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Article: The effects of sample size and sample prevalence on cellular automata simulation of urban growth

TitleThe effects of sample size and sample prevalence on cellular automata simulation of urban growth
Authors
KeywordsCellular automata
Urban growth simulation
Sample size
Sample rate
Sample prevalence
Issue Date2022
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/13658816.asp
Citation
International Journal of Geographical Information Science, 2022, v. 36 n. 1, p. 158-187 How to Cite?
AbstractThis study investigates the effects of sample size and sample prevalence on cellular automata (CA) simulation of urban growth. We take the CA models based on an artificial neural network (ANN), logistic regression (LR), and support vector machine (SVM) as examples, to simulate the urban growth of Wuhan city in China and the Wuhan Metropolitan Area under different sampling schemes. The results of the CA models based on the ANN, LR, and SVM methods are generally consistent. The sampling scheme with a small sample size and a low sample prevalence should be discarded because of the high uncertainty. Sample size determines the robustness of a CA model, whereas sample prevalence affects the performance of a CA model when there are sufficient samples. In particular, the closer the sample prevalence is to the population prevalence, the higher the simulation accuracy and the lower the shape complexity and fragmentation of the simulated urban patterns. We suggest that the optimal sampling scheme has a sample rate of 1% and a sample prevalence that is the same as the population prevalence. The selection of the optimal sampling scheme is independent of the population sizes represented by different study areas.
Persistent Identifierhttp://hdl.handle.net/10722/304846
ISSN
2020 Impact Factor: 4.186
2020 SCImago Journal Rankings: 1.294
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, B-
dc.contributor.authorXia, C-
dc.date.accessioned2021-10-05T02:36:02Z-
dc.date.available2021-10-05T02:36:02Z-
dc.date.issued2022-
dc.identifier.citationInternational Journal of Geographical Information Science, 2022, v. 36 n. 1, p. 158-187-
dc.identifier.issn1365-8816-
dc.identifier.urihttp://hdl.handle.net/10722/304846-
dc.description.abstractThis study investigates the effects of sample size and sample prevalence on cellular automata (CA) simulation of urban growth. We take the CA models based on an artificial neural network (ANN), logistic regression (LR), and support vector machine (SVM) as examples, to simulate the urban growth of Wuhan city in China and the Wuhan Metropolitan Area under different sampling schemes. The results of the CA models based on the ANN, LR, and SVM methods are generally consistent. The sampling scheme with a small sample size and a low sample prevalence should be discarded because of the high uncertainty. Sample size determines the robustness of a CA model, whereas sample prevalence affects the performance of a CA model when there are sufficient samples. In particular, the closer the sample prevalence is to the population prevalence, the higher the simulation accuracy and the lower the shape complexity and fragmentation of the simulated urban patterns. We suggest that the optimal sampling scheme has a sample rate of 1% and a sample prevalence that is the same as the population prevalence. The selection of the optimal sampling scheme is independent of the population sizes represented by different study areas.-
dc.languageeng-
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/13658816.asp-
dc.relation.ispartofInternational Journal of Geographical Information Science-
dc.subjectCellular automata-
dc.subjectUrban growth simulation-
dc.subjectSample size-
dc.subjectSample rate-
dc.subjectSample prevalence-
dc.titleThe effects of sample size and sample prevalence on cellular automata simulation of urban growth-
dc.typeArticle-
dc.identifier.emailXia, C: xia2018@connect.hku.hk-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/13658816.2021.1931237-
dc.identifier.scopuseid_2-s2.0-85107455041-
dc.identifier.hkuros325992-
dc.identifier.volume36-
dc.identifier.issue1-
dc.identifier.spage158-
dc.identifier.epage187-
dc.identifier.isiWOS:000657238400001-
dc.publisher.placeUnited Kingdom-

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