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Calibrating Spatial Stratified Heterogeneity for Heavy-Tailed Distributed Data

文献类型：期刊论文

作者Hu, Bisong3; Wu, Tingting3; Yin, Qian1; Wang, Jinfeng1; Jiang, Bin2; Luo, Jin3
刊名ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS
出版日期2024-05-23
卷号N/A页码:19
关键词Geodetector q-statistic head/tail breaks spatial stratified heterogeneity stratification structural hierarchy
ISSN号2469-4452
DOI10.1080/24694452.2024.2351002
英文摘要The phenomena with within-strata characteristics that are more similar than between-strata characteristics are ubiquitous (e.g., land-use types and image classifications). It can be summarized as spatial stratified heterogeneity (SSH), which is measured and attributed using the geographical detector (Geodetector) q-statistic. SSH is typically calibrated by stratification and hundreds of algorithms have been developed. Little is discussed about the conditions of the methods. In this work, a novel stratification method based on head/tail breaks is introduced for the purpose of better capturing the SSH of geographical variables with a heavy-tailed distribution. Compared to conventional sample-based stratifications, the presented approach is a population-based optimized stratification that indicates an underlying scaling property in geographical spaces. It requires no prior knowledge or auxiliary variables and supports a naturally determined number of strata instead of being subjectively preset. In addition, our approach reveals the inherent hierarchical structure of geographical variables, characterizes its dominant components across all scales, and provides the potential to make the stratification meaningful and interpretable. The advantages were illustrated by several case studies in natural and social sciences. The proposed approach is versatile and flexible so that it can be applied for the stratification of both geographical and nongeographical variables and is conducive to advancing SSH-related studies as well. This study provides a new way of thinking for advocating spatial heterogeneity or scaling law and advances our understanding of geographical phenomena.
WOS关键词COVID-19 EPIDEMIC ; HEAD/TAIL BREAKS ; RISK ; DISCRETIZATION ; POWER
资助项目National Natural Science Foundation of China
WOS研究方向Geography
语种英语
WOS记录号WOS:001251345700001
出版者ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
资助机构National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/206451]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Jiang, Bin
作者单位1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
2.Hong Kong Univ Sci & Technol Guangzhou, Urban Governance & Design Thrust Soc Hub, Guangzhou, Peoples R China
3.Jiangxi Normal Univ, Sch Geog & Environm, Nanchang, Peoples R China
推荐引用方式
GB/T 7714		 Hu, Bisong,Wu, Tingting,Yin, Qian,et al. Calibrating Spatial Stratified Heterogeneity for Heavy-Tailed Distributed Data[J]. ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS,2024,N/A:19.
APA Hu, Bisong,Wu, Tingting,Yin, Qian,Wang, Jinfeng,Jiang, Bin,&Luo, Jin.(2024).Calibrating Spatial Stratified Heterogeneity for Heavy-Tailed Distributed Data.ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS,N/A,19.
MLA Hu, Bisong,et al."Calibrating Spatial Stratified Heterogeneity for Heavy-Tailed Distributed Data".ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS N/A(2024):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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