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 (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique wallet addresses that initiated at least one transaction on the Worldchain blockchain within a 7-day rolling window. This metric provides insight into user engagement and adoption by tracking distinct active participants on the network. The calculation uses HyperLogLog algorithms for efficient approximate counting of distinct addresses across time windows, avoiding memory constraints when processing large datasets.

## 2. Data sources

The metric relies on the following raw data sources:
- **Worldchain transaction records**: Raw transaction data from the Worldchain blockchain containing transaction metadata, timestamps, sender addresses, and transaction hashes
- **Blockchain transaction logs**: Detailed transaction execution data including block timestamps and participant addresses

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identify unique active addresses on a daily basis from transaction activity
- **Logic**: Extract all transaction senders (`from_address`) from Worldchain transaction records, filtering for valid addresses that initiated transactions. Group transactions by day using timestamp truncation to day granularity. For each day, identify the distinct set of addresses that sent at least one transaction, creating a daily snapshot of active users.
- **Key fields**: `block_timestamp` for temporal grouping, `from_address` as the unique user identifier, `transaction_hash` for transaction validation, `user_address` as the standardized address field

### Component B: HyperLogLog sketch creation
- **Purpose**: Convert daily distinct address counts into memory-efficient probabilistic data structures for scalable aggregation
- **Logic**: Transform the daily distinct address lists into HyperLogLog sketches using a precision parameter of 15 bits. This allows for approximate distinct counting with minimal memory usage while maintaining accuracy for large datasets. Each day's active addresses are encoded into a single HLL sketch that can be efficiently merged with other time periods.
- **Key fields**: `hll` containing the encoded sketch data, `timestamp` for temporal alignment, dimensional fields for breakdown analysis

### Component C: Rolling window construction
- **Purpose**: Create 7-day rolling windows for each target date to calculate weekly active user counts
- **Logic**: Generate a datespine covering the full date range of available data. For each date in the spine, create a 7-day window spanning from 6 days prior through the current date (inclusive). This creates overlapping windows where each day serves as the end date for its own 7-day period. Join the window dates with the daily HLL sketches to align temporal data for aggregation.
- **Key fields**: `timestamp` as the window end date, `joinday` representing each day within the 7-day window, temporal alignment keys for accurate window construction

### Component D: Weekly active address calculation
- **Purpose**: Merge HyperLogLog sketches across 7-day windows to count distinct addresses without double-counting
- **Logic**: For each 7-day window, collect all HLL sketches from constituent days and merge them using HyperLogLog union operations. This produces a single merged sketch representing all distinct addresses active within the window period. Extract the approximate distinct count from the merged sketch to obtain the weekly active address count. The HLL algorithm ensures addresses active on multiple days within the window are counted only once.
- **Key fields**: `user_wau` containing the final weekly distinct address count, `timestamp` marking the window end date, dimensional breakdown fields

### Component E: Dimensional breakdown
- **Purpose**: Calculate weekly active addresses across different analytical dimensions for comprehensive reporting
- **Logic**: Group the weekly calculations by various dimensions including chain-level analysis, project-level aggregation, user type classification, and product-specific breakdowns. Each dimension provides a different analytical perspective on the same underlying address activity. Combine all dimensional breakdowns using union operations to create a comprehensive dataset supporting multiple analytical use cases.
- **Key fields**: `dimensions` indicating the analytical perspective, `chain_id` for blockchain-specific analysis, `user_type` for participant classification, `product_id` for product-specific metrics

### Final aggregation
- **Combining components**: The dimensional breakdowns are combined through union operations, creating a unified dataset with multiple analytical perspectives on weekly active addresses
- **Currency conversion**: Not applicable for this address-based metric
- **Time aggregation**: Weekly periods are calculated as 7-day rolling windows, with each day serving as the end date for its respective weekly period
- **Final calculation**: The final `active_addresses_weekly` value represents the approximate count of distinct wallet addresses that initiated at least one transaction within the 7-day window ending on the specified timestamp, calculated using merged HyperLogLog sketches for memory-efficient distinct counting

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

Unique active addresses over a rolling 7-day window.

World ChainWLD

Active addresses (weekly)

Active addresses (weekly)

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