A points of interest matching method using a multivariate weighting function with gradient descent optimization

Volunteered geographic information contains abundant valuable data, which can be applied to various spatiotemporal geographical analyses. While the useful information may be distributed in different, low-quality data sources, this issue can be solved by data integration. Generally, the primary task of integration is data matching. Unfortunately, due to the complexity and irregularities of multi-source data, existing studies have found it difficult to efficiently establish the correspondence between different sources. Therefore, we present a multi-stage method to match multi-source data using points of interest. A spatial filter is constructed to obtain candidate sets for geographical entities. The weights of non-spatial characteristics are examined by a machine learning-related algorithm with artificially labeled random samples. A case study on Fuzhou reveals that an average of 95% of instances are accurately matched. Thus, our study provides a novel solution for researchers who are engaged in data mining and related work to accurately match multi-source data via knowledge obtained by the idea and methods of machine learning.