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首页> 外文期刊>ISPRS International Journal of Geo-Information >On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies
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On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies

机译：环境研究众包中的数据质量保证和融合纠缠

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Volunteer geographical information (VGI), either in the context of citizen science or the mining of social media, has proven to be useful in various domains including natural hazards, health status, disease epidemics, and biological monitoring. Nonetheless, the variable or unknown data quality due to crowdsourcing settings are still an obstacle for fully integrating these data sources in environmental studies and potentially in policy making. The data curation process, in which a quality assurance (QA) is needed, is often driven by the direct usability of the data collected within a data conflation process or data fusion (DCDF), combining the crowdsourced data into one view, using potentially other data sources as well. Looking at current practices in VGI data quality and using two examples, namely land cover validation and inundation extent estimation, this paper discusses the close links between QA and DCDF. It aims to help in deciding whether a disentanglement can be possible, whether beneficial or not, in understanding the data curation process with respect to its methodology for future usage of crowdsourced data. Analysing situations throughout the data curation process where and when entanglement between QA and DCDF occur, the paper explores the various facets of VGI data capture, as well as data quality assessment and purposes. Far from rejecting the usability ISO quality criterion, the paper advocates for a decoupling of the QA process and the DCDF step as much as possible while still integrating them within an approach analogous to a Bayesian paradigm.
机译：在公民科学或社会媒体挖掘中，自愿地理信息（VGI）已被证明在包括自然灾害，健康状况，疾病流行病和生物监测在内的各个领域中都是有用的。但是，由于众包设置而导致的可变数据质量或未知数据仍然是将这些数据源完全集成到环境研究和潜在决策中的障碍。数据管理过程中需要质量保证（QA），通常由数据合并过程或数据融合（DCDF）中收集的数据的直接可用性驱动，将众包数据组合到一个视图中，并使用其他视图数据源。通过查看VGI数据质量的当前做法，并使用两个示例（即土地覆被验证和淹没程度估算），讨论了质量保证和DCDF之间的紧密联系。它旨在帮助确定是否有可能进行纠缠，无论是否有益，在了解数据管理过程及其将来用于众包数据的方法方面，都是可行的。通过分析整个数据策划过程中QA与DCDF之间发生纠缠以及何时发生纠缠的情况，本文探索了VGI数据捕获的各个方面以及数据质量评估和目的。本文并没有拒绝可用性ISO质量标准，而是提倡尽可能使QA流程和DCDF步骤脱钩，同时仍将它们集成在类似于贝叶斯范式的方法中。

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