Crypto Explorer

Crypto Explorer is a real-time Bitcoin data visualization tool developed for the Data Observatory at Imperial College London. Built on an existing demo created by a former MSc Computing student, Crypto Explorer retains the original functionality of visualizing Bitcoin transactions in a force-directed graph. It enhances this foundation with additional features, improved performance, and distributed rendering across multiple clients.

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Features

Distributed Rendering

Crypto Explorer distributes the rendering of the visualization across multiple clients, where each client represents a screen or web tab. Each client is assigned a portion of the graph to render based on a row-column system, ensuring efficient and scalable performance.

Dynamic Data-Driven Visualization

Transaction values and wallet balances are dynamically visualized, with edge thickness representing transaction sizes and node size reflecting wallet balances. This provides clear and intuitive insights into Bitcoin transactions.

Statistical Data Visualizations

Interactive statistical graphs, including histograms and line charts, dynamically display transaction metrics. These visualizations make it easier to identify patterns and anomalies in real-time.

Historical Data Comparison

Users can save snapshots of the current graph and statistical measures, then compare multiple snapshots side-by-side to analyze historical trends.

Enhanced Interactivity

Crypto Explorer allows users to:

• Apply filters to focus on specific transactions.
• View detailed information for selected transactions.

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Technologies Used

• Python:
  • Backend for real-time data processing.
  • Retrieves Bitcoin data using Blockchain.com's WebSocket API.
• JavaScript:
  • Visualization rendering implemented using D3.js.
• Socket.IO:
  • Facilitates real-time communication between the server and clients.

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How It Works

1. Data Processing:

   • The server retrieves real-time Bitcoin transaction data from Blockchain.com's WebSocket API.
   • Transactions are processed and added to a global graph, which maintains the entire visualization dataset.

2. Server-to-Client Communication:

   • The server broadcasts the entire graph dataset to all connected clients using Socket.IO.
   • Each client represents a screen or web tab and is assigned a specific portion of the graph to render based on its row-column position.

3. Client Rendering:

   • Clients filter the graph data they receive to extract only the nodes and edges within their assigned portion, determined by their row and column parameters.
   • Using D3.js, clients render their portion of the graph, ensuring the visualization spans multiple screens seamlessly.

4. Updates:

   • The server periodically updates the graph as new transactions are processed.
   • Updated graph data is sent to the clients, ensuring real-time changes are reflected dynamically.