The spatial statistic trinity: A generic framework for spatial sampling and inference

doi:10.1016/j.envsoft.2020.104835

	The spatial statistic trinity: A generic framework for spatial sampling and inference
	Wang, Jinfeng 2; Gao, Bingbo 1; Stein, Alfred 3
刊名	ENVIRONMENTAL MODELLING & SOFTWARE
	2020-12-01
卷号	134 页码:11
关键词	Population and sample Spatial autocorrelation (SAC) Spatial stratified heterogeneity (SSH) Variable and random variable Spatial statistic trinity (SST)
ISSN号	1364-8152
DOI	10.1016/j.envsoft.2020.104835
通讯作者	Wang, Jinfeng(wangjf@lreis.ac.cn)
英文摘要	Geospatial referenced environmental data are extensively used in environmental assessment, prediction, and management. Data are commonly obtained by nonrandom surveys or monitoring networks, whereas spatial sampling and inference affect the accuracy of subsequent applications. Design-based and model-based proced-ures (DB and MB for short) both allow one to address the gap between statistical inference and spatial data. Creating independence by sampling implies that DB may neglect spatial autocorrelation (SAC) if the sampling interval is beyond the SAC range. In MB, however, a particular sampling design can be irrelevant for inferential results. Empirical studies further showed that MSE (mean squared error) values for both DB and MB are affected by SAC and spatial stratified heterogeneity (SSH). We propose a novel framework for integrating SAC and SSH into DB and MB. We do so by distinguishing the spatial population from the spatial sample. We show that spatial independence in a spatial population results in independence in a spatial sample, whereas SAC in a spatial population is reflected in a spatial sample if sampling distances are within the range of dependence; otherwise, SAC is absent in the spatial sample. Similarly, SSH in a population may or may not be inherited in data, and this depends on the sampling method. Thus, the population, sample, and inference constitute a so-called spatial statistic trinity (SST), providing a new framework for spatial statistics, including sampling and inference. This paper shows that it greatly simplifies the choice of method in spatial sampling and inferences. Two empirical examples and various citations illustrate the theory.
资助项目	National Natural Science Foundation of China[41531179] ; National Natural Science Foundation of China[42071375]
WOS关键词	DESIGN ; GEOGRAPHY ; SCIENCE
WOS研究方向	Computer Science ; Engineering ; Environmental Sciences & Ecology
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000591373500007
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/156693]
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.China Agr Univ, Coll Land Sci & Technol, Tsinghua East Rd, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, A11 Datun Rd, Beijing 100101, Peoples R China 3.Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, NL-7514 AE Enschede, Netherlands
推荐引用方式 GB/T 7714	Wang, Jinfeng,Gao, Bingbo,Stein, Alfred. The spatial statistic trinity: A generic framework for spatial sampling and inference[J]. ENVIRONMENTAL MODELLING & SOFTWARE,2020,134:11.
APA	Wang, Jinfeng,Gao, Bingbo,&Stein, Alfred.(2020).The spatial statistic trinity: A generic framework for spatial sampling and inference.ENVIRONMENTAL MODELLING & SOFTWARE,134,11.
MLA	Wang, Jinfeng,et al."The spatial statistic trinity: A generic framework for spatial sampling and inference".ENVIRONMENTAL MODELLING & SOFTWARE 134(2020):11.