A lightweight ensemble spatiotemporal interpolation model for geospatial data. Issue 9 (1st September 2020)

• Record Type: Journal Article
• Authors: Cheng, Shifen; Peng, Peng; Lu, Feng
• Is Part Of: International journal of geographical information science. Volume 34:Issue 9(2020)
• Page Start: 1849
• Page End: 1872
• Publication Date: 2020-09-01
• Subjects: Spatiotemporal interpolation -- spatiotemporal dependence -- lightweight ensemble -- extreme learning machine; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2020.1725016
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations.