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Although the Andean region is one of the most landslide-susceptible areas in the world, limited attention has been devoted to the topic in this context in terms of research, risk reduction practice, and urban policy. Based on the collection of landslides data of the Andean city of Quito, Ecuador, this article aims to explore the predictive power of a binary logistic regression model (LOGIT) to test secondary data and an official multicriteria evaluation model for landslide susceptibility in this urban area. Cell size resampling scenarios were explored as a parameter, as the inclusion of new "urban" factors. Furthermore, two types of sensitivity analysis (SA), univariate and Monte Carlo methods, were applied to improve the calibration of the LOGIT model. A Kolmogorov–Smirnov (K-S) test was included to measure the classification power of the models. Charts of the three SA methods helped to visualize the sensitivity of factors in the models. The Area Under the Curve (AUC) was a common metric for validation in this research. Among the ten factors included in the model to help explain landslide susceptibility in the context of Quito, results showed that population and street/road density, as novel "urban factors", have relevant predicting power for landslide susceptibility in urban areas when adopting data standardization based on weights assigned by experts. The LOGIT was validated with an AUC of 0.79. Sensitivity analyses suggested that calibrations of the best-performance reference model would improve its AUC by up to 0.53%. Further experimentation regarding other methods of data pre-processing and a finer level of disaggregation of input data are suggested. In terms of policy design, the LOGIT model coefficient values suggest the need for a deep analysis of the impacts of urban features, such as population, road density, building footprint, and floor area, at a household scale, on the generation of landslide susceptibility in Andean cities such as Quito. This would help improve the zoning for landslide risk reduction, considering the safety, social and economic impacts that this practice may produce. [ABSTRACT FROM AUTHOR]

Andean cities are increasingly subjected to landslide susceptibility and events, accompanied by population and urban growth and uncertain extreme climate events. In light of this, academic and professional communities have begun to pay close attention and now face the challenge of producing more detailed knowledge and converting it into effective action. This article is based on a literature review supporting a multidimensional conceptual framework to address landslide risk reduction for the urban Andes context. It aims to complement the environmental dimension, which often shows the resulting physical condition of risk, with landslide risk root-causes by exploring socio-economic and socio-cultural dimensions and the policy and institutional apparatuses that accompany the former dimensions. We aim to identify and understand the inextricable links between the four dimensions and determine how subdimensions can operatively describe and help to understand this complexity. An example from a landslide risk-prone site in Quito illustrates the application of the framework and delivers lessons from a specific context and time. Parts of these lessons may be common for other contexts, but their understanding is critical for landslide risk reduction. • The landslide susceptibility of the Andes demands more risk reduction research to be conducted. • Landslide risk reduction practices have been proven successful by articulating bottom-up approaches. • Risk reduction networks across formal structures are more effective than cliché policies and grouped actors. • Understanding Andean informality may lead to strategic landslide risk reduction and climatic adaptation. [ABSTRACT FROM AUTHOR]