For many software projects, keeping requirements on track needs an effective and efficient path from data to decision. Visual analytics creates such a path that enables the human to extract insights by interacting with the relevant information. While various requirements visualization techniques exist, few have produced end-to-end values to practitioners. In this dissertation, we advance the literature on visual requirements analytics by characterizing its key components and relationships in a framework. We follow the goal-question-metric paradigm to define the framework by teasing out five conceptual goals (user, data, model, visualization, and knowledge), their specific operationalizations, and their interconnections. The framework allows us to not only assess existing approaches, but also create tool enhancements in a principled manner. We evaluate our enhanced tool supports both qualitatively and quantitatively. First, we evaluate our tool supports qualitatively through a case study where massive, heterogeneous, and dynamic requirements are processed, visualized, and analyzed. Working together with practitioners on a contemporary software project within its real-life context leads to the main ending that visual analytics can help tackle both open-ended visual exploration tasks and well-structured visual exploitation tasks in requirements engineering. In addition, the study helps the practitioners to reach actionable decisions in a wide range of areas relating to their project, ranging from theme and outlier identification, over requirements tracing, to risk assessment. Overall our work illuminates how the data-to-decision analytical capabilities could be improved by the increased interactivity of requirements visualization. Although many new visual analytics tools, techniques and methods are being developed, still there is a lack of understanding of how to evaluate the performance of such tools. We conducted an experiment to assess the performance (time and correctness) of our visual analytics tool support in solving requirements engineering tasks. Our study provides initial evidence and insights for visual analytics in requirements engineering and sheds light on many challenging open questions.