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Article: Using a maximum entropy model to optimize the stochastic component of urban cellular automata models

TitleUsing a maximum entropy model to optimize the stochastic component of urban cellular automata models
Authors
KeywordsUrban cellular automata
maximum entropy
stochastic component
geographic simulation
Issue Date2020
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, 2020, v. 34 n. 5, p. 924-946 How to Cite?
AbstractThe stochastic perturbation of urban cellular automata (CA) model is difficult to fine-tune and does not take the constraint of known factors into account when using a stochastic variable, and the simulation results can be quite different when using the Monte Carlo method, reducing the accuracy of the simulated results. Therefore, in this paper, we optimize the stochastic component of an urban CA model by the use of a maximum entropy model to differentially control the intensity of the stochastic perturbation in the spatial domain. We use the kappa coefficient, figure of merit, and landscape metrics to evaluate the accuracy of the simulated results. Through the experimental results obtained for Wuhan, China, the effectiveness of the optimization is proved. The results show that, after the optimization, the kappa coefficient and figure of merit of the simulated results are significantly improved when using the stochastic variable, slightly improved when using Monte Carlo methods. The landscape metrics for the simulated results and actual data are much closer when using the stochastic variable, and slightly closer when using the Monte Carlo method, but the difference between the simulated results is narrowed, reflecting the fact that the results are more reliable.
Persistent Identifierhttp://hdl.handle.net/10722/289952
ISSN
2020 Impact Factor: 4.186
2020 SCImago Journal Rankings: 1.294
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, H-
dc.contributor.authorZhang, B-
dc.contributor.authorXIA, C-
dc.contributor.authorHe, S-
dc.contributor.authorZhang, W-
dc.date.accessioned2020-10-22T08:19:49Z-
dc.date.available2020-10-22T08:19:49Z-
dc.date.issued2020-
dc.identifier.citationInternational Journal of Geographical Information Science, 2020, v. 34 n. 5, p. 924-946-
dc.identifier.issn1365-8816-
dc.identifier.urihttp://hdl.handle.net/10722/289952-
dc.description.abstractThe stochastic perturbation of urban cellular automata (CA) model is difficult to fine-tune and does not take the constraint of known factors into account when using a stochastic variable, and the simulation results can be quite different when using the Monte Carlo method, reducing the accuracy of the simulated results. Therefore, in this paper, we optimize the stochastic component of an urban CA model by the use of a maximum entropy model to differentially control the intensity of the stochastic perturbation in the spatial domain. We use the kappa coefficient, figure of merit, and landscape metrics to evaluate the accuracy of the simulated results. Through the experimental results obtained for Wuhan, China, the effectiveness of the optimization is proved. The results show that, after the optimization, the kappa coefficient and figure of merit of the simulated results are significantly improved when using the stochastic variable, slightly improved when using Monte Carlo methods. The landscape metrics for the simulated results and actual data are much closer when using the stochastic variable, and slightly closer when using the Monte Carlo method, but the difference between the simulated results is narrowed, reflecting the fact that the results are more reliable.-
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.rightsAOM/Preprint Before Accepted: his article has been accepted for publication in [JOURNAL TITLE], published by Taylor & Francis. AOM/Preprint After Accepted: This is an [original manuscript / preprint] of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI]. Accepted Manuscript (AM) i.e. Postprint This is an Accepted Manuscript of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI].-
dc.subjectUrban cellular automata-
dc.subjectmaximum entropy-
dc.subjectstochastic component-
dc.subjectgeographic simulation-
dc.titleUsing a maximum entropy model to optimize the stochastic component of urban cellular automata models-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/13658816.2019.1687898-
dc.identifier.scopuseid_2-s2.0-85074998254-
dc.identifier.hkuros316233-
dc.identifier.volume34-
dc.identifier.issue5-
dc.identifier.spage924-
dc.identifier.epage946-
dc.identifier.isiWOS:000495532200001-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl1365-8816-

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