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

TL;DR

This paper introduces a comprehensive end-to-end framework for conflating POI data from multiple sources, significantly improving data completeness and matching accuracy in urban environments.

Contribution

It presents a novel six-step POI conflation framework and demonstrates its effectiveness through a case study in Singapore, outperforming baseline methods.

Findings

Unified POI dataset was more comprehensive than individual sources.

Achieved 97.6% matching accuracy in POI conflation.

Framework is scalable for large urban datasets.

Abstract

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 minutes when matching over 12,000 POIs to result in 8,699 unique POIs, thereby demonstrating the framework's scalability for large scale implementation in dense urban contexts.