A Comprehensive Guide to Plotting Air Quality Index Data on a Map Using Python

In today's rapidly changing world, monitoring air quality has become more crucial than ever. Air quality data is readily available through various APIs, allowing us to harness the power of Python to visualize this data on a map. In this guide, we will explore the process of plotting air quality index (AQI) data on a map using Python and leveraging an air quality API.

Introduction
Air quality data plays a pivotal role in understanding and addressing environmental concerns. To access real-time air quality information, we can utilize air quality APIs. One such API is available at Ambee Data. Let's dive into the steps of plotting AQI data on a map.

Step 1: Obtain Air Quality Data
Ensure you have Python installed on your system. Additionally, install the required libraries:

pip install requests pandas folium

Step 3: Fetch Air Quality Data
Use Python's requests library to fetch air quality data from the Ambee Data API. Here's an example of how to do it:

import requests

api_key = 'YOUR_API_KEY'
url = 'https://api.ambeedata.com/latest/airquality/by-city'

headers = {
    'x-api-key': api_key
}

city = 'New York'  # Replace with your desired city
params = {
    'city': city
}

response = requests.get(url, headers=headers, params=params)
data = response.json()

Step 4: Process the Data
Once you have the data, extract the AQI values and relevant information. You can use the pandas library for data manipulation.

import pandas as pd

aqi_data = data['data']
df = pd.DataFrame(aqi_data)

# Extract relevant columns
df = df[['city', 'AQI', 'latitude', 'longitude']]

Step 5: Visualize on a Map
Now, let's create an interactive map using the folium library to plot the AQI data.

import folium

m = folium.Map(location=[df['latitude'].mean(), df['longitude'].mean()], zoom_start=10)

for _, row in df.iterrows():
    folium.Marker([row['latitude'], row['longitude']], tooltip=row['city'], icon=folium.Icon(color='blue')).add_to(m)

m.save('aqi_map.html')

Conclusion
You've successfully plotted air quality index data on a map using Python. This powerful visualization can help individuals, organizations, and governments make informed decisions to improve air quality. To stay up-to-date with the latest air quality data, you can automate the data retrieval process and regularly update your map. Now you have a valuable tool to monitor air quality in your desired locations.