Achievement

Understanding the connections in social networks, and determining the degree of modularity, has broad implications for epidemiology because diseases spread faster within than between communities. In human networks, connections among neighborhoods (and thus individuals occupying those neighborhoods) can be inferred from call flow between cell phone towers. Modeling social networks often incorporates individual attributes to explain community formation but rarely emphasizes place-based attributes. QSE3's second cohort of fellows – T. Caughlin, N. Ruktanonchai, M. Acevedo, K. Lopiano, and O. Prosper - and collaborators, recently published part of their workshop research in PLoS One. They demonstrated that place-based attributes, such as land cover and economic activity, were strongly correlated with community modularity scores using cell tower data. They showed that place-based attributes can characterize community modularity in the absence of individual-level, social-networking data.