Statistical Methods for Spatial Data Analysis

By C. Chatfield. 2004

1,268 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1998face0fddd9a32aad1cd69352db738b5e68a4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

Citations in the corpus

Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor (2011, background): “The approximation is not so easily justified from a prediction uncertainty viewpoint as the KED variance depends strongly on the variogram, except that reliable (automatic) local variography is rarely viable ( Schabenberger and Gotway 2005, pp. 425 ‐426).”
Spatial analysis of precipitation in a high-mountain region: exploring methods with multi-scale topographic predictors and circulation types (2014, methodology): “The procedure, commonly known as trans-Gaussian kriging (Schabenberger and Gotway, 2005), has been adopted in all KED experiments of the present study, using the Box–Cox power transformation (Box and Cox, 1964): Y ∗ = Y λ − 1 λ λ 6= 0 log(Y ) λ= 0 .”
Modeling Network Autocorrelation in Space–Time Migration Flow Data: An Eigenvector Spatial Filtering Approach (2011, background): “In most cases, ran- dom effects are spatially structured to capture spatial components (Schabenberger and Gotway 2005).”
Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression (2009, methodology): “We then fitted the data using eight spatial methods: lagged predictor (LagPred), lagged response (LagResp), simultaneous autoregressive (SAR), conditional autoregressive (CAR), moving average (MA), and three variants of eigenvector-based spatial filters (or spatial eigenvector mapping, SEVM) models ”
Comparing implementations of global and local indicators of spatial association (2018, neutral): “Both global and local measures may detect other forms of mis-speciﬁcation, for example, a missing variable showing spatial pattern (see McMillen 2003; Schaben-berger and Gotway 2005), or spatial heterogeneity.”
Thirty years of spatial econometrics (2010, background): “The bulk of the work in this area is carried out in geostatistics (e.g., Schabenberger and Gotway 2005).”
Geostatistical radar-raingauge combination with nonparametric correlograms: methodological considerations and application in Switzerland (2010, positive): “The semivar-iogram of a spatial process Z is deﬁned as (for greater detail see Schabenberger and Gotway, 2005, whose notation we largely follow): where Z( s i ) , Z( s j ) denote values of the process at locations s i , s j .”
Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests (2008, methodology): “…techniques discussed here can be extended to non-Gaussian (i.e., non-multivariate normal) error assumptions (Gotway and Stroup, 1997) and/or nonlinear models (Schabenberger and Pierce, 2002; Schabenberger and Gotway, 2005), we will limit the current discussion to linear Gaussian error models.”
Nonstationary models for exploring and mapping monthly precipitation in the United Kingdom (2009, neutral): “Reviews of some methods are provided by Sampson et al. (2001) and Schabenberger and Gotway (2005).”
A full scale approximation of covariance functions for large spatial data sets (2012, methodology): “In particular, spatial model fitting and spatial prediction (e.g. kriging) both involve inversion of an n×n covariance matrix for a data set of size n, which typically requires O.n3/ operations and O.n2/ memory, and is thus computationally intractable for very large n.”
Spatial designs and properties of spatial correlation: Effects on covariance estimation (2007, background): “Dalthorp et al. (2000) demonstrated with discrete data that the range and nugget parameters influence the patch configuration on a simulated landscape, although Schabenberger and Gotway (2005, p. 140) questioned the use of the range parameter as an estimate of patch size.”