Emergency response is directly related to the allocation of emergency rescue resources. Efficient emergency response can reduce loss of life and property, limit damage from the primary impact, and minimize damage from derivative impacts. An appropriate risk analysis approach in the event of accidents is one rational way to assist emergency response. In this article, a cellular automata-based systematic approach for conducting risk analysis in emergency response is presented. Three general rules, i.e., diffusive effect, transporting effect, and dissipative effect, are developed to implement cellular automata transition function. The approach takes multiple social factors such as population density and population sensitivity into consideration and it also considers risk of domino accidents that are increasing due to increasing congestion in industrial complexes of a city and increasing density of human population. In addition, two risk indices, i.e., individual risk and aggregated weighted risk, are proposed to assist decision making for emergency managers during emergency response. Individual risk can be useful to plan evacuation strategies, while aggregated weighted risk can help emergency managers to allocate rescue resources rationally according to the degree of danger in each vulnerable area and optimize emergency response programs.