Characterizing Urban Expansion Processes Using Dynamic Spatial Models – a European Application (Short Paper)

Authors Alex Hagen-Zanker , Jingyan Yu , Naratip Santitissadeekorn , Susan Hughes



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Author Details

Alex Hagen-Zanker
  • School of Sustainability, Civil and Environmental Engineering, University of Surrey, UK
Jingyan Yu
  • Institute of Geography and Sustainability (IGD), Faculty of Geosciences and Environment, University of Lausanne, Switzerland
Naratip Santitissadeekorn
  • Department of Mathematics and Physics, University of Surrey, UK
Susan Hughes
  • School of Sustainability, Civil and Environmental Engineering, University of Surrey, UK

Cite AsGet BibTex

Alex Hagen-Zanker, Jingyan Yu, Naratip Santitissadeekorn, and Susan Hughes. Characterizing Urban Expansion Processes Using Dynamic Spatial Models – a European Application (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 36:1-36:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.36

Abstract

Characterisation of the urban expansion processes using time series of binary urban/non-urban land cover data is complex due to the need to account for the initial configuration and the rate of urban expansion over the analysed period. Failure to account for these factors makes the interpretation of landscape metrics for compactness, fragmentation, or clumpiness problematic and the comparison between geographical areas and time periods contentious. This paper presents an approach for characterisation using spatio-dynamic modelling which is data-centred using a process based model, Bayesian optimization, cluster identification, and maximum likelihood classification. An application of the approach across 652 functional urban areas in Europe (1975-2014) demonstrates the consistency of the approach and its ability to identify spatial and temporal trends in urban expansion processes.

Subject Classification

ACM Subject Classification
  • Applied computing → Environmental sciences
Keywords
  • Urban expansion
  • morphology
  • spatio-temporal dynamics
  • simulation
  • compactness

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References

  1. Christina Corbane, Aneta Florczyk, Martino Pesaresi, Panagiotis Politis, and Vasileios Syrris. GHS-BUILT R2018A - GHS built-up grid, derived from Landsat, multitemporal (1975-1990-2000-2014). European Commission, Joint Research Centre (JRC), 2018. URL: https://doi.org/10.2905/jrc-ghsl-10007.
  2. Atilla R. Imre and Jan Bogaert. The Minkowski-Bouligand dimension and the interior-to-edge ratio of habitats. Fractals, 14(01):49-53, 2006. URL: https://doi.org/10.1142/S0218348X06003027.
  3. Xiaoping Liu, Xia Li, Yimin Chen, Zhangzhi Tan, Shaoying Li, and Bin Ai. A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data. Landscape ecology, 25:671-682, 2010. URL: https://doi.org/10.1007/s10980-010-9454-5.
  4. Marcello Schiavina, Ana Moreno-Monroy, Luca Maffenini, and Paolo Veneri. GHS-FUA R2019A-GHS functional urban areas, derived from GHS-UCDB R2019A,(2015). R2019A. edited by Joint Research Centre (JRC) European Commission, 2019. URL: https://doi.org/10.2905/347F0337-F2DA-4592-87B3-E25975EC2C95.
  5. Hannes Taubenböck, Michael Wurm, Christian Geiß, Stefan Dech, and Stefan Siedentop. Urbanization between compactness and dispersion: Designing a spatial model for measuring 2d binary settlement landscape configurations. International Journal of Digital Earth, 12(6):679-698, 2019. URL: https://doi.org/10.1080/17538947.2018.1474957.
  6. Jingyan Yu, Alex Hagen-Zanker, Naratip Santitissadeekorn, and Susan Hughes. Calibration of cellular automata urban growth models from urban genesis onwards-a novel application of Markov chain Monte Carlo approximate bayesian computation. Computers, Environment and Urban Systems, 90:101689, 2021. URL: https://doi.org/10.1016/j.compenvurbsys.2021.101689.
  7. Jingyan Yu, Alex Hagen-Zanker, Naratip Santitissadeekorn, and Susan Hughes. A data-driven framework to manage uncertainty due to limited transferability in urban growth models. Computers, Environment and Urban Systems, 98:101892, 2022. URL: https://doi.org/10.1016/j.compenvurbsys.2022.101892.
  8. Hui Zeng, Daniel Z. Sui, and Shujuan Li. Linking urban field theory with GIS and remote sensing to detect signatures of rapid urbanization on the landscape: Toward a new approach for characterizing urban sprawl. Urban Geography, 26(5):410-434, 2005. URL: https://doi.org/10.2747/0272-3638.26.5.410.
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