Deriving fuzzy topological relations from incomplete observations

IF 2.8 3区 地球科学 Q1 GEOGRAPHY
Subhankar Jana, Anjali Patel, Juthika Mahanta
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引用次数: 0

Abstract

Spatial objects in a geographical information system are often vague. Fuzzy topology-based intersection methods were introduced to obtain the topological relation between such vague objects by considering the objects as fuzzy sets. The elements of the intersection matrices express the nature of the intersections between topological parts of the objects. In reality, it may only be possible to calculate some of the required intersections due to the unavailability of data. This paper introduces a method to construct a fuzzy 9-intersection matrix under incomplete observations. Further, it computes the possible fuzzy topological relation using a quantitative fuzzy 9-intersection method based on fuzzy similarity measure. Some special cases of the incomplete observations and fuzzy topological relation under such situations are discussed. Finally, few applications are presented, where the proposed method is used to obtain relation between two uncertain objects under the condition that the topological properties of the objects are partially known.

Abstract Image

从不完整的观测结果中推导出模糊拓扑关系
地理信息系统中的空间对象往往是模糊的。基于模糊拓扑的交集方法被引入,通过将对象视为模糊集来获取这类模糊对象之间的拓扑关系。交集矩阵的元素表示对象拓扑部分之间交集的性质。在现实中,由于无法获得数据,可能只能计算出部分所需的交集。本文介绍了一种在不完全观测条件下构建模糊 9 交点矩阵的方法。此外,它还利用基于模糊相似度量的定量模糊 9 交点法计算了可能的模糊拓扑关系。还讨论了在这种情况下不完整观测和模糊拓扑关系的一些特殊情况。最后,还介绍了一些应用,在部分已知对象拓扑特性的条件下,利用所提出的方法获得两个不确定对象之间的关系。
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来源期刊
CiteScore
5.40
自引率
6.90%
发文量
33
期刊介绍: The Journal of Geographical Systems (JGS) is an interdisciplinary peer-reviewed academic journal that aims to encourage and promote high-quality scholarship on new theoretical or empirical results, models and methods in the social sciences. It solicits original papers with a spatial dimension that can be of interest to social scientists. Coverage includes regional science, economic geography, spatial economics, regional and urban economics, GIScience and GeoComputation, big data and machine learning. Spatial analysis, spatial econometrics and statistics are strongly represented. One of the distinctive features of the journal is its concern for the interface between modeling, statistical techniques and spatial issues in a wide spectrum of related fields. An important goal of the journal is to encourage a spatial perspective in the social sciences that emphasizes geographical space as a relevant dimension to our understanding of socio-economic phenomena. Contributions should be of high-quality, be technically well-crafted, make a substantial contribution to the subject and contain a spatial dimension. The journal also aims to publish, review and survey articles that make recent theoretical and methodological developments more readily accessible to the audience of the journal. All papers of this journal have undergone rigorous double-blind peer-review, based on initial editor screening and with at least two peer reviewers. Officially cited as J Geogr Syst
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