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unicef-ai4d-air-quality

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# UNICEF AI4D Air Quality Research

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# 📜 Description

This project explored PM2.5 estimation using Machine Learning models in Thailand. Air pollution is a pressing health concern worldwide, and thus, its monitoring is important for epidemological studies and informing public policy.

However, ground sensors (whether reference grade or low-cost) are sparse. For example, in Mueang Chiang Mai, there are only a few air quality sensors based on [OpenAQ](https://openaq.org/#/) data.

Using Machine Learning models to predict PM2.5 levels based on area predictors is a potential way to augment these ground stations, and allows us to monitor air quality even in areas without them.

![Mueang Chiang Mai Ground Sensors and Daily Model Predictions, Averaged in July 2021](https://raw.githubusercontent.com/thinkingmachines/unicef-ai4d-air-quality/main/assets/fill_the_gap.png)

Read more about the study in our [manuscript](https://drive.google.com/file/d/1lzGiUWpQ_vzyvY9FfDjWEH21Hiu-H9lz/view?usp=sharing).

This project is part of the upcoming [AI4D Research Bank](https://thinkingmachin.es/ai4d-research-bank/) that aims to support data scientists working at the intersection of machine learning (ML) and development. Check it out for other resources!

# ⚙️ Local Setup for Development

Though you are free to use any python environment manager you wish, this guide will assume the usage of [miniconda](https://docs.conda.io/en/latest/miniconda.html#:~:text=Miniconda%20is%20a%20free%20minimal,zlib%20and%20a%20few%20others.).

## Requirements

1. Python 3.7+
2. make

## 🐍 One-time Set-up
Run this the very first time you are setting-up the project on a machine to set-up a local Python environment for this project.

1. Install miniconda for your environment if you don't have it yet. Either:
* Manually download and install the appropriate version from [here](https://docs.conda.io/en/latest/miniconda.html); or
* For VMs with no GUI, this is an example of how to install from your terminal:
```bash
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
```

2. Create a local python environment and activate it.
* Note:
    * You can change the name if you want; in this example, the env name is `ai4d-air-quality`.
```bash
conda create -n ai4d-air-quality python=3.7
conda activate ai4d-air-quality
```

3. Clone this repo and navigate into the folder. For example:
```bash
git clone git@github.com:thinkingmachines/unicef-ai4d-air-quality.git
cd unicef-ai4d-air-quality
```

4. Install the project dependencies by running:
    * Note:
        * This make command installs `pip-tools` (the python dependency manager),  `pre-commit` hooks (which enforce the automated formatters), and `jupyter`/`jupyter lab`.
        * If you don't have `make` available in your system, you can refer to the commands under `Makefile` > `dev` recipe. That is, copy-paste those commands into your terminal.
```bash
make dev
```

## 📦 Dependencies

Over the course of development, you might introduce new library dependencies. When you do so, please add it in the `requirements.*` files and include those with your commits so that other devs can get the updated list of project requirements.

For example, to add `pandas` as a dependency:

1. Add it to `requirements.in`:
```bash
# Sample requirements.in contents
numpy
pandas
```

2. Run `pip-compile` to re-generate the `requirements.txt` file.
```
pip-compile -v -o requirements.txt requirements.in
```

3. Finally, run `pip-sync` to make your local env follow `requirements.txt` exactly.
```
pip-sync requirements.txt
```

Other notes:
* Alternatively, we provide a shortcut for Steps 2 and 3 by running `make requirements`.

* Running `pip-sync requirements.txt` alone is also handy for updating your local conda env after you pull changes from GitHub, if another developer has added new requirements.

# 🧠 Training a Regression Model
This workflow assumes you just want to train your own models on already existing datasets.

1. Get a copy of the latest dataset in CSV format from our [Google Drive folder](https://drive.google.com/drive/folders/1c4f1TJzW7uI9IgqXZY_08pJb1YvpevG1) and place it in your local `data` folder.
2. Create a yaml config file with the training configuration that you want inside the `config` folder (see `config/default.yaml` for a sample).
    * Note: this is where you specify the path to the CSV dataset.
3. Make sure your terminal's current working directory is the project root. Run the training script by running `make config-path=config/default.yaml train`, where you should replace `default.yaml` with the actual yaml file you created from step 2.
    * If you can't run Make commands on your system, you can also run the training script manually like this:
        * `export PYTHONPATH=.` (you only need to run this once per terminal sesion)
        * `python scripts/train.py --config-path=config/default.yaml`
            * Note: You can also just call the script without a config path `python scripts/train.py`, in which case it will use `config/default.yaml`.
4. The training script should have generated results in a dated folder under `data/outputs`. The folder should contain the best model and its params, metrics, SHAP charts, and the yaml config file used.

