The rising popularity of electric scooters (e-scooters) in the past few years has led to an increase in safety concerns. Accidents and mishaps involving e-scooters have resulted in injuries to riders as well as pedestrians. The most common injuries resulting from e-scooter accidents are head and face trauma. This paper focuses on vision obstruction-related risks involving the use of e-scooters. We propose a vision analysis methodology that integrates digital human modeling (DHM) and computer vision (CV) to quantify the visibility of e-scooter riders in a variety of traffic scenarios. The approach is presented through three case studies that analyze hypothetical scenarios involving e-scooters being operated in busy areas of Corvallis, Oregon. Each case study includes full-scale computer-aided design (CAD) models of local infrastructure and vehicles. Upon importing the models to the DHM software, Siemens Jack, dynamic footage of the traffic scene is generated for all case studies. Digital manikins of two anthropometric profiles (5th percentile female and 95th percentile male) are used to represent the e-scooter riders in each scenario. Then, a CV image segmentation process is used to quantify the visibility of the e-scooter rider at different timestamps of the videos. This approach is conducted under both clear and rainy weather conditions. The results from this study demonstrate how both anthropometry and weather impact the visibility of an e-scooter rider in situations where they are already at risk of being obstructed by other objects. Benefits of this methodology include saving time and generating more precise results than prior studies that aimed to quantify visibility.