Abstract
Point of interest (POI) matching is critical but is the most technically difficult part of multi-source POI fusion. The accurate matching of POIs from different sources is important for the effective reuse of POI data. However, the existing research on POI matching usually adopts weak constraints, which leads to a low POI matching accuracy. To address the shortcomings of previous studies, this paper proposes a POI matching method with multiple determination constraints. First, according to various attributes (name, class, and spatial location), a new calculation model considering spatial topology, name role labeling, and bottom-up class constraints is established. In addition, the optimal threshold values corresponding to the different attribute constraints are determined. Second, according to the multiattribute constraint values and optimal thresholds, a constraint model with multiple strict determination constraints is proposed. Finally, actual POI data from Baidu Map and Gaode Map in Dongying city is used to validate the method. Comparing to the existing method, the accuracy and recall of the proposed method increase 0.3% and 7.1%, respectively. The experimental results demonstrate that the proposed POI matching method attains a high matching accuracy and high feasibility.
Highlights
With the rapid development of electronic maps and mobile communication technologies, the demands for location-based services have progressively increased [1]
Point of interest (POI) matching from different sources usually refers to the process of discarding POIs representing the same objects but considering POIs representing different objects by comparing the POIs in reference and auxiliary maps with certain constraints
McKenzie et al, (2014) proposed a weighted multi-attributes strategy for matching POIs, which integrated attributes such as spatial location and distance, name attributes, and thematic similarity, and pointed out that methods combining multiple attributes can effectively solve the issue of a low matching accuracy caused by the use of a single attribute [17]
Summary
With the rapid development of electronic maps and mobile communication technologies, the demands for location-based services have progressively increased [1]. McKenzie et al, (2014) proposed a weighted multi-attributes strategy for matching POIs, which integrated attributes such as spatial location and distance, name attributes, and thematic similarity, and pointed out that methods combining multiple attributes can effectively solve the issue of a low matching accuracy caused by the use of a single attribute [17]. Based on spatial distance attributes, Huang et al, (2018) applied a nonspatial attribute, i.e., name similarity, to enhance the fusion accuracy of POI data from different sources [15]. The existing methods combining spatial and nonspatial attributes usually rely on weak constraints, which may lead to a low POI matching accuracy.
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