Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Feifei Zheng*, Ruoling Tao, Holger R. Maier, Linda See, Dragan Savic, Tuqiao Zhang, Qiuwen Chen, Thaine H. Assumpção, Dimitri Solomatine, More Authors

*Corresponding author for this work

Research output: Contribution to journalReview articleScientificpeer-review

70 Citations (Scopus)
135 Downloads (Pure)


Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing-based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.

Original languageEnglish
Pages (from-to)698-740
Number of pages43
JournalReviews of Geophysics
Issue number4
Publication statusPublished - 2019


  • big data
  • categorization
  • crowdsourcing
  • data collection
  • geophysics


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