Track Nearby Aircraft with ADS-B and Visualize Patterns

Install Python and the libraries you'll need: pip install requests pandas matplotlib numpy scikit-learn. The OpenSky Network provides a free REST API at opensky-network.org/api — no authentication needed for basic queries (though a free account increases your query limits).

Make your first API call: request all aircraft within a bounding box around your local airport. The response is a JSON array where each entry contains: ICAO24 address (unique aircraft identifier), callsign, origin country, longitude, latitude, altitude, velocity, heading, and vertical rate. Parse this with Python's requests library and print a few entries to understand the data structure.

Write a Python script that queries the API every 60 seconds and saves the results to a CSV file. Run it for several hours (or overnight) to build a dataset. Define your bounding box to cover approximately 50–100 nautical miles around your chosen airport.

Each query snapshot gives you the positions of all aircraft in your bounding box at that moment. Over time, you'll accumulate a trajectory dataset — the same aircraft appearing at multiple positions as it crosses your area. Store all data in a pandas DataFrame with columns: timestamp, icao24, callsign, latitude, longitude, altitude, velocity, heading.

Group your data by icao24 address (each aircraft has a unique one). For each aircraft that appears in at least 5 snapshots, connect its positions in time order to form a flight path. Plot all paths on a single matplotlib figure with longitude on the x-axis and latitude on the y-axis.

You'll immediately see patterns emerge: arrivals converge toward the airport from specific directions (these follow published approach procedures), departures fan out, and overflights cross at high altitude. Color-code the paths by altitude to distinguish arrivals (descending, lower altitude) from overflights (cruising at high altitude).

Use pandas grouping and aggregation to answer questions about your airport's traffic: What's the busiest hour of the day? How many unique aircraft pass through per hour? What's the altitude distribution — are most aircraft at cruise altitude (30,000–40,000 feet) or in the landing/takeoff pattern (below 10,000 feet)?

Create bar charts and histograms: traffic count by hour, altitude histogram, speed distribution. If callsigns include airline codes (e.g., "UAL" for United, "DAL" for Delta), compute a breakdown of traffic by airline. These are the same traffic statistics that airport authorities use for planning.

Use k-means clustering from scikit-learn to automatically group flights with similar patterns. For each flight track, extract simple features: average heading (direction of travel), average altitude, and minimum distance from the airport. Feed these features into k-means with k=4–6 clusters.

Plot the results, coloring each flight path by its cluster assignment. You should discover meaningful groups: northbound arrivals, southbound arrivals, eastbound departures, high-altitude overflights, etc. This is unsupervised machine learning — the algorithm found these patterns without you telling it what to look for. Experiment with different values of k and see how the groupings change.

Create a summary report or poster with your best visualizations: the flight path map (colored by cluster), traffic statistics charts, and a description of the patterns you discovered. Include insights: which runways appear to be in use (arrival paths align with runway headings), what time traffic peaks, and whether patterns change between weekdays and weekends.

Discuss data quality issues you encountered: gaps in ADS-B coverage (some aircraft don't broadcast, especially military), noisy position data, and the limitation that ground-level reception range means you only see aircraft above a certain altitude. These are real challenges that aviation data scientists deal with daily.