Abstract
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.
Highlights
Evolving patterns of geographical phenomena are usually modeled as spatio-temporal processes depicted by space-time data
A number of interpolation methods have been proposed to estimate missing observations in space-time data. Most of these methods assume that the interpolation of space-time data can be reducible to a sequence of spatial interpolations [5]
Applying spatial interpolation methods to space-time data usually leads to the loss of valuable information in the temporal dimension [6]
Summary
Evolving patterns of geographical phenomena are usually modeled as spatio-temporal processes depicted by space-time data. One is to exclude periods with missing values from data analysis, and the other is to ignore the missing data based on the tacit assumption that the data represent one continuous series [3,4] These approaches may disregard useful information and bias the analysis results [2]. To overcome these limitations, a number of interpolation methods have been proposed to estimate missing observations in space-time data. Applying spatial interpolation methods to space-time data usually leads to the loss of valuable information in the temporal dimension [6]. A space-time interpolation method considering both spatial and temporal heterogeneity is developed for treating missing data.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
