The field of social network analysis has witnessed an unprecedented growth during the past few years. Its techniques have been used by a variety of scientific fields including epidemiology, ecology, and conservation. Social network analysts have also began to incorporate time into their models producing a new class of networks known as dynamic social networks. Although there are a number of well established techniques for visualizing classical static networks,these techniques suffer from limitations when applied to dynamic networks that change with time. In this thesis, I propose SocioScape, an interactive tool for the visual exploration of spatially referenced, dynamic social networks. The tool combines a novel visual representation methodology that illustrates the evolution of social groups with a spatio-temporal visualization showing the movement of these groups in the physical environment. The visual depictions used in SocioScape provide an alternative to graphs, allowing domain scientists to easily explore the evolution of the social structure and examine the role of the external environmental factors in shaping the social behavior of populations.
The primary contributions of this thesis include:
1. A novel visual representation method for dynamic social networks that departs from traditional graph-based visualizations, revealing the evolution of social groups and association choices that actors make over time.
2. A methodology that integrates abstract representations of social interactions with a spatio-temporal visualization to facilitate the investigation of the role of environment in shaping the underlying social structure.
3. A case study in which the methodology was used by behavioral ecologists to explore the social behavior of two animal populations of endangered species.