This simple Python package provides two methods to compute betweenness centrality of road networks efficiently.
- The Betweenness-Centrality/src directory is the project’s root directory. It consists of the following components:
| Component | Description |
|---|---|
| mymodule1.py | It contains the code that supports the main functions and methods of script, such as a parent class Betweenness Centrality and two child classes Networkx, Geo. |
| mymodule2.py | It provides the script’s command line interface, which is mainly used to process input from the command line and provide help information. |
| test_mypackage.py | It contains unit tests for the components of the modules, especially the core functions in the classes. |
| main.py | It provides the executable file, that you could enter arguments from the command line. |
| betweenness_centrality.ipynb | This Jupyter notebook contains simple usage and examples of two Python packages NetworkX and OSMnx |
| research_questions.ipynb | This Jupyter notebook shows the use of this Python package in road networks research and gives a short introduction to two methods. |
- environment.yml provides a conventional file that lists the project’s external dependencies. You could use this file to automatically install the dependencies.
- README.md provides the project description and instructions for installing and running the package.
If you are installing from source, you will need:
- Python 3.12 or later
- A compiler that supports Python 3.12
We highly recommend installing on Anaconda environment.
This Python package depends on other Python libraries such as
- networkx
- osmnx
- numpy
- pandas
- geopandas
- …
You need go to the Gitlab repository: https://courses.gistools.geog.uni-heidelberg.de/sm330/05_network_analysis.git to get the source package.
git clone https://github.com/Runan-Duan/Betweenness-Centrality.git
All required packages are contained in the environment.yml file, you can install the Python environment on your computer
conda env create -f environment.yml
The environment name is betweenness-centrality, you can activate this environment to execute the main script.
conda activate betweenness-centrality
For development and further contributions, please use pre-commit hooks and the configuration file named .pre-commit-config.yaml is in the root of the repository.
- You can use the following command after activating your environment in Anaconda Prompt.
python main.py [study_area] [outfile] [route_types] [method] [--n_routes]
- There are some commands that were previously used in commandline.bat, such as
python main.py "Göttingen, Germany" .\output fastest networkx
python main.py "Würzburg, Germany" .\output fastest geographical 100
Advice for common problems or issues
- Make sure you are connected to the Internet when you run the main script.
- The main script contains help information. You can find out how to run it by using the following command.
python main.py -h
- If you choose the geo-adapted method and get an error message "the graph contains no edges" or similar, please check your input arguments [study_area] or [n_routes]. Because the program generates each route based on two randomly selected nodes, the number of routes should not exceed half the number of nodes in the network.
Runan Duan, sm330@stud.uni-heidelberg.de
This project was inspired by the lecture "Advanced Geoscripting: Introduction to scientific programming with Python" at the Geographical Institute of the University of Heidelberg.
[1] code: osmnx examples for shortest path https://github.com/gboeing/osmnx-examples/blob/main/notebooks/02-routing-speed-time.ipynb
[2] Ludwig, C., Psotta, J., Buch, A., Kolaxidis, N., Fendrich, S., Zia, M., Fürle, J., Rousell, A., and Zipf, A.: TRAFFIC SPEED MODELLING TO IMPROVE TRAVEL TIME ESTIMATION IN OPENROUTESERVICE, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W7-2023, 109–116, https://doi.org/10.5194/isprs-archives-XLVIII-4-W7-2023-109-2023, 2023.
[3] Kirkley, A., Barbosa, H., Barthelemy, M. et al. From the betweenness centrality in street networks to structural invariants in random planar graphs. Nat Commun 9, 2501 (2018). https://doi.org/10.1038/s41467-018-04978-z