DSAA-Kulimi Rwanda Data Camp Capstone Project

Lockdowns Impact on Air Quality 🌍

Intro

At the end of the DSAA-Kulimi Rwanda Data Camp program, we offer this project to allow our learners to apply their Data Science knowledge and skills while contributing to one of the hottest topics related to the impact of COVID-19 on Climate Change worldwide.

In early 2020, most cities across the globe opted for lockdowns due to the rapid spread of COVID-19. This solution, however, had other outcomes besides slowing or stopping the spread of the virus. The purpose of this study is to examine the impact of lockdowns on air quality.

Data

N.B. Normally, it is not recommended to upload data in your project's GitHub repository, however, we will do it for convenience in this project since each of the files didn't exceed the maximum size limit of 100MB

Air Quality

The World Air Quality Index project team has been taking measurements from stations planted in different cities around the world. In this project, We'll be interested only in the years 2019, 2020, and 2021. Within the dataset, we'll find the min, max, median, and standard deviation of the measurements for each of the air pollutant species (PM2.5, PM10, Ozone ...).

The dataset is structured as follows:

Date: record creation date (yyyy-mm-dd format)
Country: ISO 3166-1 alpha-2 country code
City: the city where the air quality measurement device is deployed
Specie: the air pollutant species (PM2.5, PM10, O3, Humidity, etc.)
count: the number of measurements taken a day
min: the minimum value found in the sampled values
max: the maximum value found in the sampled values
median: the median of the sampled values
variance: the variance of the sampled values

Lockdowns Dates

Besides air quality, we also need lockdowns dates for each country. This Kaggle Dataset can be a good candidate, and provides the country/region name along with the type of lockdown (if any): full or partial, along with the province name (if available).

The dataset is structured as follows:

Country/Region: name of the country
Province: name of Province
Date: date when the lockdown began, null if no lockdown exists (dd-mm-yyyy format)
Type: type of lock down (Full, Partial or None)
Reference: URL Reference of the source of information.

We found out that this dataset is not enough, this is why we scraped data from the COVID-19 lockdowns Wikipedia article

Contributing

To contribute to this repository, start by forking this repository, then create a new branch for each change (new feature, code refactoring, adding tests, adding docs, fixing a bug, etc.). Please name your branches according to these conventions.

Usage

Ideally, all the project's dependencies should be included in a requirements.txt file for reproducibility and portability purposes. In this case, you only need to create a virtual environment, activate it, then run the following command to install the required dependencies:

pip install -r requirements.txt

Code Quality Standards

We'll be using PEP 8 as a style guide for our Python code.

stoufa/Lockdowns-Impact-on-Air-Quality