A method for creating complex real-world networks using ESRI Shapefiles.

Abstract

A classic optimization problem with many real-world applications is optimal route search in graphs or networks. Graphical networks resembling real world networks are an important requirement for these studies. Python packages NetworkX and OSMnx are probably the most popular approaches in industry for creating and analyzing real world graphical networks using ESRI Shapefiles (Geospatial Vector Data). However, creating such a network is a complex and tedious process as these packages require the input data to be in a specific format. In this study, • We outline a flexible method that can be used to easily create graphical network representations in NetworkX or OSMnx using road network topology data stored in ESRI Shapefiles. • A detailed step-by-step process is outlined to successfully transform the ESRI Shapefile data into the compatible format for graph analysis libraries like OSMnx and NetworkX. • A data cleaning strategy is suggested to reduce resource consumption without distorting the actual structure of the graph.This method will allow researchers to efficiently generate graphical networks and validate their theories by evaluating their efficiencies using real-world network data of different sizes and topologies. This method could benefit, but is not limited to, research areas such as Advanced Transportation Systems (ATS), Graph Neural Networks (GNN), Multi-Objective Genetic Algorithms, to mention a few.

Keywords

• Complex Real-World Networks using Geospatial Data.