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.
Highlights
Digital transformation technologies have generated massive amounts of data over the past few decades, which is the concept known as Big Data, in which data storage grows exponentially and requires an advanced analytical tool to explore and answer research questions
Adopting spatial methods in daily observed data can lead to more intelligent cities and to urban information to identify and prevent high-risk regions using the Internet of Things (IoT) [3]
Subsequent efforts to account for these errors have a research line in spatial statistics
Summary
Digital transformation technologies have generated massive amounts of data over the past few decades, which is the concept known as Big Data, in which data storage grows exponentially and requires an advanced analytical tool to explore and answer research questions. The technical advance has opened the door to inferential models of complex phenomena, such as spatial trends and heterogeneity in information conditioned on space and time [1,2]. Adopting spatial methods in daily observed data can lead to more intelligent cities and to urban information to identify and prevent high-risk regions using the Internet of Things (IoT) [3]. Applications vary in complexity and are frequently carried out in risk surface detection, healthcare, agriculture, urban planning, economics, engineering, and rarely, smart appliances that learn based on location. This complex structure is accommodated in a flexible class of models related to observed data and spatial dependencies. In the Bayesian framework, questions are answered through an estimation procedure by combining multiple sources of information, such as previous knowledge (prior) and the acquired information in the data (likelihood) [4]
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