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Accuracy assessment of urban growth pattern classification methods using confusion matrix and ROC analysis

Classification (of information); Growth rate; Image resolution; Matrix algebra; Pattern recognition; Soft computing; Accuracy assessment; Accuracy of classifications; Classification methods; Confusion matrices; Expansion index; Receiver operating characteristic analysis; ROC analysis; Urban growth p...

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Main Authors: Ghani N.L.A., Abidin S.Z.Z., Khalid N.E.A.
Other Authors: 56940219600
Format: Conference Paper
Published: Springer Verlag 2023
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id my.uniten.dspace-22499
record_format dspace
spelling my.uniten.dspace-224992023-05-29T14:01:22Z Accuracy assessment of urban growth pattern classification methods using confusion matrix and ROC analysis Ghani N.L.A. Abidin S.Z.Z. Khalid N.E.A. 56940219600 25824609700 25634252000 Classification (of information); Growth rate; Image resolution; Matrix algebra; Pattern recognition; Soft computing; Accuracy assessment; Accuracy of classifications; Classification methods; Confusion matrices; Expansion index; Receiver operating characteristic analysis; ROC analysis; Urban growth patterns; Urban growth Urban growth pattern can be categorized as either infill, expansion or outlying. Studies on urban growth classification are focusing on the description of urban growth pattern geometric features using conventional landscape metrics. These metrics are too simple and unable to give detailed information on accuracy of the classification methods. This paper aims to assess the accuracy of classification methods that can determine urban growth patterns correctly for a specific growth area. Accuracy assessments are carried out using three different classification methods - moving window, topological relation border length and landscape expansion index. Based on confusion matrices and receiver operating characteristic (ROC) analysis, results show that landscape expansion index has the best accuracy among all. � Springer Science+Business Media Singapore 2015. Final 2023-05-29T06:01:21Z 2023-05-29T06:01:21Z 2015 Conference Paper 10.1007/978-981-287-936-3_24 2-s2.0-84946039583 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946039583&doi=10.1007%2f978-981-287-936-3_24&partnerID=40&md5=49a56e4986ab76d4452d666b63348b59 https://irepository.uniten.edu.my/handle/123456789/22499 545 255 264 Springer Verlag Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Classification (of information); Growth rate; Image resolution; Matrix algebra; Pattern recognition; Soft computing; Accuracy assessment; Accuracy of classifications; Classification methods; Confusion matrices; Expansion index; Receiver operating characteristic analysis; ROC analysis; Urban growth patterns; Urban growth
author2 56940219600
author_facet 56940219600
Ghani N.L.A.
Abidin S.Z.Z.
Khalid N.E.A.
format Conference Paper
author Ghani N.L.A.
Abidin S.Z.Z.
Khalid N.E.A.
spellingShingle Ghani N.L.A.
Abidin S.Z.Z.
Khalid N.E.A.
Accuracy assessment of urban growth pattern classification methods using confusion matrix and ROC analysis
author_sort Ghani N.L.A.
title Accuracy assessment of urban growth pattern classification methods using confusion matrix and ROC analysis
title_short Accuracy assessment of urban growth pattern classification methods using confusion matrix and ROC analysis
title_full Accuracy assessment of urban growth pattern classification methods using confusion matrix and ROC analysis
title_fullStr Accuracy assessment of urban growth pattern classification methods using confusion matrix and ROC analysis
title_full_unstemmed Accuracy assessment of urban growth pattern classification methods using confusion matrix and ROC analysis
title_sort accuracy assessment of urban growth pattern classification methods using confusion matrix and roc analysis
publisher Springer Verlag
publishDate 2023
_version_ 1806428124826566656
score 13.18916

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