Challenge: Cluster Monitoring Stations
Clustering can help you uncover patterns in environmental data that are not immediately obvious. Suppose you have a dataset containing average annual concentrations of several pollutants—such as nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and particulate matter (PM10)—from a set of air quality monitoring stations spread across a region. By grouping these stations according to their pollutant profiles, you can identify areas with similar pollution characteristics, which can inform targeted interventions and further study.
To begin, you need a pandas DataFrame representing the pollutant concentrations at each station. This DataFrame will contain rows for each station and columns for each pollutant. Once the data is prepared, you will use the KMeans algorithm from scikit-learn to cluster the stations. After clustering, you will visualize the results to interpret the spatial and environmental significance of the clusters.
Clustering is an unsupervised learning technique, meaning it does not use labeled outcomes but instead finds structure in the data itself. The choice of the number of clusters (n_clusters) is important and may require domain knowledge or experimentation. You can try different values to see which grouping makes the most sense for your environmental context.
Swipe to start coding
Cluster the following monitoring stations based on their annual pollutant concentrations and visualize the results.
- Use the provided DataFrame with columns:
Station,NO2,SO2,PM10. - Apply
KMeansclustering with 3 clusters. - Add a
Clustercolumn to the DataFrame. - Create a scatter plot of
NO2vsPM10, coloring points by cluster and labeling each point with the station name.
Data:
| Station | NO2 | SO2 | PM10 |
|---|---|---|---|
| North | 32 | 12 | 40 |
| South | 45 | 20 | 55 |
| East | 28 | 9 | 35 |
| West | 55 | 25 | 65 |
| Central | 38 | 15 | 48 |
| SuburbA | 22 | 8 | 30 |
| SuburbB | 25 | 10 | 33 |
| Industrial | 70 | 30 | 80 |
| Park | 18 | 7 | 28 |
| Airport | 60 | 22 | 70 |
Requirements:
- Import necessary libraries.
- Prepare the DataFrame.
- Perform KMeans clustering (
n_clusters=3). - Add cluster labels.
- Plot
NO2vsPM10, color by cluster, label points with station names.
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Can you show me how to prepare the DataFrame for clustering?
How do I choose the right number of clusters for KMeans?
What are some ways to visualize the clustering results?
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Clustering can help you uncover patterns in environmental data that are not immediately obvious. Suppose you have a dataset containing average annual concentrations of several pollutants—such as nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and particulate matter (PM10)—from a set of air quality monitoring stations spread across a region. By grouping these stations according to their pollutant profiles, you can identify areas with similar pollution characteristics, which can inform targeted interventions and further study.
To begin, you need a pandas DataFrame representing the pollutant concentrations at each station. This DataFrame will contain rows for each station and columns for each pollutant. Once the data is prepared, you will use the KMeans algorithm from scikit-learn to cluster the stations. After clustering, you will visualize the results to interpret the spatial and environmental significance of the clusters.
Clustering is an unsupervised learning technique, meaning it does not use labeled outcomes but instead finds structure in the data itself. The choice of the number of clusters (n_clusters) is important and may require domain knowledge or experimentation. You can try different values to see which grouping makes the most sense for your environmental context.
Swipe to start coding
Cluster the following monitoring stations based on their annual pollutant concentrations and visualize the results.
- Use the provided DataFrame with columns:
Station,NO2,SO2,PM10. - Apply
KMeansclustering with 3 clusters. - Add a
Clustercolumn to the DataFrame. - Create a scatter plot of
NO2vsPM10, coloring points by cluster and labeling each point with the station name.
Data:
| Station | NO2 | SO2 | PM10 |
|---|---|---|---|
| North | 32 | 12 | 40 |
| South | 45 | 20 | 55 |
| East | 28 | 9 | 35 |
| West | 55 | 25 | 65 |
| Central | 38 | 15 | 48 |
| SuburbA | 22 | 8 | 30 |
| SuburbB | 25 | 10 | 33 |
| Industrial | 70 | 30 | 80 |
| Park | 18 | 7 | 28 |
| Airport | 60 | 22 | 70 |
Requirements:
- Import necessary libraries.
- Prepare the DataFrame.
- Perform KMeans clustering (
n_clusters=3). - Add cluster labels.
- Plot
NO2vsPM10, color by cluster, label points with station names.
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