中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
  4. 资源与环境信息系统国家重点实验室
  5. 外文论文
Poisson means of stratified nonhomogeneity: a new method to predict spatial counts

文献类型：期刊论文

作者Shen, Fengbei1,3; Xu, Chengdong1,3; Wang, Jinfeng1,3; Hu, Maogui1,3; Hu, Yuehua2
刊名INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
出版日期2025-07-24
卷号N/A
关键词Poisson distribution spatial heterogeneity spatial statistics disease mapping
ISSN号1365-8816
DOI10.1080/13658816.2025.2536505
产权排序1
文献子类Article ; Early Access
英文摘要Spatial count data are a prevalent data type in natural and social sciences. As the data present complicated spatial autocorrelation and heterogeneity inherent in geographical analysis, the current methods lack a theoretical approach to model and predict counts, especially with limited spatial samples. To address the gap, this study develops a new method named Poisson Means of Stratified Nonhomogeneity (PoiMSN). The method considers both autocorrelation and heterogeneity but not covariates. Moreover, it incorporates local samples and out-stratum neighbors that traditional methods neglect to model and predict the latent process for data in a Poisson distribution. This study compares PoiMSN with Poisson geostatistics and traditional MSN and designs simulations to validate the model. PoiMSN outperforms the other models as it has the lowest mean absolute error and root-mean-squared error, and furthermore, at least 5% improvement in accuracy for autocorrelated and stratified Poisson data. The case study with hand, foot, and mouth disease data shows PoiMSN can precisely map the disease risks with lower uncertainty. PoiMSN has the ability to accommodate spatially non-stationary count data from autocorrelated and heterogeneous populations and leverage extensive sample information.
URL标识查看原文
WOS关键词RISK ; RATES
WOS研究方向Computer Science ; Geography ; Physical Geography ; Information Science & Library Science
语种英语
WOS记录号WOS:001536038800001
出版者TAYLOR & FRANCIS LTD
源URL[http://ir.igsnrr.ac.cn/handle/311030/215664]  
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Wang, Jinfeng
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China;
2.Chinese Ctr Dis Control & Prevent, 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		 Shen, Fengbei,Xu, Chengdong,Wang, Jinfeng,et al. Poisson means of stratified nonhomogeneity: a new method to predict spatial counts[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2025,N/A.
APA Shen, Fengbei,Xu, Chengdong,Wang, Jinfeng,Hu, Maogui,&Hu, Yuehua.(2025).Poisson means of stratified nonhomogeneity: a new method to predict spatial counts.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,N/A.
MLA Shen, Fengbei,et al."Poisson means of stratified nonhomogeneity: a new method to predict spatial counts".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE N/A(2025).

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。