Landslide susceptibility assessment forms the basis of any hazard mapping, which is one of the essential parts of quantitative risk mapping. For the same study area, different susceptibility maps can be achieved depending on the type of susceptibility mapping methods, mapping unit, and scale. Although there are various methods of obtaining susceptibility maps, the efficiency and performance of each method should be evaluated. In this study the effect of mapping unit and susceptibility mapping method on landslide susceptibility assessment is investigated. When analyzing the effect of susceptibility mapping method, logistic regression (LR) which is widely used in landslide susceptibility mapping and, spatial regression (SR), which have not been used for landslide susceptibility mapping, are selected. The susceptibility maps with logistic and spatial regression models are obtained using two different mapping units namely slope unit-based and grid-based mapping units. The procedure for investigation of effect of mapping unit on different susceptibility mapping methods is applied to Kumluca watershed, in Bartin Province of Western Black Sea Region, Turkey. 18 factor maps are prepared for landslide susceptibility assessment in the study region. Geographic information systems and remote sensing techniques are used to create the landslide factor maps, to obtain susceptibility maps and to compare the results. The relative operating characteristics (ROC) curve is used to compare the predictive abilities of each model and mapping unit and also the accuracy is evaluated depending on the observations made during field surveys. By analyzing the area under the ROC curve for grid-based and slope unit-based mapping units, it can be concluded that SR model provide better predictive performance (0.774 in grids and 0.898 in slope units) as compared to the LR model (0.744 in grids and 0.820 in slope units). This result is also supported by the accuracy analysis. For both mapping units, the SR model provides more accurate result (0.55 for grids and 0.57 for slope units) than the LR model (0.50 for grids and 0.48 for slopes). The main reason for this better performance is that the spatial correlations between the mapping units are incorporated into the model in SR while this fact is not considered in LR model.