This article reports on the results from a spatiotemporal analysis of disaggregate fire incident data. The innovative analysis presented here focuses on the exploration of spatial and temporal patterns for four principal fire incident categories: property, vehicle, secondary fires, and malicious false alarms. This research extends previous work on spatial exploration of spatiotemporal patterns by demonstrating the benefits of comaps and kernel density estimation in examining temporal and spatiotemporal dynamics in calls for services. Results indicate that fire incidents are not static in either time or space and that spatiotemporal variation is related to incident type. The application of these techniques has the potential to inform policy decisions both from a reactive, resource-allocation perspective and from a more proactive perspective, such as through spatial targeting of preventive measures.