Geography, Spatial Data Analysis, and Geostatistics: An Overview. 地理学、空间数据分析及地统计学：综述

Abstract

Geostatistics is a distinctive methodology within the field of spatial statistics. In the past, it has been linked to particular problems (e.g., spatial interpolation by kriging) and types of spatial data (attributes defined on continuous space). It has been used more by physical than human geographers because of the nature of their types of data. The approach taken by geostatisticians has several features that distinguish it from the methods typically used by human geographers for analyzing spatial variation associated with regional data, and we discuss these. Geostatisticians attach much importance to estimating and modeling the variogram to explore and analyze spatial variation because of the insight it provides. This article identifies the benefits of geostatistics, reviews its uses, and examines some of the recent developments that make it valuable for the analysis of data on areal supports across a wide range of problems.

Evaluation of TanDEM-X elevation data for geomorphological mapping and interpretation in high mountain environments — A case study from SE Tibet, China

“…Visual and geomorphometric landform analysis both implicate continuity between elevation data points, inevitably ignoring the DEM's fractal characteristics. This is feasible because terrain height also possesses a high level of positive autocorrelation for small distances, sufficiently reflecting the assumption of continuity (Hunter and Goodchild, 1997;Ehlschlaeger, 1998;Gallay, 2009;Haining et al, 2010). According to the Nyquist-Shannon sampling theorem, the intrinsic resolution of a raster DEM is twice the sampling distance of the height values actually measured for DEM generation, whereas the double of the DEM posting represents the nominal resolution (Robinson, 1994;Florinsky, 1998;Fisher and Tate, 2006;Arrell et al, 2008).…”

A Lagrangian modelling approach to assess the representativeness area of an industrial air quality monitoring station

“…The kriging interpolation method (Haining et al, 2010) was chosen as it allows to estimate the empirical semivariogram according to the values, distance and reliability of surrounding points until an established maximum distance from the starting point. The distribution of input data, as represented in the histogram, was at first normalized using the arcsin function in order to obtain a regular Gaussian distribution.…”

Measurement and interpolation uncertainties in rainfall maps from cellular communication networks

“…Ordinary kriging (OK) was employed to generate rainfall maps, because it is the simplest and most straightforward method that accounts for the local variability of the stochastic process, rainfall in this case (Cressie, 1990;Haining et al, 2010). Kriging is ideally suited for interpolation of highly irregularly spaced data points.…”