Time-use research offers a powerful lens to examine how individuals—women and men alike—allocate their time across daily activities, thereby revealing crucial insights into social, economic, and behavioral inequalities. Drawing on the American Time Use Survey Extract Builder (ATUS-X), this talk will highlight how we can visualize the gendered dimensions of time allocation, with potential applications ranging from policy-making to workplace reform advocacy.

The talk will discuss new methods for visualizing time-use data, including tempograms, transition diagrams, and activity flow paths. It will also introduce updates to R packages specifically designed for researchers analyzing ATUS-X and other time-use data to generate clear and compelling visual narratives of daily time patterns—particularly those that underscore gender gaps in paid and unpaid work, caregiving, and leisure activities.

In addition to introducing these visualization tools, the presentation will explore potential methodological innovations with sequence analysis and machine learning techniques. Sequence analysis offers opportunities to uncover temporal patterns and transitions across activities, while machine learning applications—such as clustering and predictive modeling—can enhance our understanding of time-use behaviors and their implications.