  In emergencies, high stake decisions often have to be made under time
pressure and strain. In order to support such decisions, information from
various sources needs to be collected and processed rapidly. The information
available tends to be temporally and spatially variable, uncertain, and
sometimes conflicting, leading to potential biases in decisions. Currently,
there is a lack of systematic approaches for information processing and
situation assessment which meet the particular demands of emergency situations.
To address this gap, we present a Bayesian network-based method called ERIMap
that is tailored to the complex information-scape during emergencies. The
method enables the systematic and rapid processing of heterogeneous and
potentially uncertain observations and draws inferences about key variables of
an emergency. It thereby reduces complexity and cognitive load for decision
makers. The output of the ERIMap method is a dynamically evolving and spatially
resolved map of beliefs about key variables of an emergency that is updated
each time a new observation becomes available. The method is illustrated in a
case study in which an emergency response is triggered by an accident causing a
gas leakage on a chemical plant site.
