Statistical susceptibility assessment is a common approach applied worldwide for shallow landslide studies. Identification of morphological and geological conditions is essential and still incipient to evaluate the susceptibility of landslide events in the Brazilian territory. This study aimed to develop and compare shallow landslide susceptibility scenarios based on a bivariate statistical evaluation of geological (lithology and structures) and morphological (curvature, elevation, slope, and aspect) factors in Caraguatatuba, northern coast of São Paulo State in Brazil. A compilation of geological factors from published maps was made, and morphological maps were created based on Shuttle Radar Topography Mission (30 m). A bivariate statistical application by the informative value method was used to create four susceptibility scenarios, and the validation was achieved using the area under the curve (AUC). The results indicated that lithology was the more relevant conditioning factor, followed by elevation and slope. The methodology used to determine the susceptibility was efficient (AUC values between 0.809 and 0.841). The susceptibility scenario comparison identified that conditioning factors with the highest informational value generated the most accurate mapping. This indicates that using several conditioning factors does not necessarily generate a better map. This study contributes to shallow landslides research from a methodological perspective, as it is the first analysis of its kind in Serra do Mar Paulista, which are continuously affected by mass movements. Open-source data were chosen to be used, focusing on methodological applicability in other regions of the country, since resources for landslide studies in Brazil are low.

统计敏感性评估是世界范围内用于浅层滑坡研究的一种通用方法。形态和地质条件的识别是至关重要的，并且仍然是评估巴西领土滑坡事件易感性的起步阶段。这项研究旨在基于对巴西圣保罗州北部海岸卡拉瓜塔图巴的地质（岩性和构造）和形态（曲率，高程，坡度和坡向）因素的双变量统计评估，开发和比较浅层滑坡的敏感性。从已发布的地图中汇总了地质因素，并根据航天飞机雷达地形任务（30 m）创建了形态图。利用信息价值法进行的双变量统计应用程序创建了四个易感性场景，并使用曲线下面积（AUC）进行验证。结果表明，岩性是最相关的条件因素，其次是海拔和坡度。确定磁化率的方法是有效的（AUC值在0.809和0.841之间）。敏感性情景比较确定，具有最高信息价值的条件因子会生成最准确的映射。这表明使用多个条件因子并不一定会生成更好的图。这项研究从方法论的角度为浅层滑坡研究做出了贡献，因为它是塞拉多马尔保利斯塔（Serra do Mar Paulista）中此类研究的首次分析，该研究不断受到群众运动的影响。选择使用开放源数据，重点是该方法在该国其他地区的适用性，