https://scholars.lib.ntu.edu.tw/handle/123456789/448986
標題: | Emerging patterns in multi-sourced data modeling uncertainty | 作者: | Kolovos, Alexander Smith, Lynette M. Schwab-McCoy, Aimee Gengler, Sarah HWA-LUNG YU |
關鍵字: | Bayesian maximum entropy; Binomial kriging; Minimum norm approximations; Poisson; Uncertainty | 公開日期: | 2016 | 卷: | 18 | 起(迄)頁: | 300-317 | 來源出版物: | Spatial Statistics | 摘要: | The abundance of spatial and space–time data in many research fields has led to an increasing interest in the analytics of spatial data information. This development has renewed the attention to predictive spatial methodologies and advancing geostatistical tools. In this context, the present work reviews a series of cross-discipline studies that utilize multiple monitoring sources, and promote applied approaches in spatial and spatiotemporal modeling to improve our understanding of uncertainty. As multi-sourced information gives birth to new aspects of uncertainty, we explore emerging patterns in dealing with uncertainty in sources across structured, unstructured, and incomplete spatial data. We also illustrate how additional forms of information, such as secondary data and physical models, can further support and benefit research in the characterization and modeling of natural attributes. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/448986 | ISSN: | 2211-6753 | DOI: | 10.1016/j.spasta.2016.05.005 |
顯示於: | 生物環境系統工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。