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  • 标题:Point-of-Interest (POI) Data Validation Methods: An Urban Case Study
  • 本地全文:下载
  • 作者:Lih Wei Yeow ; Raymond Low ; Yu Xiang Tan
  • 期刊名称:ISPRS International Journal of Geo-Information
  • 电子版ISSN:2220-9964
  • 出版年度:2021
  • 卷号:10
  • 期号:11
  • 页码:735
  • DOI:10.3390/ijgi10110735
  • 语种:English
  • 出版社:MDPI AG
  • 摘要: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.
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