Big Data Technology to Measure Population Mobility

StreetLight's technology contextualizes anonymous location data from mobile devices to measure population mobility patterns. Our solution transforms messy and disparate spatial data into contextual metrics for the real world.   StreetLight utilizes a multitude of data inputs in conjunction with our proprietary Route Science® engine.

Route Science® 

StreetLight's Route Science engine utilizes a unique approach to organizing data across space and time. The engine can rapidly transform billions of records into useful information.

Key innovations of Route Science:

  • Place FrameworkRoute Science is organized on a framework of road and site segments, which allows StreetLight to join behavioral data with place, context and data about the site itself.
  • Time Segment FrameworkRoute Science incorporates a new approach to organizing mobility time types that enables more insight along with faster compute time. 
  • Integration of low-fidelity and high-fidelity geospatial data exhaust:  StreetLight’s proprietary suite of algorithms and filters combines low-fidelity tracks of location data with high-fidelity geospatial data  to enhance the accuracy of our Metrics. 
  • Analyzing group patterns instead of tracking individuals: StreetLight's novel approach to analyzing and creating data about groups allows us to draw very useful information about what is happening at a particular place, without ever tracking, identifying, or targeting messaging to an individual. This goes beyond simple "aggregation" of multiple individuals into a group; the Route Science engine does not track individuals in any way. Thus, we offer value from locational data with the deepest possible privacy protection. 
  • Modularized mobilization of any 2D spatial data source: StreetLight's Route Science engine can take any set of spatial data and mobilize it. For example, if you have a map of neighborhoods where a particular type of customer lives, we can take that data and mobilize it to reveal where people from that neighborhood work, shop, recreate, etc.  This means that partners with their own proprietary data sets can add value to those sets with StreetLight.

Data Inputs

StreetLight has created a data repository that includes data from a wide variety of sources, both public and private. 

The data we use is: 

  • Archival (not real time).  
  • Anonymized and de-identified. 
  • Aggregated.

To see our publicly announced data partners, please see our Partners page.  To learn more about becoming a StreetLight data partner, please contact us.