Experimental study of urban growth pattern classification using moving window algorithm
Urban 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 inco...
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Medwell Journals
2023
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| _version_ | 1833412476085993472 |
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| author | Ghani N.L.A. Abidin S.Z.Z. |
| author2 | 56940219600 |
| author_facet | 56940219600 Ghani N.L.A. Abidin S.Z.Z. |
| author_sort | Ghani N.L.A. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Urban 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. |
| format | Article |
| id | my.uniten.dspace-22917 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Medwell Journals |
| record_format | dspace |
| spelling | my.uniten.dspace-229172023-05-29T14:13:23Z Experimental study of urban growth pattern classification using moving window algorithm Ghani N.L.A. Abidin S.Z.Z. 56940219600 25824609700 Urban 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. Final 2023-05-29T06:13:23Z 2023-05-29T06:13:23Z 2016 Article 10.3923/jeasci.2016.1639.1643 2-s2.0-85006969735 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006969735&doi=10.3923%2fjeasci.2016.1639.1643&partnerID=40&md5=b2ba39c0932ce1219daac8b4a65a5618 https://irepository.uniten.edu.my/handle/123456789/22917 11 7 1639 1643 Medwell Journals Scopus |
| spellingShingle | Ghani N.L.A. Abidin S.Z.Z. Experimental study of urban growth pattern classification using moving window algorithm |
| title | Experimental study of urban growth pattern classification using moving window algorithm |
| title_full | Experimental study of urban growth pattern classification using moving window algorithm |
| title_fullStr | Experimental study of urban growth pattern classification using moving window algorithm |
| title_full_unstemmed | Experimental study of urban growth pattern classification using moving window algorithm |
| title_short | Experimental study of urban growth pattern classification using moving window algorithm |
| title_sort | experimental study of urban growth pattern classification using moving window algorithm |
| url_provider | http://dspace.uniten.edu.my/ |