PiinPoint develops its own proprietary datasets, as well as centralized industry-leading data for use in our software platform and analytics.

Demographic data is based on the most up-to-date census information available from Statistics Canada (Canada), the U.S. Census Bureau (USA), and the Australian Bureau of Statistics (Australia). In-house projections are applied by PiinPoint's team of data scientists to select variables to represent forward-looking trends.
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For North America, POI data is sourced from a ChainXY, for best-in-class coverage of retail and restaurant locations. They collect locational data directly from the source, and validate the chain lists they collect to maintain currency and improve positional accuracy. Nearly 100% of all chain-based data is updated on a quarterly basis. Click here for more details on why ChainXY is our POI vendor of choice.  
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PiinPoint's Mobile Location Data features have been recently updated and are now built on data from data company NEAR. They collect billions of datapoints representing mobile device locations across Canada and the United States. This new partnership increases the accuracy of our data and sets the stage for future product updates.

We ingest new data from NEAR on a monthly basis, and this data acts as the foundation for our suite of mobile location data tools, including the development of trade areas, looking at visitor demographics, counts over time, and incorporating observations into our predictive models.

Mobile Location Data is a game-changer when it comes to tracking daily traffic volumes. PiinPoint's Custom Vehicle Traffic Query allows you to look at AADT (average annual daily traffic) and pedestrian counts for any major road segment in Canada and select US states. These counts sampled from mobile devices identified to be traveling within vehicles.

PiinPoint offers Geosocial data in partnership with Spatial.ai. Developed around billions of geotagged social media posts, this rich and constantly evolving dataset aggregates the personality of a neighbourhood. Through its taxonomy of over 70 different personas, it adds depth to the story your other datasets tell you about an area. 

If you'd like to learn more about the Geosocial dataset and the methodology used to collect, sort, and aggregate it, we encourage you to read Spatial.ai's The Essential Guide to Geosocial Data.
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PiinPoint has as traffic feature, based on municipal and regional studies of AADT volumes. On each traffic point, you can click on the point to get the daily count, the date, and the source of the count.

Municipal Traffic Counts: Average annual daily traffic (AADT*) is collected from individual municipalities and includes data from as recent as last year.  

Modelled Traffic Counts: These traffic counts are captured by a third-party that uses traffic devices installed on major roadways and intersections to grab daily predictions.
These are extrapolated data points, from which a daily average is calculated. These predictions take into consideration local market traits such as population, daytime population, and density. When available, certain points also include predictions of how busy an area will be throughout the day.

*Average Annual Daily Traffic (AADT) is calculated by surveying an area for a sample period (typically a 7 day week) for 24 hours each day, and then estimating what the annual traffic would be there with error and fluctuation factors considered. That result is divided by the number of days in a year (365) to identify what the estimated average traffic per day is at an intersection/point. Each municipality or third party provider uses their own technique or sensor type for the estimation.
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