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 (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique addresses that initiated at least one transaction on the Worldchain network within a rolling 30-day window. This metric provides insight into user engagement and network activity by tracking distinct transaction senders over monthly periods. The calculation uses a rolling window approach where each day represents the count of unique addresses active in the current day plus the previous 29 days, implemented using HyperLogLog approximation for efficient computation at scale.

## 2. Data sources

The calculation relies on the following raw data sources:

- **Worldchain transaction data**: Raw blockchain transaction records containing transaction hashes, block timestamps, sender addresses (`from_address`), and other transaction metadata from the Worldchain L2 network
- **Transaction timing data**: Block timestamp information used to determine when transactions occurred for daily and monthly aggregation

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identify unique transaction senders per day to establish daily active users
- **Logic**: Extract transaction sender addresses from Worldchain transaction records using the `from_address` field. Each transaction sender is classified as a "Transaction sender" user type within the "blockchains_l2" market sector. Block timestamps are truncated to day granularity to group transactions by calendar day. The system then counts distinct sender addresses per day to establish daily active user counts.
- **Key fields**: `block_timestamp` (transaction timing), `from_address` (transaction sender address), `transaction_hash` (unique transaction identifier)

### Component B: HyperLogLog sketch creation  
- **Purpose**: Convert daily distinct user counts into efficient approximate counting structures for large-scale aggregation
- **Logic**: Transform daily active user data into HyperLogLog sketches with precision parameter 15. This probabilistic data structure enables efficient approximate distinct counting across large datasets and rolling time windows. Each day's unique users are converted into an HLL sketch that can be merged with other sketches while maintaining accuracy.
- **Key fields**: `timestamp` (day-level granularity), `user_address` (distinct transaction sender), `hll` (HyperLogLog sketch representation)

### Component C: Rolling window construction
- **Purpose**: Create 30-day rolling windows for monthly active user calculation
- **Logic**: Generate a continuous datespine covering the full time range of available data to ensure no missing days in the time series. For each day in the datespine, create associations with that day plus the previous 29 days to form 30-day windows. Join this window structure with the daily HLL sketches to associate each day with all relevant historical sketch data needed for the rolling calculation.
- **Key fields**: `timestamp` (current day), `joinday` (days within the 30-day window), `hll` (associated HLL sketches)

### Component D: Multi-dimensional MAU calculation
- **Purpose**: Calculate monthly active users across different analytical dimensions
- **Logic**: Merge HLL sketches within each 30-day window and extract the final distinct count to produce Monthly Active Users (MAU). This calculation is performed across four different dimensions: project_dbt_tag level (overall project aggregation), chain level (Worldchain-specific metrics), user_type level (transaction sender breakdown), and product_id level (product-specific analysis). Each dimension provides a different analytical view of the same underlying user activity.
- **Key fields**: `user_mau` (monthly active user count), `dimensions` (analytical breakdown level), `chain_id` (blockchain identifier), `user_type` (user classification)

### Final aggregation
- **Combining components**: Union all dimensional MAU calculations into a single dataset containing multiple analytical perspectives of monthly active users
- **Currency conversion**: Not applicable for this address-based metric
- **Time aggregation**: Each day represents a 30-day rolling window, with monthly boundaries determined by the rolling calculation rather than calendar months
- **Final calculation**: Rename the `user_mau` field to `active_addresses_monthly` since for blockchain networks, active addresses are equivalent to active users (unique transaction senders). Filter to include only chain-level and project_dbt_tag-level dimensions for the final output, applying chain availability filtering to ensure data quality.

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

Unique addresses active over a rolling 30-day window.

World ChainWLD

Active addresses (monthly)

Active addresses (monthly)

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