GeoGraphNetworks: Shapefile-Derived Datasets for Accurate and Scalable Graphical Representations
The GeoGraphNetworks data repository provides a comprehensive collection of networks, including the road and railway networks of the United States of America (USA) and the road and river networks of Great Britain (GB). This dataset offers nationwide coverage for both countries.
The networks representing the road infrastructure of the USA include primary and secondary roads, covering all 50 states, 1 federal district (Washington, D.C.), and 5 territories, resulting in a total of 56 networks. The rail line infrastructure of the USA is represented as a single network that covers the entire country and includes connectivity to Canada. A total of 114 files , including 57 in Excel (.xlsx) format and 57 in JSON format, representing a total of 56 road networks and 1 rail network.
Great Britain dataset comprises a comprehensive collection of 106 files, including 53 in Excel (.xlsx) format and 53 in JSON format, representing a total of 53 unique networks, 52 road networks and 1 river network. Each file is named with a unique code assigned by Ordnance Survey, which systematically divides Great Britain into 100 km by 100 km tiles (https://www.ordnancesurvey.co.uk/documents/product-support/user-guide/os-open-roads-overview.pdf).
It allows researchers, urban planners, and anyone interested to explore how these networks connect people and places. With GeoGraphNetworks, you can easily analyze the travel routes and waterways that play a crucial role in the transportation systems of GB and the USA.
The JSON files contain graph objects created using the widely used Python library NetworkX, allowing for immediate use without the need for pre-processing. Meanwhile, the Excel files are designed to support the construction of these graph networks across various platforms and programming languages, providing users with flexibility and ease of integration into their projects. This ensures that whether you're a developer, researcher, or data analyst, you can leverage this dataset effectively in your work.
Visual representation of each network along with the code to use these networks (in Notebooks) are hosted on the Github Profile: https://github.com/Harsh9650/GeoGraphNetworks
