Temporal and Geographical Analysis of Real Economic Activities in the Bitcoin Blockchain

Paper: arXiv 2307.08616

Authors: Rafael Ramos Tubino, Remy Cazabet, Natkamon Tovanich, Celine Robardet

Abstract

We study the real economic activity in the Bitcoin blockchain that involves transactions from/to retail users rather than between organizations such as marketplaces, exchanges, or other services. We first introduce a heuristic method to classify Bitcoin players into three main categories: Frequent Receivers (FR), Neighbors of FR, and Others. We show that most real transactions involve Frequent Receivers, representing a small fraction of the total value exchanged according to the blockchain, but a significant fraction of all payments, raising concerns about the centralization of the Bitcoin ecosystem. We also conduct a weekly pattern analysis of activity, providing insights into the geographical location of Bitcoin users and allowing us to quantify the bias of a well-known dataset for actor identification.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 6.8/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper provides strong empirical analysis of on-chain Bitcoin transactions using intuitive heuristics rather than advanced mathematical modeling. Its main value lies in uncovering structural insights regarding centralization and user geography through temporal patterns, though reproducibility is slightly constrained by actor identification datasets.

Research Flowchart

  flowchart TD
    A[Research Goal: Analyze Real Economic Activity & Centralization in Bitcoin] --> B[Input: Bitcoin Blockchain Data & Wallet Entity Dataset]
    B --> C[Method: Classify Players via Heuristic Clustering]
    C --> D[Player Categories: Frequent Receivers, FR Neighbors, & Others]
    D --> E[Temporal Pattern Analysis: Weekly Activity & Timezone Mapping]
    E --> F[Findings: Retail vs. Org Centralization & Dataset Bias Identified]