Articles producció científica> Geografia

Quality of GNSS Traces from VGI: A Data Cleaning Method Based on Activity Type and User Experience

  • Datos identificativos

    Identificador: imarina:9162267
    Handle: http://hdl.handle.net/20.500.11797/imarina9162267
  • Autores:

    Avila Callau, Aitor
    Perez-Albert, Yolanda
    Serrano Gine, David
  • Otros:

    Autor según el artículo: Avila Callau, Aitor; Perez-Albert, Yolanda; Serrano Gine, David;
    Departamento: Geografia
    Autor/es de la URV: Avila Callau, Aitor / Perez Albert, Maria Yolanda / SERRANO GINÉ, DAVID
    Palabras clave: Vgi User segmentation Usability Spatial behavior Reputation Gnss data cleaning Geolocated social media data Data quality Data pre-processing Crowdsourced platforms Crowdsourced gnss traces Cluster analysis
    Resumen: 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.
    Grupo de investigación: GRATET. Anàlisi Territorial i Estudis Turístics
    Áreas temáticas: Zootecnia / recursos pesqueiros Saúde coletiva Remote sensing Medicina veterinaria Geography, planning and development Geography, physical Geografía Geociências Engenharias i Earth and planetary sciences (miscellaneous) Computers in earth sciences Computer science, information systems Ciencias sociales Ciências ambientais Ciências agrárias i Ciência da computação Biodiversidade
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: aitor.avila@urv.cat myolanda.perez@urv.cat aitor.avila@urv.cat
    Identificador del autor: 0000-0003-1634-4986
    Fecha de alta del registro: 2023-02-19
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.mdpi.com/2220-9964/9/12/727
    URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Isprs International Journal Of Geo-Information. 9 (12):
    Referencia de l'ítem segons les normes APA: Avila Callau, Aitor; Perez-Albert, Yolanda; Serrano Gine, David; (2020). Quality of GNSS Traces from VGI: A Data Cleaning Method Based on Activity Type and User Experience. Isprs International Journal Of Geo-Information, 9(12), -. DOI: 10.3390/ijgi9120727
    DOI del artículo: 10.3390/ijgi9120727
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2020
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Computer Science, Information Systems,Computers in Earth Sciences,Earth and Planetary Sciences (Miscellaneous),Geography, Physical,Geography, Planning and Development,Remote Sensing
    Vgi
    User segmentation
    Usability
    Spatial behavior
    Reputation
    Gnss data cleaning
    Geolocated social media data
    Data quality
    Data pre-processing
    Crowdsourced platforms
    Crowdsourced gnss traces
    Cluster analysis
    Zootecnia / recursos pesqueiros
    Saúde coletiva
    Remote sensing
    Medicina veterinaria
    Geography, planning and development
    Geography, physical
    Geografía
    Geociências
    Engenharias i
    Earth and planetary sciences (miscellaneous)
    Computers in earth sciences
    Computer science, information systems
    Ciencias sociales
    Ciências ambientais
    Ciências agrárias i
    Ciência da computação
    Biodiversidade
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