OptimisingForEquity

Optimising for equity: Sensor coverage, networks and the responsive city

Figures and scripts for our pre-print: 10.21203/rs.3.rs-902765/v1

Pre-Generated Figures and Data

Pre-generated networks and figures can be found in the publications/OptimisingForEquity/results directory in our upload on Zenodo: https://doi.org/10.5281/zenodo.5552876

Instructions

To generate the figures:

1. Checkout/download the correct version of the code by doing one of the following:

   • Download the source code from Zenodo: https://doi.org/10.5281/zenodo.5552876
   • Download from GitHub: https://github.com/alan-turing-institute/spatial-inequality/releases/tag/v2.2
   • From a local clone of the repo, checkout tag v2.2 or commit 2876e73.

2. Follow the setup instructions to create and activate the conda environment in the repo readme file

3. Change to the directory this file is in (cd publications/OptimisingForEquity).

4. The file config.yml defines the properties of the networks and figures that will be generated. You can leave them as the defaults used for the paper or edit the file to try running with different values.

5. Run python main.py. This may take a long time (hours) to finish, depending on the values specified in config.yml.

6. By default, results will be saved to the results directory in a sub-directory with the local authority code (E08000021 for Newcastle upon Tyne). This should include the reports with figures in Markdown (report.md) and HTML (report.html) formats, source figures in the figures directory, and saved pre-generated networks in the networks directory (in Python pickle files).