Analysing social networks provide unique insights into the fabric of social interactions among entities. Social Network Analysis (SNA) exploits the relational data structure of graphs to characterize important entities, associations, and groups within data sets. “No man is an island”, and neither are societies. All social facts are contained in some context. Actors can be considered to belong to groups which are in turn hierarchically embedded inside larger groups. These groups can refer to any contextual attribute like family, profession, community or even geography. Most social facts make little sense when abstracted from its context. The consideration of the setting in which the network belongs is thus of paramount importance for understanding many social facts that are extracted from the network structures. Though it has long been known that social phenomena are affected by the underlying context, the importance of context as a backdrop against which social networks analysis is done is only gaining traction in recent literature. SNA often neglects the point that social interactions are embedded in some geographic context, which has profound effects on the formation and hence structure of the social network. Therefore, to understand the emergence and dynamics of social interactions, the physical embeddedness of the actors must be factored in. The social networks are the outcomes of some underlying real social process (Hillier 2010). These social processes are again the outcome of the various physical processes on which it resides. Many of the well known facts of social networks such as propinquity and “six degrees of separation” are extensions of the spatial concept of distance. Geographic space is not merely a passive container, but it plays an active role in continually shaping processes that are entwined in its fabric. Despite the influences that space has on social structures, the effects of it are just starting to be studied and is far from being quantified. Incorporating a spatial component introduces a level of complexity to the data, its representation, analysis, and visualization. Despite significant attempts to ‘spatialize’ social networks, discourse on the implications of spatialization have largely been missing. My project aims at creating a framework for spatial social networks. The framework will encompass the different ways in which a social network can be made spatial, their corresponding use cases, and GIScience considerations of incorporating spatial information to social networks. The theories and the framework developed will be tested on a large grants database which is neither explicitly social nor explicitly spatial, hence allowing different conceptualizations of spatial social networks. I will also be using human social networks from around Kibale National Park, Uganda to test the frameworks validity to real world spatial social networks.
Sarkar, Dipto, Colin A. Chapman, Larry Griffin, Raja Sengupta
2015 Transactions in GIS