Journal ArticleDOI
A spatial model for high plains agriculture
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TLDR
In this paper, a random field model, consisting of a regional trend component and a local interaction component, was used to describe the spatial pattern of corn and wheat yields in the High Plains.Abstract:
A random field model, consisting of a regional trend component and a local interaction component, describes the spatial pattern of corn and wheat yields in the High Plains. The presence of spatial autocorrelation in the local component raises interesting statistical estimation problems. Temporal variations in the slope and interaction parameters are a function of climatic fluctuations. The local component reflects intercounty spatial autocorrelation in precipitation patterns.read more
Citations
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Combining Nested and Linear Sampling for Determining the Scale and Form of Spatial Variation of Regionalized Variables
TL;DR: The scale and pattern of variation of continuous spatial variables are often difficult to identify as mentioned in this paper, and the semi-variogram of regionalized variable theory provides a precise solution once the approximate scale of spatial variation is known.
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A Comparison of Three Methods of Estimation in the Context of Spatial Modeling
TL;DR: In this article, the authors compare the accuracy and precision of representative Least Squares, Maximum Likelihood and Bayesian methods of estimation using an approach similar to the jackknife, each method is repeatedly applied to subsamples of a data set on the property market in Bogota, Colombia to generate multiple estimates of the underlying explanatory spatial hedonic model.
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The Problem of Missing Data on Spatial Surfaces
TL;DR: In this review a modified version of the Orchard and Woodbury missing-information principle receives the greatest emphasis because it combines classical statistical theory with trend surface and spatial autoregressive models.
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Testing for spatial association of qualitative data using symbolic dynamics
TL;DR: A new test (Q) for spatial independence is proposed using concepts from symbolic dynamics and symbolic entropy that can be used to detect, given a spatial distribution of events, patterns of spatial association of qualitative variables in a wide variety of settings.
References
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Journal ArticleDOI
On stationary processes in the plane
TL;DR: The sampling theory of stationary processes in space is not completely analogous to that of stationary time series, due to the fact that the variate of a time series is influenced only by past values, while for a spatial process dependence extends in all directions as mentioned in this paper.
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Estimation Methods for Models of Spatial Interaction
TL;DR: In this paper, a simplified computational scheme is given and extended to mixed regressive-autoregressive models for spatial interaction, and the ML estimator is compared with several alternatives.
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Model Specification in Stationary Random Fields
TL;DR: The problems of model specification in one- and two-dimensional stationary random fields are considered, with particular reference to the class of spatial autoregressions, and the role of the autocorrelation function is discussed.
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Hail in American Agriculture
TL;DR: Hail is considered one of the more formidable of our climatic hazards as discussed by the authors, although no parts of the United States are secure from its unwelcome visitations, albeit in a few areas, albeit the best agricultural ones.