We’re used to hearing about how web sites use our data to target ads or tweak the news we see. But sometimes, researchers use similar kinds of data to gain a better understanding of our world and how we interact with each other and the environment around us.
Marta C. Gonzalez is Associate Professor of City and Regional Planning at the University of California, Berkeley, and a Physics Research faculty in the Energy Technology Area at the Lawrence Berkeley National Laboratory. She’s been using cell phone and credit card data to probe how different groups of people move around a city, and what that tells us about issues of gender and income inequality.
With a study like this, the quesiton of privacy often comes up. Gonzalez likened her usage of credit card and cell phone data to using medical records, noting that there is strict protocol when it comes to using human subject data. For instance, the data stays within the companies. They do not move anywhere. Also, it’s extremely difficult to pinpoint a particular person when they are only geographically linked to a zip code.
The data groups were put into different clusters. The “homemaker” cluster is made up of mostly older females who earn a little less than the average, and who move less. They mostly buy their food at the grocery store.
“Commuters” spend money on tolls, restaurants, and use the internet for many of their purchases.
The “young” group doesn’t use a car typically and relies on taxis. They also often make purchases on the internet, but a lot of their money goes towards groceries and eating out.
“High-tech” people don’t go to restaurants. They are much more plugged into computers, and their core transaction was purchasing groceries from the internet.
The “dinner out” group is social and has a higher mobility diversity, meaning they visit many different places.
Gonzalez believes that this information can help in a variety of ways.
For instance, it can be a helpful application for urban planning. When planners are familiar with the patterns of their populations, they can adapt with their infrastructure and neighborhood mapping, using what Gonzalez thinks, is data for social good.
