The objective of this research is to understand and improve the resource coordination and dispatch mechanisms used by first responders in smart and connected communities. In prior art, as well as practice, incident forecasting and response are typically siloed by category and department, reducing effectiveness of prediction and precluding efficient coordination of resources. This research project provides a unique opportunity to study the problem by integrating both the data and emergency resources from distinct urban agencies in the City of Nashville along with other widely available data such as pedestrian traffic, road characteristics, traffic congestion, and weather. This will allow development of models for anticipating heterogeneous incidents, such as distinct categories of crime, as well as vehicular accidents. With these models we can develop decision support tools to optimize both resource allocation and response times. These tools will help the emergency responders determine which units to dispatch (police, fire, or both) in order to minimize expected response time, and what equipment is most appropriate, taking into account the time, location, and nature of incidents, as well as those predicted to occur in the future. Ultimately, the methods developed in this research can be applied to other domains where multi-resource spatio-temporal scheduling is a challenge. The technical aspects of this project will require us to develop methods for solving the algorithmic challenge related to continuous-time forecasting of spatio-temporal time series of heterogeneous incidents. In tackling the forecasting task, we will develop methods to cluster incidents taking into account multiple features, and use the resulting groupings to develop distinct continuous-time models that forecast incident occurrence distributions based on survival analysis. The optimization framework, in turn, requires a scalable solution for integrated spatio-temporal allocation of heterogeneous emergency responders, making use of developed integrated forecasting methods. The proposed optimization methods will transform the incident response problem into a transportation problem with heterogeneous resources, which can be formalized as a network-flow linear program, augmented to account for heterogeneity in the resources and incidents that these resources can address. The developed solutions will be made available to the community for maximal dissemination. This research has the potential to impact actual operational planning at the Metro Nashville Police Department and Nashville Fire Department, by optimally coordinating responses. Broader impacts also include involvement in educational activities, including STEM-related projects for High School students at the School for Science and Math at Vanderbilt, undergraduate and graduate teaching, and active engagement of undergraduates and graduates in research.