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A Hybrid Method for Interpolating Missing Data in Heterogeneous Spatio-Temporal Datasets

Department of Geo-Informatics, Central South University, Changsha 410083, China
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2016, 5(2), 13;
Received: 9 October 2015 / Accepted: 3 February 2016 / Published: 6 February 2016
PDF [3137 KB, uploaded 18 February 2016]


Space-time interpolation is widely used to estimate missing or unobserved values in a dataset integrating both spatial and temporal records. Although space-time interpolation plays a key role in space-time modeling, existing methods were mainly developed for space-time processes that exhibit stationarity in space and time. It is still challenging to model heterogeneity of space-time data in the interpolation model. To overcome this limitation, in this study, a novel space-time interpolation method considering both spatial and temporal heterogeneity is developed for estimating missing data in space-time datasets. The interpolation operation is first implemented in spatial and temporal dimensions. Heterogeneous covariance functions are constructed to obtain the best linear unbiased estimates in spatial and temporal dimensions. Spatial and temporal correlations are then considered to combine the interpolation results in spatial and temporal dimensions to estimate the missing data. The proposed method is tested on annual average temperature and precipitation data in China (1984–2009). Experimental results show that, for these datasets, the proposed method outperforms three state-of-the-art methods—e.g., spatio-temporal kriging, spatio-temporal inverse distance weighting, and point estimation model of biased hospitals-based area disease estimation methods. View Full-Text
Keywords: spatio-temporal interpolation; heterogeneity; spatio-temporal covariance; clustering spatio-temporal interpolation; heterogeneity; spatio-temporal covariance; clustering

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Deng, M.; Fan, Z.; Liu, Q.; Gong, J. A Hybrid Method for Interpolating Missing Data in Heterogeneous Spatio-Temporal Datasets. ISPRS Int. J. Geo-Inf. 2016, 5, 13.

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