Open AccessSocial weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace
Author(s) -
Zhu Yifan,
Zhang Sifan,
Li Yinan,
Lu Hao,
Shi Kaize,
Niu Zhendong
Publication year - 2020
Publication title -
geoscience data journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.125
H-Index - 11
ISSN - 2049-6060
DOI - 10.1002/gdj3.85
Subject(s) - crowdsourcing , data science , cyberspace , computer science , data collection , service (business) , multidisciplinary approach , knowledge management , bridge (graph theory) , categorization , world wide web , the internet , artificial intelligence , business , political science , medicine , statistics , mathematics , law , marketing
Crowdsourcing has significantly motivated the development of meteorological services. Starting from the beginning of 2010s and highly motivating after 2014, crowdsourcing‐driven meteorological services have evolved from a single collection and observation of data to the systematic acquisition, analysis and application of these data. In this review, by focusing on papers and databases that have combined crowdsourcing methods to promote or implement meteorological knowledge services, we analysed the relevant literature in three dimensions: data collection, information analysis and meteorological knowledge applications. First, we selected the potential data sources for crowdsourcing and discussed the characteristics of the collected data in four dimensions: consciousness, objectiveness, mobility and multidisciplinary. Second, based on the purpose of these studies and the extent of utilizing data as well as knowledge, we categorize the crowdsourcing‐based meteorological analysis into three levels: relationship discovery, knowledge generalization and systemized service. Third, according to the application scenario, we discussed the applications that have already been put into use, and we suggest current challenges and future research directions. These previous studies show that the use of crowdsourcing in social space can expand the coverage as well as enhance the performance of meteorological service. It was also evident that current researches are contributing towards a systemic and intelligent knowledge service to establish a better bridge among academic, industrial and individual community.