USAGE_CLOCK
The Rhythm of Zcash
- Transactions
- 2,788,414
- Busiest hour
- 16:00 UTC
- Quietest hour
- 02:00 UTC
- Peak / quietest
- 1.64×
Daily activity
Selected hour · UTCBar length shows transaction volume relative to the busiest hour. The inner ring gets stronger as more nodes enter daylight.
Daylight simulation for today at the selected hour · 482 observed nodes. Sun over the western Atlantic; daylight near Americas, S. America, Europe, Africa. Explore nodes
Daylight correlation
Pearson correlation between hourly transactions and the share of observed nodes in daylight. Ranges from −1 to +1; it does not establish a cause or a user location.
Timing & node geography
Compare a modeled timing mix with observed node locations. Timing weights describe how well three daily profiles fit the activity curve; they do not locate users.
Thick bars: fitted timing weights. Thin bars: observed node shares. Node locations can reflect hosting or VPN endpoints, rather than where people live.
Activity vs. baseline
Observed share of transactions minus a daily routine model weighted by node locations, in percentage points. A difference is not evidence of bots or automated activity.
Seven days, twenty-four hours
Low → high · UTCHow to read these charts
Activity is grouped by UTC block timestamp across the selected period. Heatmap cells are total transactions for each weekday/hour combination, not daily averages. The dial uses a zero baseline and compares each hour with the busiest hour.
The daylight layer uses today’s solar declination at the selected UTC hour. It is a simulation, not a live transaction stream or a reconstruction of historical sunlight. Node observations describe the current available network snapshot.
The timing model fits three fixed routine profiles at UTC−6, UTC+1 and UTC+8 in 2% weight steps. The baseline weights the same routine by node counts, with offsets approximated from longitude. Neither model measures people’s locations; no adjustment is made for daylight saving or regional behavior.
Block timestamps describe when transactions were recorded. Node geography, timing weights and baseline differences cannot identify users or distinguish human activity from automation. Explore privacy metrics.