Spatiotemporal Cartographic Generalization: Conceptual Framework and Algorithmic Extensions

Abstract Multitemporal cartographic generalization poses challenges beyond those of conventional map generalization, requiring methods that not only enhance map legibility but also preserve and effectively communicate meaningful changes over time. This paper addresses two key requirements: (1) a systematic and reproducible framework for describing and comparing multitemporal generalization processes and (2) algorithmic extensions of spatiotemporal generalization operations that explicitly account for temporal characteristics while maintaining the legibility of significant change events. To this end, we propose a conceptual framework for spatiotemporal generalization that integrates the temporal dimension into the description of generalization operations, their levels of application, relevant temporal properties (including scale, resolution, duration, and lag), and generalization objectives. Particular emphasis is placed on preserving and emphasizing major change events and suppressing minor changes throughout the generalization process. The utility of the proposed framework and its associated algorithmic extensions is demonstrated through two representative operations—data classification and line simplification—which illustrate how temporal considerations can be systematically incorporated to improve both the consistency and interpretability of multitemporal map representations.