FAQs about car passage and footfall data

In this article, you will find various questions and their answers that you may have about our data sources regarding footfall and car passage.

What is the timeframe of the data?

• The footfall set is based on 12 months data. We often add new data if it becomes available.
• Footfall & car passage predictions are on weekly basis.

What is the source of the footfall data?

Footfall dataset is based on a wide selection of mobile app data. We get our mobile app data from different providers (we do not say which providers). Our providers collect mobile location data via an SDK anonymously (Mobile app publishers). The selection of apps is very broad and contains apps of various types (weather, traffic, TV guide, entertainment, etc.), ensuring that we do not have over  or under representation of certain profiles.

How do we translate raw mobile app data to a footfall value for road segment?

This paragraph describes the processing of mobile data through an advanced algorithmic developed by RetailSonar. Here are the key steps:

• Data Filtering: High-quality data points are retained by evaluating sampling frequency, GPS accuracy, and outliers. Devices with movement typically provide around 100 measurements daily, covering about 15 hours.
• Population Extrapolation: Weights are assigned to devices based on ZIP-code penetration rates to represent the entire population and account for local variations. If a Zipcode has 10% penetration, each device from that area is weighted accordingly.
• Data Aggregation: Weighted trajectory data is aggregated to create passage maps, detailing movement patterns on road segments.
• Validation and Calibration: Car passage statistics are validated against induction loop data (90% accuracy). Footfall and visitor data are verified using the RetailSonar Activity Map, which estimates annual visits to points of interest using various proxies (e.g., hospital beds, retail area).
• Mode Classification: Movement data is classified into "stay" or "move" episodes. For movement, transport modes (car or bike/walk) are identified using attributes like speed and distance.
• Trajectory Mapping: Discrete location data is converted into continuous trajectories via map-matching and interpolation, considering the transport mode.

The datapacks can be consulted in the platform via our customers. They provide us valuable user feedback as well to increase the data quality and acknowledge the large added value compared to manual counting.

Why do not all road segments get a footfall or car value?

We only predict footfall within shopping areas. If a road segment is not located within a shopping area, we will not show a footfall value. Typically footfall below 2000 passants/week is not included in the map.

For car passage; if the vehicles/ week is below a threshold value it also won't be indicated as a main road segment on the map.