TY - CHAP
T1 - Bayesian Methods for Geospatial Data Analysis
AU - Tu, Wei
AU - Yu, Lili
N1 - Publisher Copyright:
© Higher Education Press 2022.
PY - 2022/7/1
Y1 - 2022/7/1
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
UR - https://digitalcommons.georgiasouthern.edu/geo-facpubs/214
UR - https://doi.org/10.1007/978-981-19-3816-0_14
UR - https://www.scopus.com/pages/publications/85161250738
U2 - 10.1007/978-981-19-3816-0_14
DO - 10.1007/978-981-19-3816-0_14
M3 - Chapter
SN - 9789811938153
T3 - New Thinking in GIScience
SP - 119
EP - 128
BT - New Thinking in GIScience
ER -