Land policies for landslide risk reduction in Andean cities Highlights• 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.

The Metropolitan Area of Quito has experienced exponential growth in recent decades, especially in peri-urban sectors. The literature has described this process as “urban sprawl”, a phenomenon that is changing the landscape by increasing land consumption and forming conurbations with the nearest populated centers. This article proposes a new, broader and more complex metropolitan structure for the metropolis of Quito, the linking of neighboring and conurbed areas to the form a new metropolitan area based on the case study of the Metropolitan District of Quito (DMQ). This new metropolitan area identification considers the interpretation of satellite images and the classification of land uses, highlighting the main urban growth areas located outside, but contiguous to the administrative limit of the DMQ, over a period of 19 years (1998 to 2017), the demographics analysis, particularly the densification of new urban peripheral areas close to the DMQ border and the mobility links of population, goods and services between human settlements that allows the configuration of urban corridors and the integration of the territory. The main findings evaluated are the need to evaluate urban planning strategies aimed at sustainable development.

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.