A georeferenced graph model for geospatial data matching by optimising measures of similarity across multiple scales. Issue 11 (2nd November 2021)

• Record Type: Journal Article
• Authors: Zhang, Wen-Bin; Ge, Yong; Leung, Yee; Zhou, Yu
• Is Part Of: International journal of geographical information science. Volume 35:Issue 11(2021)
• Page Start: 2339
• Page End: 2355
• Publication Date: 2021-11-02
• Subjects: Geospatial data matching -- georeferenced graph -- multiscale similarity relationships -- object matching -- optimisation; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2020.1858301
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: The growth of georeferenced data sources calls for advanced matching methods to improve the reliability of geospatial data processing, such as map conflation. Existing matching methods mainly focus on similarity measures at the entity scale or area scale. A measure that combines entity-scale and area-scale similarities can provide sound matching results under various circumstances. In this paper, we propose a georeferenced-graph model that integrates multiscale similarities for data matching. Specifically, a match of correspondent data objects is identified by the entity-scale measure under the constraint of the area-scale measure. Nodes in the proposed georeferenced graph model represent polygons by their centroids, whereas the links in the graph connect the nodes (i.e. centroids) according to pre-defined rules. Then, we develop an algorithm to identify many-to-many matches. We demonstrate the proposed graph model and algorithm in real-world experiments using OpenStreetMap data. The experimental results show that the proposed georeferenced-graph model can effectively integrate the context and the location-and-form distance of geospatial data matches across different datasets.