5 citations · 5 across the 8 of their papers we have counts for
10 papers
HypLTSF: A Hyperbolic Geometric View of Multi-Scale Hierarchies for Long-Term Time Series Forecasting
Namwoo Kim, Hyungryul Baik, Yoonjin Yoon
Multi-scale modeling has become an effective approach for long-term time series forecasting, capturing temporal patterns that range from fine-grained local dynamics to coarse globa…
Synergistic Fusion of Topological Structure and Temporal Semantics of Mobility for Urban Region Embedding
Namwoo Kim, Jeeyun Chang, Kanghoon Lee +1
Urban region embeddings have shown promising results in diverse urban sensing tasks such as crime, income, and service-call prediction. Recent methods improve representation qualit…
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images
Juneyoung Ro, Namwoo Kim, Yoonjin Yoon
Effectively understanding urban scenes requires fine-grained spatial reasoning about objects, layouts, and depth cues. However, how well current vision-language models (VLMs), pret…
USE-LFA: A Data-Driven Framework for UAM Site Evaluation using Latent Factor Analysis
Sungmin Sohn, Namwoo Kim, Mark Hansen +1
Urban air mobility (UAM) introduces new challenges for infrastructure planning, requiring data driven approaches for sustainable site selection. This study proposes USE-LFA (Urban…
TopoCL: Topological Contrastive Learning for Time Series
Namwoo Kim, Hyungryul Baik, Yoonjin Yoon
Universal time series representation learning is challenging but valuable in real-world applications such as classification, anomaly detection, and forecasting. Recently, contrasti…
MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations
Namwoo Kim, Takahiro Yabe, Chanyoung Park +1
Recently, learning effective representations of urban regions has gained significant attention as a key approach to understanding urban dynamics and advancing smarter cities. Exist…
Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)
Namwoo Kim, Yoonjin Yoon
Revealing the hidden patterns shaping the urban environment is essential to understand its dynamics and to make cities smarter. Recent studies have demonstrated that learning the r…
Running the COVID-19 marathon: the behavioral adaptations in mobility and facemask over 27 weeks of pandemic in Seoul, South Korea
Jungwoo Cho, Yuyol Shin, Seyun Kim +4
Battle with COVID-19 turned out to be a marathon, not a sprint, and behavioral adjustments have been unavoidable to stay viable. In this paper, we employ a data-centric approach to…
The effect of adaptive mobility policy to the spread of COVID-19 in urban environment: intervention analysis of Seoul, South Korea
Yoonjin Yoon, Soohwan Oh, Jungwoo Cho +4
Although severe mobility restrictions are recognized as the key enabler to contain COVID-19, there has been few scientific studies to validate such approach, especially in urban co…
COVID-19 Mobility Data Collection of Seoul, South Korea
Jungwoo Cho, Soohwan Oh, Seyun Kim +4
The relationship between pandemic and human mobility has received considerable attention from scholars, as it can provide an indication of how mobility patterns change in response…