Elevation Data Sorting Visualizer

Live Demo Video: https://www.youtube.com/watch?v=Vpd3DNyJ0XE

A Python-based project that demonstrates how common sorting algorithms work — using real elevation data from NOAA’s global elevation dataset. This project visualizes the step-by-step sorting of elevation values sampled from randomly selected geographic coordinates, making it easier to understand how each algorithm functions.

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Problem

Sorting algorithms can be hard to understand in the abstract. Raw elevation data is also tough to interpret on its own. This project addresses both problems by combining them: visualizing sorting algorithms using real-world elevation data.

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Motivation

• Help students better understand how sorting algorithms behave over time.
• Make sense of NOAA elevation data through color-coded visualizations.
• Provide interactive exploration of different sorting strategies.

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Features

• Retrieves elevation data for a random 5.3° x 5.3° bounding box using the BRIDGES elevation dataset.
• Visualizes a sample of the elevation data as a bar graph using matplotlib.
• Animates the sorting process using various algorithms:
  • Bubble Sort
  • Insertion Sort
  • Selection Sort
  • Merge Sort
  • Heap Sort
  • Quick Sort
• Displays the time taken to sort and the real-world location of the highest elevation point in the dataset using reverse geocoding.
• Allows selection of sorting algorithm from the command line.

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How It Works

1. Data Collection: Random geographic coordinates are generated and used to pull elevation data from NOAA via the BRIDGES API.
2. Flattening: The elevation grid is flattened into a list for sorting.
3. Visualization: A matplotlib animation shows the sorting progress on a sample of the data.
4. Reverse Lookup: The highest elevation is reverse-geocoded to find its real-world location.

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Tools & Libraries

• Python 3.11+
• matplotlib – for animation/visualization
• numpy – for efficient array manipulation
• requests – for reverse geocoding lookups
• bridges – for accessing NOAA elevation data

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Setup Instructions

1. Clone the repository:

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the visualizer:

   python main.py quick     # options: quick, heap, merge, bubble, insertion, selection