An increasingly fast-paced world and a collective sense of urgency has led to a recent rise of self-awareness methods and tools that provide users with insights into various aspects of their life. Meanwhile, recent studies associate our movement patterns and habits with our physical and mental well-being. In the light of the above, this work introduces Location-Aware Insights, a self-awareness platform that enables users to retrospectively reflect upon their whereabouts and helps them to better understand where and how they spend their precious time. In addition, our work supports users to identify "bad"patterns and eventually adapt their behavior towards a higher quality of life. For the purposes of achieving a deeper understanding of the users' visit patterns and promoting a healthier life style, our Insights dashboard attempts to particularly highlight factors that have been proven to affect our well-being. On one hand, this is done by utilizing an extended locations graph that goes beyond containing the typical hierarchical relations and considers additional semantic location attributes that are related with our well-being, such as in-/outdoor, green, bright, quite and open/closed spaces. On the other hand, we focus on similarly important well-being-related statistical features such as visit frequency, regularity and periodicity. Finally, the art of presentation plays a major role in the self-reflection process. For this reason, the presented demo explores a large variety of ways for presenting the generated insights back to the user.
CITATION STYLE
Karatzoglou, A. (2021). Location-aware insights: A visual analytics dashboard for location-relevant self-awareness & reflection (demo paper). In Proceedings of the 14th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2021. Association for Computing Machinery, Inc. https://doi.org/10.1145/3486629.3490690
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