Yang Chizhong filtering and estimation methods for spatial data interpolation

doi:10.11947/j.AGCS.2025.20240370

Abstract

Abstract:

Geoscience research generally faces the problem of missing data and sparse distribution. Spatial interpolation is one of the primary and core tasks of geoscience data analysis. Statistics- and geometry- based spatial interpolation methods are widely used in spatial data analysis; however, they have long had defects in modeling non-stationary spatial processes. Yang Chizhong filtering and interpolation methods (abbreviated as Yang Chizhong methods) integrate geometric and statistical strategy to quantify spatial autocorrelation structure, which provides a new idea for modeling non-stationary spatial processes. Yang Chizhong methods have unique advantages in modeling small samples and non-stationary spatial processes, such as geometrisation of ore body. In this study, we first overcome two long-standing theoretical shortcomings of Yang Chizhong methods. Then, we focus on the new progress of Yang Chizhong methods in recent years. Finally, we provide an outlook on future research directions. Yang Chizhong methods can widely serve the analysis of distribution characteristics and discovery of evolutionary laws of geo-phenomena.

Key words: spatial statistics, spatial autocorrelation, nonstationary spatial processes, spatial compound variable, Yang Chizhong filtering and interpolation

CLC Number:

P208

Qiliang LIU, Jie YANG, Xiancheng MAO, Zhankun LIU, Min DENG. Yang Chizhong filtering and estimation methods for spatial data interpolation[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(8): 1501-1517.

Figures/Tables 13

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