Sifting through large amounts of data to

determine which variables to use for the assessment of things like the health of a city's population is challenging. Researchers often choose these variables based on their personal experience. They might decide that adult obesity rates, mortality rates, and life expectancy are important variables for calculating a generalized metric of the residents' overall health. But are these the best variables to use? Are there other more important ones to consider?
Matteo Convertino of Hokkaido University in Japan and Joseph Servadio of the University of Minnesota in the U.S. have introduced a novel probabilistic method that allows the visualization of the relationships between variables in  for . The approach is based on "maximum transfer entropy," which probabilistically measures the strength of relationships between multiple variables over time.