Exploring Data Model Relations in OpenStreetMap

Pruvost, Hippolyte; Mooney, Peter

doi:10.3390/fi9040070

Cited by 2 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This was reported to occur in OSM and its effect on completeness was assessed for the case of road data in the U.S. [45]. Bulk imports were also raised as a possible reason for similar distributions in tags for OSM relations between cities [71]. However, the impact of POI bulk imports on positional and thematic accuracy is less understood and could be an area of future work.…”

Section: Extrinsic and Intrinsic Methodsmentioning

confidence: 99%

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

Yeow

Low

Tan

et al. 2021

IJGI

View full text Add to dashboard Cite

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.

show abstract

Section: Extrinsic and Intrinsic Methodsmentioning

confidence: 99%

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

Yeow

Low

Tan

et al. 2021

IJGI

View full text Add to dashboard Cite

show abstract

“…These crowdsourced addresses can be collected by users on site and at the street level using a mobile device with a global navigation satellite system (GNSS) sensor or by carrying out digitalization using a map platform such as JOSM. OSM address data, such as house numbers, are collected by users and can be geometrically associated ("node") with any geographic feature and establish relationships with another element [55]. The semantic component of the address can then be linked to the geographic position in front of an entrance or at the top of a building, and some studies analyze the quality relation of crowdsourced information.…”

Section: Crowdsourcing Dataset From the Openstreetmap Projectmentioning

confidence: 99%

Development of an Algorithm to Evaluate the Quality of Geolocated Addresses in Urban Areas

Sierra Requena,

Martínez-Llario,

Lorenzo-Sáez

et al. 2023

IJGI

View full text Add to dashboard Cite

The spatial and semantic data of geographic addresses are extremely important for citizens, governments, and companies. The addresses can georeference environmental, economic, security, health, and demographic parameters in urban areas. Additionally, address components can be used by users to locate any point of interest (POI) with location-based systems (LBSs). For this reason, errors in address data can affect the geographic location of events, map representations, and spatial analyses. Thus, this paper presents the development of an algorithm for evaluating the quality of semantic and geographic information in any geospatial address dataset. The reference datasets are accessible using open data platforms or spatial data infrastructure (SDI) and volunteered geographic information (VGI), and both have been compared with commercial datasets using geocoding web services. Address quality analysis was developed using several open-source data science code libraries combined with spatial databases and geographic information systems. In addition, the quality of geographic addresses was evaluated by carrying out normalized tests in accordance with International Geospatial Standards (ISO 19157). Finally, this methodology assesses the quality of authorized and VGI address datasets that can be used for geocoding any relevant information in specific urban areas.

show abstract

Exploring Data Model Relations in OpenStreetMap

Cited by 2 publications

References 39 publications

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

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

Development of an Algorithm to Evaluate the Quality of Geolocated Addresses in Urban Areas

Contact Info

Product

Resources

About