Quantifying Pan-Map Information Content: A Multi-Factor Weighting Model

This study introduces a multi-factor, cognition-weighted model designed to quantitatively evaluate map information content in response to the growing diversity of pan-map representations. Drawing on cartographic-cognition theory, map elements are initially grouped into three overarching categories-text, graphics, and boundaries-and are subsequently subdivided into eight distinct types, each assigned a specific cognitive weight reflecting its relative contribution to information perception. The total information content (I) of a map is calculated by multiplying the number of elements in each category by their respective cognitive weights and summing the resulting products, while normalized information entropy (H_norm) is used to assess the distribution balance across categories, indicating the degree of evenness in information presentation. A total of fifteen representative maps of Chengdu, including standard, thematic, electronic, and hand-drawn varieties, were analyzed within a standardized 800 × 800 pixels framework to ensure comparability. The analysis reveals that standard maps exhibit the highest values of I and H_norm, suggesting a dense yet well-organized information structure that supports efficient comprehension; hand-drawn maps display highly variable values, reflecting the individualized and flexible nature of manual cartographic design; electronic maps tend to regulate information density dynamically through scale adjustments, resulting in clustering at moderate-to-high levels; thematic maps, which prioritize specific types of knowledge, generally carry lower overall information content and exhibit uneven category distribution. These findings highlight that map information content, when quantified using cognition-weighted approaches, not only captures the volume of information but also provides insights into the structural balance and readability of diverse map forms, offering a practical metric for evaluating map quality and guiding future cartographic design.