Explicit incorporation of social and organizational factors into Level 1 Probabilistic Risk Assessment (PRA) has been improved theoretically and methodologically and is now in the development stage for application at Nuclear Power Plants (NPPs). The goal of this study is to initiate a similar paradigm for Level 3 PRA. Explicit incorporation of location-specifi c social factors into Level 3 PRA can drastically aff ect decisions related to emergency planning, preparedness, and response. The population’s response to a radiological accident, e.g., the 2011 Fukushima Daiichi NPP, demonstrated that understanding the social makeup of the population in the vicinity of NPPs can give policy makers valuable information regarding the eff ects of their decisions. This research presents a fi rst-time approach for combining a nuclear accident consequence code, MACCS2, with the Social Vulnerability Index utilizing Geographic Information Systems to explicitly consider social factors of the local population in risk models for severe NPP accidents.