KNU‑GeoAI
Lab

The lab maps how cities shape human health — reading the spatial and temporal patterns of disease, environment, and exposure, and turning them into evidence for healthier places.

도시의 구조가 사람의 건강에 어떻게 영향을 미치는지 — 질병·환경·노출의 시공간 패턴을 지도로 읽어내고, 더 건강한 도시를 위한 근거로 만듭니다.

Led by Changmin Im, an urban geographer trained in GIScience and spatial statistics, KNU-GeoAI Lab sits at the meeting point of two questions: how the urban environment shapes public health, and how spatial data — ever larger and more complex — can be modeled to answer it.

The current focus is the coupled spread of illicit drugs and depression, read through a spatio-temporal "syndemic" lens. Earlier work ranged across infectious disease — COVID-19, tuberculosis, African swine fever, avian influenza — and non-communicable outcomes such as preterm birth.

Methodologically, the lab has moved from classical spatial statistics toward spatial machine learning, causal inference, and explainable AI (GeoAI) — tools for making sense of spatial big data without losing sight of place.

현재는 마약과 우울증이 함께 확산되는 양상을 시공간 신데믹(syndemic) 관점에서 분석하고 있습니다. 공간통계에서 출발해 공간 머신러닝·인과추론·설명가능한 AI(GeoAI)로 방법론을 넓히고 있으며, 지방소멸과 관계인구, 소아비만 건강격차 등 지역 기반 연구도 함께 수행합니다.