This paper presents a novel data science solution for analyzing and clustering wildfire data. It incorporates human cultural factors—particularly, ones that are not readily measurable—when gathering insights from a database through clustering algorithms. In addition, we modify Minkowski distance to generalize across a variety of clustering algorithms. We evaluate our data science solution with the modified Minkowski distance on large USA wildfires data collected for years 1992 to 2020. Evaluation results help get insights into how cultural practices (e.g., daylight savings time, holidays) play into patterns associated with natural or human-caused disasters like wildfires. This, in turn, helps establish disaster-resilient societies.