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

arXiv: https://arxiv.org/abs/2109.06073 · HTML: https://mlsanity.com/paper/2109.06073/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 74 references that are in the map.
- First posted on arXiv 13 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)

## Built on by (most cited first, 1 of 1)

- [Enhancing personalised thermal comfort models with Active Learning for improved HVAC controls](https://mlsanity.com/paper/2309.09073/) (2023)

## Abstract (arXiv)

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.