Acta Geographica Sinica ›› 1998, Vol. 53 ›› Issue (s1): 61-66.doi: 10.11821/xb1998s1008

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Dang Among   

  1. GIS Lab. of Architecture College, Tsinghua University, Beijing 100084
  • Received:1997-05-09 Revised:1998-08-23 Online:1998-12-15 Published:1998-12-15

Abstract: A series of classification quantitative standard have been worked out since the research of G. F. Jecks and M. R. C. Coulson (1963). However, the characteristics of statistical data has been considered so much that the mapping effect of classification scheme was hardly cared. In 1980s, mapping information content based on the entropy function was used to evaluate classifi-cation scheme which took the statistical precision and mapping effect into account at the same time. This article uses mapping classification information content to determine the visualization effect of GIS. A new formula of calculating classification information content is worked out based on the general mapping information concept.Using one of the general formulas of numerical sequence and progression, lots of classifi-cation schemes can be acquired with many sets of parameters (x,d,rBk) values within their do-main. This is the basic condition for classification optimization. However, the class intervals or class breaks of classification scheme changing in a single regularity from the minimum value to the maximum value of mapping element. And the change regularity of mapping element data it-self are not always so simple. The real situations are different from one to another and as com-plex as several types of changing rules composed together. Author proposes a new idea of "par-tial numerical sequence and progression classification method". The idea of this new method is that dividing mapping elements data series into several subseries at first; and then, using differ-ent classification method to acquire classification scheme for each subseries; finally, generalizing all of the subseries classification schemes to determine the final optimal classification scheme.

Key words: GIS, visualization, thematic mapping, quantitative standard, classifi-cation method, classification mapping information content

CLC Number: 

  • P208