Spatial Non-Stationarity-Based Landslide Susceptibility Assessment Using PCAMGWR Model

Abstract

Landslide Susceptibility Assessment (LSA) is a fundamental component of landslide risk management and a substantial area of geospatial research. Previous researchers have considered the spatial non-stationarity relationship between landslide occurrences and Landslide Conditioning Factors (LCFs) as fixed effects. The fixed effects consider the spatial non-stationarity scale between different LCFs as an average value, which is represented by a single bandwidth in the Geographically Weighted Regression (GWR) model. The present study analyzes the non-stationarity scale effect of the spatial relationship between LCFs and landslides and explains the influence of factor correlation on the LSA. A Principal-Component-Analysis-based Multiscale GWR (PCAMGWR) model is proposed for landslide susceptibility mapping, in which hexagonal neighborhoods express spatial proximity and extract LCFs as the model input. The area under the receiver operating characteristic curve and other statistical indicators are used to compare the PCAMGWR model with other GWR-based models and global regression models, and the PCAMGWR model has the best prediction effect. Different spatial non-stationarity scales are obtained and improve the prediction accuracy of landslide susceptibility compared to a single spatial non-stationarity scale.

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

“…To address this deficiency, Fotheringham et al (2017) (Fotheringham, et al [ 25 ]) proposed the multiscale GWR (MGWR) model, which fits an appropriate bandwidth for each explanatory variable. MGWR has been successfully used in the analysis of local impact factors on housing prices [ 26 ], the urban built environment [ 27 ], COVID-19 incidence rates [ 28 ], landslide susceptibility [ 29 ], ecological environment quality [ 30 ], and soil carbon storage [ 31 ]. Therefore, MGWR has great potential in determining the local pollution sources of As and proposing targeted pollution control measures.…”