This paper is an exploration of Layers in Time, a mapping process to visualize water-related events in New Orleans. This paper shares how the project was conceived, including the considerations and aspirations that guided its development, the design process for selecting tools and protocols for mapping, and the limitations and future possibilities of the work. Designed using Python, Layers in Time guides viewers through the accompanying dissertation by chapter, while also allowing them to explore its features in sandbox mode. A case study will be examined to illustrate how this mapping process can be used to understand historical contingency, evaluate existing narratives and claims, and generate new knowledge.