ABSTRACT

Previous research has tended to use a global threshold of proximity to determine neighbors, neglecting spatial heterogeneity. Flexible thresholds implemented by adaptive search radii methods account for either the spatial structures or the non-spatial similarities of objects, but few consider both. By combining the spatial and non-spatial information of objects, we propose a novel approach that can automatically determine the neighbors that are strongly related to the object of interest. We introduce the sparse reconstruction technique from the signal processing domain, which aims to remove trivial relationships in a dataset. We extend the sparse reconstruction model by assuring three principles in spatial data, including retention of the correlation of data in the non-spatial attribute domain, preservation of local dependencies in the spatial domain, and removal of trivial relationships. Extensive experiments, based on road network missing value imputation and building clustering, show that our approach can make better use of both spatial and non-spatial information than a simple addition of them.

摘要

以前的研究倾向于使用全局邻近阈值来确定邻居，而忽略了空间异质性。通过自适应搜索半径方法实现的灵活阈值既可以考虑空间结构，也可以考虑对象的非空间相似性，但很少有人同时考虑这两者。通过结合对象的空间和非空间信息，我们提出了一种新方法，可以自动确定与感兴趣对象密切相关的邻居。我们从信号处理领域引入了稀疏重建技术，旨在消除数据集中的琐碎关系。我们通过确保空间数据中的三个原则来扩展稀疏重建模型，包括保留非空间属性域中的数据相关性，保留空间域中的局部依赖关系，并删除琐碎的关系。基于道路网络缺失值估算和建筑物聚类的大量实验表明，我们的方法可以更好地利用空间和非空间信息，而不是简单地添加它们。