Bayesian Methods for Geospatial Data Analysis

Wei Tu; Lili Yu

doi:10.1007/978-981-19-3816-0_14

Bayesian Methods for Geospatial Data Analysis

Wei Tu, Lili Yu

Research output: Contribution to book or proceeding › Chapter

1 Scopus citations

Abstract

This chapter provides an applied introduction to model two types of point-based geospatial data using Bayesian methods. Unlike frequentist inference, Bayesian inference describes unknownstatistical parameterswith a prior distribution. With this foundation, Bayesian approach provides a valuable alternative to analyze geospatial data.We begin the chapter by introducing the basic concepts and benefits of Bayesian inference and survey four selected Bayesianmodels and methods, including Bayesian spatial interpolation, spatial epidemiology/diseasemapping, Bayesian hierarchical models, and Bayesian spatial autoregressive models, for their applications in geospatial data analysis. Then we discuss some popular software packages to perform Bayesian analysis. We conclude the chapter by encouraging geospatial researchers and practitioners to add Bayesian methods in their toolboxes.

Original language	American English
Title of host publication	New Thinking in GIScience
DOIs	https://doi.org/10.1007/978-981-19-3816-0_14
State	Published - Jul 1 2022

Keywords

Bayesian hierarchical models
Bayesian inference
Bayesian spatial autoregressive (SAR) models
Bayesian spatial interpolation
Case and count data
Geospatial data analysis
Markov Chain Monte Carlo (MCMC)
Spatial epidemiology/disease mapping

DC Disciplines

Geology
Geography

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https://doi.org/10.1007/978-981-19-3816-0_14

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title = "Bayesian Methods for Geospatial Data Analysis",

abstract = "This chapter provides an applied introduction to model two types of point-based geospatial data using Bayesian methods. Unlike frequentist inference, Bayesian inference describes unknownstatistical parameterswith a prior distribution. With this foundation, Bayesian approach provides a valuable alternative to analyze geospatial data.We begin the chapter by introducing the basic concepts and benefits of Bayesian inference and survey four selected Bayesianmodels and methods, including Bayesian spatial interpolation, spatial epidemiology/diseasemapping, Bayesian hierarchical models, and Bayesian spatial autoregressive models, for their applications in geospatial data analysis. Then we discuss some popular software packages to perform Bayesian analysis. We conclude the chapter by encouraging geospatial researchers and practitioners to add Bayesian methods in their toolboxes.",

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author = "Wei Tu and Lili Yu",

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N2 - This chapter provides an applied introduction to model two types of point-based geospatial data using Bayesian methods. Unlike frequentist inference, Bayesian inference describes unknownstatistical parameterswith a prior distribution. With this foundation, Bayesian approach provides a valuable alternative to analyze geospatial data.We begin the chapter by introducing the basic concepts and benefits of Bayesian inference and survey four selected Bayesianmodels and methods, including Bayesian spatial interpolation, spatial epidemiology/diseasemapping, Bayesian hierarchical models, and Bayesian spatial autoregressive models, for their applications in geospatial data analysis. Then we discuss some popular software packages to perform Bayesian analysis. We conclude the chapter by encouraging geospatial researchers and practitioners to add Bayesian methods in their toolboxes.

AB - This chapter provides an applied introduction to model two types of point-based geospatial data using Bayesian methods. Unlike frequentist inference, Bayesian inference describes unknownstatistical parameterswith a prior distribution. With this foundation, Bayesian approach provides a valuable alternative to analyze geospatial data.We begin the chapter by introducing the basic concepts and benefits of Bayesian inference and survey four selected Bayesianmodels and methods, including Bayesian spatial interpolation, spatial epidemiology/diseasemapping, Bayesian hierarchical models, and Bayesian spatial autoregressive models, for their applications in geospatial data analysis. Then we discuss some popular software packages to perform Bayesian analysis. We conclude the chapter by encouraging geospatial researchers and practitioners to add Bayesian methods in their toolboxes.

KW - Bayesian hierarchical models

KW - Bayesian inference

KW - Bayesian spatial autoregressive (SAR) models

KW - Bayesian spatial interpolation

KW - Case and count data

KW - Geospatial data analysis

KW - Markov Chain Monte Carlo (MCMC)

KW - Spatial epidemiology/disease mapping

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DO - 10.1007/978-981-19-3816-0_14

M3 - Chapter

BT - New Thinking in GIScience

ER -

Bayesian Methods for Geospatial Data Analysis

Abstract

Keywords

DC Disciplines

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