Developing capacitated p-median location-allocation model in the spopt library to allow UCL student teacher placements using public transport
Nick Bearman
Geospatial Training Solutions and University College London, London, UK
Rongbo Xu
Urban Big Data Centre, University of Glasgow, Glasgow, UK
Patrick J. Roddy
Advanced Research Computing, University College London, London, UK
James D. Gaboardi
Geospatial Science and Human Security Division, Oak Ridge National Laboratory, USA
Qunshan Zhao
Urban Big Data Centre, University of Glasgow, Glasgow, UK
Huanfa Chen
CASA, University College London, London, UK
Levi Wolf
University of Bristol, Bristol, UK
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Joost de Vries, Fanny M. Monteiro, Alex J. Poulton, Nicola A. Wiseman, and Levi J. Wolf
EGUsphere, https://doi.org/10.1101/2025.09.11.675535, https://doi.org/10.1101/2025.09.11.675535, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
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Calcifying phytoplankton ('coccolithophores') are a diverse group of organisms which play a key role in the ocean's carbon cycle. Despite the diversity of these organisms, they are generally viewed as a single group with a uniform response to climate change. Here we show using global machine learning stock estimates that doing so risks biasing our understanding of the role of these organisms in the carbon cycle and their response to environmental changes.
Haiyan Huang, Qimin Cheng, Duowang Zhu, Xiao Huang, and Qunshan Zhao
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-G-2025, 383–389, https://doi.org/10.5194/isprs-annals-X-G-2025-383-2025, https://doi.org/10.5194/isprs-annals-X-G-2025-383-2025, 2025
Sebastian Hafner, Qunshan Zhao, Angela Abascal, Manuella Comerio de Paulo, Grant Tregonning, Alexandra Middleton, Adenike Shonowo, Monika Kuffer, Ryan Engstrom, Dana R. Thomson, Francis C. Onyambu, Caroline Kabaria, Peter Elias, Oluwatoyin Odulana, Bunmi Alugbin, Kehinde Baruwa, and João Porto de Albuquerque
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-7-2025, 221–228, https://doi.org/10.5194/isprs-archives-XLVIII-M-7-2025-221-2025, https://doi.org/10.5194/isprs-archives-XLVIII-M-7-2025-221-2025, 2025
Y. Li, Q. Zhao, and M. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 537–543, https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-537-2022, https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-537-2022, 2022