Exploring Time Diaries Using Semi-Automated Activity Pattern Extraction

Identifying patterns of activities within individuals’ time diaries and studying similarities and deviations between individuals in a population is of interest in time use research. So far, activity patterns in a population have mostly been studied either by visual inspection, searching for occurrences of specific activity sequences and studying their distribution in the population, or statistical methods such as time series analysis in order to analyse daily behaviour. We describe a new approach for extracting activity patterns from time diaries that uses, instead, sequential data mining techniques. We have implemented an algorithm that searches the time diaries and automatically extracts all activity patterns meeting user-defined criteria of what constitutes a valid pattern of interest for the research question. Amongst the many criteria which can be applied are: a time window containing the pattern, and minimum and maximum number of people that perform the pattern. The extracted activity patterns can then be interactively filtered, visualized and analyzed to reveal interesting insights using the VISUAL-TimePAcTS application. To demonstrate the value of this approach we consider and discuss sequential activity patterns at a population level, from a single day perspective, with focus on the activity “paid work” and some activities surrounding it. Questions can be posed such as: Which activities appear frequently in activity patterns related to paid work? Which are the activities surrounding work at different hours of the day? What differences are revealed between the sexes in the patterns? Exploration of the results of each pattern search may result in new hypotheses which can be subsequently explored by altering the search criteria.