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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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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