Visitor Journey
Visitor Journey on Places
Where a location's visitors go before and after visiting, as directed flows between places and each destination's share of those journeys.
What does Visitor Journey tell me?
Visitor Journey shows the places visitors go to before and after a chosen location, and how strongly locations connect. Each row is a pair of locations with a direction of travel and the share of journeys that run between them.
The unit is journeys, not people: one visitor making several onward trips counts once per trip.
What questions does it answer?
- Where do this location's visitors come from and go next?
- Which locations are commonly visited in sequence with it?
- Does an attraction draw from nearby hubs and lodging, or stand alone?
- Which onward destinations should transport or retail planning account for?
Reading this cadence
Journeys are summarised over the period this page covers, with alternative time-of-day, day-of-week, and immediacy views of the same journeys.
How it's produced
When the same visitor is seen at one location and then another, that movement is recorded as a journey with an origin and a destination, then aggregated into directed flows between location pairs.
How to read it
- Direction matters. Each pair is inbound to or outbound from the anchor location; read the two directions separately.
- Shares add up to 100%. For a given location and direction, the shares across all related locations add up to about 1 (100%), so 0.30 means 30% of those journeys go to that related place.
- Each extra column is a separate view. Time-of-day, day-of-week, and immediacy are each their own set of shares over the same journeys, so use one view at a time and do not add them together.
- Journeys, not people, so someone who makes the same trip repeatedly is counted each time.
Related metrics
- Trade Areas, where visitors live and work (origins, not sequences).
- Foot Traffic, how busy each location in the flow is.
Country- and dataset-specific behaviour (coverage, geography units, exclusions, release timing) is documented on each dataset in its country catalog.
Related techniques
The techniques applied to the signal behind this dataset.
Home & work inference
Estimating the likely home and work areas of visitor groups, always at area level and never an address, to enable resident / worker / visitor breakdowns, trade areas, and visitor-only scoping for Visit Length.
Extrapolation
Scaling the observed sample of devices up to population-level visit estimates, so counts reflect real-world volume rather than sample size.
Privacy approach
Pine59 products are built for statistical use only, and no output can be linked to an individual person.