Reduced Basis Kriging for Big Spatial Fields

Fields as a Generic Data Type for Big Spatial Data

This paper defines the Field data type for big spatial data. Most big spatial data sets provide information about properties of reality in continuous way, which leads to their representation as fields. We develop a generic data type for fields that can represent different types of spatiotemporal data, such as trajectories, time series, remote sensing and, climate data. To assess its power of ge...

Mapping Spatial Thematic Accuracy Using Indicator Kriging

Part 4: Spatial Prediction and Kriging

where the covariance structure of U(s) is unknown. The prediction of U(s) at a new spatial site s0 is known as kriging (though the term has been mainly used for the construction of a spatial predictor using a model with known parameters). A generalization is to predict the joint value at several points, or an integral such as u(A) = ∫ A u(s) ds doe some set A (commonly of interest in areal data...

Interpolation of Spatial Data: Some Theory for Kriging

Want to get experience? Want to get any ideas to create new things in your life? Read interpolation of spatial data some theory for kriging now! By reading this book as soon as possible, you can renew the situation to get the inspirations. Yeah, this way will lead you to always think more and more. In this case, this book will be always right for you. When you can observe more about the book, y...

Kriging and Cross-validation for Massive Spatial Data

Spatial prediction such as kriging involves the inversion of a covariance matrix. When the number of locations is very large as in many studies, inversion of the covariance matrix may not be practical. Covariance tapering, predictive process models and low rank kriging are some methods for overcoming the large matrix problem, all of which can be regarded as approximations to the underlying stat...