Landslide Susceptibility Mapping for Quito with Logistic Regression and Sensitivity Analysis
The Andean region is one of the highest landslide-susceptible areas worldwide, while still limited attention has been devoted to the topic in this context in terms of research, risk reduction practice and urban policy. Given the collection of a few early datasets for landslides after a dozen years by the Andean city of Quito, this article aims to explore the predictive power of a binary logistic regression model (LOGIT) to test the provided data and multicriteria evaluation for landslide
... or landslide susceptibility in this urban area. Furthermore, two types of sensitivity analysis, the univariate and the MonteCarlo methods, were applied in order to suggest a better calibration of the LOGIT model. Amongst the ten variables included in the model for the context of Quito, which help to explain its landslide susceptibility, results showed that population and road density have relevant predicting power for high landslide susceptibility when adopting a weights-based data normalization for the model. This model was validated with a receiving operating characteristic of 0.79. Sensitivity analyses suggested calibrations of the model different from the referential one that would improve this metric up to 2%. The calibration process suggests further experimentation regarding other methods of normalization and a finer level of disaggregation of data. In terms of policy design, the LOGIT model coefficients values suggest the need of a deep analysis of the impacts that urban features such as population, road density, building footprint and floor area at a household scale have on the generation of landslide susceptibility in Andean cities like Quito.