A generalized linear model approach to spatial data analysis and prediction

• Published in: Journal of agricultural, biological, and environmental statistics Vol. 2; no. 2; pp. 157 - 178
• Main Authors: Gotway, C.A. (Centers For Disease Control and Prevention, Clifton, Atlanta, GA.), Stroup, W.W
• Format: Journal Article
• Language: English
• Published: American Statistical Association and the International Biometric Society 01-06-1997
• Subjects: Correlations; Data analysis; DISTRIBUCION ESPACIAL; DISTRIBUTION SPATIALE; Estimation methods; Generalized linear model; GEOSTATISTICS; Kriging; Mathematical independent variables; MATHEMATICAL MODELS; Matrices; METHODE STATISTIQUE; METODOS ESTADISTICOS; MODELE MATHEMATIQUE; Modeling; MODELOS MATEMATICOS; Spatial data; SPATIAL DISTRIBUTION; STATISTICAL METHODS; Statistical variance
• ISSN: 1085-7117; 1537-2693
• DOI: 10.2307/1400401

The theory of generalized linear models and quasi-likelihood provides a flexible framework for analyzing non-normal data. In this article, we demonstrate how this theory can be extended to include the analysis of discrete and categorical spatial data. This theory can be used to estimate parameters and test treatment effects in a designed experiment involving discrete or categorical spatial responses. It also provides a flexible method for spatial prediction using non-normal data and includes universal kriging and indicator kriging as special cases. Examples are given, including one where the focus is on comparing treatments in a designed experiment in which spatial correlation is present, and two others where spatial prediction or mapping is the desired goal. The methods presented here provide an additional set of tools for the analysis of spatial data that will be useful to researchers in a variety of disciplines, including hydrology, soil science, entomology, agronomy, and ecology.