Application of fuzzy measures in multi-criteria evaluation in GIS
Application of fuzzy measures in multi-criteria evaluation in GIS
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DOI：
10.1080/136588100240903
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发表时间：
2000-03-01
影响因子：
5.7
通讯作者：
Eastman, JR
中科院分区：
文献类型：
--
作者：
Jiang, H;Eastman, JR
多标准评估（MCE）可能是地理信息系统（GIS）中最基本的决策支持操作。本文回顾了 GIS 中使用的两种主要 MCE 方法，即布尔和加权线性组合 (WLC)，并讨论了与这两种方法相关的问题。为了解决这两种方法之间的概念差异，本文提出了模糊测度的应用，这是一个更广泛但包括模糊集隶属度的概念，并认为MCE的标准化因子属于模糊测度的一般类别和模糊集隶属度的更具体实例。这一观点为因素标准化及其后续聚合提供了强有力的理论基础。在这种情况下，我们讨论了一种新的聚合运算符，它适应并扩展了布尔和 WLC 方法：有序加权平均。采用肯尼亚纳库鲁产业配置的案例研究来说明不同的方法。
Multi-criteria evaluation (MCE) is perhaps the most fundamental of decision support operations in geographical information systems (GIS). This paper reviews two main MCE approaches employed in GIS, namely Boolean and Weighted Linear Combination (WLC), and discusses issues and problems associated with both. To resolve the conceptual differences between the two approaches, this paper proposes the application of fuzzy measures, a concept that is broader but that includes fuzzy set membership, and argues that the standardized factors of MCE belong to a general class of fuzzy measures and the more specific instance of fuzzy set membership. This perspective provides a strong theoretical basis for the standardization of factors and their subsequent aggregation. In this context, a new aggregation operator that accommodates and extends the Boolean and WLC approaches is discussed: the Ordered Weighted Average. A case study of industrial allocation in Nakuru, Kenya is employed to illustrate the different approaches.