Estimating and modeling space–time correlation structures

Cesare, L. De; Myers, Donald E.; Posa, D.

doi:10.1016/s0167-7152(00)00131-0

Cited by 146 publications

(98 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The following class of valid product-sum covariance models was introduced in De Cesare et al (2001b) and further developed in De Iaco et The typical example of subsampled IASI Level 2 XCH 4 (altitude below 4 km) data for a selected estimation location (yellow circle). The colors of the observations show semivariance between observation and estimation locations (blue: lowest; red: highest).…”

Section: Characterization Of Spatio-temporal Covariancementioning

confidence: 99%

Spatio-temporal approach to moving window block kriging of satellite data v1.0

et al. 2017

View full text Add to dashboard Cite

Abstract. Numerous existing satellites observe physical or environmental properties of the Earth system. Many of these satellites provide global-scale observations, but these observations are often sparse and noisy. By contrast, contiguous, global maps are often most useful to the scientific community (i.e., Level 3 products). We develop a spatio-temporal moving window block kriging method to create contiguous maps from sparse and/or noisy satellite observations. This approach exhibits several advantages over existing methods: (1) it allows for flexibility in setting the spatial resolution of the Level 3 map, (2) it is applicable to observations with variable density, (3) it produces a rigorous uncertainty estimate, (4) it exploits both spatial and temporal correlations in the data, and (5) it facilitates estimation in real time. Moreover, this approach only requires the assumption that the observable quantity exhibits spatial and temporal correlations that are inferable from the data. We test this method by creating Level 3 products from satellite observations of CO 2 (XCO 2 ) from the Greenhouse Gases Observing Satellite (GOSAT), CH 4 (XCH 4 ) from the Infrared Atmospheric Sounding Interferometer (IASI) and solar-induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2). We evaluate and analyze the difference in performance of spatio-temporal vs. recently developed spatial kriging methods.

show abstract

Section: Characterization Of Spatio-temporal Covariancementioning

confidence: 99%

Spatio-temporal approach to moving window block kriging of satellite data v1.0

et al. 2017

View full text Add to dashboard Cite

show abstract

“…In order to evaluate the ST-2SMR method, we compared three existing methods (ST-kriging [31], P-BSHADE [16,32] and ST-HC [2,33]), each constrained in three different ways (i.e., increasing coarse-grained interpolation, increasing the sliding window or both; Table 2). We adopted mean absolute error (MAE), mean relative error (MRE) and the ratio of construction (RC) as the evaluation criteria to verify the performances of the proposed method.…”

Section: Evaluation Criteriamentioning

confidence: 99%

A Two-Step Method for Missing Spatio-Temporal Data Reconstruction

Cheng

2017

IJGI

View full text Add to dashboard Cite

Abstract:Missing 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.

show abstract

“…In the separable model, the spatio-temporal covariance function is treated as either a sum or product of separate spatial and temporal covariance functions [30]. In the non-separable model, the spatio-temporal covariance function is treated as a non-linear, multiplicative version of the spatial and temporal covariance functions [6,10,[31][32][33]. However, spatio-temporal kriging methods assume that a space-time process has a constant mean and variance (i.e., second order stationarity) in space and time [13,15].…”

Section: Related Workmentioning

confidence: 99%

“…Most of these methods assume that the interpolation of space-time data can be reducible to a sequence of spatial interpolations [5]. However, applying spatial interpolation methods to space-time data usually leads to the loss of valuable information in the temporal dimension [6]. On that account, space-time interpolation methods that consider both spatial and temporal dimensions have been paid more attention and have been widely used in geoscience [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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

Deng

Zide

Liu

et al. 2016

IJGI

View full text Add to dashboard Cite

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 (1984China ( -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.

show abstract

Estimating and modeling space–time correlation structures

Cited by 146 publications

References 14 publications

Spatio-temporal approach to moving window block kriging of satellite data v1.0

Spatio-temporal approach to moving window block kriging of satellite data v1.0

A Two-Step Method for Missing Spatio-Temporal Data Reconstruction

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

Contact Info

Product

Resources

About