Graph Theory Applications in Advanced Geospatial Research

TL;DR

This paper reviews how graph theory algorithms are crucial in geospatial sciences, enabling efficient modeling and analysis of spatial relationships across various applications like environmental monitoring and infrastructure planning.

Contribution

It provides a comprehensive overview of graph theory applications in geospatial research, highlighting key algorithms and their role in solving spatial problems.

Findings

Graph theory algorithms enhance spatial connectivity analysis.

They improve network modeling in GIS.

Applications include digital twins and infrastructure planning.

Abstract

Geospatial sciences include a wide range of applications, from environmental monitoring transportation to infrastructure planning, as well as location-based analysis and services. Graph theory algorithms in mathematics have emerged as indispensable tools in these domains due to their capability to model and analyse spatial relationships efficiently. This article explores the applications of graph theory algorithms in geospatial sciences, highlighting their role in network analysis, spatial connectivity, geographic information systems, and various other spatial problem-solving scenarios like digital twin. The article provides a comprehensive idea about graph theory's key concepts and algorithms that assist the geospatial modelling processes and insights into real-world geospatial challenges and opportunities. It lists the extensive research, innovative technologies and methodologies implemented in this domain.