Quality of GNSS Traces from VGI: A Data Cleaning Method Based on Activity Type and User Experience
VGI (Volunteered Geographic Information) refers to spatial data collected, created, and shared voluntarily by users. Georeferenced tracks are one of the most common components of VGI, and, as such, are not free from errors. The cleaning of GNSS (Global Navigation Satellite System) tracks is usually...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/9/12/727 |
| _version_ | 1827700712423817216 |
|---|---|
| author | Aitor Àvila Callau Yolanda Pérez-Albert David Serrano Giné |
| author_facet | Aitor Àvila Callau Yolanda Pérez-Albert David Serrano Giné |
| author_sort | Aitor Àvila Callau |
| collection | DOAJ |
| description | VGI (Volunteered Geographic Information) refers to spatial data collected, created, and shared voluntarily by users. Georeferenced tracks are one of the most common components of VGI, and, as such, are not free from errors. The cleaning of GNSS (Global Navigation Satellite System) tracks is usually based on the detection and removal of outliers using their geometric characteristics. However, according to our experience, user profile differentiation is still a novelty, and studies delving into the relationship between contributor efficiency, activity, and quality of the VGI produced are lacking. The aim of this study is to design a procedure to filter GNSS traces according to their quality, the type of activity pursued, and the contributor efficiency with VGI. Source data are obtained Wikiloc. The methodology includes tracks classification according mobility types, box plot analysis to identify outliers, bivariate user segmentation according to level of activity and efficiency, and the study of its spatial behavior using kernel-density maps. The results reveal that out of 44,326 tracks, 8096 (18.26%) are considered erroneous, mainly (73.02%) due to contributors’ poor practices and the remaining being due to bad GNSS reception. The results also show a positive correlation between data quality and the author’s efficiency collecting VGI. |
| first_indexed | 2024-03-10T14:17:51Z |
| format | Article |
| id | doaj.art-d00383fff14440cdbb98ba754d4df00b |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T14:17:51Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-d00383fff14440cdbb98ba754d4df00b2023-11-20T23:40:53ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-12-0191272710.3390/ijgi9120727Quality of GNSS Traces from VGI: A Data Cleaning Method Based on Activity Type and User ExperienceAitor Àvila Callau0Yolanda Pérez-Albert1David Serrano Giné2Department of Geography, Universitat Rovira i Virgili, c/Joanot Martorell, 15, 43480 Vila-seca, Tarragona, SpainDepartment of Geography, Universitat Rovira i Virgili, c/Joanot Martorell, 15, 43480 Vila-seca, Tarragona, SpainDepartment of Geography, Universitat Rovira i Virgili, c/Joanot Martorell, 15, 43480 Vila-seca, Tarragona, SpainVGI (Volunteered Geographic Information) refers to spatial data collected, created, and shared voluntarily by users. Georeferenced tracks are one of the most common components of VGI, and, as such, are not free from errors. The cleaning of GNSS (Global Navigation Satellite System) tracks is usually based on the detection and removal of outliers using their geometric characteristics. However, according to our experience, user profile differentiation is still a novelty, and studies delving into the relationship between contributor efficiency, activity, and quality of the VGI produced are lacking. The aim of this study is to design a procedure to filter GNSS traces according to their quality, the type of activity pursued, and the contributor efficiency with VGI. Source data are obtained Wikiloc. The methodology includes tracks classification according mobility types, box plot analysis to identify outliers, bivariate user segmentation according to level of activity and efficiency, and the study of its spatial behavior using kernel-density maps. The results reveal that out of 44,326 tracks, 8096 (18.26%) are considered erroneous, mainly (73.02%) due to contributors’ poor practices and the remaining being due to bad GNSS reception. The results also show a positive correlation between data quality and the author’s efficiency collecting VGI.https://www.mdpi.com/2220-9964/9/12/727data pre-processingdata qualityGNSS data cleaningcrowdsourced GNSS tracescrowdsourced platformsVGI |
| spellingShingle | Aitor Àvila Callau Yolanda Pérez-Albert David Serrano Giné Quality of GNSS Traces from VGI: A Data Cleaning Method Based on Activity Type and User Experience ISPRS International Journal of Geo-Information data pre-processing data quality GNSS data cleaning crowdsourced GNSS traces crowdsourced platforms VGI |
| title | Quality of GNSS Traces from VGI: A Data Cleaning Method Based on Activity Type and User Experience |
| title_full | Quality of GNSS Traces from VGI: A Data Cleaning Method Based on Activity Type and User Experience |
| title_fullStr | Quality of GNSS Traces from VGI: A Data Cleaning Method Based on Activity Type and User Experience |
| title_full_unstemmed | Quality of GNSS Traces from VGI: A Data Cleaning Method Based on Activity Type and User Experience |
| title_short | Quality of GNSS Traces from VGI: A Data Cleaning Method Based on Activity Type and User Experience |
| title_sort | quality of gnss traces from vgi a data cleaning method based on activity type and user experience |
| topic | data pre-processing data quality GNSS data cleaning crowdsourced GNSS traces crowdsourced platforms VGI |
| url | https://www.mdpi.com/2220-9964/9/12/727 |
| work_keys_str_mv | AT aitoravilacallau qualityofgnsstracesfromvgiadatacleaningmethodbasedonactivitytypeanduserexperience AT yolandaperezalbert qualityofgnsstracesfromvgiadatacleaningmethodbasedonactivitytypeanduserexperience AT davidserranogine qualityofgnsstracesfromvgiadatacleaningmethodbasedonactivitytypeanduserexperience |