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International Conference on Soft Computing in Data Science

SCDS 2015: Soft Computing in Data Science pp 255–264Cite as

Accuracy Assessment of Urban Growth Pattern Classification Methods Using Confusion Matrix and ROC Analysis

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 545))

Abstract

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.

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Author information

Authors and Affiliations

  1. College of Computer Science & Information Technology, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000, Kajang, Selangor, Malaysia

    Nur Laila Ab Ghani

  2. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia

    Siti Zaleha Zainal Abidin & Noor Elaiza Abd Khalid

Authors
  1. Nur Laila Ab Ghani
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  2. Siti Zaleha Zainal Abidin
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  3. Noor Elaiza Abd Khalid
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Corresponding author

Correspondence to Nur Laila Ab Ghani .

Editor information

Editors and Affiliations

  1. University of Tennessee, Knoxville, Tennessee, USA

    Michael W. Berry

  2. Universiti Teknologi MARA, Shah Alam, Malaysia

    Azlinah Mohamed

  3. Universiti Teknologi MARA, Shah Alam, Malaysia

    Bee Wah Yap

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© 2015 Springer Science+Business Media Singapore

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Ghani, N.L.A., Abidin, S.Z.Z., Khalid, N.E.A. (2015). Accuracy Assessment of Urban Growth Pattern Classification Methods Using Confusion Matrix and ROC Analysis. In: Berry, M., Mohamed, A., Yap, B. (eds) Soft Computing in Data Science. SCDS 2015. Communications in Computer and Information Science, vol 545. Springer, Singapore. https://doi.org/10.1007/978-981-287-936-3_24

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  • DOI: https://doi.org/10.1007/978-981-287-936-3_24

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-935-6

  • Online ISBN: 978-981-287-936-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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