Modeling of spatial stratified heterogeneity
文献类型：期刊论文
| 作者 | Guo, Jiangang2,3; Wang, Jinfeng2,3; Xu, Chengdong2,3; Song, Yongze1 |
| 刊名 | GISCIENCE & REMOTE SENSING
 |
| 出版日期 | 2022-12-31 |
| 卷号 | 59期号:1页码:1660-1677 |
| 关键词 | Spatial stratified heterogeneity stratification cluster analysis spatial statistics geographical detector |
| ISSN号 | 1548-1603 |
| DOI | 10.1080/15481603.2022.2126375 |
| 通讯作者 | Wang, Jinfeng(wangjf@lreis.ac.cn) |
| 英文摘要 | Spatial stratified heterogeneity (SSH) refers to the geographical phenomena in which the geographical attributes within-strata are more similar than the between-strata, which is ubiquitous in the real world and offers information implying the causation of nature. Stratification, a primary approach to SSH, generates strata using a priori knowledge or thousands of supervised and unsupervised learning methods. Selecting reasonable stratification methods for spatial analysis in specific domains without prior knowledge is challenging because no method is optimal in a general sense. However, a systematic review of a large number of stratification methods is still lacking. In this article, we review the methods for stratification, categorize the existing typical stratification methods into four classes - univariate stratification, cluster-based stratification, multicriteria stratification, and supervised stratification - and construct their taxonomy. Finally, we present a summary of the software and tools used to compare and perform stratification methods. Given that different stratification methods reflect distinct human understandings of spatial distributions and associations, we suggest that further studies are needed to reveal the nature of geographical attributes by integrating SSH, advanced algorithms, and interdisciplinary methods. |
| WOS关键词 | GEOGRAPHICALLY WEIGHTED REGRESSION ; CLASSIFICATION APPROACH ; VISUAL ASSESSMENT ; CLUSTERS ; NUMBER ; REGIONALIZATION ; ASSOCIATION ; ALGORITHMS ; REGION ; STRATEGIES |
| 资助项目 | National Natural Science Foundation of China[42071375] |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000860361300001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/185053]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Jinfeng |
| 作者单位 | 1.Curtin Univ, Sch Design & Built Environm, Perth, WA, Australia 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Jiangang,Wang, Jinfeng,Xu, Chengdong,et al. Modeling of spatial stratified heterogeneity[J]. GISCIENCE & REMOTE SENSING,2022,59(1):1660-1677. |
| APA | Guo, Jiangang,Wang, Jinfeng,Xu, Chengdong,&Song, Yongze.(2022).Modeling of spatial stratified heterogeneity.GISCIENCE & REMOTE SENSING,59(1),1660-1677. |
| MLA | Guo, Jiangang,et al."Modeling of spatial stratified heterogeneity".GISCIENCE & REMOTE SENSING 59.1(2022):1660-1677. |
入库方式： OAI收割
来源：地理科学与资源研究所
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