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Of course, here's a Python solution using matplotlib to visualize your aircraft height data:

```python
import matplotlib.pyplot as plt
import numpy as np

def visualize_aircraft_heights(height_data):
    """
    Visualize aircraft height data where:
    - keys are heights (integers)
    - values are lists of aircraft at that height
    
    Parameters:
    height_data (dict): Dictionary with heights as keys and lists of aircraft as values
    """
    # Sort heights to ensure they're in ascending order
    heights = sorted(height_data.keys())
    
    # Get all unique aircraft IDs
    all_aircraft = set()
    for aircraft_list in height_data.values():
        all_aircraft.update(aircraft_list)
    all_aircraft = sorted(list(all_aircraft))
    
    # Create a figure with two subplots
    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 10))
    
    # First plot: Count of aircraft at each height
    counts = [len(height_data[h]) for h in heights]
    ax1.plot(heights, counts, marker='o', linestyle='-', linewidth=2)
    ax1.set_title('Number of Aircraft at Each Height')
    ax1.set_xlabel('Height (ft)')
    ax1.set_ylabel('Count of Aircraft')
    ax1.grid(True)
    
    # Second plot: Individual aircraft presence at different heights
    # Create a matrix to represent aircraft presence
    presence_matrix = np.zeros((len(all_aircraft), len(heights)))
    
    for i, height in enumerate(heights):
        aircraft_at_height = height_data[height]
        for j, aircraft in enumerate(all_aircraft):
            if aircraft in aircraft_at_height:
                presence_matrix[j, i] = 1
    
    # Plot each aircraft's presence
    for i, aircraft in enumerate(all_aircraft):
        ax2.step(heights, presence_matrix[i], where='mid', label=aircraft, linewidth=2)
    
    ax2.set_title('Individual Aircraft Presence by Height')
    ax2.set_xlabel('Height (ft)')
    ax2.set_ylabel('Present (1) / Absent (0)')
    ax2.set_yticks([0, 1])
    ax2.set_ylim(-0.1, 1.1)
    ax2.legend(loc='center left', bbox_to_anchor=(1, 0.5))
    ax2.grid(True)
    
    plt.tight_layout()
    plt.show()
    
    # Bonus: Display a heatmap view
    fig, ax3 = plt.subplots(figsize=(12, 6))
    im = ax3.imshow(presence_matrix, aspect='auto', cmap='Blues')
    
    # Set ticks and labels
    ax3.set_xticks(np.arange(len(heights)))
    ax3.set_xticklabels(heights)
    ax3.set_yticks(np.arange(len(all_aircraft)))
    ax3.set_yticklabels(all_aircraft)
    
    ax3.set_title('Aircraft Presence Heatmap')
    ax3.set_xlabel('Height (ft)')
    ax3.set_ylabel('Aircraft')
    
    # Add a colorbar
    cbar = plt.colorbar(im)
    cbar.set_ticks([0, 1])
    cbar.set_ticklabels(['Absent', 'Present'])
    
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.show()

# Example usage
if __name__ == "__main__":
    # Sample data
    aircraft_heights = {
        0: ['Aircraft1', 'Aircraft2', 'Aircraft3'],
        1000: ['Aircraft1', 'Aircraft2', 'Aircraft4', 'Aircraft5'],
        2000: ['Aircraft1', 'Aircraft3', 'Aircraft4'],
        3000: ['Aircraft2', 'Aircraft3', 'Aircraft5'],
        4000: ['Aircraft1', 'Aircraft4'],
        5000: ['Aircraft3', 'Aircraft5'],
        6000: ['Aircraft1', 'Aircraft2'],
        7000: ['Aircraft4'],
        8000: ['Aircraft1', 'Aircraft3', 'Aircraft5'],
        9000: ['Aircraft2'],
        10000: ['Aircraft1', 'Aircraft3', 'Aircraft4']
    }
    
    visualize_aircraft_heights(aircraft_heights)
```

This code creates three visualizations:
1. A line chart showing the count of aircraft at each height
2. A step chart showing the presence/absence of each aircraft at different heights
3. A heatmap visualization showing all aircraft and their presence at each height

To use it with your data, just replace the `aircraft_heights` dictionary with your own. The keys should be integers representing heights, and the values should be lists of aircraft identifiers at those heights.​​​​​​​​​​​​​​​​