My research interests lie in developing theories, methodologies, and applications for analyzing spatio-temporal dynamics of human and environmental phenomena by adopting statistics, data mining, and machine learning techniques.
I am currently involved in an interdisciplinary research project, MOASIS (Mobility, Activity and Social Interaction Study) as a postdoctoral researcher at the University Research Priority Program 'Dynamics of Healthy Aging' and the Department of Geography, University of Zurich.
My current research projects include but not limited to:
- Theories and Statistical Methodology Development for Measuring Spatial and Temporal Heterogeneity and Bursty Patterns of Geographic Events;
- Human Mobility Pattern Analysis using Multi-Sensor Datasets including GPS, Accelerometry, Audio data, and Momentary Survey Data;
- Automatic Place Detection and Semantic Trajectory Analysis using Machine Learning;
- Longitudinal and Spatial Analysis of Older Adults' Mental Health and their Socio-Environmental Status.
- Fillekes, M. P., Giannouli, E., Kim, E.-K., Zijlstra, W., & Weibel, R. (2019). Towards a comprehensive set of GPS-based indicators reflecting the multidimensional nature of daily mobility for applications in health and aging research. International Journal of Health Geographics, 18(1), 17. DOI
- Shook, E., Bowlick, F., Kemp, K., Ahlqvist, O., Carbajales, P., DiBiase, D., Kim, E.-K., Lathrop, S., Ricker, B., Rickles, P., Rush, J., Swift, J., & Wang, S. (2019). Cyber Literacy for GIScience: Toward Formalizing Geospatial Computing Education. The Professional Geographer, 71(2), 221-238. DOI
- Kim, E.-K. (2018). Stay-Move Tree for Summarizing Spatiotemporal Trajectories (Short Paper). Raubal, M., Wang, S., Guo, M., Jonietz, D., & Kiefer, P. (Eds.) Proceedings of Spatial Big Data and Machine Learning in GIScience, Workshop at GIScience 2018, Melbourne, Australia, August 2018. DOI
- Perez, L., Kim, E.-K., & Sengupta, R. (Eds.) (2018). Agent-Based Models and Complexity Science in the Age of Geospatial Big Data. In Balram, S. & Dragicevic, S. (Series Eds.), ‘Advances in Geographic Information Science’ Series, Springer International Publishing. DOI
- Kim, E.-K. & Jo, H.-H. (2016). Measuring Burstiness for Finite Event Sequences. Physical Review E, 94(3): 032311. DOI
Supervising Research Projects
Tian Tian (Fall 2018 ~): Master Thesis on "Longitudinal Spatial Analysis of the Impact of Sociodemographic Status on Mental and Physical Well-being of the Elderly"
Course Development and Teaching
GEO 881: Advanced Spatial Analysis II
- A course is served for master and doctoral students.
- Lectures and tutorials were redesigned revolving around spatial prediction and spatial machine learning.
Currently Organizing Events
- Applied Machine Learning Days (AMLD) 2020, 'AI & Cities' track on January 25-29, 2020 in Lausanne, Switzerland.
- Peer Mentoring Group "Applied Machine Learning for Social & Environmental Problems (ML4SEP)" in January - December, 2019.
Past events since 2019
- ML4SEP 2019: The Workshop on Applied Machine Learning for Social & Environmental Problems on UZH Irchel on October 3-4, 2019.
- ECTQG 2019 Special Sessions, Mobilities and Health: Advances in theoretical and quantitative methods on September 5-9, 2019 in Mondorf-Les-Bains, Luxembourg.