Big geodata mining: Objective, connotations and research issues
文献类型：期刊论文
| 作者 | Pei, Tao1,2 ; Song, Ci1,2; Guo, Sihui1,2; Shu, Hua1,2; Liu, Yaxi1,2; Du, Yunyan1,2; Ma, Ting1,2; Zhou, Chenghu1,2
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| 刊名 | JOURNAL OF GEOGRAPHICAL SCIENCES
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| 出版日期 | 2020-02-01 |
| 卷号 | 30期号:2页码:251-266 |
| 关键词 | big earth observation data big human behavior data geographical spatiotemporal pattern spatio-temporal heterogeneity knowledge discovery |
| ISSN号 | 1009-637X |
| DOI | 10.1007/s11442-020-1726-7 |
| 通讯作者 | Pei, Tao(peit@lreis.ac.cn) |
| 英文摘要 | The objective, connotations and research issues of big geodata mining were discussed to address its significance to geographical research in this paper. Big geodata may be categorized into two domains: big earth observation data and big human behavior data. A description of big geodata includes, in addition to the "5Vs" (volume, velocity, value, variety and veracity), a further five features, that is, granularity, scope, density, skewness and precision. Based on this approach, the essence of mining big geodata includes four aspects. First, flow space, where flow replaces points in traditional space, will become the new presentation form for big human behavior data. Second, the objectives for mining big geodata are the spatial patterns and the spatial relationships. Third, the spatiotemporal distributions of big geodata can be viewed as overlays of multiple geographic patterns and the characteristics of the data, namely heterogeneity and homogeneity, may change with scale. Fourth, data mining can be seen as a tool for discovery of geographic patterns and the patterns revealed may be attributed to human-land relationships. The big geodata mining methods may be categorized into two types in view of the mining objective, i.e., classification mining and relationship mining. Future research will be faced by a number of issues, including the aggregation and connection of big geodata, the effective evaluation of the mining results and the challenge for mining to reveal "non-trivial" knowledge. |
| WOS关键词 | GEOGRAPHICALLY WEIGHTED REGRESSION ; SUPPORT VECTOR MACHINES ; LAND-COVER ; NEURAL-NETWORKS ; CLASSIFICATION ; SPACE ; GAME ; GO |
| 资助项目 | National Natural Science Foundation of China[41525004] ; National Natural Science Foundation of China[41421001] |
| WOS研究方向 | Physical Geography |
| 语种 | 英语 |
| WOS记录号 | WOS:000520220500005 |
| 出版者 | SCIENCE PRESS |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/133323]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Pei, Tao |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Pei, Tao,Song, Ci,Guo, Sihui,et al. Big geodata mining: Objective, connotations and research issues[J]. JOURNAL OF GEOGRAPHICAL SCIENCES,2020,30(2):251-266. |
| APA | Pei, Tao.,Song, Ci.,Guo, Sihui.,Shu, Hua.,Liu, Yaxi.,...&Zhou, Chenghu.(2020).Big geodata mining: Objective, connotations and research issues.JOURNAL OF GEOGRAPHICAL SCIENCES,30(2),251-266. |
| MLA | Pei, Tao,et al."Big geodata mining: Objective, connotations and research issues".JOURNAL OF GEOGRAPHICAL SCIENCES 30.2(2020):251-266. |
入库方式： OAI收割
来源：地理科学与资源研究所
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