# 📚 Generating a training dataset
This section describes how to generate a dataset used for ML  training and evaluation. The example is based on the generation of an OpenAQ-based dataset for our experiments.

If you wish to generate your own custom dataset (e.g. generate dataset for a different year), feel free to do so - just modify the parameters accordingly.

## OpenAQ Training Dataset Example

*Notes: Make sure your terminal's current working directory is at the project root.*

1. Collect raw OpenAQ data
    * Run the collection script (this example is for collecting 2021 Thailand data through the OpenAQ API):
    ```
    export PYTHONPATH=. && \
    python scripts/collect_openaq.py \
	--start-date=2021-01-01 \
	--end-date=2021-12-31 \
	--country-code=TH
    ```
    * This script will do a bit of pre-processing and generate 2 csv files in your `data/` folder (`<timestamp>` is the datetime you ran the script):
        * `daily-pm25-<timestamp>.csv`
        * `station-list-<timestamp>.csv`
    * Feel free to rename these files.
    * *Note: Over the course of development, the OpenAQ API seems to have been under active development and we encountered intermittent errors a few times. If this happens, code has to be updated to match the API changes.*
2. Add features to the OpenAQ data
    * Take note of the filenames generated by the previous step, as they are the input to the next script for collecting features.
    * Download pre-requisite files to your local:
        * Get a copy of the contents of this [GDdrive data folder](https://drive.google.com/drive/u/0/folders/1Ni-OWGovH-4gV2VhJeao0jABMW_Wp2k_) and place them in your local `data` folder.
        * The most important here are the admin boundaries (`tha_admin_bounds_adm3/`) and population data (`tha_general_2020.tif`).
    * Sign-up for a [Google Earth Engine account](https://signup.earthengine.google.com/) if you don't have one yet, as the script uses the GEE API to collect some of the features. It will ask you to log-in when you run it.
    * Finally, run the script with the appropriate parameters. The ff. is an example, but you should change the `--locations-csv` and `--ground-truth-csv` arguments accordingly to the files generated from step 1:
    ```
    export PYTHONPATH=. && python scripts/generate_features.py \
	--locations-csv=data/2022-05-06-openaq-th-stations.csv \
	--ground-truth-csv=data/2022-05-06-openaq-daily-pm25.csv \
	--admin-bounds-shp=data/tha_admin_bounds_adm3/tha_admbnda_adm3_rtsd_20220121.shp \
	--hrsl-tif=data/tha_general_2020.tif \
	--start-date=2021-01-01 \
	--end-date=2021-12-31
    ```
    * This should generate an ML-ready file of the format: `generated_data_<timestamp>.csv` in your `data/` folder. As usual, feel free to rename the file if you wish.

# 🌍 Predicting PM2.5 levels at a target location
We provide a sample notebook for illustrating how one might use a trained model on a location in Thailand. The notebook can be found in the `notebooks/2022-05-18-prediction-example` folder. This notebook contains more explanations, and has some light EDA and viz on sample predictions for a district in Chiang Mai.

There is also a script version for just running predictions on an input CSV file of locations (the expected format of this is described in the notebook). Please run `export PYTHONPATH=. && python scripts/predict.py --help` to see details on the usage.

![Mueang Chiang Mai Daily Model Predictions, Averaged per Month for 2021](https://raw.githubusercontent.com/thinkingmachines/unicef-ai4d-air-quality/main/assets/model_predictions_throughout_year.gif)

*Mueang Chiang Mai Daily Model Predictions, Averaged Monthly for 2021*

# Acknowledgements
This work was supported by the [UNICEF Venture Fund](https://www.unicef.org/innovation/venturefund) in collaboration with the [UNICEF East Asia and Pacific Regional Office (EAPRO)](https://blogs.unicef.org/east-asia-pacific/about/).

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