r5py-example

A repository demonstrating how to use the r5py python package for travel time estimation. It is intended for demonstration and educational purposes only and should not be used for any other purpose.

Overview

The aim of this example explores travel time estimation, by different modalities, to a subset of supermarkets across the Newport, Wales area.

The example uses r5py to calculate travel times from a set of origins (Output Area Population weight centroids) to a set of destinations (supermarkets). The example demonstrates how to use the r5py package to calculate travel times using different modes of transport (public transit, walking, cycling, driving) and how to visualise the results.

The data/ directory contains the input data required to run this example. It is a small, limited subset of the publicly available data covering only the example area of interest. For more information on these data, please see the data/README.md.

Installation

To run this example, you will need to:

1. Have Java installed on your system (required by r5py, see the r5py Java installation documentation).
2. Install the required packages. You can do this by running the commands below (uses venv and pip to create a virtual environment and install the required packages).:

Note: It is also possible to use conda to create the virtual environment and install the packages. In this case, use conda to install r5py v0.1.0 before installing the requirements.txt, since this will install a suitable OpenJDK version into the conda environment too.

Clone the repository:

git clone https://github.com/datasciencecampus/r5py-example.git

Create the virtual environment:

python3 -m venv .venv

Activate the virtual environment:

source .venv/bin/activate

Note: On Windows, you should use .\.venv\Scripts\activate to activate the virtual environment.

Upgrade pip:

pip install --upgrade pip

Install the required packages:

pip install -r requirements.txt

Optional: Install the pre-commit hooks:

pre-commit install

Example File

The example is all self-contained within the notebook r5py_example.ipynb.

To run the notebook, make sure you have followed the installation instructions above and have selected the correct kernel in the notebook to match the virtual environment created.

You should then be able to follow the notebook from start to finish to see how to use r5py to calculate travel times and visualise the results.

Data Science Campus

At the Data Science Campus we apply data science, and build skills, for public good across the UK and internationally. Get in touch with the Campus at [email protected].

Licence

The code, unless otherwise stated, is released under the MIT Licence.

The documentation for this work is subject to © Crown copyright and is available under the terms of the Open Government 3.0 licence.