Pattern-mining approach for conflating crowdsourcing road networks with POIs

Abstract

Crowdsourcing geospatial data mainly collected by public citizens have brought about a profound transformation on data acquisition and utilization. However, the unpredictable positional accuracies, unstructured semantic descriptions, and invalid spatial relations occur to crowdsourcing geospatial data, causing difficulties for conflating heterogeneous data sets collected by different professional agencies or volunteers. We thus propose a novel pattern-mining approach to conflate crowdsourcing road networks with points of interest (POIs) geometrically and semantically. The proposed method mines the geometric patterns between road networks and POIs respectively and generates the pattern-related skeleton graphs for them. Then, corresponding points are determined between the two skeleton graphs to align POIs and road networks geometrically, and the road-related semantic data between the associated POIs and the road segments are compared to check the data quality of POIs and infer the road names of the road segments. Experimental results show the advantages of our proposed method, demonstrating a functional and promising solution for enriching POIs and road network geometrically and semantically.

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