Exploring time diaries using semi-automated activity pattern extraction
2009 (English)In: electronic International Journal of Time Use Research (eIJTUR), Vol. 6, no 1, 1-25 p.Article in journal (Refereed) Published
Identifying patterns of activities in time diaries in order to understand the variety of daily life in terms of combinationsof activities performed by individuals in different groups is of interest in time use research. So far, activitypatterns have mostly been identified by visually inspecting representations of activity data or by using sequencecomparison methods, such as sequence alignment, in order to cluster similar data and then extract representativepatterns from these clusters. Both these methods are sensitive to data size, pure visual methods becometoo cluttered and sequence comparison methods become too time consuming. Furthermore, the patterns identifiedby both methods represent mostly general trends of activity in a population, while detail and unexpectedfeatures hidden in the data are often never revealed. We have implemented an algorithm that searches the timediaries and automatically extracts all activity patterns meeting user-defined criteria of what constitutes a validpattern of interest for the user’s research question. Amongst the many criteria which can be applied are a timewindow containing the pattern, minimum and maximum occurrences of the pattern, and number of people thatperform it. The extracted activity patterns can then be interactively filtered, visualized and analyzed to revealinteresting insights. Exploration of the results of each pattern search may result in new hypotheses which can besubsequently explored by altering the search criteria. To demonstrate the value of the presented approach weconsider and discuss sequential activity patterns at a population level, from a single day perspective.
Place, publisher, year, edition, pages
2009. Vol. 6, no 1, 1-25 p.
Time-geography, diaries, everyday life, activity patterns, visualization, data mining, sequential pattern mining
National CategoryEngineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-51231OAI: oai:DiVA.org:liu-51231DiVA: diva2:273589