## 1 Introduction  
Landslides are catastrophic natural hazards frequently posing risks to the major societal, economic, and environmental on an international scale [1]. According to the report from EM-DAT [2], 21,412 landslides occurred worldwide between 1900 and 2014, resulting in 38,521,499 fatalities, with 7,229,487,068 people affected and total direct economic losses exceeding $2.7 trillion. Landslide susceptibility assessment has long been recognized as a useful tool for landslide hazard management through land use planning and better decision making in landslide prone areas [3]. It is generally based on heuristic, statistical, or deterministic models [4; 5; 6; 7; 8]. Heuristic models are subjective and much susceptible to the expectation of the results [9; 10]. Deterministic models have been reported with higher accuracy, but are limited by the difficulty of obtaining detailed landslide database [11; 12]. Statistical models are the most widely used models due to their simplicity and high efficiency [13; 14]. Many remarkable studies on the above aspects have been made, laying a solid foundation for landslide susceptibility mapping. However, in general, most of the previous studies consider the relationship between triggering factors and landslide occurrence as a fixed effect within an area, whereas different degrees of parameter influence may occur, such that, with the change of location, the effect of parameters can be consequently changed. The uncertainties due to this varied relationship remain a scientific challenge. The second law of geography suggests that there exists variability over space of a given relationship between variables widely in spatial data [15], which is the so-called spatial non-stationarity. In view of that landslide susceptibility assessment is heavily based on spatial data, the relationship between influencing factors and landslide susceptibility may also have the characteristics of spatial non-stationarity. Previous studies, e.g., [16], have also suggested that the effective parameters in the occurrence of a natural disaster phenomenon do not have the same importance in different parts of an area. The existence of spatial non-stationarity indicates that average relationships fitted to the whole study area of traditional models might be inappropriate since they do not accurately fit local conditions [17]. This spatial non-stationarity characteristics in the data pose difficulties in landslide susceptibility assessment based on the traditional models. Geographically weighted regression (GWR), the most popular local regression format, shows great capability in dealing with spatial non-stationary relationships [18]. It allows the relationship between dependent and independent variables to vary over space, as well as that regression coefficients in the model are calculated for each spatial zone [19]. This method has been applied in various fields of study such as social economics, geography, and meteorology [20; 21; 22]. However, previous studies applying GWR model for the assessment of geological hazard susceptibility have not yet been reported. One difficulty limiting the application of GWR model in landslide susceptibility assessment is spatial proximity. It is the basic input condition and core problem for GWR model, and the issue regarding an adequate expression for the spatial proximity at different locations directly affects the modeling ability of GWR model [23; 24]. Spatial proximity is the distance relationship between two units in space, and the closer the distance, the greater the impact. The key to determining the spatial proximity is segmenting the study area into map units to effectively express the spatial adjacency relationship between landslide data. The relationship should satisfy the requirements of GWR for good internal homogeneity and between-units heterogeneity. The commonly used segmentation methods in previous studies relating to GWR model can be categorized into two major kinds, i.e., administrative units [25] and grid units [26].