jeanbayiha24/DataPreprocessing

Comparative study of air quality data of Senegal(Dakar) and Cameroon(Douala) # DataPreprocessing – Air Quality Dakar vs Douala This project performs data preprocessing and exploratory analysis on **air quality measurements** for two African coastal cities: - **Dakar, Senegal** - **Douala, Cameroon** The aim is to transform raw data from the OpenAQ platform into a clean, well‑structured format and then compare air quality indicators between the two locations over a common time window (23 September 2024 to 27 October 2024). --- ## Context Air quality is a critical environmental and public health issue, especially in rapidly growing urban areas. This project focuses on Dakar and Douala, using data collected by **AirGradient** sensors and accessed via OpenAQ. The work was carried out at **AIMS‑Senegal** by Group 5 “Open Air Quality”, supervised by Dr. Rockefeller (Stellenbosch University). --- ## Data Sources The notebook uses two CSV files (downloaded from OpenAQ): - `data/air-qualdakarsept_oct.csv` – measurements for Dakar - `data/air_qualdoualasept_oct.csv` – measurements for Douala Each raw dataset contains, among others, the following columns: - `location_id`, `location_name` - `sensor_id` (in the original API responses) - `parameter` (type of pollutant or meteorological variable) - `value`, `unit` - `datetimeUtc`, `datetimeLocal`, `timezone` - `latitude`, `longitude` - `owner_name`, `provider` - Mobility/monitor flags --- ## Preprocessing Objectives The preprocessing in the notebook is designed to: 1. **Transform raw, long-format measurements** into a tidy **wide-format** dataset where each parameter becomes a separate column. 2. **Align temporal information** (local datetime, date, and time) and select a common analysis window for both cities. 3. **Select relevant air quality and meteorological variables** for comparative analysis. 4. Prepare data structures that are convenient for plotting time series and computing summary statistics. --- ## Preprocessing Steps ### 1. Load Raw Data For each city, the notebook: - Read …