Wonder how AirSage leverages people’s movement data to provide best-in-class location insights? Find answers to all your questions here.
AirSage is a US-based technology company specializing in collecting and analyzing anonymous location data, such as cell phone and GPS data, processing more than 15 billion mobile locations every day – and turns them into meaningful and actionable information.
Our mission is to provide Insights from when, where, and how to help answer the why and what related to the movement of people.
We proudly serve blue-chip companies such as NASA and Stantec with industry-leading accuracy and reliability.
Company in numbers:
- Over 5 billion high accuracy location signals and more than 200 million mobile devices evaluated to accurate human movement insights;
- Over 200 satisfied clients and more than 500 projects;
- Over 20 years of experience;
- More than 60 unique proprietary algorithms;
- 4 patents covering “the use of wireless data to support transportation planning and engineering”.
Learn about how we ensure data privacy on Privacy & Consumer Rights page.
AirSage taps into the power of billions of location signals. We extract geospatial insights from raw data using a patented big data approach. Our team leverages the experience of 20 years as a market innovator to deliver industry-leading accuracy.
We offer multiple products and insights to serve the different needs of our clients.
Quantitatively describe the trip patterns between multiple zones in a given area. Each trip matrix includes a wide array of attributes for person trips like origin, destination, and home zones down to a census block group.
Nationwide Trip Matrix attributes include:
- Origins, Destinations, and Home Locations;
- Time-of-Day and Weekday Segmentation;
- Resident / Visitor Classification;
- Trip Purpose Classification (home/work/other);
- Person trips (extrapolated to the entire population);
We know the where and when of >1 billion trips made every day in the US.
Trip Patterns attributes include:
- Origins & Destinations;
- Time-of-Day / Day-Part Segmentation;
- Resident / Visitor Classification;
- Trip Purpose Classification;
- Home / Work Classification;
- Home & Work Locations;
- Long Distance Filtering;
- Transit zone indicators;
- Real-time traffic monitoring.
Supporting Travel and Tourism industries, destination Insights self-service platform enabling market studies. Thus, users can understand insights of visitors to their market – both local/in-market residents and visitors from afar.
Destinations insights include:
- Visitor counts by home location (at state, MSA, county, and census tract levels);
- Average lengths of stay;
- Seasonality trends;
- Event impacts;
- Pass-Through Trips vs. Daytrip vs. Overnight Trips;
- Historical data (back to January 2017).
Activity Density provides direct insight into the living population density heat map or daytime population density.
Activity Density attributes include:
- 10m, 100m, 1,000m grid cells;
- Hourly reporting;
- Total number of unique devices per grid;
- Possible filtering based on Home, Work, Other or transient locations.
POI Insights unlocks an understanding of visitor characteristics for any point of interest in North America.
Properties attributes include:
- Trade area development;
- Foot traffic count;
- Demographic analysis;
- POI comparisons;
- Custom dayparts;
- Frequency and Duration distributions.
We collect and analyze the movement data from all over North America. Consumer and market research companies, travel and tourism organizations, and government agencies can use AirSage aggregated information to model, evaluate and analyze the location, movement, and flow of people and assets.
We put customers in the center of our attention and provide the best quality insights and services. So why do companies choose AirSage?
AirSage is a pioneer in the Location Intelligence market. Over 20 years ago, AirSage started to serve its first clients, continuously delivering high results. Our blue-chip clients appreciate our knowledge in providing cutting-edge location insights.
Focus on Outcome
AirSage has built a reputation for developing a deep understanding and delivering robust insights to support your business case.
Our experience in location intelligence is unmatched; our latest algorithms boast 99.9% accuracy in identifying users’ mobility status.
No dependency on carriers
Unlike other providers, we use aggregate app-based location data.
Size of the data panel
AirSage provides one of the largest data panels in the market.
Adherence to regulatory concerns
AirSage meets all existing regulations (incl. CCPA, GDPR) and exceeds their requirements, with no impact on the insights or data we provide. Our deliverables contain no sensitive or personally identifiable information (PII) data, eliminating the privacy risk for you.
You can schedule a discovery call with one of our experts here.
Absolutely! AirSage experts offer free 30 minutes discovery call where you can ask any related questions.
Yes, we already supported multiple NGOs, research groups and universities. Contact us to learn how can we help you in your mission.
About AirSage Data
With more than a decade of experience with sourcing various types of anonymous location data (carrier data, connected car data, fleet data, smartphone data, and more), and 5 years specifically in sourcing App data, AirSage has developed a unique skill in sourcing the best available data and building an optimal data panel.
