Next Article in Journal
A Generic Model to Exploit Urban Regulation Knowledge
Previous Article in Journal
An Assessment of Urban Surface Energy Fluxes Using a Sub-Pixel Remote Sensing Analysis: A Case Study in Suzhou, China
Article Menu

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2016, 5(2), 13; doi:10.3390/ijgi5020013

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

1
Department of Geo-Informatics, Central South University, Changsha 410083, China
2
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Received: 9 October 2015 / Accepted: 3 February 2016 / Published: 6 February 2016
View Full-Text   |   Download PDF [3137 KB, uploaded 18 February 2016]   |  

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. View Full-Text
Keywords: spatio-temporal interpolation; heterogeneity; spatio-temporal covariance; clustering spatio-temporal interpolation; heterogeneity; spatio-temporal covariance; clustering
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top