Spatio-temporal simulation of future urban growth trends using an integrated CA-Markov model

Aburas, MM; Ho, YM; Pradhan, B; Salleh, AH; Alazaiza, MYD

Spatio-temporal simulation of future urban growth trends using an integrated CA-Markov model

Aburas, MM Ho, YM Pradhan, B

Salleh, AH Alazaiza, MYD

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Publisher:: SPRINGER HEIDELBERG
Publication Type:: Journal Article
Citation:: Arabian Journal of Geosciences, 2021, 14, (2)
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Aburas, MM
dc.contributor.author	Ho, YM
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Salleh, AH
dc.contributor.author	Alazaiza, MYD
dc.date.accessioned	2021-05-05T00:51:48Z
dc.date.available	2021-05-05T00:51:48Z
dc.date.issued	2021-01-01
dc.identifier.citation	Arabian Journal of Geosciences, 2021, 14, (2)
dc.identifier.issn	1866-7511
dc.identifier.issn	1866-7538
dc.identifier.uri	http://hdl.handle.net/10453/148652
dc.description.abstract	Urban growth, a dynamic and demographic phenomenon, refers to the increased spatial value of urban areas, such as cities and towns, due to social and economic forces. Nowadays, urban lands are rapidly increasing, replacing non-urban lands such as agricultural, forest, water, rural, and open lands. In this study, a CA-Markov model was utilized to predict the growth of urban lands and their spatial trends in Seremban, Malaysia. The performance of the CA-Markov model was also assessed. The Markov chain model was applied to produce the quantitative values of transition probabilities for urban and non-urban lands. Subsequently, the CA model was used to predict the dynamic spatial trends of land changes. The change in urban and non-urban land use from 1984 to 2010 was modeled using the CA-Markov model for calibration purposes and to compute optimal CA transition rules, as well as to predict future urban growth. For accuracy assessment, the CA-Markov model was validated using a kappa coefficient. An 83% overall accuracy was observed for the kappa index statistics, which indicates the excellent performance of the proposed model. Finally, based on the CA transition rules and the transition area matrix produced from the Markov chain model-based calibration process, the future urban growth in Seremban for 2020 and 2030 was simulated.
dc.language	English
dc.publisher	SPRINGER HEIDELBERG
dc.relation.ispartof	Arabian Journal of Geosciences
dc.relation.isbasedon	10.1007/s12517-021-06487-8
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in [Arabian Journal of Geosciences, 2021, 14, (2)]. The final authenticated version is available online at: https://link.springer.com/article/10.1007%2Fs12517-021-06487-8]”
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0403 Geology
dc.title	Spatio-temporal simulation of future urban growth trends using an integrated CA-Markov model
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	0403 Geology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	recently_added	*
dc.date.updated	2021-05-05T00:51:46Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	2

Abstract:

Urban growth, a dynamic and demographic phenomenon, refers to the increased spatial value of urban areas, such as cities and towns, due to social and economic forces. Nowadays, urban lands are rapidly increasing, replacing non-urban lands such as agricultural, forest, water, rural, and open lands. In this study, a CA-Markov model was utilized to predict the growth of urban lands and their spatial trends in Seremban, Malaysia. The performance of the CA-Markov model was also assessed. The Markov chain model was applied to produce the quantitative values of transition probabilities for urban and non-urban lands. Subsequently, the CA model was used to predict the dynamic spatial trends of land changes. The change in urban and non-urban land use from 1984 to 2010 was modeled using the CA-Markov model for calibration purposes and to compute optimal CA transition rules, as well as to predict future urban growth. For accuracy assessment, the CA-Markov model was validated using a kappa coefficient. An 83% overall accuracy was observed for the kappa index statistics, which indicates the excellent performance of the proposed model. Finally, based on the CA transition rules and the transition area matrix produced from the Markov chain model-based calibration process, the future urban growth in Seremban for 2020 and 2030 was simulated.

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http://hdl.handle.net/10453/148652