An End-to-End Point of Interest (POI) Conflation Framework

Point of interest (POI) data serves as a valuable source of semantic information for places of interest and has many geospatial applications in real estate, transportation, and urban planning. With the availability of different data sources, POI conflation serves as a valuable technique for enrichin...

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Bibliographic Details
Published in:ISPRS international journal of geo-information 2021-11, Vol.10 (11), p.779
Main Authors: Low, Raymond, Tekler, Zeynep Duygu, Cheah, Lynette
Format: Article
Language:English
Subjects:
Crowdsourcing
data conflation
data fusion
data integration
Data sources
Datasets
Feasibility studies
Frameworks
Geography
Government agencies
Land use
machine learning
Matching
natural language processing
Real estate
Semantics
Social networks
Taxonomy
Transport
Urban planning
volunteered geographic information
Wireless networks
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Summary:Point of interest (POI) data serves as a valuable source of semantic information for places of interest and has many geospatial applications in real estate, transportation, and urban planning. With the availability of different data sources, POI conflation serves as a valuable technique for enriching data quality and coverage by merging the POI data from multiple sources. This study proposes a novel end-to-end POI conflation framework consisting of six steps, starting with data procurement, schema standardisation, taxonomy mapping, POI matching, POI unification, and data verification. The feasibility of the proposed framework was demonstrated in a case study conducted in the eastern region of Singapore, where the POI data from five data sources was conflated to form a unified POI dataset. Based on the evaluation conducted, the resulting unified dataset was found to be more comprehensive and complete than any of the five POI data sources alone. Furthermore, the proposed approach for identifying POI matches between different data sources outperformed all baseline approaches with a matching accuracy of 97.6% with an average run time below 3 min when matching over 12,000 POIs to result in 8699 unique POIs, thereby demonstrating the framework’s scalability for large scale implementation in dense urban contexts.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi10110779