World Chain is designed as a Layer 2 blockchain built on the OP Stack and optimized for verified humans. It leverages World ID, a privacy-preserving proof of personhood, to prioritize real users over bots and Sybil attackers. It integrates with World App and other identity wallets using Safe smart contract infrastructure.

World Chain is an Ethereum Layer 2 chain that enables applications to distinguish between bots and verified humans. It forms a core part of the Worldcoin ecosystem, aiming to power more human-centric onchain activity by prioritizing real people in blockspace allocation and transaction processing.

World Chain is developed by the team behind Worldcoin, which is currently governed by the Worldcoin Foundation. Worldcoin's investors include a16z, Multicoin Capital, 1confirmation, CoinFund, among others.

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World Chain

# World Chain Active addresses (daily) Methodology

## 1. Overview

Daily active addresses measures the number of unique addresses that initiated at least one transaction on the Worldchain network within a 24-hour period. For blockchain networks, an "active address" is defined as any address that sent a transaction, making it equivalent to an active user metric. This metric answers the fundamental question: "How many unique addresses are actively using Worldchain each day?" The calculation uses HyperLogLog sketches for memory-efficient distinct counting of transaction senders.

## 2. Data sources

- **Worldchain transaction records**: Raw blockchain transaction data containing transaction metadata including timestamps, transaction hashes, and sender addresses
- **Chain availability configuration**: Internal configuration data that defines chain availability periods and data quality filters

## 3. Step-by-step calculation

### Component A: Transaction sender extraction
- **Purpose**: Extract and standardize transaction sender information from raw blockchain data
- **Logic**: Raw Worldchain transaction records are processed to identify unique transaction senders. Each transaction record contains a sender address in the `from_address` field along with a `block_timestamp`. The data is standardized by casting addresses to strings and timestamps to proper timestamp format. Additional metadata is added including chain identifier set to 'worldchain', market sector as 'blockchains_l2', business segment as 'blockchain', and user type as 'Transaction sender'
- **Key fields**: `block_timestamp` (transaction timing), `from_address` (transaction sender), `transaction_hash` (unique transaction identifier)

### Component B: Daily user identification
- **Purpose**: Count distinct active users (addresses) per day using efficient HyperLogLog sketches
- **Logic**: Transaction data is grouped by truncating timestamps to daily boundaries using day-level granularity. For each day, distinct sender addresses are identified and processed through HyperLogLog initialization with precision parameter 15. This creates memory-efficient sketches that can estimate distinct counts without storing all individual addresses. The process handles incremental updates by looking back a configurable number of days from the latest partition to capture any late-arriving data
- **Key fields**: `timestamp` (daily boundary), `user_address` (unique sender), `user_dau` (daily active user identifier)

### Component C: Multi-dimensional aggregation
- **Purpose**: Create breakdowns of daily active users across different analytical dimensions
- **Logic**: The daily HyperLogLog sketches are aggregated across multiple dimensions to support different analytical views. Four separate breakdowns are created: project-level aggregation combining all data under 'project_dbt_tag' dimension, chain-level breakdown grouping by chain identifier, user-type breakdown grouping by user classification, and product-level breakdown grouping by product identifier. Each breakdown uses HyperLogLog merge operations to combine sketches while maintaining distinct count accuracy. The final step extracts actual counts from the merged HyperLogLog sketches
- **Key fields**: `dimensions` (breakdown category), `chain_id` (chain identifier), `user_dau` (extracted distinct count)

### Final aggregation
- **Combining components**: The multi-dimensional aggregation results are filtered to include only 'chain' and 'project_dbt_tag' dimensions, effectively selecting chain-level and overall project-level active address counts while excluding user-type and product-level breakdowns
- **Currency conversion**: Not applicable for this address-based metric
- **Time aggregation**: Daily boundaries are maintained throughout the calculation with timestamps truncated to day-level granularity. Missing days are filled with zero values to ensure continuous time series
- **Final calculation**: The final active addresses count is the extracted HyperLogLog distinct count of unique sender addresses for each day and dimension combination. Chain availability filters are applied to ensure only valid time periods are included in the final results

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Unique active addresses over a fixed 24-hour window.

World ChainWLD

Active addresses (daily)

Active addresses (daily)

Competitive landscape

Chain breakdown