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Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment

1
School of Resource and Environment Science, Wuhan University, Wuhan 430079, China
2
School of Environment and Resource, Zhejiang A&F University, Hangzhou 311300, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(8), 332; https://doi.org/10.3390/ijgi8080332
Received: 17 May 2019 / Revised: 24 July 2019 / Accepted: 25 July 2019 / Published: 27 July 2019
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Abstract

Logistic regression methods have been widely used for landslide research. However, previous studies have seldom paid attention to the frequent occurrence of spatial autocorrelated residuals in regression models, which indicate a model misspecification problem and unreliable results. This study accounts for spatial autocorrelation by implementing eigenvector spatial filtering (ESF) into logistic regression for landslide susceptibility assessment. Based on a landslide inventory map and 11 landslide predisposing factors, we developed the eigenvector spatial filtering-based logistic regression (ESFLR) model, as well as a conventional logistic regression (LR) model and an autologistic regression (ALR) model for comparison. The three models were evaluated and compared in terms of their prediction capability and model fit. The ESFLR model performed better than the other two models. The overall predictive accuracy of the ESFLR model was 90.53%, followed by the ALR model (76.21%) and the LR model (74.76%), and the areas under the ROC curves for the ESFLR, ALR and LR models were 0.957, 0.828 and 0.818, respectively. The ESFLR model adequately addressed the spatial autocorrelation of residuals by reducing the Moran’s I value of the residuals to 0.0270. In conclusion, the ESFLR model is an effective and flexible method for landslide analysis. View Full-Text
Keywords: landslide; logistic regression; spatial autocorrelation; eigenvector spatial filtering landslide; logistic regression; spatial autocorrelation; eigenvector spatial filtering
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Li, H.; Chen, Y.; Deng, S.; Chen, M.; Fang, T.; Tan, H. Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment. ISPRS Int. J. Geo-Inf. 2019, 8, 332.

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