HAVERSINE Documentation

Complete guide to integrating and using HAVERSINE spatial analytics.

Introduction
Understanding HAVERSINE's approach to spatial on-chain analytics

HAVERSINE is an AI-powered spatial analytics platform designed specifically for the Solana blockchain. By mapping wallet behaviour, transaction patterns, and liquidity flows into spatial coordinates, HAVERSINE provides unprecedented insights into on-chain activity.

The platform uses advanced machine learning algorithms to identify patterns, detect anomalies, and cluster similar behaviours across the Solana ecosystem. This spatial approach enables analysts and developers to visualize complex on-chain relationships in an intuitive, data-driven manner.

Core Concepts
Fundamental principles behind spatial analytics

Spatial Coordinates

Every wallet and transaction is mapped to a multi-dimensional coordinate space based on behavioural features including transaction frequency, volume, token interactions, and temporal patterns.

Behavioural Distance

The Haversine formula, adapted for on-chain data, measures the "distance" between wallet behaviours. Smaller distances indicate similar patterns, while larger distances suggest divergent activity.

Clustering & Classification

Machine learning algorithms automatically group wallets into clusters based on their spatial proximity, revealing communities, trading patterns, and potential risk indicators.

Behaviour Distance Model
Mathematical foundation of spatial analysis

The behaviour distance model is based on a modified Haversine formula that calculates the great-circle distance between two points on a sphere. HAVERSINE adapts this to multi-dimensional behavioural space:

d = 2r × arcsin(√(sin²((φ₂-φ₁)/2) + cos(φ₁)cos(φ₂)sin²((λ₂-λ₁)/2)))

Where φ represents behavioural latitude (transaction patterns), λ represents behavioural longitude (token interactions), and r is the radius of the behavioural space.

Key Features

  • Transaction volume weighting
  • Temporal decay functions
  • Token affinity scoring
  • Network effects normalization
API Reference
REST API endpoints for accessing HAVERSINE data

Wallet Similarity

Calculate behavioural distance between two wallets

POST https://api.haversine.network/v1/similarity
Content-Type: application/json

{
  "wallet_a": "7xKX...abc",
  "wallet_b": "9yPQ...xyz",
  "time_range": "30d"
}

Cluster Analysis

Identify wallet clusters and communities

GET https://api.haversine.network/v1/clusters?token=SOL&min_size=10

Response:
{
  "clusters": [
    {
      "id": "cluster_1",
      "size": 1247,
      "centroid": {...},
      "characteristics": ["high_frequency", "defi_focused"]
    }
  ]
}

Liquidity Flow

Track liquidity trajectories over time

POST https://api.haversine.network/v1/liquidity/flow
Content-Type: application/json

{
  "token": "SOL",
  "start_time": "2024-01-01T00:00:00Z",
  "end_time": "2024-01-31T23:59:59Z",
  "granularity": "hourly"
}
Security & Data Integrity
Privacy and security best practices

Data Privacy

HAVERSINE analyzes only publicly available on-chain data. No private keys, personal information, or off-chain data is collected or stored. All analysis is performed on anonymized wallet addresses.

API Authentication

API access requires authentication via API keys or wallet signatures. Rate limits apply to all endpoints to ensure service availability.

Authorization: Bearer YOUR_API_KEY
X-Wallet-Signature: SIGNED_MESSAGE

Data Integrity

All on-chain data is verified against multiple Solana RPC nodes. Analytics results include confidence scores and data freshness indicators to ensure reliability.