Abstract: Summit County in Ohio, USA, is well known for the occurrence of frequent landslides along the Cuyahoga River valley. This study investigates the factors that affect the frequency and distribution of landslides in Summit County using different numerical models in Geographic Information Systems (GIS) database. The landslide locations in the county were identified from aerial photographs, field checks, and the existing literature, and a landslide inventory map was prepared for the region at a scale of 1:24,000. The occurrence of landslides in a given area generally depends upon the complex interaction of different dependent and independent factors like slope angle, slope aspect, soil type, erodible soil, depth to groundwater, land cover pattern, distance from the river, etc. These factors were imported as raster data layers in ArcGIS for the landslide susceptibility analysis in Summit County. Each of the above-listed factors was classified and coded using a numerical scale corresponding to the physical conditions of the region. In order to investigate the role of each factor in controlling the spatial distribution of landslides, susceptibility priority number model, landslide susceptibility index model, and logistic regression model were generated using the Summit County digital dataset. Each model was superimposed on the landslide inventory map and was evaluated for its suitability. The logistic regression model was found to be the best model for predicting the landslide susceptibility for Summit County, Ohio. The results indicate that the factors such as slope angle, soil type, distance from the river, and the erodible soil are statistically significant in controlling the slope movement, whereas liquidity index, precipitation, land cover, and depth to water table are not very significant and, thus, were excluded from the model. The data from this model were used in ArcGIS to produce a landslide susceptibility map of Summit County. The landslide susceptibility was classified into three categories: low, moderate, and high. The results of the study demonstrate that landslide susceptibility of a region can be effectively modeled using GIS technology and logistic regression analysis.