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Introduction: Mobility as a multidimensional concept has rarely been examined as a day-to-day varying phenomenon in its within-person association with older adults’ daily well-being. This study examined associations between daily mobility and daily well-being in community-dwelling older adults with a set of GPS-derived mobility indicators that were representative of older adults’ daily mobility. Methods: Participants wore a custom-built mobile GPS sensor (“uTrail”) and completed smartphone-based experience sampling questionnaires on momentary affective states (7 times per day) and daily life satisfaction (in the evening). Analyses included data across 947 days from 109 Swiss older adults aged 65–89 years. Results: Multilevel modeling showed that, within persons, a day with a larger life space area, more time spent in passive transport modes, and a higher number of different locations was associated with higher daily life satisfaction but not daily positive or negative affect. Follow-up analysis showed that the daily maximum distance from home was positively associated with daily life satisfaction, providing a first indication that exposure to non-habitual environments might be a possible underlying mechanism to explain the effects of mobility. Conclusions: Traveling a long distance away from home and visiting diverse locations may be a way to improve life satisfaction. Results are discussed in the context of research on healthy aging.

Summarizing spatiotemporal trajectories of a large number of individual objects or events provides insight into collective patterns of phenomena. A well-defined data model can serve as a vehicle for classifying and analyzing data sets efficiently. This paper proposes the Stay-Move tree (SM tree) to represent frequency distributions for types of trajectories by introducing concepts of stay and move. The proposed tree model was applied to analyzing the Korean Household Travel Survey data. The preliminary results show that the proposed SM trees can potentially be employed to compare/classify spatiotemporal trajectories of different groups (e.g., demographic groups or species of animals). The methodology can potentially be useful to summarize big trajectory data observed from both human and natural phenomena. Cite as: Eun-Kyeong Kim. (2018). Stay-Move Tree for Summarizing Spatiotemporal Trajectories. In Martin Raubal, Shaowen Wang, Mengyu Guo, David Jonietz, & Peter Kiefer. (editors). Spatial Big Data and Machine Learning in GIScience, Workshop at GIScience 2018, Melbourne, Australia, 2018. http://doi.org/10.5281/zenodo.3402236, {"references":["Laube, P., Imfeld, S., & Weibel, R. (2005). Discovering relative motion patterns in groups of moving point objects. International Journal of Geographical Information Science, 19(6), 639-668.","Sinha, G. & Mark, D. M. (2005). Measuring similarity between geospatial lifelines in studies of environmental health. Journal of Geographical Systems,7(1), 115-136.","Dodge, S., Weibel, R., & Lautenschütz, A. K. (2008). Towards a taxonomy of movement patterns. Information visualization, 7(3-4), 240-252.","Hornsby, K. S., & Li, N. (2009). Modeling Spatiotemporal Paths for Single Moving Objects. In Research Trends in Geographic Information Science (pp. 151-167). Springer, Berlin, Heidelberg.","Schneider, C. M., Belik, V., Couronné, T., Smoreda, Z., & González, M. C. (2013). Unravelling daily human mobility motifs. Journal of The Royal Society Interface, 10(84), 20130246.","Inoue, R., & Tsukahara, M. (2016). Travel pattern analysis from trajectories based on hierarchical classification of stays. In International Conference on GIScience Short Paper Proceedings (Vol. 1, No. 1).","Kim, E.-K. (2009). 3D sequential object clustering for spatio-temporal data analysis. (master's thesis, Kyung Hee University, 2009)."]}

The unprecedented availability of geospatial data and technologies is driving innovation and discovery but not without the risk of losing focus on the geographic foundations of space and place in this vast “cyber sea” of data and technology. There is a pressing need to educate a new generation of scientists and citizens who understand how space and place matter in the real world and who understand and can keep pace with technological advancements in the computational world. We define cyberliteracy for GIScience (cyberGIScience literacy) and outline eight core areas that serve as a framework for establishing the essential abilities and foundational knowledge necessary to navigate and thrive in this new technologically rich world. The core areas are arranged to provide multiple dimensions of learning ranging from a technological focus to a problem solving focus or a focus on GIScience or computational science. We establish a competency matrix as a means of assessing and evaluating levels of cyberGIScience literacy across the eight core areas. We outline plans to catalyze the collaborative development and sharing of instructional materials to embed cyberGIScience literacy in the classroom and begin to realize a cyberliterate citizenry and academe.

A broad scope of concepts and methodologies from complexity science – including Agent-Based Models, Cellular Automata, network theory, chaos theory, and scaling relations – has contributed to a better understanding of spatial/temporal dynamics of complex geographic patterns and process. Recent advances in computational technologies such as Big Data, Cloud Computing and CyberGIS platforms, and Sensor Networks (i.e. the Internet of Things) provides both new opportunities and raises new challenges for ABM and complexity theory research within GIScience. Challenges include parameterization of complex models with volumes of georeferenced data being generated, scale model applications to realistic simulations over broader geographic extents, explore the challenges in their deployment across large networks to take advantage of increased computational power, and validate their output using real-time data, as well as measure the impact of the simulation on knowledge, information and decision-making both locally and globally via the world wide web. The scope of this workshop proceedings is to explore novel complexity science approaches to dynamic geographic phenomena and their applications, addressing challenges and enriching research methodologies in geography in a Big Data Era. The topics include (1) Multisensor Data Fusion for parameterizing complex models, (2) Integrating theory with practice, and (3) Output Validation.