This repo serves as the code implementation of the project assigned in the module ELEC0054 in the Department of Electronic and Electrical Engineering in UCL. In this project, we design and implement an integrated air quality monitoring system with the functionalities of forecasts and visualization. To be more specific, we focus on predicting as well as mapping the concentration of six main air pollutants (namely PM2.5, PM10, CO, NO2, SO2, O3) at any location within London areas over considerable time in the future.
- Python 3.7+ (Python 3.7.11 is recommended which is the version used in the development.)
- Required modules
- check requirement.txt to ensure all modules included have been installed
- or run
pip3 install -r requirements.txtin terminal to install with ease
- Clone this repo into your local directory
<dir_path>.
git clone https://github.com/UCL-SmartCity/Air-quality-forcast-and-visualization.git <dir_path>- Download the database backup from Google Drive and restore from the folder
<folder_path>using the command below.
mongorestore -h 127.0.0.1:27017 -d ucl-smartcity <folder_path>- Run
database_update/main.pyto update the air quality and weather data to the latest.
cd <dir_path>/database_update
python3 main.py- Open
forecast.ipynbto carry out a series of data preprocessing and modeling process. Need to mention that the Data Analysis and Modeling (demo) section in the notebook is for the propose of detailed illustration of the whole procedure and not necessary to execute. Skip it for a quick run. - Run
visualization(pure_py)to visualize the data using plotly. A website will open automatically to show the mapping. Play with it.
cd <dir_path>
python3 visualization(pure_py).py
- Run
visualization(py+js)to visualize the data using Google Maps API. Open the urlhttp://0.0.0.0:5000/in your browser to see the mapping.
cd <dir_path>
python3 visualization(py+js).py
If you run into problems, please either file a github issue or send an email to [email protected].