Encyclopedia of GIS

2008 Edition
| Editors: Shashi Shekhar, Hui Xiong

Hierarchical Spatial Models

  • Ali Arab
  • Mevin B. Hooten
  • Christopher K. Wikle
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-35973-1_564


Hierarchical dynamic spatio-temporal models; Geostatistical models; Hierarchies; Autoregressive models; Process model


A hierarchical spatial model is the product of conditional distributions for data conditioned on a spatial process and parameters, the spatial process conditioned on the parameters defining the spatial dependencies between process locations, and the parameters themselves.

Historical Background

Scientists across a wide range of disciplines have long recognized the importance of spatial dependencies in their data and the underlying process of interest. Initially due to computational limitations, they dealt with such dependencies by randomization and blocking rather than the explicit characterization of the dependencies in their models. Early developments in spatial modeling started in the 1950's and 1960's motivated by problems in mining engineering and meteorology [11], followed by the introduction of Markov random fields [2]. The application...


Spatial Process Markov Random Field Geographically Weight Regression Areal Data Kriging Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Recommended Reading

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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Ali Arab
    • 1
  • Mevin B. Hooten
    • 2
  • Christopher K. Wikle
    • 1
  1. 1.Department of StatisticsUniversity of Missouri-ColumbiaColumbiaUSA
  2. 2.Department of Mathematics and StatisticsUtah State UniversityLoganUSA