| 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. |
