Empirical Strategies for Coupling Models of Social and Physical Systems

Quantifying interactions between social systems and the physical environment we live within has long been a major scientific challenge.   A better empirical understanding of dynamic interactions between the built environment and urban social structure is necessary to support predictions of how cities will respond to climate change, ensure energy and water security for their residents, and to facilitate urban sustainability and resilience.  In this talk,  I will describe three projects using different datasets, methods, and theoretical backgrounds that are all aimed at developing empirical strategies to increase our understanding of how social and physical systems are coupled. Using Japanese language twitter data, I describe how the online social system responded to the Tohoku earthquake and tsunami in March 2011.  Using household level water consumption and infrastructure data, I show that peer influence at the neighborhood scale does increase a home’s likelihood of participating in a turf removal incentive program.  And finally, using a dataset of historical disease cases in the US, I consider the role that the physical and technological environment plays in influencing socio-economic outcomes in urban environments, and demonstrate that the invention of a commercially viable Diphtheria vaccine occurred at the same time as the scaling relationship between disease cases and city size failed. In all of these projects, sources of information that have been are newly available or were invented in the era of cheap computing are allowing novel research strategies into the relationship between human social systems and the physical environment.