This paper presents evomap, a Python package for dynamic mapping. Mapping methods are widely used across disciplines to visualize relationships among objects as spatial representations, or maps. However, most existing statistical software supports only static mapping, which captures objects' relationships at a single point in time and lacks tools to analyze how these relationships evolve. evomap fills this gap by implementing the dynamic mapping framework EvoMap, originally proposed by Matthe, Ringel, and Skiera (2023), which adapts traditional static mapping methods for dynamic analyses. The package supports multiple mapping techniques, including variants of Multidimensional Scaling (MDS), Sammon Mapping, and t-distributed Stochastic Neighbor Embedding (t-SNE). It also includes utilities for data preprocessing, exploration, and result evaluation, offering a comprehensive toolkit for dynamic mapping applications. This paper outlines the foundations of static and dynamic mapping, describes the architecture and functionality of evomap, and illustrates its application through an extensive usage example.
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