Nearly all data available in the open market for large scale sourcing has been evaluated and considered by AirSage to enter its panel. Each such candidate passes a thorough and efficient evaluation process that ultimately reveals its data volume, coverage, uniqueness, and multiple other quality metrics, all relevant for the AirSage analytics use cases.
Data that has been chosen to enter the panel goes through similar ongoing evaluation to make sure that the highest quality standards are also kept through time. Data feeds that fail to maintain such standards are removed from the panel.
AirSage supports the ingestion of data from multiple different data providers (publishers, 1st party data providers and aggregators) and has also evaluated other providers that we don’t support.
Our experience is that the current data we use is among the largest panel with the most sufficiently high-quality devices for us to be able to select a large enough sample of a consistently high enough quality so that we can adjust for things like variable sample sizes.
We select our sample using a per device abstract monthly metric that measures both the visibility and mobility of each device to ensure that we have a sample of devices that behave consistently.
Our metric was defined by staff that also worked with telecom data, which offered better visibility than app data.
This is a key differentiator between us and competitors. Much of this is IP and, therefore, cannot be expanded upon.
Sourced data is normalized and archived in AirSage’s Big Data system in a secure and accessible format. Irrespectively of the final use case, proprietary pre-processing is run on the data. This includes some unique features such as:
- Accurate point classification: every user location is classified whether it represents a person in motion or stationary. This is then very critical, for example, when trying to count visitation to a location and differentiate between people who just passed by and those who actually spent time at the location. In a recent comparison of the AirSage point classification with an independent source, we found that the AirSage data was more than 99.9% accurate.
- Home/Work assignment: to serve several use cases, the need for high-quality assignment of the location in which the mobile device holders live and work are critical. These assignments need to be able to cope with extraordinary cases, such as people relocating to a vacation home for a few weeks or even months, people working night shifts regularly, etc.
AirSage cleanses the data we use on ingest. We apply point types to sightings and various other important metadata for our individual product processing. Further, we don’t use bid-stream data like other providers.
One of the biggest challenges in the geospatial Big-Data analytics space is translating the results generated from a varying sample of mobile devices into insights about the full population. AirSage has developed the most efficient extrapolation methodologies to do so. This is done by maximizing and validating the correlation to independent sources such as updated census data, high-quality traffic counts, and attendance reports.
We provide our output as CSV files for maximum compatibility with our customer’s systems.
About Data Visualization
Our data can easily be imported as attribute data to be joined with standard Census shapefiles generically, allowing the use of your preferred GIS suite.
We can accept Shapefiles, GeoJson, and delimited text files with WKT or Hexified WKB.
Our customers can convert our output into their own GeoJson datasets to use it with Kepler and Superset. These do support the ability to import CSV data into the database to which it’s connected.
About Location Insights
This is not an issue for us. AirSage uses the mobile advertiser ID to uniquely identify devices. AirSage’s data is coalesced at the device level, so we do not distinguish between different apps or SDKs.
We control sample bias by having a diverse data panel to get a better representation of all people. Our data panel includes tens of millions of unique devices and is comprised of apps in every bucket. After receiving the aggregated data, we implement an accuracy metric and device quality score to exclude some noise. Some things we take into consideration:
- How often the device moves around.
- How frequently devices are seen.
- The duration of each sighting.
There are some other known biases that would be hard to avoid, such as age bias when looking at usage during particular times of the day (waking/sleeping hours typically vary depending on age). There could also be a vacation bias that may increase activity when one is on vacation compared with regular daily activities. Another possible bias would be income bias where more affluent areas may have more devices (i.e., people from affluent areas may have more than 1 device).
We discern user behavior, for example, at home/work vs. moving through a reported point vs. at a stationary location.
We like to also consider “home” and “work” locations as “daytime” and “evening” locations. These locations are based on where devices ping the most during the daytime and late evening.
Total Devices counts distinct devices present at the location of interest during the reporting period. Total Sightings counts the total number of individual records produced by all devices present at the location of interest during the reporting period.
About AirSage Self-service Interface
Yes, either by drawing a new POI or by uploading them during study creation.
We support Shapefile (.zip) and GeoJSON (.json, .geojson).
We do. However this is possible only in the context of a Destination Location Analysis (DLA) study. DLA used mainly by the travel, tourism and hospitality industries.
It’s possible to easily extract data in CSV format.