The Interval Categorizer Tesselation-Based Model for High Perfomance Computing

Authors

Marilton S. de Aguiar
Gracaliz P. Dimuro
Antonio C. da Rocha Costa
Rafael K.S. Silva

Publication Date

4-2005

Comments

UTEP-CS-04-10a.

Short version published in Proceedings of the Workshop on State-of-the-Art in Scientific Computing PARA'04, Lyngby, Denmark, June 20-23, 2004; extended version published in Jack Dongarra, Kaj Madsen, and Jerzy Wasniewski (eds.), PARA'04 Workshop on State-of-the-Art in Scientific Computing, Springer Lecture Notes in Computer Science, 2006, Vol. 3732, pp. 83-92.

Abstract

The paper presents the results obtained by an implementation of the interval tessellation-based model for categorization of geographic regions according the analysis of the relief function declivity, called ICTM. The analysis of the relief declivity, which is embedded in the rules of the model ICTM, categorizes each tessellation cell, with respect to the whole considered region, according to the (positive, negative, null) signal of the declivity of the cell. Such information is represented in the states assumed by the cells of the model. The overall configuration of such cells allows the division of the region into sub-regions of cells belonging to the same category, that is, presenting the same declivity signal. In order to control the errors coming from the discretization of the region into tessellation cells, or resulting from numerical computations, interval techniques are used.