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Examining the Controls on the Spatial Distribution of Landslides Triggered by the 2008 Wenchuan Ms 8.0 Earthquake, China, Using Methods of Spatial Point Pattern Analysis

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  • Guangshun Bai

    (Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China
    Key Laboratory of Geohazard Forecast and Geoecological Restoration in Plateau Mountainous Area, Ministry of Natural Resources of China and Yunnan Province, Kunming 650093, China)

  • Xuemei Yang

    (Yunnan Gaozheng Geo-Exploration Co., Ltd., Kunming 650041, China)

  • Guangxin Bai

    (Qingdao Hongqiao Construction Co., Ltd., Qingdao 266400, China)

  • Zhigang Kong

    (Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China
    Key Laboratory of Geohazard Forecast and Geoecological Restoration in Plateau Mountainous Area, Ministry of Natural Resources of China and Yunnan Province, Kunming 650093, China)

  • Jieyong Zhu

    (Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China
    Key Laboratory of Geohazard Forecast and Geoecological Restoration in Plateau Mountainous Area, Ministry of Natural Resources of China and Yunnan Province, Kunming 650093, China)

  • Shitao Zhang

    (Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China
    Key Laboratory of Geohazard Forecast and Geoecological Restoration in Plateau Mountainous Area, Ministry of Natural Resources of China and Yunnan Province, Kunming 650093, China)

Abstract

Landslide risk management contributes to the sustainable development of the region. Understanding the spatial controls on the distribution of landslides triggered by earthquakes (EqTLs) is difficult in terms of the prediction and risk assessment of EqTLs. In this study, landslides are regarded as a spatial point pattern to test the controls on the spatial distribution of landslides and model the landslide density prediction. Taking more than 190,000 landslides triggered by the 2008 Wenchuan Ms 8.0 earthquake (WcEqTLs) as the research object, the relative density estimation, Kolmogorov–Smirnov testing based on cumulative distribution, receiver operating characteristic curve (ROC) analysis, and Poisson density modeling are comprehensively applied to quantitatively determine and discuss the different control effects of seven factors representing earthquakes, geology, and topography. The distance to the surface ruptures (dSR) and the distance to the epicenter (dEp) show significant and strong control effects, which are far stronger than the other five factors. Using only the dSR, dEp, engineering geological rock group (Eg), and the range, a particularly effective Poisson model of landslide density is constructed, whose area under the ROC (AUC) reaches 0.9244 and whose very high-density (VHD) zones can contain 50% of landslides and only comprise 3.9% of the study areas. This research not only deepens our understanding of the spatial distribution of WcEqTLs but also provides new technical methods for such investigation and analysis.

Suggested Citation

  • Guangshun Bai & Xuemei Yang & Guangxin Bai & Zhigang Kong & Jieyong Zhu & Shitao Zhang, 2024. "Examining the Controls on the Spatial Distribution of Landslides Triggered by the 2008 Wenchuan Ms 8.0 Earthquake, China, Using Methods of Spatial Point Pattern Analysis," Sustainability, MDPI, vol. 16(16), pages 1-24, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6974-:d:1456369
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    References listed on IDEAS

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    1. A. Baddeley & R. Turner & J. Møller & M. Hazelton, 2005. "Residual analysis for spatial point processes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 617-666, November.
    2. Mark Berman, 1986. "Testing for Spatial Association between a Point Process and Another Stochastic Process," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 35(1), pages 54-62, March.
    3. Baddeley, Adrian & Turner, Rolf, 2005. "spatstat: An R Package for Analyzing Spatial Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i06).
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