Projects per year
Abstract
Urban expansion models are widely used to understand, analyze and predict any peculiar scenario based on input probabilities. Modelling and uncertainty are concomitant, and can occur due to reasons ranging from–discrepancies in input variables, unpredictable model parameters, spatio-temporal variability between observations, or malfunction in linking model variables under two different spatio-temporal scenarios. However, uncertainties often occur because of the interplay of model elements, structures, and the quality of data sources employed; as input parameters influence the behavior of cellular automaton (CA) models. Our study aims to address these uncertainties. While most studies consider neighborhood effects, timestep and spatial resolution, our study uniquely focuses on the susceptibility of multi density classes and varying cell size on uncertainty. Hence this chapter offers a theoretical elucidation of the concepts, sources, and strategies for managing uncertainty under various criteria as well as an algorithm for enumerating the model’s accuracy for Wallonia, Belgium.
Original language | English |
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Title of host publication | Intelligence for Future Cities. CUPUM 2023 |
Subtitle of host publication | International Conference on Computers in Urban Planning and Urban Management |
Editors | Robert Goodspeed, Raja Sengupta, Marketta Kyttä, Christopher Pettit |
Place of Publication | Cham |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 325-341 |
Number of pages | 17 |
ISBN (Electronic) | 978-3-031-31746-0 |
ISBN (Print) | 978-3-031-31745-3 |
DOIs | |
Publication status | Published - 2 Jun 2023 |
Externally published | Yes |
Publication series
Name | Urban Book Series |
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Volume | Part F270 |
Bibliographical note
Funding Information:Funding: This research was funded by the INTER program and co-funded by the Fond National de la Recherche, Luxembourg (FNR) and the Fund for Scientific Research-FNRS, Belgium (F.R.S— FNRS), T.0233.20,—‘Sustainable Residential Densification’ project (SusDens, 2020–2023).
Keywords
- Uncertainty analysis
- Urban CA modelling
- Urban densification
Projects
- 1 Finished
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SusDens: Sustainable Residential Densification
Omrani, H., Teller, J., Gorczynska-Angiulli, M., Gerber, P., Skoczylas, K., Zieba-Kulawik, K. & El Hajjar, S.
Luxembourg National Research Fund (FNR), Luxembourg Institute of Socio-Economic Research (LISER)
1/03/20 → 29/02/24
Project: Research