FOSS4G-Asia 2023 Seoul

Eun-Kyeong Kim

Eun-Kyeong Kim, Ph.D. is a geographer and is passionate to contribute to geographic data science for understanding spatiotemporal processes of human behaviors, health, and climate change with spatiotemporal statistics, GeoAI, and multi-sensor trajectory analytics. She is currently a Spatial Data Science Lead and Research Associate at Luxembourg Institute of Socio-Economic Research. She has been working in the field of GIScience for +17 years in Luxembourg, Switzerland, USA, and South Korea.


Individual Mobility Analytics with Open Data and Software
Eun-Kyeong Kim

Individual mobility is a crucial indicator of personal health and behavior. To assess individual mobility and activity, mobile sensing using global positioning systems (GPS) and surveys on smartphones are increasingly being employed, providing high spatial and temporal resolution data. This advancement has facilitated in-depth studies into the interactions between individuals' real-life behaviors, health, personal characteristics, and environments at a micro spatiotemporal scale. For such studies related to personal health and mobility, several open-source software packages (e.g., R/Python libraries) and open trajectory data (e.g., GPS data) have been developed.

In this presentation, we will introduce our R package development project for individual mobility analytics, focusing on GPS-based trajectory data processing and analysis at an individual level. The open-source package includes: (1) GPS data preprocessing, (2) construction of semantically enriched trajectory data from GPS data using automated methods for home detection, stop-move detection, and transport mode detection, and (3) computation of mobility indicators at two different aggregation levels (daily vs. by-person). We will also showcase case studies using open GPS data (such as Geolife) and datasets from other research projects. Furthermore, we will discuss the challenges faced during this project and outline its future directions.

Academic Track(Talks, Online Talks, Lightning Talks, Posters)
Circle Room
COVID-Contrail: Automated Contrail Detection through Deep Learning Model
Eun-Kyeong Kim

Contrails potentially influence the Earth’s energy balance by altering the radiative forcing (i.e., heating or cooling), particularly in middle latitudes where a cold and sometimes moist upper-troposphere (UT) intersects the mean jet-flight path altitude. Such contrail radiative forcing should be amplified when multiple occurrences of these anthropogenic clouds are grouped spatio-temporally as ‘contrail outbreaks’ and contrail cirrus on at least 1 × 103 km2, extending over approximately 104~105 km2, and persisting for approximately 1-6 h. To more definitively determine the net radiative forcing of contrails and their role in contemporary and future climate changes, and to establish appropriate climate change mitigation strategies (e.g., jet flight activity regulation), it is essential to reliably detect contrail incidence and assess the spatio-temporal distribution of contrail outbreaks as well as understand the underlying mechanisms of contrail formation.

Thanks to recent advanced technologies, one can obtain flight tracking data as well as automate an individual contrail occurrence detection on the satellite or aerial images and alleviate the burden of labor-intensive manual contrail detection tasks. In this context, our COVID-Contrail project aims to (1) assess the changes of contrail occurrence patterns and relevant atmospheric factors in the continental United States (CONUS) before and during COVID-19 and (2) develop an automated data processing and analysis system to construct and publish a massive contrail incidence database in high spatio-temporal resolution. This project is expected to make significant advances in diverse aspects of contrail and climate studies. In the poster presentation, we will introduce our COVID-Contrail project and present work in progress of our project.

Academic Track(Talks, Online Talks, Lightning Talks, Posters)
Gallery 3