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Published in:
2015 | OriginalPaper | Chapter
Accuracy Assessment of Urban Growth Pattern Classification Methods Using Confusion Matrix and ROC Analysis
Authors : Nur Laila Ab Ghani, Siti Zaleha Zainal Abidin, Noor Elaiza Abd Khalid
Published in: Soft Computing in Data Science
Publisher: Springer Singapore
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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.