Files and links (1)
Published VersionCC BY V4.0, Open Access
Journal article
Point-of-Interest (POI) Data Validation Methods: An Urban Case Study
ISPRS International Journal of Geo-Information, Vol.10(11), pp.1-27
2021
Abstract
Point-of-interest (POI) data from map sources are increasingly used in a wide range of applications, including real estate, land use, and transport planning. However, uncertainties in data quality arise from the fact that some of this data are crowdsourced and proprietary validation workflows lack transparency. Comparing data quality between POI sources without standardized validation metrics is a challenge. This study reviews and implements the available POI validation methods, working towards identifying a set of metrics that is applicable across datasets. Twenty-three validation methods were found and categorized. Most methods evaluated positional accuracy, while logical consistency and usability were the least represented. A subset of nine methods was implemented to assess four real-world POI datasets extracted for a highly urbanized neighborhood in Singapore. The datasets were found to have poor completeness with errors of commission and omission, although spatial errors were reasonably low (< 60 m). Thematic accuracy in names and place types varied. The move towards standardized validation metrics depends on factors such as data availability for intrinsic or extrinsic methods, varying levels of detail across POI datasets, the influence of matching procedures, and the intended application of POI data.
Details
- Title
- Point-of-Interest (POI) Data Validation Methods: An Urban Case Study
- Authors
- Lih Wei Yeow - Singapore University of Technology and DesignRaymond Low - Singapore University of Technology and DesignYu Xiang Tan - Singapore University of Technology and DesignLynette Cheah (Corresponding Author) - Singapore University of Technology and Design
- Publication details
- ISPRS International Journal of Geo-Information, Vol.10(11), pp.1-27
- Publisher
- MDPI AG
- Date published
- 2021
- DOI
- 10.3390/ijgi10110735
- ISSN
- 2220-9964
- Copyright note
- © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Data Availability
- OpenStreetMap data analyzed in this study are publicly available.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991029387902621
- Output Type
- Journal article
Metrics
16 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Computer Science, Information Systems
- Geography, Physical
- Remote Sensing
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites