Development of high resolution population and social dynamics models and databases

High resolution population distribution data is critical for successfully addressing critical issues ranging from energy and socio-environmental research to public health to homeland security. Commonly available population data from Census is constrained both in space and time and does not capture the population dynamics as functions of space and time. This imposes a significant negative consequence on the fidelity of event based simulation models with sensitive space-time resolution. Such limitations, to a large degree, can be overcome by developing population data with a finer resolution in both space and time at sub-Census levels. Geodemographic data at such scales will represent a more realistic non-uniform distribution of population. Using an innovative approach with Geographic Information System and Remote Sensing, Oak Ridge National Laboratory (ORNL) has made significant progress towards solving this problem. ORNL, as part of its LandScan global population project, has developed the finest resolution global and US population distribution models. This talk will describe ongoing development of the computational framework for spatial data integration and modeling framework for LandScan. Discussions will cover development of algorithms to utilize population infrastructure datasets (such as residences, business locations, academic institutions, correctional facilities, and public offices) along with behavioral or activity-based mobility datasets for representing temporal dynamics of population. In addition, we will discuss development and integration of transportation, physical and behavioral science computational algorithms; the integration of these models that address different scales and different time frames; and the development of dynamic optimization routines to take advantage of real-time data from sensor networks.