Yes, we have supported multiple NGOs, research groups and universities. Contact us to learn how we can help you in your mission.
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 more than 20 years as a market innovator to deliver industry-leading accuracy.
We offer multiple data products to serve the different needs of our clients:
Trip Matrix data quantitatively describes 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.
AirSage trajectory data is obtained from a continuous supply of connected vehicles (CVs). View details such as unique vehicle IDs, location, speeds, roadways and zones traversed, home locations, timestamps, and more.
Connected vehicle (CV) insights like vehicle speed and harsh-braking events can supplement data-driven roadway risk assessments and lead to better-informed safety planning decisions.
This powerful tool focuses on Slow Transient Points, capturing mobile devices moving at less than 1.5 m/s, providing precise insights into high-density areas of non-motorized activity.
Activity Density provides direct insight into where devices are located throughout the day for any given “hot spot” location or special event on a defined geographical space
Target Location Analysis unlocks an understanding of visitor characteristics for any point of interest in North America during a specified time period.
You can schedule a discovery call with one of our experts here.
We collect and analyze the movement data from all over North America. Consulting firms, government agencies, and private businesses can use AirSage aggregated information to model, evaluate and analyze the location, movement, and flow of people and assets.
AirSage is a US-based technology company that specializes in collecting and analyzing anonymous location data (such as mobile device, GPS, and connected vehicle data) processing more than 15 billion mobile locations every day – and turns it 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 government agencies like NASA and Departments of Transportation as well as top consulting firms with industry-leading accuracy and reliability.
Company in numbers:
Learn about how we ensure data privacy on Privacy & Consumer Rights page.
We put customers in the center of our attention and provide the best quality insights and services.
Industry Expertise
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.
Customized solutions
AirSage has built a reputation for developing a deep understanding and delivering robust insights to support your business case.
Pinpoint accuracy
Our latest algorithms boast 99.9% accuracy in identifying users’ mobility status.
No dependency on carriers
We use aggregate app-based location data from multiple sources.
Size of data panel
AirSage provides one of the largest data panels in the market.
Focus on privacy and security
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.
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.
Sourced data is normalized and archived in AirSage’s Big Data system in a secure and accessible format. Irrespective of the final use case, proprietary pre-processing is run on the data. This includes some unique features such as:
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.
With more than a decade of experience sourcing various types of anonymous location data (carrier data, connected vehicle 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 candidate passes a thorough and efficient evaluation process that ultimately reveals its data volume, coverage, uniqueness, and other quality metrics, all relevant for 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 kept through time. Data feeds that fail to maintain such standards are removed from the panel.
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.
We provide our output as CSV files for maximum compatibility with our customer’s systems.
Our customers can convert our output into their own GeoJson datasets to use with Kepler and Superset. These tools support the ability to import CSV data.
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 also have a platform available that can be customized to your use case.
We can accept Shapefiles, GeoJson, and delimited text files with WKT or Hexified WKB.
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 category. After receiving the aggregated data, we implement an accuracy metric and device quality score to exclude some noise. Some things we take into consideration:
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.
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 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.
