Novel data sources to model human behavior at urban scale include but are not limited to: Probe/GPS data, Credit Card Transactions, Traffic and Mobile phone data. Urban mobility models are important in a wide range of application areas. Current mainstream models require socio-demographic information from costly manual surveys, which are in small sample sizes and updated in low frequency. In this study, we propose a novel individual mobility modeling framework, TimeGeo, that extracts all required features from ubiquitous, passive, and sparse digital traces in the information age. In the second part of the talk I analyze sequence of purchases in credit card data reveal life styles in urban populations. I present a framework using a text compression technique on the sequences of credit card purchase to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity, we detect five consumer groups. Remarkably, individuals in each consumer group are also similar in age, total expenditure, gender, and the diversity of their social and mobility networks extracted by their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior.
Note: This talk was originally scheduled on March 13, 2018, and is now confirmed on March 20, 2018.
The Berkeley Distinguished Lectures in Data Science, co-hosted by the Berkeley Institute for Data Science (BIDS) and the Berkeley Division of Data Sciences, feature faculty doing visionary research that illustrates the character of the ongoing data, computational, inferential revolution. All campus community members are welcome and encouraged to attend. Arrive at 3:30pm for tea, coffee and discussion prior to the formal presentation.
University of California, Berkeley
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 (ETA) at the Lawrence Berkeley National Laboratory (Berkeley Lab). With the support of several companies, cities and foundations, her research team develops computer models to analyze digital traces of information mediated by devices. They process this information to manage the demand in urban infrastructures in relation to energy and mobility. Her recent research uses billions of mobile phone records to understand the appearance of traffic jams and the integration of electric vehicles into the grid, smart meter data records to compare the policy of solar energy adoption and card transactions. Credit to identify habits in spending behavior. Prior to joining Berkeley, Marta worked as an Associate Professor of Civil and Environmental Engineering at MIT, a member of the Operations Research Center and the Center for Advanced Urbanism. She is a member of the scientific council of technology companies such as Gran Data, PTV and the Pecan Street Project consortium.