Crols, T orcid.org/0000-0002-9379-7770, White, R, Uljee, I et al. (3 more authors) (2017) High-resolution simulations of population-density change with an activity-based cellular automata land-use model. In: Proceedings of GeoComputation 2017. GeoComputation 2017, 04-07 Sep 2017, Leeds, UK. Centre for Computational Geography, University of Leeds
Abstract
The MOLAND model is a cellular automata (CA) land-use change model that has often been applied to simulate urban growth. A more recent alternative model makes the simulations more multifunctional by also computing different activities (population and employment) for every cell. However, the equation to update population density in time in this activity-based CA model could not deal with high population growth rates in some existing urban centres. Therefore, we experimented with two alternative equations. A semi-automated calibration routine was used to compare errors of the different model versions at a continuous range of resolutions in two study areas: the Greater Dublin Region, Ireland, and Flanders and Brussels, Belgium. The two new population density equations turn out to solve the particular problem of fast changes in high-density neighbourhoods and generally improve regional errors in the Belgian application, but can unfortunately introduce larger errors in low-density areas or in the land-use simulations.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017, The Authors. |
Keywords: | Population density; Cellular automata; Densification; Land-use change; Semi-automated calibration |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Nov 2017 13:12 |
Last Modified: | 16 Nov 2017 13:12 |
Published Version: | http://www.geocomputation.org/2017/papers/79.pdf |
Status: | Published |
Publisher: | Centre for Computational Geography, University of Leeds |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:124181 |