Abstract: Geographic information classification is the core content of cartography. With the coming of the ubiquitous information society, “spatio-temporal ubiquitous” is gradually becoming a new qualitative feature of geographic information, which has brought new challenges to traditional geographic information classification models. This paper takes ubiquitous map information as its research subject and puts forward a four-level information classification model of “instances→features↔dimensions→themes” to satisfy the need for location-based aggregation application. Then it designs a verifying method for the model, which tags ubiquitous maps’ thematic features based on the feature system and information dimensions, realizes the unified expression of ubiquitous map information in the vector space, and uses a hierarchical clustering algorithm to generate the classification and grading system of ubiquitous map information. At last, it verifies the feasibility of the model through a “meteorological theme” classification experiment.In essence, the model is to automatically build a classification and grading system, which is data-driven and constrained by cognitive patterns. And its feature level has extended the “instance→dimension↔theme” structure of traditional geographic information classification models, enabling ubiquitous map information classification in fine-grained semantic description while staying in the hierarchical cognitive structure.

Key words: information classification, ubiquitous map information, location-based aggregation, information dimension, hierarchical clustering