  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.
