Data Methodology
Raw GPS data
We generate our footfall dataset using GPS information acquired from app data aggregators. We maintain transparency regarding the attributes of the raw data, such as the size of the panel and the volume of location signals. We also share our methodology for cleansing, normalising, and extrapolating the data to create our final data product.
Easy to access
Data is stored in comma separated values (CSV) format files. These can be accessed via AWS, Google and Azure clouds and will soon be added to the Snowflake and Databricks market places. Our goal is to smooth the road to getting value from the data.
Aggregated to H3 grid
After being cleaned to remove noise the GPS data is aggregated to H3 resolution 10 grids to provide footfall by hour. The data is fully anonymised and aggregated with no personal data. H3 resolution 10 grids have an average edge length of ~70m, making them optimal for analysing a high street or city block.
Easy to use
Join with any other data aggregated to H3. If the data you need to join to is at a lower resolution than 10, no problem, H3 makes it simple to join through its hierarchical index. Connect to your points of interest, sales data, census, demographics and more.
GPS data cleaned and normalised
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Exclude extra columns with no meaning, check column data formats for formal validity and filter records by location accuracy.
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Apply consistent rules rather than data provider rules.
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Convert source timestamp into a recognisable format, normalise it and explode hour, day of the week and year components.
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Exclude ‘locality centre’ hotspots brought up by assisted geolocation technologies (if a device is unable to catch GPS signal, it locates itself in an arbitrary ‘district centre’, which is always approximate and has no real meaning), and other dense anomalies like traffic hubs.
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Delete signals from stationary devices like GSM locks and users that sit tight and don’t move.
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Determine motion type from pre and post location and timestamps.
Granular footfall and mobility data
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H3 is the open source spatial index that makes it easy to join geo data, for example sales data for store to footfall. Resolution 10 is on average equivalent circle with a radius of 155m
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Both a count of the actual number of devices seen in the cell and a modelled number for total number of people this would indicate
Flexible purchase and delivery
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Choose any or all of the available 132 towns, cities and London bororughs.
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Choose any number of specific places (using a latitude and longitude) and get data for a 1 minute walk isochrone.
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Pick the month or months you want data for.
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You can buy for any month since January 2023.
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You can subscribe to receive updated data every month. Data is available from the 15th of the month that follows the month period
Download the schema