IGSNRR OpenIR
A Two-Step Method for Missing Spatio-Temporal Data Reconstruction
Cheng, Shifen1,2; Lu, Feng1,2,3
2017-07-01
Source PublicationISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
ISSN2220-9964
Volume6Issue:7Pages:25
Corresponding AuthorLu, Feng(luf@lreis.ac.cn)
AbstractMissing data reconstruction is a critical step in the analysis and mining of spatio-temporal data; however, few studies comprehensively consider missing data patterns, sample selection and spatio-temporal relationships. As a result, traditional methods often fail to obtain satisfactory accuracy or address high levels of complexity. To combat these problems, this study developed an effective two-step method for spatio-temporal missing data reconstruction (ST-2SMR). This approach includes a coarse-grained interpolation method for considering missing patterns, which can successfully eliminate the influence of continuous missing data on the overall results. Based on the results of coarse-grained interpolation, a dynamic sliding window selection algorithm was implemented to determine the most relevant sample data for fine-grained interpolation, considering both spatial and temporal heterogeneity. Finally, spatio-temporal interpolation results were integrated by using a neural network model. We validated our approach using Beijing air quality data and found that the proposed method outperforms existing solutions in term of estimation accuracy and reconstruction rate.
Keywordspatio-temporal interpolation spatio-temporal heterogeneity dynamic sliding window neural network
DOI10.3390/ijgi6070187
WOS KeywordSPATIAL INTERPOLATION ; DATA IMPUTATION ; NETWORKS
Indexed BySCI
Language英语
Funding ProjectState Key Research Development Program of China[2016YFB0502104] ; National Natural Science Foundation of China[41631177] ; Key Research Program of the Chinese Academy of Sciences[ZDRW-ZS-2016-6-3]
Funding OrganizationState Key Research Development Program of China ; National Natural Science Foundation of China ; Key Research Program of the Chinese Academy of Sciences
WOS Research AreaPhysical Geography ; Remote Sensing
WOS SubjectGeography, Physical ; Remote Sensing
WOS IDWOS:000407506900004
PublisherMDPI AG
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/61565
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLu, Feng
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Fujian, Peoples R China
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Cheng, Shifen,Lu, Feng. A Two-Step Method for Missing Spatio-Temporal Data Reconstruction[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2017,6(7):25.
APA Cheng, Shifen,&Lu, Feng.(2017).A Two-Step Method for Missing Spatio-Temporal Data Reconstruction.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,6(7),25.
MLA Cheng, Shifen,et al."A Two-Step Method for Missing Spatio-Temporal Data Reconstruction".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 6.7(2017):25.
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