Machine Learning

Machine Learning Transforms Location Data into Predictive Intelligence

Sense Founder, Dr. Tony Jebara“Machine learning intersects computer science and statistics to develop new frameworks for learning from data and produces important applications in vision, spatio-temporal datasets, interfaces, bioinformatics and text.”  

Professor Tony Jebara, Sense Networks Co-Founder, Chief Scientist and Director of Columbia University’s Machine Learning Laboratory


Breakthrough Algorithms

Sense Networks’ experts in machine learning have developed a series of breakthrough algorithms that reduce the dimensionality of location data, and can characterize places according to the activity and movement between them.

From massive high dimensional location data, these algorithms uncover trends, meaning, and relationships to eventually produce human, understandable representations. It then becomes possible to use such data to automatically make intelligent predictions and find important matches and similarities between places and people.

The Process of Discovering Behavior through Location

This video animation depicts the below process of identifying the primary tribe of the top 200 nightlife destinations in San Francisco during a certain period of the night:

1) Identify and isolate the top 200 nightlife destinations
2) Create a network of movement between these locations
3) Machine learning algorithms analyze each location in the context of the overall movement and categorize it (colored dots added) by examining everyone’s point of origin, and where everyone goes afterwards
4) The categorized places are now grouped by behavioral similarity, not proximity (convergent dots represent places with the most similar “type” of people present, in real-time, versus geographical proximity)
5) The spatial behavioral map is overlaid onto a spatial geographical map and the system continuously learns as new live data is received

Harnessing Big Data

Analyzing locations in the real world requires an ability to process hundreds of thousands of data dimensions. Due to the temporal sensitivity of the number and type of people frequenting a place (which can be far more dynamic than a static web page on the internet), the raw data describing places in the real world requires a staggering number of dimensions.

Sense Networks attributes 487,500 dimensions to every place in a city, thus identifying a unique and complex “DNA” which describes it completely. The dimensions are based on the movement of people in and out of that place over time, and the places those people visit before and afterwards. Proprietary MVE (Minimum Volume Embedding) algorithms reduce the dimensionality of location and temporal data to 2 dimensions while retaining over 90% of the information. This allows for visualizations of data to better understand key dimensions and data relationships.