Information and Complexity Analysis of Spatial Data

TL;DR

This paper reviews recent information-theoretic approaches to analyzing spatial data, emphasizing complexity and uncertainty quantification across various scientific fields, including spatial point patterns and multifractal data.

Contribution

It provides a comprehensive review of recent methods and directions in the informational analysis of spatial data from an evolutionary perspective.

Findings

Highlights the importance of information theory in spatial data analysis

Discusses recent advances in complexity and risk analysis for spatial data

Addresses multifractal spatial point patterns

Abstract

Information Theory provides a fundamental basis for analysis, and for a variety of subsequent methodological approaches, in relation to uncertainty quantification. The transversal character of concepts and derived results justifies its omnipresence in scientific research, in almost every area of knowledge, particularly in Physics, Communications, Geosciences, Life Sciences, etc. Information-theoretic aspects underlie modern developments on complexity and risk. A proper use and exploitation of structural characteristics inherent to spatial data motivates, according to the purpose, special considerations in this context. In this paper, some of the most relevant approaches introduced, in particular recent contributions and directions, regarding the informational analysis of spatial data and related aspects concerning complexity analysis, are reviewed under a conceptually connective evolutionary perspective. The discussion involves the cases of spatial data from magnitude measurements and spatial point patterns, with the latter possibly being of a multifractal nature.