Examining Spatial Correlation-Oriented Area Entity Matching Method for Multisource Vector Data

Currently, the establishment of association relationships between multisource data through geometric matching techniques is a prominent topic in the area of spatial association. In response to this area of interest, a spatial association-oriented area entity matching method for multisource vector data is proposed. This method establishes a mapping relationship between the area entities of multisource vector data by geometrically matching them. Experiments were conducted using the Shenzhen standard datasets and Shenzhen City data downloaded from OpenStreetMap (OSM). The experimental results demonstrate that the choice of threshold value significantly impacts the matching accuracy. With a carefully selected threshold value, the proposed algorithm can more effectively perform the geometric matching between area entities, thereby establishing a correlation between the area entities of the multisource vector data with high precision and reliability, proving its practical utility.