Semantic similarity measurement for building polygon aggregation in multi-scale map space

doi:10.11947/j.AGCS.2022.20210074

Abstract

Abstract: Map generalization is a process of spatial similarity transformation in multi-scale map spaces. Cartographers generalize under the guidance of the similarity principle; at the same time, map readers form mental maps and reconstruct the real world from maps containing similarity. Thus, it is of great significance to study and measure the similarity relations with respect to the scale reduces in multi-scale map spaces. However, due to the poor computability of similarity and the purpose of its computation is to reveal deeper information, there are few achievements on similarity relations especially semantic relations in multi-scale map spaces. To solve this problem, semantic similarities in city block aggregation (from approximately 1:1750 to 1:14 000) under the constraint of semantic functional units are computed, and the method for measuring the semantic similarity is the matching-distance model based on ontology and set theory. By the experiment of different city block generalization, the semantic similarity values at key scales were obtained and the results were analyzed and evaluated. The experimental results have shown that the building aggregation under the constraint of semantic functional units is in accordance with map readers' cognitive needs. The method described in this paper is helpful for map to play a better role as a carrier of information transmission.

Key words: semantic similarity, city block settlement aggregation, map generalization, multi-scale map space, matching-distance model

CLC Number:

P208

GAO Xiaorong, YAN Haowen, LU Xiaomin. Semantic similarity measurement for building polygon aggregation in multi-scale map space[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(1): 95-103.

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