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Spatial Interpolation

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Book cover Semantic Kriging for Spatio-temporal Prediction

Part of the book series: Studies in Computational Intelligence ((SCI,volume 839))

Abstract

This chapter presents a background study on spatial interpolation methods, its extended variants for spatio-temporal interpolation and some probabilistic approaches for different spatial analyses. It also focuses on the issues of modeling terrestrial dynamics for meteorological applications. Modeling LULC knowledge of the terrain, evaluating semantic associations between them and enabling interoperability among the spatial data sources, have been studied extensively for spatial applications. For spatial interpolation methods and its variants, one frequency of comparison graph or a popularity graph is proposed, depicting their frequency of being chosen for comparative analysis in 85 selected articles. This facilitates us to identify the most popular, moderately popular, least popular groups of spatial interpolation methods. The most popular group members can further be chosen for the empirical comparison with the proposed approach. A brief description of each of those methods (of the most popular group) is also presented here.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Technical University of Munich (TUM), Munich, Bayern, Germany

    Shrutilipi Bhattacharjee & Dr. Jia Chen

  2. Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India

    Soumya Kanti Ghosh

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  1. Shrutilipi Bhattacharjee
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  2. Soumya Kanti Ghosh
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Correspondence to Shrutilipi Bhattacharjee .

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Bhattacharjee, S., Ghosh, S.K., Chen, J. (2019). Spatial Interpolation. In: Semantic Kriging for Spatio-temporal Prediction. Studies in Computational Intelligence, vol 839. Springer, Singapore. https://doi.org/10.1007/978-981-13-8664-0_2

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