As cities grow, public services must scale up to meet demand. While useful data is being collected by sensors throughout most modern cities, key data needed to make decisions may be siloed, and models derived from it may be static. These limitations are especially apparent where high quality data matters most – public safety and emergency response. To address this problem, we propose Twin TAK, a complete digital twin solution that will help police, firefighters, first responders, and policymakers make more informed decisions based on real-time data. As incidents occur, the locations of incidents, first responders, and traffic bottlenecks will be fed into our system. This raw data will form the basis for a digital twin, which can be employed to optimize responses and identify blind spots in ongoing incident responses. A “What-If” capability will extend this further, allowing analysts to prepare for hypothetical scenarios based on real world data.