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Abstract
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.
Highlights
VGI (Volunteered Geographic Information) refers to spatial data that are voluntarily collected, created, and shared by users [1]
The results show a positive correlation between data quality and the author’s efficiency collecting VGI
A method of filtering to detect and discard GNSS traces with errors from Wikiloc was developed
Summary
VGI (Volunteered Geographic Information) refers to spatial data that are voluntarily collected, created, and shared by users [1]. VGI presents many challenges [8,9], among which the following are of note: (1) its quality is highly variable and is undocumented; (2) when it is generated, the scientific principles of collecting geographic data are rarely followed; (3) its authors are not professionals, so they do not have the same training or commitment as professionals in the process of acquiring data; and (4) in many cases, data present varying levels of detail because they have been captured via different methods or devices. The quality and reliability of data stand out [10]
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