Project Overview

This repository holds the files to reproduce the results found in the article "A composite indicator of liveability based on sociodemographic and Uber quality service dimensions: a data-driven approach".

The Uber Estimated Time of Arrival (ETA) data are available for the neighborhoods and Human Development Units (HDU). However, it is worth mentioning this data are already preprocessed, imputed, and grouped by spatial level to obtain the final means and standard deviations. The raw data are not available due to the rules of the Uber Terms of Use.

Environment Setup

Create a conda environment with environment.yml:

conda env create --file environment.yml

To remove an environment in your terminal window run:

conda remove --name myenv --all

To list all available environments run:

conda env list

The pip requirements.txt file is also available and it is possible to install some dependencies by running:

pip install -r requirements.txt

Reproduce the results

After the installation, navigate to the folder where your notebooks are located, and start Jupyter server in your terminal window:

jupyter notebook

You can also open the notebooks using Visual Studio Code, being recommended to install the extensions: Python, Jupyter and Jupyter Notebook Renderers.

After that, you will be able to manipulate and reproduce the results by running the jupyter notebooks eda.ipynb and indicator.ipynb. In both notebooks, you can set the variable spatial_unit as "neighborhood" or "hdu" and run all cells to see the results.