The Paonia-McClure Pass area of Colorado has been recognized as a region highly susceptible to mass movement. Because of the dynamic nature of the surface, accurate methods are needed to predict susceptibility to movement of these slopes. The area between Paonia and McClure Pass was evaluated by a coupling of a geographic information system (GIS) with logistic regression methods to assess susceptibility to landslides. We mapped 735 shallow landslides in the area. Seventeen factors, as predictor variables of landslides, were mapped from aerial photographs, available public data archives, ETM+ satellite data, published literature and frequent field surveys. A logistic regression model was run using landslides as the dependent factor and landslide-causing factors as covariates (independent factors). Different techniques of sampling landslide and non-landslide data were developed and the capabilities of this information to assess landslide susceptibility were evaluated. Landslide data were collected from the landslide masses, landslide scarps, center of mass of the landslides, center of scarp of the landslides, and an equal amount of data were collected from areas void of mass movement. Models of susceptibility to landslides for each sampling technique were developed first. Second, landslides were classified as debris-dominated flows, debris-dominated slides, rock-dominated slides and soil-dominated slides and then models of susceptibility to landslides were created for each type of landslide. Fourteen models of susceptibility to landslides were created. The prediction accuracies of each model were compared using the ROC curve technique. The model using samples from landslide scarps has the highest prediction accuracy, and the model using samples from centers of mass of the landslides has the lowest prediction accuracy among the models developed from the four techniques of data sampling. Likewise, the model developed for debris-dominated slides has the highest prediction accuracy, and the model developed for soil-dominated slides has the lowest prediction accuracy among four types of landslides. Furthermore, prediction from the model developed by combining four models of four types of landslides is better than the prediction from the model developed by using all landslides together.