Considerations on Geospatial Big Data

6th Digital Earth Summit

地球科学;计算机科学

Liu, Zhen^1 ; Guo, Huadong^1 ; Wang, Changlin^1

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing

100094, China^1

关键词: Data driven;    Evolving patterns;    Geo-spatial data;    Quality issues;    Scientific knowledge;    Scientific researches;    Spatial correlations;    Spatial visualization;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/46/1/012058/pdf
DOI  :  10.1088/1755-1315/46/1/012058

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Geospatial data, as a significant portion of big data, has recently gained the full attention of researchers. However, few researchers focus on the evolution of geospatial data and its scientific research methodologies. When entering into the big data era, fully understanding the changing research paradigm associated with geospatial data will definitely benefit future research on big data. In this paper, we look deep into these issues by examining the components and features of geospatial big data, reviewing relevant scientific research methodologies, and examining the evolving pattern of geospatial data in the scope of the four 'science paradigms'. This paper proposes that geospatial big data has significantly shifted the scientific research methodology from 'hypothesis to data' to 'data to questions' and it is important to explore the generality of growing geospatial data 'from bottom to top'. Particularly, four research areas that mostly reflect data-driven geospatial research are proposed: spatial correlation, spatial analytics, spatial visualization, and scientific knowledge discovery. It is also pointed out that privacy and quality issues of geospatial data may require more attention in the future. Also, some challenges and thoughts are raised for future discussion.