Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
  • Handle: RePEc:plo:pone00:0249044
    DOI: 10.1371/journal.pone.0249044
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    Cited by:

    1. Yongke Wu & Xiankun Yang & Zhiqiang Jia & Jinnian Wang, 2023. "Building Density Dynamics and Habitability Evaluation of China’s Nanning City," Sustainability, MDPI, vol. 15(9), pages 1-20, May.
    2. Qikang Zhong & Bo Li & Tian Dong, 2024. "Building sustainable slow communities: the impact of built environments on leisure-time physical activities in Shanghai," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-21, December.
    3. Franz Schug & David Frantz & Dominik Wiedenhofer & Helmut Haberl & Doris Virág & Sebastian van der Linden & Patrick Hostert, 2023. "High‐resolution mapping of 33 years of material stock and population growth in Germany using Earth Observation data," Journal of Industrial Ecology, Yale University, vol. 27(1), pages 110-124, February.
    4. André Hartmann & Martin Behnisch & Robert Hecht & Gotthard Meinel, 2024. "Prediction of residential and non-residential building usage in Germany based on a novel nationwide reference data set," Environment and Planning B, , vol. 51(1), pages 216-233, January.
    5. Kiatkulchai Jitt-Aer & Graham Wall & Dylan Jones & Richard Teeuw, 2022. "Use of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(1), pages 185-211, August.
    6. David Frantz & Franz Schug & Dominik Wiedenhofer & André Baumgart & Doris Virág & Sam Cooper & Camila Gómez-Medina & Fabian Lehmann & Thomas Udelhoven & Sebastian Linden & Patrick Hostert & Helmut Hab, 2023. "Unveiling patterns in human dominated landscapes through mapping the mass of US built structures," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    7. Sandra Hadam, 2023. "Experimentelle georeferenzierte Bevölkerungszahl auf Basis der Bevölkerungsfortschreibung und Mobilfunkdaten [Experimental georeferenced population figure based on intercensal population updates an," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 17(1), pages 35-69, March.

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