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A novel soft spatial weights matrix method based on soft sets

机译:基于软集的一种新的软空间权重矩阵方法

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摘要

Soft sets are efficient and flexible tools to describe uncertainty and fuzziness. In this paper, we integrate soft set theory into the specification of fuzzy spatial dependent relationship and provide a framework for spatial weight description. We treat the configuration of spatial location relationship as soft sets and propose a new dependence measure based on operations of soft sets. The proposed soft spatial weights matrix efficiently combines information of 'spatial adjacent relation' and 'spatial distance'. Further, Chinese regional industrial agglomeration data are applied to empirical analysis. The spatial autoregressive error panel model (SEM) with our new matrix performs better than that of Moran's Ⅰ, log-likelihood and interpretation.
机译:软件集是描述不确定性和模糊性的有效且灵活的工具。在本文中,我们将软集理论整合到模糊空间相关关系的规范中,并提供了空间权重描述的框架。我们将空间位置关系的配置视为软集合,并基于软集合的操作提出了一种新的依赖度量。所提出的软空间权重矩阵有效地结合了“空间相邻关系”和“空间距离”的信息。此外,将中国区域工业集聚数据用于实证分析。具有新矩阵的空间自回归误差面板模型(SEM)的性能优于Moran的Ⅰ,对数似然和解释。

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