Defining accurate models for real-world social networks is instrumental in several research fields, e.g., in sociology [1], epidemiology [2] or marketing [3]. In combination with computer simulations these models may represent a valuable tool to understand social phenomena, along with classic analytical studies. Dynamic processes, such as the spread of a disease or a rumour, can be represented upon suitable networks that encode the patterns of connection and interaction among the individuals of a population. Moreover, the comparison of synthetic networks produced by different generative models helps to infer how each factor contributes to the emergence of experimentally measured properties of real networks [4]. In this paper, we present a novel computational model for urban social networks, that combines a data-driven framework with a set of adjustable parameters. A fully operational open source implementation of the model is available under the GPL v3 at gitlab.com/cranic-group/usn. The software allows to generate a synthetic social network of "strong ties" [5] among geo-referenced and age-stratified individuals. The graph encodes information on the urban social fabric and, as such, it increases the plausibility of dynamic (e.g., transmission) processes that may be influenced by preferences and actions of agents and groups of related agents. On the one hand, our social graph may be used to simulate the fact that friends and relatives may go out together, organize public or private meetings, and are, in general, more likely to interact.