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About the Project Fire events have had a devastating impact on Canadian communities. In 2001 alone, there were 55,300 reported fire incidents leading to 337 civilian deaths and over 1.4 billion (Canadian dollars) in property damage. Accordingly, there is a clear need for improved decision making in fire planning and response in order to save lives, to minimize injuries and to protect property. Real-time, participatory Multi-Criteria Spatial Decision Support Systems (MCSDSS) can improve multi-agency coordination, and fire response decision making. Geomatics play a critical role in fire modeling (including modeling the spread of fire, the dispersion of poisonous fumes, and the spatial distribution of vulnerable people). Developing a location-aware low-cost emergency data sensor system is essential but challenging and more research is required to enhance the utility of participatory GIS-based multi-criteria evaluation methods, particularly for use in fire dispatching, incident reporting, data collection, and incident command systems. Moreover, existing fire decision support tools are typically deployed at an Emergency Operations Centre (EOC) after a state of emergency has been declared. Accordingly, the system herein proposed is designed to provide valuable information for real-time fire decision-making (before and during the disaster) and to harness advances in geomatics and emergency management. Objectives The first objective is to develop a real-time, participatory fire management MCSDSS capable of extending the decision support capabilities of current fire dispatching, incident reporting, and incident command systems. To this end, the proposed research will design and implement an interactive computing environment that integrates decision analytic tools, geospatial information technologies, graphical user interfaces, and Mobile Asset Emergency Management (MAEM) in order to facilitate complex resource allocation decisions. Key issues involve developing location-aware low-cost sensor systems (for real-time fire data acquisition, in-sensor embedded data management, data transmission and access) and making time-sensitive and urgent decisions under conditions of uncertainty and complexity. The second objective is to apply the fire MCSDSS to improve inter-agency communication and to facilitate collaborative (group) decision making thereby helping to find pareto-optimal compromise solutions. This involves modeling dynamic changes in the preference structures and values of fire experts. Finally, the operational implementation of these two objectives is demonstrated with fieldwork and performance testing by our project partners.