Urban planning and design approaches that aim to leverage walking as a sustainable means of transportation require a thorough understanding of the built environment. Information regarding density, diversity, accessibility, and attractiveness of streets is critical to assess walkability, yet it is also resource-intensive to acquire through traditional methods. We present a computational analysis method that captures and aggregates information on walkability indicators encapsulated in the 3d morphology, street-view imagery, and POI data of streets, using a 3d component called the Street Void . This component builds on the Convex and Solid-Void models  which are 3d representations of open-urban spaces informed by the interrelationships between topography, surrounding buildings and other immediate physical boundaries, and facilitates the quantitative evaluation of walkability attributes. The method is unique in that it allows for the walkability evaluation of urban open spaces in the micro level, with a semi-automated algorithm and utilizing remotely accessible urban data. We present the implementation of this analysis on four neighborhoods of Istanbul and Lisbon, demonstrating insight drawn from its quantitative output. The research interconnects knowledge in the domains of computational design, behavioral psychology, urban management, and planning; with the contribution of a novel quantitative analysis of streets to inform urban decision-making processes.