CAREER: A Research and Educational Framework for Incorporating Spatial Heterogeneity into Social Network Analysis

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Social networks are representations of the relationships between individuals or groups, ranging from household ties in an isolated village to billions of online social media connections. Analyzing these structures can tell us who is well-connected, who is important in the network, and more advanced structural properties like how information will flow through the network. Yet, these analyses are typically conducted without references to where the network is located in geographic space. Mapping the locations of the people or organizations (nodes) in a social network and the relationships between them (edges) allows us to measure new properties such as obstacles to meeting, important nearby facilities, and paths of knowledge and information flow across space. Using models, technologies and methods from Geographic Information Science (GIS) and Social Network Analysis (SNA), the project will create a public Spatial Social Network (SSN) data repository that includes SSNs from a wide variety of subjects including epidemiology, sociology, anthropology, history, political science, organization science, and public health. In addition to the data, the project will develop new models and statistical tests to help better understand these networks. To help promote and communicate the study of SSNs, the investigator will lead the development of educational and research infrastructure through software, tutorials, curriculum guidance and the support of an online community of SSN researchers. This Faculty Early-Career Development (CAREER) award will support the investigation of how the features of the built environment and physical environment play a role in how social networks form and develop, and the relationships that ensue within the provisions of geographic space. The investigator will fuse both SNA and GIS principles by measuring node and graph properties, and linking these properties to environmental features for simultaneous assessment (e.g. hot spots and network centrality). She will compare real SSNs to simulations of those networks based on principles of movement, distance decay, city formation and the urban hierarchy, as well as social network characteristics such as preferential attachment, structural holes and small-world models. This research will show how choices in scale, scope, sampling, and simplification affect statistical results, and design best practices for analyzing and modeling social networks in geographic space. The project will contribute to improving models used in the physical sciences by incorporating features of places, land use, topography, points of interest, administrative districts, population density, and transportation features. This project also emphasizes the visualization of SSNs on maps, which helps both exploratory spatial data analysis and engagement with a broad audience. The expected outcomes are innovative methods for testing how social networks interact with their surroundings, and a new understanding of how and why individuals or groups form relationships with one another in the context of their surroundings and provisions of the built environment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.