By Enlarged 12th Plenary Session of the 8th Central Committee of the CPC
The legitimate statement of the Cultural Revolution with phots of Mao and Lin Piao.
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A spline is a type of piecewise polynomial, which is preferable to a simple polynomial interpolation because more parameters can be defined including the amount of smoothing. The smoothing spline function also assumes that there is a (measurement) error in the data that needs to be smoothed locally. There are many versions and modifications of spline interpolators. The most widely used techniques are thin-plate splines (Hutchinson, 1995) and regularized spline with tension and smoothing (Mitasova and Mitas, 1993).
2, spatial prediction can even be fully automated so that a user needs only to provide quality inputs and the system will select the most suitable technique. ✹✶ 12 ✶ ✷ Geostatistical mapping Trans-Gaussian (variable transformed using some link function); Localization of analysis — whether the model uses all sampling locations or only locations in local proximity: ✸ Local or moving window analysis (a local sub-sample; local models applicable); ✹ Global (all samples; the same model for the whole area); ✺ Convexity effect — whether the model makes predictions outside the range of the data: ✻ Convex (all predictions are within the range); ✼ Non-convex (some predictions might be outside the range); ✽ ✾ ✶✵ ✶✶ Support size — whether the model predicts at points or for blocks of land: Point-based or punctual prediction models; Area-based or block prediction models; Regularity of support — whether the output data structure is a grid or a polygon map: ✶✷ Regular (gridded outputs); ✶✸ Irregular (polygon maps); ✶✹ Quantity of target variables — whether there is one or multiple variables of interest: ✶✺ Univariate (model is estimated for one target variable at a time); ✶✻ Multivariate (model is estimated for multiple variables at the same time); ✶✼ ✶✽ ✶✾ ✷✵ ✷✶ ✷✷ ✷✸ ✷✹ ✷✺ ✷✻ ✷✼ ✷✽ ✷✾ ✸✵ ✸✶ ✸✷ ✸✸ ✸✹ Another way to look at spatial prediction models is to consider their ability to represent models of spatial variation.
13: Comparison of spatial prediction techniques for mapping ❩✐♥❝ (sampling locations are shown in Fig. 9). Note that inverse distance interpolation (✳✐❞) and kriging (✳♦❦) are often quite similar; the moving trend surface (✳tr; 2nd order polynomial) can lead to artifacts (negative values) — locally where the density of points is poor. The regression-based (✳❧♠) predictions were produced using distance from the river as explanatory variable (see further §5). , 2004). 2)), this time by averaging the predictors: zˆ❖▲❙ (s0 ) = ˆb0 + ˆb1 · q1 (s0 ) + .