https://github.com/alan-turing-institute/spatial-inequality - (Revised v2) Optimising for equity: Sensor coverage, networks and the responsive city

v2.2: Revised figures.

Code for generating figures in the paper "Optimising for equity: Sensor coverage, networks and the responsive city", pre-print available here: https://doi.org/10.21203/rs.3.rs-902765/v1

Includes the ability to run a single-objective greedy algorithm or multi-objective genetic algorithm (NSGA2) on any local authority in England & Wales.

What's Changed

• Update paper figures by @jack89roberts in https://github.com/alan-turing-institute/spatial-inequality/pull/37
• Add API end-point to compute coverage for user-defined networks by @jack89roberts in https://github.com/alan-turing-institute/spatial-inequality/pull/39
• R2 reviewer comments by @jack89roberts in https://github.com/alan-turing-institute/spatial-inequality/pull/51

Full Changelog: https://github.com/alan-turing-institute/spatial-inequality/compare/v2.0...v2.2

- Jupyter Notebook
Published by jack89roberts about 4 years ago

https://github.com/alan-turing-institute/spatial-inequality - (Revised) Optimising for equity: Sensor coverage, networks and the responsive city

v2.1: Revised figures.

Code for generating figures in the paper "Optimising for equity: Sensor coverage, networks and the responsive city", pre-print available here: https://doi.org/10.21203/rs.3.rs-902765/v1

Includes the ability to run a single-objective greedy algorithm or multi-objective genetic algorithm (NSGA2) on any local authority in England & Wales.

- Jupyter Notebook
Published by jack89roberts over 4 years ago

https://github.com/alan-turing-institute/spatial-inequality - Optimising for equity: Sensor coverage, networks and the responsive city

Code for generating figures in the paper "Optimising for equity: Sensor coverage, networks and the responsive city", pre-print available here: https://doi.org/10.21203/rs.3.rs-902765/v1

Includes the ability to run a single-objective greedy algorithm or multi-objective genetic algorithm (NSGA2) on any local authority in England & Wales.

- Jupyter Notebook
Published by jack89roberts almost 5 years ago

https://github.com/alan-turing-institute/spatial-inequality - Greedy optimisation for Residents and Workplace

Optimisation code and API with: - Greedy algorithm - Output area stats - Residential population by age - Place of work - Population weighted output area centroids as sensor sites. - Weighted combination of residential population (by age) and workplace in a single objective optimisation.

- Jupyter Notebook
Published by jack89roberts about 6 years ago