A lightweight ensemble spatiotemporal interpolation model for geospatial data

doi:10.1080/13658816.2020.1725016

	A lightweight ensemble spatiotemporal interpolation model for geospatial data
	Cheng, Shifen 2,3; Peng, Peng 2,3; Lu, Feng 1,2,3,4
刊名	INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
	2020-02-14
页码	24
关键词	Spatiotemporal interpolation spatiotemporal dependence lightweight ensemble extreme learning machine
ISSN号	1365-8816
DOI	10.1080/13658816.2020.1725016
通讯作者	Lu, Feng(luf@lreis.ac.cn)
英文摘要	Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations.
资助项目	National Key Research and Development Program of China[2016YFB0502104] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23100500] ; China Postdoctoral Science Foundation[2019M660774]
WOS关键词	EXTREME LEARNING-MACHINE ; ARTIFICIAL NEURAL-NETWORKS ; DATA RECONSTRUCTION ; TRAFFIC FLOW ; TIME ; COMPLETION
WOS研究方向	Computer Science ; Geography ; Physical Geography ; Information Science & Library Science
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000513756800001
资助机构	National Key Research and Development Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; China Postdoctoral Science Foundation
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/132124]
专题	中国科学院地理科学与资源研究所
通讯作者	Lu, Feng
作者单位	1.Fuzhou Univ, Acad Digital China, Fuzhou, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Peoples R China
推荐引用方式 GB/T 7714	Cheng, Shifen,Peng, Peng,Lu, Feng. A lightweight ensemble spatiotemporal interpolation model for geospatial data[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2020:24.
APA	Cheng, Shifen,Peng, Peng,&Lu, Feng.(2020).A lightweight ensemble spatiotemporal interpolation model for geospatial data.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,24.
MLA	Cheng, Shifen,et al."A lightweight ensemble spatiotemporal interpolation model for geospatial data".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2020):24.