Knowledge‐Guided Automated Cartographic Generalization Process Construction: A Case Study Based on Map Analysis of Public Maps of China

Abstract

The efficacy of conveying information through maps heavily depends on the quality of map generalization. However, automating map generalization poses a complex decision‐making challenge, requiring a profound understanding of the process—specifically, knowledge about the generalization procedure. Currently, there is a scarcity of research on the sequence of generalization operations, particularly for cartographic generalization involving symbolization and labeling. On the contrary, customary maps generated in practical applications consistently adhere to the specified generalization and symbolization protocol, which makes it feasible and credible to construct this overall process based on expert knowledge. To reconcile this incongruity, this paper presents a knowledge‐guided automated cartographic generalization process construction. Firstly, an exhaustive examination of the sequential procedures involved in manual generalization and a well‐applied automated generalization system are delineated, drawing upon map analysis methodologies, observations, and expert interviews. Then, elaborate guidelines governing each phase within this process, particularly concerning the symbolization and labeling of map features, are explored. Ultimately, details of the expert interview are described and a map generalized by the well‐applied system is analyzed. The results show that the automated generalization system follows the knowledge‐guided process in this paper can significantly improve production efficiency in practice, this study serves as a connection between cartographers and developers and may help achieve a higher level of automated cartographic generalization.