模糊环境观

B. Bajat, D. Joksic, Zoran Nedeljković
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引用次数: 0

摘要

自从基于地理信息系统(GIS)的第一批应用问世以来,已经过去了50年。GIS不仅影响了空间数据的方法、收集技术、处理、操作和可视化的发展。它还影响了地球科学研究以及从事空间分析的技术学科的扩大。如今,GIS正在成为验证和实际实施在基础科学学科框架内开发的模型和算法的工具。GIS这个缩写词的含义与地理或地理信息科学的术语越来越相关。以这种方式更加强调日益应用于地理信息系统的科学概念。地理信息系统的计算技术,也需要地理数据模型的发展,应该有效地支持地理信息系统的操作。这些模型代表了人们在观察地理现象时使用的概念模型的形式等价。过去,空间现象被映射为具有已知坐标的明确定义的点,或连接相同点的线,或具有精确定义边界的多边形。它们以前以模拟形式绘制,现在则以数字形式绘制。这种空间感知、数据分析和空间查询可视化的方法仅限于布尔代数和二进制逻辑的基本规则的应用,最终结果呈现为经典的主题地图。由于需要一个数学模型来描述空间数据的不确定性,在空间分析中引入了模糊集理论。此外,该模型将为没有明确边界的类的空间现象的可视化和分组提供解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy view of environment
A period of fifty years has been reached since the introduction of the first applications based upon geographical information systems (GIS). GIS has not only influenced the development of methods, collection techniques, processing, manipulation and visualization of spatial data. It influenced also the expansion of scientific research in geosciences, as well as the technical disciplines that are engaged in spatial analysis. Nowadays, GIS is becoming the tool for verification and practical implementation of models and algorithms that have been developed within the frame of basic scientific disciplines. The meaning of the GIS acronym is becoming more and more related to term of Geographical or Geo Information Sciences. Scientific concepts that are increasingly applied in GIS are more emphasized in that way. GIS computational techniques, required also the development of geographical data models that should effectively support GIS operations. These models represent formal equivalents of conceptual models used by people in observing geographic phenomena. Spatial phenomena used to be mapped as clearly defined points with known coordinates, or as lines which connect the very same points, or as polygons with exactly defined borders. They were mapped previously in analog form and nowadays in digital format. This approach of perceiving a space, data analyses and visualization of spatial quires is limited on the application of basic rules of Boolean algebra and binary logic, with final results presented as classical thematic maps. The need for a mathematical model that would describe uncertainty of spatial data, resulted in the introduction of the theory of fuzzy sets in spatial analysis. Moreover, this model will provide a solution for visualization and grouping up of spatial phenomena in classes which do not have clearly defined borders.
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