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Experimental study of urban growth pattern classification using moving window algorithm

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dc.contributor.authorGhani N.L.A.en_US
dc.contributor.authorAbidin S.Z.Z.en_US
dc.contributor.authorid56940219600en_US
dc.contributor.authorid25824609700en_US
dc.date.accessioned2023-05-29T06:13:23Z
dc.date.available2023-05-29T06:13:23Z
dc.date.issued2016
dc.description.abstractUrban growth pattern can be generally categorized as either infill, expansion or outlying growth. Moving window algorithm determines urban growth pattern based on moving window analysis and a set of classification rules. However, literatures are concerned that the existing algorithm may produce incorrect classification result as it is strongly influenced by the size of moving window frame and classification rule. This study aims to investigate the effect of different moving window frames on the classification results and proposed an improvement to moving window algorithm with new classification rules. Results show that the existing algorithm is only able to classify outlying growth whereas the improved algorithm is not only able to classify outlying growth, it can also classify infill growth. � Medwell Journals, 2016.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.3923/jeasci.2016.1639.1643
dc.identifier.epage1643
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85006969735
dc.identifier.spage1639
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85006969735&doi=10.3923%2fjeasci.2016.1639.1643&partnerID=40&md5=b2ba39c0932ce1219daac8b4a65a5618
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22917
dc.identifier.volume11
dc.publisherMedwell Journalsen_US
dc.sourceScopus
dc.sourcetitleJournal of Engineering and Applied Sciences
dc.titleExperimental study of urban growth pattern classification using moving window algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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