Spatial Statistical Models: An Overview under the Bayesian Approach

Author(s)

Louzada, Francisco ^[1] ; Nascimento, Diego Carvalho do ^[2] ; Egbon, Osafu Augustine ^[1]

Affiliation

^[1] Univ Sao Paulo, Inst Math Sci & Comp, BR-13566590 Sao Carlos - Brazil

^[2] Univ Atacama, Fac Ingn, Dept Matemat, Copiapo 1530000 - Chile

• Document type: Journal article
• Source: AXIOMS; v. 10, n. 4 DEC 2021.
• Web of Science Citations: 0

Abstract

Spatial documentation is exponentially increasing given the availability of Big Data in the Internet of Things, enabled by device miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns in space through prior knowledge and data likelihood. However, this class of modeling is not yet well explored when compared to adopting classification and regression in machine-learning models, in which the assumption of the spatiotemporal independence of the data is often made, that is an inexistent or very weak dependence. Thus, this systematic review aims to address the main models presented in the literature over the past 20 years, identifying the gaps and research opportunities. Elements such as random fields, spatial domains, prior specification, the covariance function, and numerical approximations are discussed. This work explores the two subclasses of spatial smoothing: global and local. (AU)

• FAPESP's process: 20/09174-5 - Recommendation system of interest items for BeeNet users
• Grantee: Diego Carvalho do Nascimento
• Support Opportunities: Scholarships in Brazil - Technical Training Program - Technical Training