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Open AccessArticle

An Approach for a Spatial Data Attribute Similarity Measure Based on Granular Computing Closeness

1
College of Mathematics and Information Science, Guangxi University, Nanning 530004, China
2
School of Mathematics and Stadistics, Nanning Normal University, Nanning 530001, China
3
State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(13), 2628; https://doi.org/10.3390/app9132628
Received: 1 June 2019 / Revised: 17 June 2019 / Accepted: 25 June 2019 / Published: 28 June 2019
(This article belongs to the Section Earth Sciences and Geography)
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PDF [2515 KB, uploaded 28 June 2019]
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Abstract

This paper proposes a spatial data attribute similarity measure method based on granular computing closeness. This method uses the distance and membership degree of different index levels of spatial entities to measure the similarity of attributes. It not only reflects the degree of similarity of spatial entity types at different index levels but also reflects the integration similarity between spatial entity types under a comprehensive index. This method embodies the layered idea of granular computing and can provide a basis for spatial problem decision making and for spatial entity classification. Finally, the feasibility and applicability of the method are verified by taking the similarity measure of the land-use type attribute in Guangxi as an example. View Full-Text
Keywords: closeness; granular computing; similarity measure; spatial data; land use closeness; granular computing; similarity measure; spatial data; land use
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Liao, W.; Hou, D.; Jiang, W. An Approach for a Spatial Data Attribute Similarity Measure Based on Granular Computing Closeness. Appl. Sci. 2019, 9, 2628.

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