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A taxonomy of quality assessment methods for volunteered and crowdsourced geographic information
Author(s) -
Degrossi Lívia Castro,
Porto de Albuquerque João,
Santos Rocha Roberto dos,
Zipf Alexander
Publication year - 2018
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12329
Subject(s) - taxonomy (biology) , data science , computer science , volunteered geographic information , quality (philosophy) , quality assessment , crowdsourcing , data quality , social media , knowledge management , world wide web , engineering , evaluation methods , ecology , biology , metric (unit) , philosophy , operations management , epistemology , reliability engineering
The growing use of crowdsourced geographic information (CGI) has prompted the employment of several methods for assessing information quality, which are aimed at addressing concerns on the lack of quality of the information provided by non‐experts. In this work, we propose a taxonomy of methods for assessing the quality of CGI when no reference data are available, which is likely to be the most common situation in practice. Our taxonomy includes 11 quality assessment methods that were identified by means of a systematic literature review. These methods are described in detail, including their main characteristics and limitations. This taxonomy not only provides a systematic and comprehensive account of the existing set of methods for CGI quality assessment, but also enables researchers working on the quality of CGI in various sources (e.g., social media, crowd sensing, collaborative mapping) to learn from each other, thus opening up avenues for future work that combines and extends existing methods into new application areas and domains.