Description of PIE Viz
The Populations, Infrastructures, and Exposures Visualization (PIE Viz) web app is intended for use by students, educators, researchers, planners, and policy-makers. It integrates and overlays data from multiple sources to show correlations at the county level between power outages, extreme heat, pollution levels, and social isolation, across the United States. We envision this tool as a powerful way to identify localities most likely to experience combined climate and environmental exposure-related risks and, within those localities, to identify areas with heightened percentages of vulnerable populations.
The overall idea of PIE Viz is to combine human population data and infrastructure data with environmental exposure data, since we know that built environment and infrastructure (like the power grid or drinking water infrastructure) are primary mediators of exposure to environmental contaminants for human populations, particularly as humans spend more time indoors.
With PIE Viz, users can explore the interactions between these factors by clicking on a state to zoom in, and then overlaying different layers of data and selecting ranges for each layer.
Description of Synthetic Population
The synthetic population is a set of synthetic people and households, located geographically, each associated with demographic variables and typical daily activity patterns. It is constructed by integrating a variety of databases from commercial and public sources. The process preserves the confidentiality of the individuals in the original data sets, yet produces realistic attributes and demographics for the synthetic individuals. The resulting model is a dynamic representation of human mobility and interaction over the course of a normative day. From this we can also induce a social contact network, which is an interaction-based graph whose vertices are synthetic people, labeled by their demographics, and whose edges represent estimated contacts, labeled by duration of contact and type of activity. This social contact network is specific to a geographic location because of its dependence on "contingent realities" for the area – demographics of people who live there and the distribution of actual activity locations. It provides a plausible, bottom-up mechanism for generating large scale structure without making assumptions about hierarchies. It also makes it possible to model realistic interaction with the built environment.