In this paper we examine the case of a mobile robot that is part of a human-robot urban search and rescue (USAR) team. During USAR scenarios, we would like the robot to have a geometrical-functional understanding of space, using which it can infer where to perform planned tasks in a manner that mimics human behavior. We assess the situation awareness of rescue workers during a simulated USAR scenario and use this as an empirical basis to build our robot's spatial model. Based upon this spatial model, we present " functional mapping " as an approach to identify regions in the USAR environment where planned tasks are likely to be optimally achievable. The system is deployed and evaluated in a simulated rescue scenario.