Evacuation is a critical part of wildfire emergencies. Emergency managers must be prepared to issue evacuation notices to areas that could be in the path of a wildfire to protect lives. There are methods of establishing when an evacuation should be called for any given urban area. One option is the creation of trigger boundaries around populated areas. This boundary is formed so that, should the wildfire cross it, a triggered evacuation will be complete before the wildfire becomes a threat to the area by threatening the community. Models already exist for calculating the trigger boundary around an area, and each have their strengths and limitations. WUIVAC by Li et. al. (2015) is a robust tool that can calculate the trigger boundary of an urban area, for any one simulated fire. However, it can only model trigger boundaries for one wildfire at a time and thus cannot capture the susceptibility of the area to any possible wildfire. Ramirez et. al. (2019) created a tool to calculate the probabilistic trigger boundary of an area by simulating all possible fires that may threaten the urban area. This tool is effective for management and planning, but is incredibly resource-intensive and limited in terms of letting the user specify which area is most likely to suffer from a wildfire. To address this, the k-PERIL algorithm was developed to calculate the trigger boundary of an urban area, as an upgraded version of the PERIL algorithm (Mitchell et. al, 2019). The user can load a single simulated wildfire, through software like FARSITE, and an evacuation time and get an exact trigger boundary for a specified urban area. The user can also load multiple wildfire simulation results for a given area, and K-PERIL will then generate a probabilistic trigger area. The user can then choose to retrieve a singular boundary of the area based on a probability value; It was made as part of the WUINITY project, by Ronchi et. al (2020), a self-contained wildfire simulation and evacuation planning tool. To introduce k-PERIL as a tool of calculating trigger boundaries, this paper presents the a study on creating trigger boundaries for Roxborough Park, an urban area near Denver, CO, USA. A fire ignition area was specified and a number of wildfires with varying weather inputs were created, and a probabilistic boundary was created. The testing program allowed the user to specify the average weather values and standard deviations of their area of interest, specify an area where ignition is most likely, and specify the number of simulated wildfires. Testing for single-wildfire boundaries will be conducted in the full paper. The results show that the results follow the current understanding of wildfire propagation. The resulting shapes are more accurate when more test cases are loaded, and are wider when the input variables are more varied. K-PERIL can thusly be used to create both specific and probabilistic boundaries around an inhabited area, either for immediate response or long-term planning.