Z. Wang1, L.X. Li2, Z.H. Wu3, J. Xiao1

1 - The School of Resource and Environment Science,Wuhan University, Wuhan, China

2 - The School of Science,Wuhan University of Technology, Wuhan, China

3 - LIESMARS, Wuhan University, Wuhan, China



The prime problem in the automatic map generalization is quantifying the efficiency. With the development of algorithms for Point of Interest (PoI) generalization, this paper attempts to evaluate the efficiency of them in an information theoretic approach.

In order to evaluate the result of Point Cluster generalization with reason, a hierarchical model of map linguistics is proposed as basis referencing to the mature method of Semiotic Linguistics, which includes three levels: syntactic level, semantic level, pragmatic level. Furthermore, these three levels are step-up, namely a higher level should be established on the basis of the lower-level and in turn guide the lower-levels generalization. It should be noted that the model of Semiotic Linguistics and McMaster&Sheas division Model are independent with each other, and should be used synthetically.

This paper mainly focuses on the syntactic level. In this level, the entropy of metric information defined by Li&Huang is employed as an evaluating indicator, which is computed via Voronoi diagram whose attributes of region influence for geographical entities manifest the content of syntactic level information precisely. Then, a quantitative changing rule when Point Cluster is generalized under the typification generalization constraint can be concluded via mathematical theory. (For Point Cluster, typification means preserving spatial distribution properties, scilicet, preserving the density contrast of the different region correctly which is the most important and comprehensive request for generalizing Point Cluster, but is rarely considered actually in the existing automatic generalization methods.) The rule will be expressed as follows: on the syntactic level generalization, considering typification model, generalization will reduce metric information, and the reductions relate to generalization degree (selection ratio). Further, the greater the generalized degree is (the smaller the selection ratio is), the more information will be lost, and the amount of changing between the information of raw map and generalized map will follow the rule of. Based on this rule, an instance of evaluation is showed, which proves that the rule meets the practice of map generalization and is useful to evaluate effective transmission of maps.