Point-of-Interest (POI) Data Validation Methods: An Urban Case Study

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 t...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Lih Wei Yeow, Raymond Low, Yu Xiang Tan, Lynette Cheah
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	point of interest volunteered geographic information (VGI) data quality Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/5b11dfd080c045009232a52a578f0af7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5b11dfd080c045009232a52a578f0af7
record_format	dspace
spelling	oai:doaj.org-article:5b11dfd080c045009232a52a578f0af72021-11-25T17:52:49ZPoint-of-Interest (POI) Data Validation Methods: An Urban Case Study10.3390/ijgi101107352220-9964https://doaj.org/article/5b11dfd080c045009232a52a578f0af72021-10-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/735https://doaj.org/toc/2220-9964Point-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.Lih Wei YeowRaymond LowYu Xiang TanLynette CheahMDPI AGarticlepoint of interestvolunteered geographic information (VGI)data qualityGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 735, p 735 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	point of interest volunteered geographic information (VGI) data quality Geography (General) G1-922
spellingShingle	point of interest volunteered geographic information (VGI) data quality Geography (General) G1-922 Lih Wei Yeow Raymond Low Yu Xiang Tan Lynette Cheah Point-of-Interest (POI) Data Validation Methods: An Urban Case Study
description	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.
format	article
author	Lih Wei Yeow Raymond Low Yu Xiang Tan Lynette Cheah
author_facet	Lih Wei Yeow Raymond Low Yu Xiang Tan Lynette Cheah
author_sort	Lih Wei Yeow
title	Point-of-Interest (POI) Data Validation Methods: An Urban Case Study
title_short	Point-of-Interest (POI) Data Validation Methods: An Urban Case Study
title_full	Point-of-Interest (POI) Data Validation Methods: An Urban Case Study
title_fullStr	Point-of-Interest (POI) Data Validation Methods: An Urban Case Study
title_full_unstemmed	Point-of-Interest (POI) Data Validation Methods: An Urban Case Study
title_sort	point-of-interest (poi) data validation methods: an urban case study
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5b11dfd080c045009232a52a578f0af7
work_keys_str_mv	AT lihweiyeow pointofinterestpoidatavalidationmethodsanurbancasestudy AT raymondlow pointofinterestpoidatavalidationmethodsanurbancasestudy AT yuxiangtan pointofinterestpoidatavalidationmethodsanurbancasestudy AT lynettecheah pointofinterestpoidatavalidationmethodsanurbancasestudy
_version_	1718411862442246144

Point-of-Interest (POI) Data Validation Methods: An Urban Case Study

Ejemplares similares