aalto1 untyped-item.component.html

Graphlet decomposition dataset of Tallinn's road network from January 2020 OpenStreetMap data

Loading...
Thumbnail Image

Access rights

openAccess
CC BY

Creative Commons license

Except where otherwised noted, this item's license is described as openAccess
publishedVersion

URL

Journal Title

Journal ISSN

Volume Title

Data Article

Major/Subject

Mcode

Degree programme

Language

en

Pages

12

Series

Data in Brief, Volume 61

Abstract

This paper presents a comprehensive dataset of graphlet decomposition for the road network of Tallinn, Estonia, based on OpenStreetMap (OSM) data representing the road network state as of 1 January 2020. Graphlets, which are small subgraphs, serve as powerful tools for analyzing and classifying local street structures in urban networks. The dataset includes counts of all possible four-node graphlet configurations for each intersection in the road network, provided in both Comma Separated Values (CSV) and Environmental Systems Research Institute (ESRI) Shapefile formats for maximum accessibility. The methodology for extracting these graphlets using Python and the Python ORbit Counting Algorithm (PyORCA) library is explained in details. The processing pipeline includes graph construction from spatial data, node-centric graphlet counting, and conversion back to geographic format. The resulting dataset enables researchers to identify recurring patterns in urban street networks, study urban morphology, and compare structural similarities between different urban areas. The code is designed for reproducibility, allowing researchers to apply the same analysis to other cities. This dataset contributes to the growing field of quantitative urban morphology and can support studies in urban planning, transportation network analysis, sustainable development, and comparative urban studies.

Description

Publisher Copyright: © 2025

Other note

Citation

Rasoulinezhad, M, Eslamirad, N & Partanen, J 2025, 'Graphlet decomposition dataset of Tallinn's road network from January 2020 OpenStreetMap data', Data in Brief, vol. 61, 111776. https://doi.org/10.1016/j.dib.2025.111776

Endorsement

Review

Supplemented By

Referenced By