## 3 Methodology  
### General Procedure  
When following a standard procedure, the preparation of susceptibility maps requires, first, defining the factors that in the authors' opinion are most decisively involved in the dynamics that trigger ground movements. Before creating the susceptibility maps, each factor must be separately analysed using a bivariate statistical method. This allows the researcher to discern the true impact of each variable and accurately classify the variable map as having direct or inverse proportionality with landslides. That can be accomplished by calculating the weights of each class factor by means of FR.  
Several methods are commonly applied in the susceptibility map literature to obtain the weights of each factor involved. An approach known as Spatial Multicriteria Evaluation (SMCE) [29] is widely used for landslide susceptibility zoning. This approach combines both landslide information and expert knowledge. In SMCE, factor weights for landslide susceptibility are determined using the Analytic Hierarchy Process (AHP) through pairwise comparisons of selected factors [30]. The Landslide Susceptibility Index (LSI) for each pixel is then calculated using a weighted linear sum of the factors and factor class weights, as shown in Equation (1).  
\[LSI_{i}=\sum_{j=1}^{n}w_{j}\times x_{ji} \tag{1}\]  
where LSI is calculated for each i pixel, wj is the weight of the factor j (according to AHP), and xji is the weight of the factor class i in criterion j according to FR methods for three factors. The landslide susceptibility maps were created through this landslide susceptibility index calculated for a specific area.  
The factors that appear in Equation (1) are generally publicly available variables provided by official bodies that describe the characteristics of the terrain relative to the possibility of a landslide. An inventory of landslides in the study area is also necessary for the calculation of the FR and subsequent validation processes.  
To calculate LSI (landslide susceptibility index) values, classes are established for the selected variables. The first objective of this study is to work on the optimisation of one of the common methods for obtaining the weighting of classes (the previously mentioned frequency ratio method or FR) and including new calculation procedures. In total, four methods for obtaining FR were used. All these methods are discussed at length in Section 2.2.  
The second objective is to differentiate between suitable classifications for landslide susceptibility maps. Thus, some of the more conventional classifications, such as those based on diagnostic tests of sensitivity and specificity, are discussed using the tools provided by the ROC space. In total, four classifications have been discussed, and these are explained in greater detail in Section 3.3.  
To better compare the results obtained, the selected geographical area is divided into three sectors, defined by the provincial boundaries, and the methods proposed are applied separately. With all the results obtained, a comparison will be made with the inventory data by calculating a series of quantitative adjustment indicators. Finally, these comparisons are synthesised and summarised so conclusions may be reached on the best combinations of frequency ratio method and classification maps.