Share:


Automatic data classification based on the triangular graph for thematic maps

    Zdena Dobesova Affiliation

Abstract

A triangular point graph helps in the process of data classification for a thematic map. A triangular graph can be used for a situation that is described by three variables. The total sum of variables is 100%. The proportion of three variables is plotted in an equilateral triangular graph where each side represents a coordinate for one variable. A triangular graph displays the proportions of the three variables. The position of the point indicates the type (class) of the situation in the triangular graph. The typology of the situation can be subsequently expressed in the map.


We have created a “Triangular Graph” program which represents a helpful automatic tool for ArcGIS software. This new program classifies input data based on a triangular graph. It is realized by two python scripts located in a custom toolbox as two programs. The first program calculates X and Y coordinates in an equilateral triangular graph. The second program compares plotted points and suggested zones of a division produced by the first program. Finally, a new attribute is added to the source data. The user can create a new thematic map, based on that attribute in order to express the typology of the given situation.


The programming language Python and essential module ArcPy have been used for solving these tasks. To test the created programs several maps were made, based on the classification often used in demography. For example, the new program helped to create a sample map of age categories in districts of the Czech Republic.


The program is available to download from the Esri web pages and web pages of the Department of Geoinformatics, Palacký University Olomouc.

Keyword : cartography, thematic map, classification, triangular graph, ArcGIS, Python

How to Cite
Dobesova, Z. (2015). Automatic data classification based on the triangular graph for thematic maps. Geodesy and Cartography, 41(1), 1-8. https://doi.org/10.3846/20296991.2015.1024457
Published in Issue
Apr 1, 2015
Abstract Views
512
PDF Downloads
647
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.